From b58a2e703e8179a03dbd91817313c9b2ddc1a797 Mon Sep 17 00:00:00 2001 From: Raghav Dixit <34462078+raghavdixit99@users.noreply.github.com> Date: Sat, 2 Mar 2024 14:28:47 -0500 Subject: [PATCH 01/32] app update (#153) --- README.md | 2 +- examples/imagebind_demo/README.md | 4 +--- examples/imagebind_demo/downloader.py | 2 +- 3 files changed, 3 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index c629da34..092cb1ae 100644 --- a/README.md +++ b/README.md @@ -58,7 +58,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Accelerate Vector Search Applications Using OpenVINO](/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| | [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| | [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301) | -| [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| +| [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| diff --git a/examples/imagebind_demo/README.md b/examples/imagebind_demo/README.md index 3dd48aae..2fec470d 100644 --- a/examples/imagebind_demo/README.md +++ b/examples/imagebind_demo/README.md @@ -3,9 +3,7 @@ A gradio app showcasing multi-modal capabilities of Imagebind supported via lanceDB API ## Usage -you can run it locally by cloning the project as mentioned below, or access via Spaces: - hf spaces - +you can run it locally by cloning the project as mentioned below, or access via Spaces: hf spaces ```bash git clone https://github.com/lancedb/vectordb-recipes.git diff --git a/examples/imagebind_demo/downloader.py b/examples/imagebind_demo/downloader.py index af1440d0..d0a9891c 100644 --- a/examples/imagebind_demo/downloader.py +++ b/examples/imagebind_demo/downloader.py @@ -8,7 +8,7 @@ "https://github.com/raghavdixit99/assets/raw/main/dragon-growl-37570.wav", "https://github.com/raghavdixit99/assets/raw/main/car_audio.wav", "https://github.com/raghavdixit99/assets/raw/main/acoustic-guitar.wav", - "https://github.com/raghavdixit99/assets/raw/main/witch-103635.wav", + "https://github.com/raghavdixit99/assets/raw/main/witch.wav", "https://github.com/raghavdixit99/assets/raw/main/thunder-25689.wav", ] image_urls = [ From dde8a2f4ce2c0dd38bccbe4180c52b446ae51042 Mon Sep 17 00:00:00 2001 From: Akash A Desai <62583018+akashAD98@users.noreply.github.com> Date: Sun, 10 Mar 2024 20:24:02 +0530 Subject: [PATCH 02/32] Example/query expansion reranker & superagent -autogen lancedb (#154) * Create README.md * Add files via upload * Rename main_query_reranker.ipynb to main.ipynb * Update README.md * Update README.md * Update README.md * Create README.md * Add files via upload * Rename lancedb_autogen_langchain_main.ipynb to main.ipynb * Update README.md * Update README.md * Update README.md * Update README.md for superagent -autogen lancedb --- README.md | 3 + examples/Query_expansion_&_Reranker/README.md | 21 + .../Query_expansion_&_Reranker/main.ipynb | 5251 +++++++++++++++++ .../SuperAgent_Autogen_with_lancedb/README.md | 50 + .../main.ipynb | 698 +++ 5 files changed, 6023 insertions(+) create mode 100644 examples/Query_expansion_&_Reranker/README.md create mode 100644 examples/Query_expansion_&_Reranker/main.ipynb create mode 100644 examples/SuperAgent_Autogen_with_lancedb/README.md create mode 100644 examples/SuperAgent_Autogen_with_lancedb/main.ipynb diff --git a/README.md b/README.md index 092cb1ae..9c6c3d17 100644 --- a/README.md +++ b/README.md @@ -49,6 +49,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)| | [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)| | [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)| +| [Query_expansion and Reranker ](/examples/Query_expansion_&_Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| | [RAG Fusion](/examples/RAG_Fusion/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)| | [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)| | [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| | @@ -59,6 +60,8 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| | [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301) | | [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| +| [SuperAgent_Autogen_with_LanceDB](/examples/SuperAgent_Autogen_with_lancedb/) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases/)| + diff --git a/examples/Query_expansion_&_Reranker/README.md b/examples/Query_expansion_&_Reranker/README.md new file mode 100644 index 00000000..c10f77d3 --- /dev/null +++ b/examples/Query_expansion_&_Reranker/README.md @@ -0,0 +1,21 @@ +## Enhancing RAG with Query Expansion & Reranking Models +![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/8d1c28a3-4129-43f6-a76c-b633348e61a7) + + +### Overview +This project explores the enhancement of Retrieval-Augmented Generation (RAG) systems through query +expansion and the integration of advanced reranking models such as Cross Encoders, ColBERT v2, and FlashRank. +Our focus is on improving the precision and recall of document retrieval processes, thereby refining the performance of RAG models in handling information retrieval tasks. + +### Key Features +- Query Expansion: Utilizes Large Language Models (LLMs) to generate meaningful expansions of search queries, addressing issues like query ambiguity and improving document matching. +- Reranking Methods: Implements advanced reranking models including Cross-Encoder reranking, ColBERT v2, and FlashRank to refine search results and prioritize relevant documents. + +### code +Google Colab walkthrough Open In Colab + +### Learn deeper in Our Blog +For a detailed exploration of the concepts and methodologies discussed in this project, +visit our blog + +[Read the Blog Post](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/) diff --git a/examples/Query_expansion_&_Reranker/main.ipynb b/examples/Query_expansion_&_Reranker/main.ipynb new file mode 100644 index 00000000..842bc3a4 --- /dev/null +++ b/examples/Query_expansion_&_Reranker/main.ipynb @@ -0,0 +1,5251 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + 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/usr/local/lib/python3.10/dist-packages (from requests>=2.31.0->lancedb) (2.0.7)\n", + "Requirement already satisfied: decorator>=3.4.2 in /usr/local/lib/python3.10/dist-packages (from retry>=0.9.2->lancedb) (4.4.2)\n", + "Requirement already satisfied: py<2.0.0,>=1.4.26 in /usr/local/lib/python3.10/dist-packages (from retry>=0.9.2->lancedb) (1.11.0)\n", + "Requirement already satisfied: greenlet!=0.4.17 in /usr/local/lib/python3.10/dist-packages (from SQLAlchemy<3,>=1.4->langchain) (3.0.3)\n", + "Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\n" + ] + } + ], + "source": [ + "!pip install lancedb langchain openai tiktoken pypdf tantivy sentence-transformers" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# download your choice pdf file & upload here\n", + "\n", + " https://github.com/akashAD98/dummy_data/blob/main/microsoft_annual_report_2022.pdf" + ], + "metadata": { + "id": "je__kyDIyI62" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import time\n", + "import lancedb\n", + "import getpass\n", + "import pandas as pd\n", + "from lancedb.embeddings import EmbeddingFunctionRegistry\n", + "from lancedb.pydantic import LanceModel, Vector\n", + "from lancedb.embeddings import get_registry\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain.document_loaders import PyPDFLoader\n", + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.embeddings import HuggingFaceEmbeddings\n", + "from langchain_community.vectorstores import LanceDB\n", + "\n", + "\n", + "# Set the OPENAI_API_KEY environment variable\n", + "os.environ['OPENAI_API_KEY'] = 'sk-yourkey'" + ], + "metadata": { + "id": "QcfZ_lr6FrEA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def word_wrap(string, n_chars=72):\n", + " # Wrap a string at the next space after n_chars\n", + " if len(string) < n_chars:\n", + " return string\n", + " else:\n", + " return string[:n_chars].rsplit(' ', 1)[0] + '\\n' + word_wrap(string[len(string[:n_chars].rsplit(' ', 1)[0])+1:], n_chars)" + ], + "metadata": { + "id": "NtbIb3QPjfuW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We are using langchain fro loading the pdf file & doing prprocessing" + ], + "metadata": { + "id": "7JSJy4AROGOM" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "#your pdf file\n", + "loader = PyPDFLoader(\"/content/microsoft_annual_report_2022.pdf\")\n", + "documents = loader.load_and_split()\n", + "\n", + "# split it into chunks\n", + "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "docsn = text_splitter.split_documents(documents)\n", + "\n", + "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n", + "\n", + "docsearch = LanceDB.from_documents(docsn, embeddings)\n", + "\n", + "query = \"What were the most important factors that contributed to increases in revenue?\"\n", + "docsnew = docsearch.similarity_search(query)\n", + "print(docsnew[0].page_content)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0TUtpO0_jkUY", + "outputId": "919b95f7-1a6e-4f1c-c063-fde2d978f57e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", + " warn_deprecated(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "35 \n", + "Reportable Segments \n", + "Fiscal Year 2022 Compared with Fiscal Year 2021 \n", + "Productivity and Business Processes \n", + "Revenue increased $9.4 billion or 18%. \n", + "• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \n", + "Commercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \n", + "medium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \n", + "products revenue declined 22% driven by continued customer shift to cloud offerings. \n", + "• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \n", + "Consumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \n", + "• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \n", + "and advertising demand in our Marketing Solutions business. \n", + "• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \n", + "Operating income increased $5.3 billion or 22%. \n", + "• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \n", + "margin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \n", + "gross margin percentage increased 2 points driven by improvement across all cloud services. \n", + "• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \n", + "Gross margin and operating income both included an unfavorable foreign currency impact of 2%. \n", + "Intelligent Cloud \n", + "Revenue increased $15.2 billion or 25%. \n", + "• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \n", + "services. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \n", + "services. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \n", + "SQL Server running in multi -cloud environments. \n", + "• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \n", + "Operating income increased $6.6 billion or 25%. \n", + "• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \n", + "percentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \n", + "was relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \n", + "mix shift to Azure and other cloud services. \n", + "• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \n", + "Revenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \n", + "More Personal Computing \n", + "Revenue increased $5.6 billion or 10%. \n", + "• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \n", + "Commercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \n", + "market, which has higher revenue per license. Windows Commercial products and cloud services revenue \n", + "increased 11% driven by demand for Microsoft 365.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "L9y74fpPfHag" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Query expansion" + ], + "metadata": { + "id": "nr7_uISgfHo4" + } + }, + { + "cell_type": "code", + "source": [ + "#we are using openai for generating query\n", + "import os\n", + "import openai\n", + "from openai import OpenAI\n", + "openai.api_key = os.environ['OPENAI_API_KEY']\n", + "openai_client = OpenAI()\n", + "\n", + "\n", + "def augment_query_generated(query, model=\"gpt-3.5-turbo\"):\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"You are a helpful expert financial research assistant. Provide an example answer to the given question, that might be found in a document like an annual report. \"\n", + " },\n", + " {\"role\": \"user\", \"content\": query}\n", + " ]\n", + "\n", + " response = openai_client.chat.completions.create(\n", + " model=model,\n", + " messages=messages,\n", + " )\n", + " content = response.choices[0].message.content\n", + " return content" + ], + "metadata": { + "id": "B7B0XA3ajsFY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### its generating query & hypothetica answer" + ], + "metadata": { + "id": "-2v8mN9XQy7Y" + } + }, + { + "cell_type": "code", + "source": [ + "original_query = \"What were the most important factors that contributed to increases in revenue?\"\n", + "hypothetical_answer = augment_query_generated(original_query)\n", + "# we are combining our orignal query + hypothetical_answer\n", + "joint_query = f\"{original_query} {hypothetical_answer}\"\n", + "print(word_wrap(joint_query))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IPBqpky3jtwM", + "outputId": "51c6b216-d80d-458a-f058-79df1c69b37f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "What were the most important factors that contributed to increases in\n", + "revenue? \"In the fiscal year 2021, our company experienced significant\n", + "growth in revenue due to several key factors. Firstly, our successful\n", + "market expansion efforts into emerging markets led to a substantial\n", + "increase in sales volume from new customer segments. Additionally,\n", + "strategic partnerships with key industry players resulted in lucrative\n", + "collaborations that boosted our revenue streams. Furthermore, our focus\n", + "on innovation and product development led to the launch of highly\n", + "successful new product lines that resonated well with our target\n", + "audience. Lastly, our robust marketing campaigns and enhanced digital\n", + "presence played a crucial role in driving higher sales and revenue\n", + "growth for the year.\"\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# we are passing the joint_query & fining the similar docs for this query\n", + "retrieved_documents = docsearch.similarity_search(joint_query,k=4)\n", + "print(retrieved_documents)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "daOypkNqjwIh", + "outputId": "830749bc-36bc-4d27-cc42-dced1fdfd0b2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Document(page_content='35 \\nReportable Segments \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nProductivity and Business Processes \\nRevenue increased $9.4 billion or 18%. \\n• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \\nCommercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \\nmedium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \\nproducts revenue declined 22% driven by continued customer shift to cloud offerings. \\n• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \\nConsumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \\n• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \\nand advertising demand in our Marketing Solutions business. \\n• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \\nOperating income increased $5.3 billion or 22%. \\n• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \\nmargin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \\ngross margin percentage increased 2 points driven by improvement across all cloud services. \\n• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nIntelligent Cloud \\nRevenue increased $15.2 billion or 25%. \\n• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \\nservices. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \\nservices. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \\nSQL Server running in multi -cloud environments. \\n• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \\nOperating income increased $6.6 billion or 25%. \\n• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \\npercentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \\nwas relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \\nmix shift to Azure and other cloud services. \\n• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \\nRevenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \\nMore Personal Computing \\nRevenue increased $5.6 billion or 10%. \\n• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \\nCommercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \\nmarket, which has higher revenue per license. Windows Commercial products and cloud services revenue \\nincreased 11% driven by demand for Microsoft 365.', metadata={'vector': [0.027368593961000443, -0.014953545294702053, -0.0041684047318995, 0.03653769567608833, -0.009155231527984142, -0.01824110560119152, -0.034734394401311874, 0.045415498316287994, -0.014287710189819336, 0.0345679335296154, 0.05373843386769295, -0.019212115556001663, 0.00569427665323019, -0.04400059953331947, -0.07263150066137314, 0.03933975100517273, -0.02145930752158165, -0.005413377657532692, 0.023595528677105904, 0.009189910255372524, -0.03531700000166893, 0.004102514591068029, -0.008537947200238705, 0.034734394401311874, -0.016035526990890503, -0.005881542805582285, 0.014273838140070438, 0.013753654435276985, 0.02448330819606781, 0.015799710527062416, 0.002777780406177044, -0.003655156819149852, -0.026758244261145592, -0.009828002192080021, 0.05662371963262558, 0.001376752625219524, 0.016243599355220795, -0.05432103946805, 0.05512559041380882, 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competitive, with frequent changes in both technologies and business models. Each \\nindustry shift is an opportunity to conceive new products, new technologies, or new ideas that can further transform the \\nindustry and our business. At Microsoft, we push the boundaries of what is possible through a broad range of research \\nand development activities that seek to identify and address the changing demands of customers and users, industry \\ntrends, and competitive forces. \\nEconomic Conditions, Challenges, and Risks \\nThe markets for software, devices, and cloud -based services are dynamic and highly competitive. Our competitors are \\ndeveloping new software and devices, while also deploying competing cloud -based services for consumers and \\nbusinesses. The devices and form factors customers prefer evolve rapidly, and influence how users access services in \\nthe cloud, and in some cases, the user’s choice of which suite of cloud -based services to use. We must continue to evolve \\nand adapt over an extended time in pace with this changing environment. The investments we are making in infrastructure \\nand devices will continue to increase our operating costs and may decrease our operating margins. \\nOur success is highly dependent on our ability to attract and retain qualified employees. We hire a mix of university and \\nindustry talent worldwide. We compete for talented individuals globally by offering an exceptional working environment, \\nbroad customer reach, scale in resources, the ability to grow one’s career across many different products and businesses, \\nand competitive compensation and benefits. Aggregate demand for our software, services, and devices is correlated to \\nglobal macroeconomic and geopolitical factors, which remain dynamic. \\nOur devices are primarily manufactured by third -party contract manufacturers, some of which contain certain components \\nfor which there are very few qualified suppliers. For these components, we have limited near -term flexibility to use other \\nmanufacturers if a current vendor becomes unavailable or is unable to meet our requirements. Extended disruptions at \\nthese suppliers and/or manufacturers could lead to a similar disruption in our ability to manufacture devices on time to \\nmeet consumer demand. \\nOur international operations provide a significant portion of our total revenue and expenses. Many of these revenue and \\nexpenses are denominated in currencies other than the U.S. dollar. As a result, changes in foreign exchange rates may \\nsignificantly affect revenue and expenses. Fluctuations in the U.S. dollar relative to certain foreign currencies did not hav e \\na material impact on reported revenue or expenses from our international operations in fiscal year 2022. \\nRefer to Risk Factors in our fiscal year 2022 Form 10 -K for a discussion of these factors and other risks. \\nSeasonality \\nOur revenue fluctuates quarterly and is generally higher in the second and fourth quarters of our fiscal year. Second \\nquarter revenue is driven by corporate year -end spending trends in our major markets and holiday season spending by \\nconsumers, and fourth quarter revenue is driven by the volume of multi -year on -premises contracts executed during the \\nperiod. \\nReportable Segments \\nWe report our financial performance based on the following segments: Productivity and Business Processes, Intelligent \\nCloud, and More Personal Computing. The segment amounts included in MD&A are presented on a basis consistent with \\nour internal management reporting. Additional information on our reportable segments is contained in Note 19 – Segment \\nInformation and Geographic Data of the Notes to Financial Statements in our fiscal year 2022 Form 10 -K. \\nMetrics \\nWe use metrics in assessing the performance of our business and to make informed decisions regarding the allocation of', metadata={'vector': [0.015208840370178223, -0.03496319428086281, -4.293437450542115e-05, 0.023085245862603188, 0.012839212082326412, -0.040924523025751114, -0.04849541187286377, 0.029419157654047012, -0.026960110291838646, 0.009553029201924801, 0.06366699188947678, -0.019195478409528732, 0.017287854105234146, -0.03162484988570213, -0.07803379744291306, 0.036542944610118866, -0.023651571944355965, -0.02155020460486412, 0.02842063643038273, 0.017466694116592407, -0.0331449881196022, 0.011527719907462597, -0.025961587205529213, 0.0442032553255558, -0.0012230038410052657, -0.0024534594267606735, 0.011758721433579922, 0.005100661888718605, 0.019463738426566124, 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Document(page_content='33 Dynamics products and cloud services revenue growth Revenue from Dynamics products and cloud services, \\nincluding Dynamics 365, comprising a set of intelligent, \\ncloud -based applications across ERP, CRM, Customer \\nInsights, Power Apps, and Power Automate; and on -\\npremises ERP and CRM applications \\n \\nLinkedIn revenue growth Revenue from LinkedIn, including Talent Solutions, \\nMarketing Solutions, Premium Subscriptions, and Sales \\nSolutions \\n \\nServer products and cloud services revenue growth Revenue from Server products and cloud services, \\nincluding Azure and other cloud services; SQL Server, \\nWindows Server, Visual Studio, System Center, and \\nrelated Client Access Licenses (“CALs”); and Nuance and \\nGitHub \\nMore Personal Computing \\nMetrics related to our More Personal Computing segment assess the performance of key lines of business within this \\nsegment. These metrics provide strategic product insights which allow us to assess the performance across our \\ncommercial and consumer businesses. As we have diversity of target audiences and sales motions within the Windows \\nbusiness, we monitor metrics that are reflective of those varying motions. \\n \\nWindows OEM revenue growth Revenue from sales of Windows Pro and non -Pro licenses sold \\nthrough the OEM channel \\n \\nWindows Commercial products and cloud \\nservices revenue growth Revenue from Windows Commercial products and cloud services, \\ncomprising volume licensing of the Windows operating system, \\nWindows cloud services, and other Windows commercial offerings \\n \\nSurface revenue growth Revenue from Surface devices and accessories \\n \\nXbox content and services revenue growth Revenue from Xbox content and services, comprising first - and third -\\nparty content (including games and in -game content), Xbox Game \\nPass and other subscriptions, Xbox Cloud Gaming, third -party disc \\nroyalties, advertising, and other cloud services \\n \\nSearch and news advertising revenue, \\nexcluding TAC, growth Revenue from search and news advertising excluding traffic \\nacquisition costs (“TAC”) paid to Bing Ads network publishers and \\nnews partners \\nSUMMARY RESULTS OF OPERATIONS \\n \\n(In millions, except percentages and per share amounts) 2022 2021 Percentage \\nChange \\n \\nRevenue $ 198,270 $ 168,088 18% \\nGross margin 135,620 115,856 17% \\nOperating income 83,383 69,916 19% \\nNet income 72,738 61,271 19% \\nDiluted earnings per share 9.65 8.05 20% \\n \\nAdjusted net income (non -GAAP) 69,447 60,651 15% \\nAdjusted diluted earnings per share (non -GAAP) 9.21 7.97 16% \\nAdjusted net income and adjusted diluted earnings per share (“EPS”) are non -GAAP financial measures which exclude \\nthe net income tax benefit related to transfer of intangible properties in the first quarter of fiscal year 2022 and the', metadata={'vector': [0.04153840243816376, -0.017145821824669838, -0.008022157475352287, 0.0258274357765913, 0.0013270620256662369, -0.009420781396329403, -0.06156843528151512, 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Refer to the Non -GAAP Financial Measures section below for a reconciliation of our financial results reported in \\naccordance with GAAP to non -GAAP financial results. See Note 12 – Income Taxes of the Notes to Financial Statements \\nin our fiscal year 2022 Form 10 -K for further discussion. \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nRevenue increased $30.2 billion or 18% driven by growth across each of our segments. Intelligent Cloud revenue \\nincreased driven by Azure and other cloud services. Productivity and Business Processes revenue increased driven by \\nOffice 365 Commercial and LinkedIn. More Personal Computing revenue increased driven by Search and news \\nadvertising and Windows. \\nCost of revenue increased $10.4 billion or 20% driven by growth in Microsoft Cloud. \\nGross margin increased $19.8 billion or 17% driven by growth across each of our segments. \\n• Gross margin percentage decreased slightly. Excluding the impact of the fiscal year 2021 change in \\naccounting estimate for the useful lives of our server and network equipment, gross margin percentage \\nincreased 1 point driven by improvement in Productivity and Business Processes. \\n• Microsoft Cloud gross margin percentage decreased slightly to 70%. Excluding the impact of the change in \\naccounting estimate, Microsoft Cloud gross margin percentage increased 3 points driven by improvement \\nacross our cloud services, offset in part by sales mix shift to Azure and other cloud services. \\nOperating expenses increased $6.3 billion or 14% driven by investments in cloud engineering, LinkedIn, Gaming, and \\ncommercial sales. \\nKey changes in operating expenses were: \\n• Research and development expenses increased $3.8 billion or 18% driven by investments in cloud \\nengineering, Gaming, and LinkedIn. \\n• Sales and marketing expenses increased $1.7 billion or 8% driven by investments in commercial sales and \\nLinkedIn. Sales and marketing included a favorable foreign currency impact of 2%. \\n• General and administrative expenses increased $793 million or 16% driven by investments in corporate \\nfunctions. \\nOperating income increased $13.5 billion or 19% driven by growth across each of our segments. \\nCurrent year net income and diluted EPS were positively impacted by the net tax benefit related to the transfer of \\nintangible properties, which resulted in an increase to net income and diluted EPS of $3.3 billion and $0.44, respectively. \\nPrior year net income and diluted EPS were positively impacted by the net tax benefit related to the India Supreme Court \\ndecision on withholding taxes, which resulted in an increase to net income and diluted EPS of $620 million and $0.08, \\nrespectively. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nSEGMENT RESULTS OF OPERATIONS \\n \\n(In millions, except percentages) 2022 2021 Percentage \\nChange \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 18% \\nIntelligent Cloud 75,251 60,080 25% \\nMore Personal Computing 59,655 54,093 10% \\nTotal $ 198,270 $ 168,088 18% \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 22% \\nIntelligent Cloud 32,721 26,126 25% \\nMore Personal Computing 20,975 19,439 8% \\nTotal $ 83,383 $ 69,916 19%', metadata={'vector': [0.04216254875063896, -0.013476958498358727, -0.004202566109597683, 0.03570127487182617, 0.0005548379267565906, -0.016125807538628578, -0.0518270842730999, 0.045393187552690506, -0.034113336354494095, 0.015482417307794094, 0.05869903042912483, -0.022833485156297684, 0.035098955035209656, -0.02413395419716835, -0.06264150142669678, 0.023162024095654488, -0.01989031955599785, -0.0056091248989105225, 0.01709773577749729, -0.0009077611030079424, -0.051224760711193085, 0.02058846689760685, -0.012730900198221207, 0.022614458575844765, -0.014962229877710342, 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Office 365 \n", + "Commercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \n", + "medium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \n", + "products revenue declined 22% driven by continued customer shift to cloud offerings. \n", + "• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \n", + "Consumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \n", + "• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \n", + "and advertising demand in our Marketing Solutions business. \n", + "• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \n", + "Operating income increased $5.3 billion or 22%. \n", + "• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \n", + "margin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \n", + "gross margin percentage increased 2 points driven by improvement across all cloud services. \n", + "• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \n", + "Gross margin and operating income both included an unfavorable foreign currency impact of 2%. \n", + "Intelligent Cloud \n", + "Revenue increased $15.2 billion or 25%. \n", + "• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \n", + "services. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \n", + "services. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \n", + "SQL Server running in multi -cloud environments. \n", + "• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \n", + "Operating income increased $6.6 billion or 25%. \n", + "• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \n", + "percentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \n", + "was relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \n", + "mix shift to Azure and other cloud services. \n", + "• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \n", + "Revenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \n", + "More Personal Computing \n", + "Revenue increased $5.6 billion or 10%. \n", + "• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \n", + "Commercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \n", + "market, which has higher revenue per license. Windows Commercial products and cloud services revenue \n", + "increased 11% driven by demand for Microsoft 365.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Reranker model" + ], + "metadata": { + "id": "slsNEwLV-NWS" + } + }, + { + "cell_type": "markdown", + "source": [ + "we are using landb for colbertv2 implementation\n", + "COlbertv2 is reranker method helpful for improving the ranking of results its returning top ranking\n", + "results as the most relevant to your particular query.\n", + "\n", + "for more technical details please check the blog :" + ], + "metadata": { + "id": "nRDEYibze8xA" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "\n", + "db = lancedb.connect(\"/tmp/db\")\n", + "registry = EmbeddingFunctionRegistry.get_instance()\n", + "func = registry.get(\"openai\").create()\n", + "\n", + "class Words(LanceModel):\n", + " text: str = func.SourceField()\n", + " vector: Vector(func.ndims()) = func.VectorField()\n", + "\n", + "table = db.create_table(\"wordsllm\", schema=Words,mode=\"overwrite\")\n", + "\n", + "# data from retriver\n", + "formatted_data = [{\"text\": doc.page_content} for doc in retrieved_documents]\n", + "\n", + "\n", + "# ingest docs with auto-vectorization\n", + "table.add(formatted_data)\n", + "# Create the FTS index on the 'text' field\n", + "table.create_fts_index(['text'],replace=True)\n", + "\n", + "\n", + "# normal search\n", + "query = \"technologies and business models\"\n", + "actual = table.search(query).limit(1).to_pydantic(Words)[0]\n", + "print(actual.text)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZHjbtdCpkB-r", + "outputId": "a33b3216-7764-4fc7-8f43-66371c954dc6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "82 \n", + "In addition, certain costs incurred at a corporate level that are identifiable and that benefit our segments are allocated to \n", + "them. These allocated costs include legal, including settlements and fines, information technology, human resources, \n", + "finance, excise taxes, field selling, shared facilities services, and customer service and support. Each allocation is \n", + "measured differently based on the specific facts and circumstances of the costs being allocated. \n", + "Segment revenue and operating income were as follows during the periods presented: \n", + " \n", + "(In millions) \n", + " \n", + "Year Ended June 30, 2022 2021 2020 \n", + " \n", + "Revenue \n", + " \n", + "Productivity and Business Processes $ 63,364 $ 53,915 $ 46,398 \n", + "Intelligent Cloud 75,251 60,080 48,366 \n", + "More Personal Computing 59,655 54,093 48,251 \n", + "Total $ 198,270 $ 168,088 $ 143,015 \n", + " \n", + "Operating Income \n", + " \n", + "Productivity and Business Processes $ 29,687 $ 24,351 $ 18,724 \n", + "Intelligent Cloud 32,721 26,126 18,324 \n", + "More Personal Computing 20,975 19,439 15,911 \n", + "Total $ 83,383 $ 69,916 $ 52,959 \n", + "No sales to an individual customer or country other than the United States accounted for more than 10% of revenue for \n", + "fiscal years 2022, 2021, or 2020. Revenue, classified by the major geographic areas in which our customers were located, \n", + "was as follows: \n", + " \n", + "(In millions) \n", + " \n", + "Year Ended June 30, 2022 2021 2020 \n", + " \n", + "United States (a) $ 100,218 $ 83,953 $ 73,160 \n", + "Other countries 98,052 84,135 69,855 \n", + "Total $ 198,270 $ 168,088 $ 143,015 \n", + "(a) Includes billings to OEMs and certain multinational organizations because of the nature of these businesses and the \n", + "impracticability of determining the geographic source of the revenue. \n", + "Revenue, classified by significant product and service offerings, was as follows: \n", + " \n", + "(In millions) \n", + " \n", + "Year Ended June 30, 2022 2021 2020 \n", + " \n", + "Server products and cloud services $ 67,321 $ 52,589 $ 41,379 \n", + "Office products and cloud services 44,862 39,872 35,316 \n", + "Windows 24,761 22,488 21,510 \n", + "Gaming 16,230 15,370 11,575 \n", + "LinkedIn 13,816 10,289 8,077 \n", + "Search and news advertising 11,591 9,267 8,524 \n", + "Enterprise Services 7,407 6,943 6,409 \n", + "Devices 6,991 6,791 6,457 \n", + "Other 5,291 4,479 3,768 \n", + "Total $ 198,270 $ 168,088 $ 143,015 \n", + "We have recast certain previously reported amounts in the table above to conform to the way we internally manage and \n", + "monitor our business.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Colbertv2 reranker model" + ], + "metadata": { + "id": "DWriE9uWMgPb" + } + }, + { + "cell_type": "code", + "source": [ + "from lancedb.rerankers import ColbertReranker\n", + "reranker_colbert = ColbertReranker()\n", + "results_colbert = table.search(\"technologies and business models\", query_type=\"hybrid\").rerank(reranker=reranker_colbert).to_pandas()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368, + "referenced_widgets": [ + "512a4a1ac0a54087b19b3ab709d408a5", + "e4fffd410f144690a30509cd6ebfe3d0", + 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"eb07c72549434e3bbac0c78daf97341f", + "e8a29343ae5a49d4b71c0cee39903eba", + "71457dc5c8ea4b69b0118a3972fc7f5f", + "8aa6071ab59b4e1b867d6538261decc9", + "34f4a5a1851a4268a0ab749c967066d9", + "edc4db29c82744b1929358793c22cecb", + "22a7472d547a48d684e208aa80a48daa", + "98185bbf7b794e3189ca9868a0a8c548", + "79ecc557cfba4409876c543ad9fd4d1d", + "d51840e06afa471ca5d09e63d02d7af5", + "5323184c79864acfb798ee3d0903e5e3", + "c17a522cbf9341d796fa7cacacded1cd", + "0d67cc434d514e7fbb844462fe613936", + "dc06c0ec5c7e40d98a8008d03b162a40", + "ad17228a04184934a4fd107307c59c9c", + "bf1598b78a01422c99a77b0c00704f24", + "240ed6a8d0df400d9b5b8a86dbb89d03", + "1cc8951a55584143a4f24b11d4221d0b", + "f52c290c9f5d4c7d830578bd139ab4a9", + "0ec0c7c3998d41e6890903c713f518bc", + "6b3936ecd33a41f2a61c368c86498865", + "93bd0eca9ace4bf4a10bd0467223ed90", + "ac8d646d12e6417ca32eb45e478a9cfe", + "ba1f368d05fd40578d7bca97269fffab", + "9a5d372096cb4f759778026b9b57b376", + "81d022b3239647509d245b7ab890badb", + "832bc71baafb44118b338d3f124b9927", + "9fa2d08290ea419a8455e06ea92db3e8", + "ce808eccce44475a9628cf45c7cf7e56", + "67ca36dd2e844fa6bb17ab776cb25b1f", + "f586fe78b39244d097f774a380f976fc", + "e668274af14544148104c94f1a1cfc6a", + "6588d853f27c4cc795e41e146eab356f", + "3a1134aa7e19470bbac3d88c0ad63c0c", + "e5bd14e45ee04cfaaa0028f2b3f03341", + "3302a4bb82ff4d5989c01c0acfae4997", + "f13df4048fc04e3394cab184b2fbadb0", + "df6656e2d170478ca0078d65677871f8" + ] + }, + "id": "-JCKr0_n6jjb", + "outputId": "dc215c45-5d2c-4014-a5c6-b8bfc1076fea" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/content/lancedb/python/lancedb/rerankers/colbert.py:40: FutureWarning: promote has been superseded by promote_options='default'.\n", + " combined_results = self.merge_results(vector_results, fts_results)\n", + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/405 [00:00\n", + "
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vectortext_relevance_score
0[-0.0043462967, -0.022663297, -0.014687485, -0...35 \\nReportable Segments \\nFiscal Year 2022...0.580270
1[0.0073030023, -0.018824346, 0.001970756, -0.0...82 \\nIn addition, certain costs incurred at ...0.580032
2[-0.0026444048, -0.023148028, -0.008980748, -0...33 Dynamics products and cloud services revenu...0.579247
3[-0.014430126, -0.01449687, 0.0075287614, -0.0...34 net income tax benefit related to an India ...0.526167
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Another way to think about this is\n", + "your re-ranking model scores each of the results conditioned on the\n", + "query, and those with the highest score are the most\n", + "relevant.\n", + "Then you can just select the top ranking\n", + "results as the most relevant to your particular query.\n", + "So let's take a look at how to do this in practice" + ], + "metadata": { + "id": "hn9bxtkQeQBf" + } + }, + { + "cell_type": "code", + "source": [ + "\n", + "import numpy as np\n", + "#cross encoder reranker\n", + "from sentence_transformers import CrossEncoder\n", + "cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')\n", + "\n", + "# Extract text content from Document objects and convert to strings\n", + "document_texts = [doc.page_content for doc in retrieved_documents]\n", + "query_text = \"What were the most important factors that contributed to increases in revenue?\"\n", + "# Create pairs as strings\n", + "pairs = [[query_text, doc_text] for doc_text in document_texts]\n", + "# Predict scores for pairs\n", + "scores = cross_encoder.predict(pairs)\n", + "# Print scores\n", + "print(\"Scores:\")\n", + "for score in scores:\n", + " print(score)\n", + "\n", + "\n", + "print(\"New Ordering:\")\n", + "for o in np.argsort(scores)[::-1]:\n", + " print(o+1)" + ], + "metadata": { + "id": "Bhg8NKLzk716", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351, + "referenced_widgets": [ + "9607864fc48046f18b029ee3fc460998", + "a66b8790b1d649dfbe2b8b23810f8b08", + "4830ede53f4e481a844fceed058d3709", + "035029d168a945c6825e95d7e640a649", + "3b84205b280940b7b5910a3a1bc59428", + "f80e6d5c5324449aae87bcb498060ad6", + "e66b07711d8145d0837735c52ac6ae5b", + "fd47bda4836b4c47a6eab7f4adad746c", + "a10be6ba5286402390e7e800df2773c5", + "86ceb09a7cea4cbebdc833b18c06eee1", + "80d0de24b4974c56944acee7dac4cd3c", + "cfa8b359cbe841c590022567b2912936", + "cfbbebe5f3834beba9860b664b14263b", + "607a9a07aead4d1ba536b9a037746307", + "8989a92722094eaa87b78e03a3006ee5", + "4c4724a005d94c948c9df8accc4bd9be", + "f0e02ea03aff47b3b86b31e856f6f979", + "ec1eaf8260da4275a77ee827a3eee933", + "3d98c4e94ef54414a1aabf4b57ca8fad", + "0aadb5b86e344bd482a973ce507fcb9b", + "7ad7b106ae1b4104aea6faaa4451e12b", + "5228d72006e6422ab0fdc39241008622", + "371783f879314d9f97372bc35d73b43a", + "d6a4461d6f004844ac3e939d232d4ea4", + "beb93bd7017649269a9725173bfe095a", + "363f2fe905e24d7db098c85e0e6cac0d", + "49d9663243e94c9485839bbc9c0fdd6d", + "0e14830f5dcf4fb4af8d5a2762cac409", + "b12276a43b47439f8bc21b36a16c83bd", + "907b6225eef84efbadfb53b4f749cfa7", + "9632d557ac41490abee15f80f89d1e8c", + "f9052f766c6644349111e72f9579a09b", + "1e49b79aaa654886b6c06544deb160f0", + "d6ee90511c1843b1b680df60afc66d95", + "61f362cebf6e4f259d7cb1e20b6f1aea", + "06e798bf472645fc807917f7320b7ca0", + "679e1bd10e79449a9240872fccc4d819", + "1cca0d4e0444459da89c234b54f25ff9", + "6d98a47526f6447ea853aa2a17a07414", + "292c462527c54d35b04807262a35bd71", + "2aeb4c955a5748459179106ea10bb1a5", + "c6bd6b236f4e42b997d502dc27b95bba", + "6cdc965edc9648aebd9f3d89cae3955d", + "ea381193a01b4d85a457a70362182454", + "00c97c56f748495db23e999b1daa823b", + "e5766214355249e1a00ca05c9cd72e5f", + "7e2cca6135484b578c9a822d8f14ad0a", + "9b7b7a7a9684476aac925a44e9b391b7", + "fba748349bac44eab84f9e9361ef076d", + "1f68f5e201fd4a86ba134a39bf4a1727", + "a2ffb345d1ea467a92f952dbacf80b27", + "004e0f18fe604aa3974ea4dfd8421b31", + "4cc07587af674134a9e644eb3eb1674f", + "a349191878374a2282ab194818e80d31", + "9f6f8810125e4a12ba9d00274bce61dc" + ] + }, + "outputId": "05e40b97-832b-4ff3-f043-986269ed529e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/794 [00:00flashrank) (15.0.1)\n", + "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime->flashrank) (23.5.26)\n", + "Requirement already satisfied: packaging in 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tokenizers->flashrank) (0.20.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (3.13.1)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (2023.6.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (6.0.1)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (4.9.0)\n", + "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime->flashrank) (10.0)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime->flashrank) (1.3.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Metadata is optional, Id can be your DB ids from your retrieval stage or simple numeric indices.\n", + "from flashrank import Ranker, RerankRequest\n", + "#query = \"How to speedup LLMs?\"\n", + "\n", + "query = 'What were the most important factors that contributed to increases in revenue?'\n", + "\n", + "ranker = Ranker(model_name=\"ms-marco-MiniLM-L-12-v2\", cache_dir=\"/opt\")\n", + "\n", + "rerankrequest = RerankRequest(query=query, passages=formatted_data)\n", + "results = ranker.rerank(rerankrequest)\n", + "print(results)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4NzbLIaRZ9lo", + "outputId": "3a7f2b44-4d28-4976-e7de-2403034f9736" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[{'text': '35 \\nReportable Segments \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nProductivity and Business Processes \\nRevenue increased $9.4 billion or 18%. \\n• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \\nCommercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \\nmedium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \\nproducts revenue declined 22% driven by continued customer shift to cloud offerings. \\n• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \\nConsumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \\n• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \\nand advertising demand in our Marketing Solutions business. \\n• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \\nOperating income increased $5.3 billion or 22%. \\n• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \\nmargin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \\ngross margin percentage increased 2 points driven by improvement across all cloud services. \\n• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nIntelligent Cloud \\nRevenue increased $15.2 billion or 25%. \\n• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \\nservices. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \\nservices. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \\nSQL Server running in multi -cloud environments. \\n• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \\nOperating income increased $6.6 billion or 25%. \\n• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \\npercentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \\nwas relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \\nmix shift to Azure and other cloud services. \\n• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \\nRevenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \\nMore Personal Computing \\nRevenue increased $5.6 billion or 10%. \\n• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \\nCommercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \\nmarket, which has higher revenue per license. Windows Commercial products and cloud services revenue \\nincreased 11% driven by demand for Microsoft 365.', 'score': 0.14885372}, {'text': '34 net income tax benefit related to an India Supreme Court decision on withholding taxes in the third quarter of fiscal year \\n2021. Refer to the Non -GAAP Financial Measures section below for a reconciliation of our financial results reported in \\naccordance with GAAP to non -GAAP financial results. See Note 12 – Income Taxes of the Notes to Financial Statements \\nin our fiscal year 2022 Form 10 -K for further discussion. \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nRevenue increased $30.2 billion or 18% driven by growth across each of our segments. Intelligent Cloud revenue \\nincreased driven by Azure and other cloud services. Productivity and Business Processes revenue increased driven by \\nOffice 365 Commercial and LinkedIn. More Personal Computing revenue increased driven by Search and news \\nadvertising and Windows. \\nCost of revenue increased $10.4 billion or 20% driven by growth in Microsoft Cloud. \\nGross margin increased $19.8 billion or 17% driven by growth across each of our segments. \\n• Gross margin percentage decreased slightly. Excluding the impact of the fiscal year 2021 change in \\naccounting estimate for the useful lives of our server and network equipment, gross margin percentage \\nincreased 1 point driven by improvement in Productivity and Business Processes. \\n• Microsoft Cloud gross margin percentage decreased slightly to 70%. Excluding the impact of the change in \\naccounting estimate, Microsoft Cloud gross margin percentage increased 3 points driven by improvement \\nacross our cloud services, offset in part by sales mix shift to Azure and other cloud services. \\nOperating expenses increased $6.3 billion or 14% driven by investments in cloud engineering, LinkedIn, Gaming, and \\ncommercial sales. \\nKey changes in operating expenses were: \\n• Research and development expenses increased $3.8 billion or 18% driven by investments in cloud \\nengineering, Gaming, and LinkedIn. \\n• Sales and marketing expenses increased $1.7 billion or 8% driven by investments in commercial sales and \\nLinkedIn. Sales and marketing included a favorable foreign currency impact of 2%. \\n• General and administrative expenses increased $793 million or 16% driven by investments in corporate \\nfunctions. \\nOperating income increased $13.5 billion or 19% driven by growth across each of our segments. \\nCurrent year net income and diluted EPS were positively impacted by the net tax benefit related to the transfer of \\nintangible properties, which resulted in an increase to net income and diluted EPS of $3.3 billion and $0.44, respectively. \\nPrior year net income and diluted EPS were positively impacted by the net tax benefit related to the India Supreme Court \\ndecision on withholding taxes, which resulted in an increase to net income and diluted EPS of $620 million and $0.08, \\nrespectively. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nSEGMENT RESULTS OF OPERATIONS \\n \\n(In millions, except percentages) 2022 2021 Percentage \\nChange \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 18% \\nIntelligent Cloud 75,251 60,080 25% \\nMore Personal Computing 59,655 54,093 10% \\nTotal $ 198,270 $ 168,088 18% \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 22% \\nIntelligent Cloud 32,721 26,126 25% \\nMore Personal Computing 20,975 19,439 8% \\nTotal $ 83,383 $ 69,916 19%', 'score': 0.04124021}, {'text': '33 Dynamics products and cloud services revenue growth Revenue from Dynamics products and cloud services, \\nincluding Dynamics 365, comprising a set of intelligent, \\ncloud -based applications across ERP, CRM, Customer \\nInsights, Power Apps, and Power Automate; and on -\\npremises ERP and CRM applications \\n \\nLinkedIn revenue growth Revenue from LinkedIn, including Talent Solutions, \\nMarketing Solutions, Premium Subscriptions, and Sales \\nSolutions \\n \\nServer products and cloud services revenue growth Revenue from Server products and cloud services, \\nincluding Azure and other cloud services; SQL Server, \\nWindows Server, Visual Studio, System Center, and \\nrelated Client Access Licenses (“CALs”); and Nuance and \\nGitHub \\nMore Personal Computing \\nMetrics related to our More Personal Computing segment assess the performance of key lines of business within this \\nsegment. These metrics provide strategic product insights which allow us to assess the performance across our \\ncommercial and consumer businesses. As we have diversity of target audiences and sales motions within the Windows \\nbusiness, we monitor metrics that are reflective of those varying motions. \\n \\nWindows OEM revenue growth Revenue from sales of Windows Pro and non -Pro licenses sold \\nthrough the OEM channel \\n \\nWindows Commercial products and cloud \\nservices revenue growth Revenue from Windows Commercial products and cloud services, \\ncomprising volume licensing of the Windows operating system, \\nWindows cloud services, and other Windows commercial offerings \\n \\nSurface revenue growth Revenue from Surface devices and accessories \\n \\nXbox content and services revenue growth Revenue from Xbox content and services, comprising first - and third -\\nparty content (including games and in -game content), Xbox Game \\nPass and other subscriptions, Xbox Cloud Gaming, third -party disc \\nroyalties, advertising, and other cloud services \\n \\nSearch and news advertising revenue, \\nexcluding TAC, growth Revenue from search and news advertising excluding traffic \\nacquisition costs (“TAC”) paid to Bing Ads network publishers and \\nnews partners \\nSUMMARY RESULTS OF OPERATIONS \\n \\n(In millions, except percentages and per share amounts) 2022 2021 Percentage \\nChange \\n \\nRevenue $ 198,270 $ 168,088 18% \\nGross margin 135,620 115,856 17% \\nOperating income 83,383 69,916 19% \\nNet income 72,738 61,271 19% \\nDiluted earnings per share 9.65 8.05 20% \\n \\nAdjusted net income (non -GAAP) 69,447 60,651 15% \\nAdjusted diluted earnings per share (non -GAAP) 9.21 7.97 16% \\nAdjusted net income and adjusted diluted earnings per share (“EPS”) are non -GAAP financial measures which exclude \\nthe net income tax benefit related to transfer of intangible properties in the first quarter of fiscal year 2022 and the', 'score': 0.00040568286}, {'text': '82 \\nIn addition, certain costs incurred at a corporate level that are identifiable and that benefit our segments are allocated to \\nthem. These allocated costs include legal, including settlements and fines, information technology, human resources, \\nfinance, excise taxes, field selling, shared facilities services, and customer service and support. Each allocation is \\nmeasured differently based on the specific facts and circumstances of the costs being allocated. \\nSegment revenue and operating income were as follows during the periods presented: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 $ 46,398 \\nIntelligent Cloud 75,251 60,080 48,366 \\nMore Personal Computing 59,655 54,093 48,251 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 $ 18,724 \\nIntelligent Cloud 32,721 26,126 18,324 \\nMore Personal Computing 20,975 19,439 15,911 \\nTotal $ 83,383 $ 69,916 $ 52,959 \\nNo sales to an individual customer or country other than the United States accounted for more than 10% of revenue for \\nfiscal years 2022, 2021, or 2020. Revenue, classified by the major geographic areas in which our customers were located, \\nwas as follows: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nUnited States (a) $ 100,218 $ 83,953 $ 73,160 \\nOther countries 98,052 84,135 69,855 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\n(a) Includes billings to OEMs and certain multinational organizations because of the nature of these businesses and the \\nimpracticability of determining the geographic source of the revenue. \\nRevenue, classified by significant product and service offerings, was as follows: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nServer products and cloud services $ 67,321 $ 52,589 $ 41,379 \\nOffice products and cloud services 44,862 39,872 35,316 \\nWindows 24,761 22,488 21,510 \\nGaming 16,230 15,370 11,575 \\nLinkedIn 13,816 10,289 8,077 \\nSearch and news advertising 11,591 9,267 8,524 \\nEnterprise Services 7,407 6,943 6,409 \\nDevices 6,991 6,791 6,457 \\nOther 5,291 4,479 3,768 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\nWe have recast certain previously reported amounts in the table above to conform to the way we internally manage and \\nmonitor our business.', 'score': 0.00026307348}]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# this how you can use this methods & pass this data to llm & get better results fro your RAG applications" + ], + "metadata": { + "id": "bzHa6zld2wQa" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "GowqlU-y2wda" + }, + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/examples/SuperAgent_Autogen_with_lancedb/README.md b/examples/SuperAgent_Autogen_with_lancedb/README.md new file mode 100644 index 00000000..29b3653c --- /dev/null +++ b/examples/SuperAgent_Autogen_with_lancedb/README.md @@ -0,0 +1,50 @@ +# Super Agent: Integrating Autogen Technologies and Vector Databases + +## Overview +![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/dafa79aa-2eb8-4fc9-8e01-048a08ab0319) + + +The Super Agent project showcases the integration of cutting-edge technologies including Ollama, LiteLLM, Autogen, LanceDB, and LangChain to create a powerful AI agent. This agent leverages the strengths of vector databases and conversational AI to provide sophisticated data management and context-aware interactions. The goal is to harness the capabilities of LanceDB's vector database and Autogen's conversational AI framework to build a Super Agent that excels in understanding and processing complex queries. + +## Installation + +For detailed installation instructions, please refer to our comprehensive guide available in the Google Colaboratory notebook: + +Google Colab Notebook Open In Colab + + +This guide will walk you through setting up each component of the Super Agent, ensuring a seamless integration process. + +## Building the Super Agent + +### Setting Up Autogen + +Autogen plays a pivotal role in developing sophisticated conversational AI agents. It enables the creation of dynamic agents capable of engaging in complex dialogues, significantly enhancing user interaction. + +### Integrating LiteLLM and Ollama + +- **LiteLLM**: Facilitates easy API connectivity with large language models, allowing for seamless integration and interaction. +- **Ollama**: Used for deploying large language models (LLMs) on a local system, Ollama ensures that the power of LLMs can be harnessed directly within your environment. + +### Leveraging LangChain for Contextual Awareness + +LangChain is instrumental in enhancing the Super Agent's ability to comprehend and act upon the context within user queries, providing more accurate and relevant responses. + +### Utilizing LanceDB for Efficient Data Management + +LanceDB is a vector database that excels in managing and querying large datasets efficiently, making it an essential component for the Super Agent. It allows for quick and precise searches, facilitating better data access and manipulation. + +## Implementation Steps + +The Super Agent's implementation involves several key steps: + +1. **Vector Store Creation**: Index the target PDF with LanceDB to create a searchable vector store. +2. **LangChain Setup**: Implement a Question-Answer (QA) chain with LangChain to enable context-aware querying. +3. **Autogen Agents Integration**: Configure user and assistant agents using Autogen for interactive and dynamic querying. + +## Resources + +- **Google Colab**: For hands-on examples and detailed code snippets for implementing the Super Agent, visit our [Google Colab Notebook](https://colab.research.google.com/drive/12LjrteGKfFOgJL8gB7KTZmJGvXda4KsJ?usp=sharing). + +- **Blog Post**: For a more in-depth exploration of the concepts and methodologies discussed in this project, please visit our [blog post](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases/). + diff --git a/examples/SuperAgent_Autogen_with_lancedb/main.ipynb b/examples/SuperAgent_Autogen_with_lancedb/main.ipynb new file mode 100644 index 00000000..7446400f --- /dev/null +++ b/examples/SuperAgent_Autogen_with_lancedb/main.ipynb @@ -0,0 +1,698 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "This notebook illustrates how you can use the langchain with custom pdf data & chat it using autogen agent" + ], + "metadata": { + "id": "lHFbPb4JU9xF" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3poVgyh-bZJ-", + "outputId": "ad799a6e-7eec-4e14-dae3-f7e86c9e67cc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.8/88.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m811.8/811.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m226.7/226.7 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.9/111.9 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m284.0/284.0 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.2/295.2 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.0/77.0 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.4/239.4 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.7/55.7 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.6/21.6 MB\u001b[0m \u001b[31m34.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.3/38.3 MB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.4/55.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "%pip install pyautogen~=0.1.0 langchain openai tiktoken lancedb pypdf -q -U" + ] + }, + { + "cell_type": "code", + "source": [ + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.document_loaders import PyPDFLoader\n", + "from langchain.memory import ConversationBufferMemory\n", + "from langchain.llms import OpenAI\n", + "from langchain.chains import ConversationalRetrievalChain\n", + "from langchain_community.vectorstores import LanceDB\n" + ], + "metadata": { + "id": "0tLTTT9ucFEb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Requirements\n", + "AutoGen requires Python>=3.8. To run this notebook example, please install pyautogen:\n" + ], + "metadata": { + "id": "ObNypsHRV3mz" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install pyautogen" + ], + "metadata": { + "id": "6RuVu12whCG0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import lancedb\n", + "\n", + "embeddings = OpenAIEmbeddings(openai_api_key='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')" + ], + "metadata": { + "id": "sUFdvTyVh8xF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "kztlyFIXU8m-" + } + }, + { + "cell_type": "code", + "source": [ + "# !wget -O uniswap_v3.pdf https://uniswap.org/whitepaper-v3.pdf\n", + "!wget -O food.pdf https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ODKg12trdhX-", + "outputId": "a7041322-f633-496c-a8e8-126a81cbb5d9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-02-11 04:40:16-- https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf\n", + "Resolving pdf.usaid.gov (pdf.usaid.gov)... 23.7.61.67, 2600:1408:ec00:380::1923, 2600:1408:ec00:38f::1923\n", + "Connecting to pdf.usaid.gov (pdf.usaid.gov)|23.7.61.67|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6419525 (6.1M) [application/pdf]\n", + "Saving to: ‘food.pdf’\n", + "\n", + "food.pdf 100%[===================>] 6.12M --.-KB/s in 0.1s \n", + "\n", + "2024-02-11 04:40:16 (42.7 MB/s) - ‘food.pdf’ saved [6419525/6419525]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "create OAI_CONFIG_LIST.json file in pwd & upload\n", + "in it\n", + "\n", + "\n", + "[\n", + " {\n", + " \"model\": \"gpt-4\",\n", + " \"api_key\": \"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\"\n", + " }\n", + "]" + ], + "metadata": { + "id": "1oC3NAFyd4Kb" + } + }, + { + "cell_type": "code", + "source": [ + "import autogen\n", + "\n", + "config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST.json\",\n", + " filter_dict={\n", + " \"model\": [\"gpt-4\"],\n", + " },\n", + ")" + ], + "metadata": { + "id": "H1bRXWu-cE_C" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# create file name with OAI_CONFIG_LIT.json & put below authentications code" + ], + "metadata": { + "id": "yV0pNPiRPy8h" + } + }, + { + "cell_type": "code", + "source": [ + "# create file name with OAI_CONFIG_LIT.\n", + "[\n", + " {\n", + " \"model\": \"gpt-4\",\n", + " \"api_key\": \"sk-yourapikey\"\n", + " }\n", + "]" + ], + "metadata": { + "id": "yWDhjTDMcFBi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "loaders = [ PyPDFLoader('./food.pdf') ]\n", + "docs = []\n", + "for l in loaders:\n", + " docs.extend(l.load())\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\n", + "docs = text_splitter.split_documents(docs)" + ], + "metadata": { + "id": "5gapqmsscFG-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import lancedb\n", + "\n", + "embeddings = OpenAIEmbeddings(openai_api_key='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')\n", + "\n", + "db = lancedb.connect(\"/tmp/lancedb\")\n", + "table = db.create_table(\n", + " \"my_table\",\n", + " data=[\n", + " {\n", + " \"vector\": embeddings.embed_query(\"Hello food\"),\n", + " \"text\": \"Hello food\",\n", + " \"id\": \"1\",\n", + " }\n", + " ],\n", + " mode=\"overwrite\",\n", + ")\n", + "\n", + "vectorstore = LanceDB.from_documents(docs, embeddings, connection=table)\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5dLkCqa0dLXV", + "outputId": "28ab5984-c875-4281-95e5-d48bfdd12e99" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", + " warn_deprecated(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "qa = ConversationalRetrievalChain.from_llm(\n", + " OpenAI(temperature=0,openai_api_key='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'),\n", + " vectorstore.as_retriever(),\n", + " memory=ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YMBoF5kucFMJ", + "outputId": "13d7edab-5f3d-4698-fe6f-40f33dcd865a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.llms.openai.OpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.\n", + " warn_deprecated(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def answer_food_question(question):\n", + " response = qa({\"question\": question})\n", + " return response[\"answer\"]" + ], + "metadata": { + "id": "HjSVygLIcSEX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "question = 'what is good food'\n", + "answer_food_question(question)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 160 + }, + "id": "XCqxSaQSepsW", + "outputId": "c1fc1bdc-9f2e-467b-cb51-fdde3fc964ae" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", + " warn_deprecated(\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "' Good food is food that provides the recommended amounts of nutrients for the body to perform all its physiological activities. It is important to eat the right food, at the right time, in the right amounts, and prepared correctly in order to maintain a balanced diet and promote good nutrition. Good food is essential for physical and cognitive development and can improve overall health and quality of life.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install pyautogen" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BY4Fz-l7cUCA", + "outputId": "3a10b926-3659-46d3-d76b-7e083daf8fca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pyautogen in /usr/local/lib/python3.10/dist-packages (0.1.14)\n", + "Requirement already satisfied: diskcache in /usr/local/lib/python3.10/dist-packages (from pyautogen) (5.6.3)\n", + "Requirement already satisfied: flaml in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.1.1)\n", + "Requirement already satisfied: openai<1 in /usr/local/lib/python3.10/dist-packages (from pyautogen) (0.28.1)\n", + "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from pyautogen) (1.0.1)\n", + "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.4.0)\n", + "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (2.31.0)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (4.66.1)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (3.9.3)\n", + "Requirement already satisfied: NumPy>=1.17.0rc1 in /usr/local/lib/python3.10/dist-packages (from flaml->pyautogen) (1.23.5)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.6)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2024.2.2)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.3.1)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (23.2.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.4.1)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (6.0.5)\n", + "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.9.4)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (4.0.3)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### 4. Set up AutoGen user agent and assistant agent with function calling enabled." + ], + "metadata": { + "id": "anvuLAIycaqb" + } + }, + { + "cell_type": "code", + "source": [ + "llm_config={\n", + " \"request_timeout\": 600,\n", + " \"seed\": 42,\n", + " \"config_list\": config_list,\n", + " \"temperature\": 0,\n", + " \"functions\": [\n", + " {\n", + " \"name\": \"answer_food_question\",\n", + " \"description\": \"Answer to any quetion related to food & provided answer_food_question \",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"question\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The question to ask in relation to food\",\n", + " }\n", + " },\n", + " \"required\": [\"question\"],\n", + " },\n", + " }\n", + " ],\n", + "}" + ], + "metadata": { + "id": "Vca8Y_khcUID" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# create an AssistantAgent instance named \"assistant\"\n", + "assistant = autogen.AssistantAgent(\n", + " name=\"assistant\",\n", + " llm_config=llm_config,\n", + ")\n", + "# create a UserProxyAgent instance named \"user_proxy\"\n", + "user_proxy = autogen.UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " human_input_mode=\"NEVER\",\n", + " max_consecutive_auto_reply=10,\n", + " code_execution_config={\"work_dir\": \".\"},\n", + " llm_config=llm_config,\n", + " system_message=\"\"\"Reply TERMINATE if the task has been solved at full satisfaction.\n", + "Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\"\"\",\n", + " function_map={\"answer_food_question\": answer_food_question}\n", + ")\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "1XHjzIYAcfE7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# the assistant receives a message from the user, which contains the task description\n", + "user_proxy.initiate_chat(\n", + " assistant,\n", + " message=\"\"\"\n", + "what is good food?\n", + "\"\"\"\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hOZKxakHchZ4", + "outputId": "7ca36fe0-a211-409a-abb0-57e3cd05e429" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user_proxy (to assistant):\n", + "\n", + "\n", + "what is good food?\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "***** Suggested function Call: answer_food_question *****\n", + "Arguments: \n", + "\n", + "{\n", + " \"question\": \"what is good food?\"\n", + "}\n", + "*********************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + ">>>>>>>> EXECUTING FUNCTION answer_food_question...\n", + "user_proxy (to assistant):\n", + "\n", + "***** Response from calling function \"answer_food_question\" *****\n", + " Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", + "*****************************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n", + "user_proxy (to assistant):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# the assistant receives a message from the user, which contains the task description\n", + "user_proxy.initiate_chat(\n", + " assistant,\n", + " message=\"\"\"\n", + "please explain me essential minerals, sources, functions and symptoms of\n", + "deficiency?\n", + "\"\"\"\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UDXo2V06fNjz", + "outputId": "37cf6766-9b68-4a81-e0c6-245d2af28a30" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user_proxy (to assistant):\n", + "\n", + "\n", + "please explain me essential minerals, sources, functions and symptoms of\n", + "deficiency?\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "Sure, here are some essential minerals, their sources, functions, and symptoms of deficiency:\n", + "\n", + "1. Calcium:\n", + " - Sources: Dairy products, leafy green vegetables, fish with edible bones (like sardines and salmon), fortified foods.\n", + " - Functions: Necessary for bone health, muscle function, nerve transmission, blood clotting.\n", + " - Deficiency Symptoms: Osteoporosis, rickets in children, muscle cramps, dental problems.\n", + "\n", + "2. Iron:\n", + " - Sources: Red meat, poultry, eggs, fruits, green vegetables, fortified bread.\n", + " - Functions: Essential for the production of red blood cells, helps in oxygen transport.\n", + " - Deficiency Symptoms: Anemia, fatigue, weakness, immune system problems.\n", + "\n", + "3. Magnesium:\n", + " - Sources: Nuts, seeds, whole grains, green leafy vegetables, fish, beans, yogurt.\n", + " - Functions: Helps in over 300 enzyme reactions, including regulation of blood pressure, supports immune system.\n", + " - Deficiency Symptoms: Loss of appetite, nausea, fatigue, weakness, muscle cramps, numbness.\n", + "\n", + "4. Potassium:\n", + " - Sources: Bananas, oranges, cantaloupe, honeydew, apricots, grapefruit, cooked spinach, cooked broccoli, potatoes, sweet potatoes, mushrooms, peas, cucumbers, zucchini, eggplant, pumpkins, leafy greens.\n", + " - Functions: Maintains fluid balance, helps in nerve transmission and muscle contraction.\n", + " - Deficiency Symptoms: Fatigue, weakness, constipation, muscle cramps.\n", + "\n", + "5. Zinc:\n", + " - Sources: Meat, shellfish, legumes, seeds, nuts, dairy, eggs, whole grains.\n", + " - Functions: Necessary for immune function, protein synthesis, DNA synthesis, cell division, wound healing.\n", + " - Deficiency Symptoms: Growth retardation, loss of appetite, impaired immune function, hair loss, diarrhea, delayed sexual maturation.\n", + "\n", + "Please note that this is not an exhaustive list and there are other essential minerals as well. Also, the symptoms of deficiency can vary from person to person and can often be symptoms of other conditions as well. Always consult with a healthcare provider for accurate information.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "user_proxy (to assistant):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# the assistant receives a message from the user, which contains the task description\n", + "user_proxy.initiate_chat(\n", + " assistant,\n", + " message=\"\"\"\n", + "which food Keeps eyes healthy ?\n", + "\"\"\"\n", + ")" + ], + "metadata": { + "id": "UrlFGYW0g0sJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e82396e5-3dca-402c-889c-394557aeea0d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user_proxy (to assistant):\n", + "\n", + "\n", + "which food Keeps eyes healthy ?\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "***** Suggested function Call: answer_food_question *****\n", + "Arguments: \n", + "\n", + "{\n", + " \"question\": \"which food Keeps eyes healthy ?\"\n", + "}\n", + "*********************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + ">>>>>>>> EXECUTING FUNCTION answer_food_question...\n", + "user_proxy (to assistant):\n", + "\n", + "***** Response from calling function \"answer_food_question\" *****\n", + " Foods that are rich in Vitamin A, such as yellow/orange fruits and vegetables, dark green and deep yellow fruits and vegetables, liver, egg yolk, dairy products, and margarine can help maintain healthy eyes.\n", + "*****************************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "Foods that are rich in Vitamin A can help maintain healthy eyes. These include:\n", + "\n", + "1. Yellow/orange fruits and vegetables: These include carrots, sweet potatoes, pumpkins, and apricots.\n", + "2. Dark green and deep yellow fruits and vegetables: These include spinach, kale, and other leafy greens.\n", + "3. Liver: This is a great source of Vitamin A.\n", + "4. Egg yolk: This is another good source of Vitamin A.\n", + "5. Dairy products: These include milk, cheese, and yogurt.\n", + "6. Margarine: This is also a good source of Vitamin A.\n", + "\n", + "Including these foods in your diet can help keep your eyes healthy.\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n", + "user_proxy (to assistant):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "En6-kvjcjaid" + }, + "execution_count": null, + "outputs": [] + } + ] +} From 66dec3ba157bf74ec8f7bfe0cf2d3d394036c3dd Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Thu, 14 Mar 2024 03:04:38 +0530 Subject: [PATCH 03/32] Fix (#155) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test --- .github/workflows/examples-test.yml | 4 +- README.md | 7 +- assets/RAG-flow.png | Bin 0 -> 71519 bytes assets/query-expansion.png | Bin 0 -> 34488 bytes assets/superagent-autogen.png | Bin 0 -> 107325 bytes compile_testing.js | 2 +- .../Inbuilt_Hybrid_Search_with_LanceDB.ipynb | 1969 ++++++ .../README.md | 11 +- examples/QueryExpansion&Reranker/main.ipynb | 5266 +++++++++++++++++ .../Query_expansion_&_Reranker/main.ipynb | 5251 ---------------- .../README.md | 10 +- examples/SuperAgent_Autogen/main.ipynb | 697 +++ .../main.ipynb | 698 --- .../RAG-from-Scratch/RAG_from_Scratch.ipynb | 2341 ++++++++ 14 files changed, 10293 insertions(+), 5963 deletions(-) create mode 100644 assets/RAG-flow.png create mode 100644 assets/query-expansion.png create mode 100644 assets/superagent-autogen.png create mode 100644 examples/Inbuilt-Hybrid-Search/Inbuilt_Hybrid_Search_with_LanceDB.ipynb rename examples/{Query_expansion_&_Reranker => QueryExpansion&Reranker}/README.md (67%) create mode 100644 examples/QueryExpansion&Reranker/main.ipynb delete mode 100644 examples/Query_expansion_&_Reranker/main.ipynb rename examples/{SuperAgent_Autogen_with_lancedb => SuperAgent_Autogen}/README.md (80%) create mode 100644 examples/SuperAgent_Autogen/main.ipynb delete mode 100644 examples/SuperAgent_Autogen_with_lancedb/main.ipynb create mode 100644 tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb diff --git a/.github/workflows/examples-test.yml b/.github/workflows/examples-test.yml index b437aea6..012a173e 100644 --- a/.github/workflows/examples-test.yml +++ b/.github/workflows/examples-test.yml @@ -46,7 +46,7 @@ jobs: run: | for folder in *; do echo "$folder"; - if [[ $folder == multimodal_clip ]]; then + if [[ $folder == multimodal_clip_diffusiondb ]]; then continue fi if [ ! -f "$folder"/test.py ]; then @@ -59,7 +59,7 @@ jobs: echo "$file"; python -m pip install -r "$file"; pip uninstall lancedb -y - pip install "lancedb @ git+https://github.com/lancedb/lancedb.git#egg=subdir&subdirectory=python" + pip install lancedb fi done for file in *; do diff --git a/README.md b/README.md index 9c6c3d17..fd1959b3 100644 --- a/README.md +++ b/README.md @@ -33,6 +33,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | | | | | [Youtube transcript search bot](/examples/Youtube-Search-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Youtube-Search-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Youtube-Search-QA-Bot/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#)|| | [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Code-Documentation-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Code-Documentation-QA-Bot/index.js)[![LLM](https://img.shields.io/badge/openai-api-white)](#)|| +| [Inbuilt Hybrid Search](/examples/Inbuilt-Hybrid-Search) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) || | [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/reducing_hallucinations_ai_agents/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/reducing_hallucinations_ai_agents/index.js)[![LLM](https://img.shields.io/badge/openai-api-white)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f)| | [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_clip_diffusiondb/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939)| | [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_video_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939)| @@ -49,7 +50,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)| | [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)| | [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)| -| [Query_expansion and Reranker ](/examples/Query_expansion_&_Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| +| [Query Expansion and Reranker ](/examples/QueryExpansion&Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| | [RAG Fusion](/examples/RAG_Fusion/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)| | [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)| | [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| | @@ -60,7 +61,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| | [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301) | | [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| -| [SuperAgent_Autogen_with_LanceDB](/examples/SuperAgent_Autogen_with_lancedb/) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases/)| +| [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases)| @@ -89,6 +90,8 @@ Looking to get started with LLMs, vectorDBs, and the world of Generative AI? The | Tutorial | Interactive Environment | Blog Link | | --------- | -------------------------- | ----------- | | | | | +| [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | | +| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | | | [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [LLMs, RAG, & the missing storage layer for AI](https://medium.com/etoai/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | diff --git a/assets/RAG-flow.png 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get_registry().get(\"openai\").create()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8eKRYd2F7v5n" + }, + "source": [ + "# Load `Chunks` of data from [BeIR Dataset](https://huggingface.co/datasets/BeIR/scidocs)\n", + "\n", + "Note: This is a dataset built specially for retrieval tasks to see how good your search is working" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 313 + }, + "id": "l0ezDr7suAf_", + "outputId": "0424f34c-f5de-43af-88a9-148ce5d7cf45" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":5: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " docs[\"num_words\"] = docs[\"text\"].apply(lambda x: len(x.split())) # Insert some Metadata for a more \"HYBRID\" search\n" + ] + }, + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \"docs\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"_id\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"9d1940f843c448cc378214ff6bad3c1279b1911a\",\n \"8e508720cdb495b7821bf6e43c740eeb5f3a444a\",\n \"4b53f660eb6cfe9180f9e609ad94df8606724a3d\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Shape-aware Instance Segmentation\",\n \"Learning Scalable Deep Kernels with Recurrent\\nStructure\",\n \"Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"We address the problem of instance-level semantic segmentation, which aims at jointly detecting, segmenting and classifying every individual object in an image. In this context, existing methods typically propose candidate objects, usually as bounding boxes, and directly predict a binary mask within each such proposal. As a consequence, they cannot recover from errors in the object candidate generation process, such as too small or shifted boxes. In this paper, we introduce a novel object segment representation based on the distance transform of the object masks. We then design an object mask network (OMN) with a new residual-deconvolution architecture that infers such a representation and decodes it into the final binary object mask. This allows us to predict masks that go beyond the scope of the bounding boxes and are thus robust to inaccurate object candidates. We integrate our OMN into a Multitask Network Cascade framework, and learn the resulting shape-aware instance segmentation (SAIS) network in an end-to-end manner. Our experiments on the PASCAL VOC 2012 and the CityScapes datasets demonstrate the benefits of our approach, which outperforms the state-of-the-art in both object proposal generation and instance segmentation.\",\n \"Many applications in speech, robotics, finance, and biology deal with sequential data, where ordering matters and recurrent structures are common. However, this structure cannot be easily captured by standard kernel functions. To model such structure, we propose expressive closed-form kernel functions for Gaussian processes. The resulting model, GP-LSTM, fully encapsulates the inductive biases of long short-term memory (LSTM) recurrent networks, while retaining the non-parametric probabilistic advantages of Gaussian processes. We learn the properties of the proposed kernels by optimizing the Gaussian process marginal likelihood using a new provably convergent semi-stochastic gradient procedure, and exploit the structure of these kernels for scalable training and prediction. This approach provides a practical representation for Bayesian LSTMs. We demonstrate state-of-the-art performance on several benchmarks, and thoroughly investigate a consequential autonomous driving application, where the predictive uncertainties provided by GP-LSTM are uniquely valuable.\",\n \"In this paper a novel approach is proposed to predict intraday directional-movements of a currency-pair in the foreign exchange market based on the text of breaking financial news-headlines. The motivation behind this work is twofold: First, although market-prediction through text-mining is shown to be a promising area of work in the literature, the text-mining approaches utilized in it at this stage are not much beyond basic ones as it is still an emerging field. This work is an effort to put more emphasis on the text-mining methods and tackle some specific aspects thereof that are weak in previous works, namely: the problem of high dimensionality as well as the problem of ignoring sentiment and semantics in dealing with textual language. This research assumes that addressing these aspects of text-mining have an impact on the quality of the achieved results. The proposed system proves this assumption to be right. The second part of the motivation is to research a specific market, namely, the foreign exchange market, which seems not to have been researched in the previous works based on predictive text-mining. Therefore, results of this work also successfully demonstrate a predictive relationship between this specific market-type and the textual data of news. Besides the above two main components of the motivation, there are other specific aspects that make the setup of the proposed system and the conducted experiment unique, for example, the use of news article-headlines only and not news article-bodies, which enables usage of short pieces of text rather than long ones; or the use of general financial breaking news without any further filtration. In order to accomplish the above, this work produces a multi-layer algorithm that tackles each of the mentioned aspects of the text-mining problem at a designated layer. The first layer is termed the Semantic Abstraction Layer and addresses the problem of co-reference in text mining that is contributing to sparsity. Co-reference occurs when two or more words in a text corpus refer to the same concept. This work produces a custom approach by the name of Heuristic-Hypernyms Feature-Selection which creates a way to recognize words with the same parent-word to be regarded as one entity. As a result, prediction accuracy increases significantly at this layer which is attributed to appropriate noise-reduction from the feature-space. The second layer is termed Sentiment Integration Layer, which integrates sentiment analysis capability into the algorithm by proposing a sentiment weight by the name of SumScore that reflects investors\\u2019 sentiment. Additionally, this layer reduces the dimensions by eliminating those that are of zero value in terms of sentiment and thereby improves prediction accuracy. The third layer encompasses a dynamic model creation algorithm, termed Synchronous Targeted Feature Reduction (STFR). It is suitable for the challenge at hand whereby the mining of a stream of text is concerned. It updates the models with the most recent information available and, more importantly, it ensures that the dimensions are reduced to the absolute minimum. The algorithm and each of its layers are extensively evaluated using real market data and news content across multiple years and have proven to be solid and superior to any other comparable solution. The proposed techniques implemented in the system, result in significantly high directional-accuracies of up to 83.33%. On top of a well-rounded multifaceted algorithm, this work contributes a much needed research framework for this context with a test-bed of data that must make future research endeavors more convenient. The produced algorithm is scalable and its modular design allows improvement in each of its layers in future research. This paper provides ample details to reproduce the entire system and the conducted experiments. 2014 Elsevier Ltd. All rights reserved. A. Khadjeh Nassirtoussi et al. / Expert Systems with Applications 42 (2015) 306\\u2013324 307\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_words\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 193,\n \"min\": 138,\n \"max\": 621,\n \"num_unique_values\": 5,\n \"samples\": [\n 187,\n 138,\n 621\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "

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359ea16bc34448ca9d713f4501f1a6215a26746372A survey of software testing practices in albertaSoftware organizations have typically de-empha...236
429d1940f843c448cc378214ff6bad3c1279b1911aShape-aware Instance SegmentationWe address the problem of instance-level seman...187
314b53f660eb6cfe9180f9e609ad94df8606724a3dText mining of news-headlines for FOREX market...In this paper a novel approach is proposed to ...621
21b579366db457216b0548220bf369ab9eb183a0ccAn analysis on the significance of ticket anal...Software even though intangible should undergo...232
298e508720cdb495b7821bf6e43c740eeb5f3a444aLearning Scalable Deep Kernels with Recurrent\\...Many applications in speech, robotics, finance...138
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\n" + ], + "text/plain": [ + " _id \\\n", + "35 9ea16bc34448ca9d713f4501f1a6215a26746372 \n", + "42 9d1940f843c448cc378214ff6bad3c1279b1911a \n", + "31 4b53f660eb6cfe9180f9e609ad94df8606724a3d \n", + "21 b579366db457216b0548220bf369ab9eb183a0cc \n", + "29 8e508720cdb495b7821bf6e43c740eeb5f3a444a \n", + "\n", + " title \\\n", + "35 A survey of software testing practices in alberta \n", + "42 Shape-aware Instance Segmentation \n", + "31 Text mining of news-headlines for FOREX market... \n", + "21 An analysis on the significance of ticket anal... \n", + "29 Learning Scalable Deep Kernels with Recurrent\\... \n", + "\n", + " text num_words \n", + "35 Software organizations have typically de-empha... 236 \n", + "42 We address the problem of instance-level seman... 187 \n", + "31 In this paper a novel approach is proposed to ... 621 \n", + "21 Software even though intangible should undergo... 232 \n", + "29 Many applications in speech, robotics, finance... 138 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "queries = load_dataset(\"BeIR/scidocs\", \"queries\")[\"queries\"].to_pandas()\n", + "full_docs = (\n", + " load_dataset(\"BeIR/scidocs\", \"corpus\")[\"corpus\"].to_pandas().dropna(subset=\"text\")\n", + ")\n", + "\n", + "docs = full_docs.head(64) # just random samples for faster embed demo\n", + "docs[\"num_words\"] = docs[\"text\"].apply(\n", + " lambda x: len(x.split())\n", + ") # Insert some Metadata for a more \"HYBRID\" search\n", + "docs.sample(5)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HJf8xZmX8VJC" + }, + "source": [ + "# Build New Table" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "GkhFiOZAwVWw" + }, + "outputs": [], + "source": [ + "!rm -rf ./db" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "5aljyqpUiViE" + }, + "outputs": [], + "source": [ + "class Documents(LanceModel):\n", + " vector: Vector(embeddings.ndims()) = embeddings.VectorField()\n", + " text: str = embeddings.SourceField()\n", + " title: str\n", + " num_words: int\n", + "\n", + "\n", + "data = docs.apply(\n", + " lambda row: {\n", + " \"title\": row[\"title\"],\n", + " \"text\": row[\"text\"],\n", + " \"num_words\": row[\"num_words\"],\n", + " },\n", + " axis=1,\n", + ").values.tolist()\n", + "\n", + "db = lancedb.connect(\"./db\")\n", + "table = db.create_table(\"documents\", schema=Documents)\n", + "\n", + "table.add(data) # ingest docs with auto-vectorization\n", + "table.create_fts_index(\"text\") # Create a fts index before the hybrid search" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "OS-soek-yYm4", + "outputId": "9c66ba15-11a8-44e4-eacb-1a2a57fc72f6" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \" query_type=\\\"fts\\\")\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"vector\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Hierarchical Pitman-Yor Process priors are compelling methods for learning language models, outperforming point-estimate based methods. However, these models remain unpopular due to computational and statistical inference issues, such as memory and time usage, as well as poor mixing of sampler. In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees. Then, we develop an efficient approximate inference scheme in this framework that has a much lower memory footprint compared to full HPYP and is fast in the inference time. The experimental results illustrate that our model can be built on significantly larger datasets compared to previous HPYP models, while being several orders of magnitudes smaller, fast for training and inference, and outperforming the perplexity of the state-of-the-art Modified Kneser-Ney countbased LM smoothing by up to 15%.\",\n \"Software organizations have typically de-emphasized the importance of software testing. In this paper, the results of a regional survey of software testing and software quality assurance techniques are described. Researchers conducted the study during the summer and fall of 2002 by surveying software organizations in the Province of Alberta. Results indicate that Alberta-based organizations tend to test less than their counterparts in the United States. The results also indicate that Alberta software organizations tend to train fewer personnel on testing-related topics. This practice has the potential for a two-fold impact: first, the ability to detect trends that lead to reduced quality and to identify the root causes of reductions in product quality may suffer from the lack of testing. This consequence is serious enough to warrant consideration, since overall quality may suffer from the reduced ability to detect and eliminate process or product defects. Second, the organization may have a more difficult time adopting methodologies such as extreme programming. This is significant because other industry studies have concluded that many software organizations have tried or will in the next few years try some form of agile method. Newer approaches to software development like extreme programming increase the extent to which teams rely on testing skills. Organizations should consider their testing skill level as a key indication of their readiness for adopting software development techniques such as test-driven development, extreme programming, agile modelling, or other agile methods.\",\n \"We introduce a long short-term memory recurrent neural network (LSTM-RNN) approach for real-time facial animation, which automatically estimates head rotation and facial action unit activations of a speaker from just her speech. Specifically, the time-varying contextual non-linear mapping between audio stream and visual facial movements is realized by training a LSTM neural network on a large audio-visual data corpus. In this work, we extract a set of acoustic features from input audio, including Mel-scaled spectrogram, Mel frequency cepstral coefficients and chromagram that can effectively represent both contextual progression and emotional intensity of the speech. Output facial movements are characterized by 3D rotation and blending expression weights of a blendshape model, which can be used directly for animation. Thus, even though our model does not explicitly predict the affective states of the target speaker, her emotional manifestation is recreated via expression weights of the face model. Experiments on an evaluation dataset of different speakers across a wide range of affective states demonstrate promising results of our approach in real-time speech-driven facial animation.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Compressed Nonparametric Language Modelling\",\n \"A survey of software testing practices in alberta\",\n \"Speech-driven 3 D Facial Animation with Implicit Emotional Awareness : A Deep Learning Approach\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_words\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 51,\n \"min\": 112,\n \"max\": 236,\n \"num_unique_values\": 5,\n \"samples\": [\n 134,\n 236,\n 171\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"score\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 0.3946595562275909,\n \"min\": 6.037307262420654,\n \"max\": 6.8111042976379395,\n \"num_unique_values\": 5,\n \"samples\": [\n 6.782823085784912,\n 6.037307262420654,\n 6.157275199890137\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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Clickbait dissatisfies users because the article content does not match their expectation. Thus, clickbait detection has attracted more and more attention recently. Traditional clickbait-detection methods rely on heavy feature engineering and fail to distinguish clickbait from normal headlines precisely because of the limited information in headlines. A convolutional neural network is useful for clickbait detection, since it utilizes pretrained Word2Vec to understand the headlines semantically, and employs different kernels to find various characteristics of the headlines. However, different types of articles tend to use different ways to draw users\\u2019 attention, and a pretrained Word2Vec model cannot distinguish these different ways. To address this issue, we propose a clickbait convolutional neural network (CBCNN) to consider not only the overall characteristics but also specific characteristics from different article types. Our experimental results show that our method outperforms traditional clickbait-detection algorithms and the TextCNN model in terms of precision, recall and accuracy.\",\n \"a r t i c l e i n f o a b s t r a c t We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks.\",\n \"This paper proposes quiz-style information presentation for interactive systems as a means to improve user understanding in educational tasks. Since the nature of quizzes can highly motivate users to stay voluntarily engaged in the interaction and keep their attention on receiving information, it is expected that information presented as quizzes can be better understood by users. To verify the effectiveness of the approach, we implemented read-out and quiz systems and performed comparison experiments using human subjects. In the task of memorizing biographical facts, the results showed that user understanding for the quiz system was significantly better than that for the read-out system, and that the subjects were more willing to use the quiz system despite the long duration of the quizzes. 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However, these models remain unpopular due to computational and statistical inference issues, such as memory and time usage, as well as poor mixing of sampler. In this work we propose a novel framework which represents the HPYP model compactly using compressed suffix trees. Then, we develop an efficient approximate inference scheme in this framework that has a much lower memory footprint compared to full HPYP and is fast in the inference time. The experimental results illustrate that our model can be built on significantly larger datasets compared to previous HPYP models, while being several orders of magnitudes smaller, fast for training and inference, and outperforming the perplexity of the state-of-the-art Modified Kneser-Ney countbased LM smoothing by up to 15%.\",\n \"We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.\",\n \"a r t i c l e i n f o a b s t r a c t We present an automatic approach to the construction of BabelNet, a very large, wide-coverage multilingual semantic network. Key to our approach is the integration of lexicographic and encyclopedic knowledge from WordNet and Wikipedia. In addition, Machine Translation is applied to enrich the resource with lexical information for all languages. We first conduct in vitro experiments on new and existing gold-standard datasets to show the high quality and coverage of BabelNet. We then show that our lexical resource can be used successfully to perform both monolingual and cross-lingual Word Sense Disambiguation: thanks to its wide lexical coverage and novel semantic relations, we are able to achieve state-of the-art results on three different SemEval evaluation tasks.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Compressed Nonparametric Language Modelling\",\n \"Deep Voice 2 : Multi-Speaker Neural Text-to-Speech\",\n \"BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_words\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 23,\n \"min\": 133,\n \"max\": 187,\n \"num_unique_values\": 5,\n \"samples\": [\n 134,\n 151,\n 133\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"_relevance_score\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 5,\n \"samples\": [\n 0.6744159460067749,\n 0.1125519722700119,\n 0.25645574927330017\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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Remove all the rows which contain a specific term, in out case, `\"dual-band\"`\n", + "2. Keep only the rows which have `num_words > 100`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 206 + }, + "id": "vfMIXT2vE9yv", + "outputId": "90b28f90-c614-446a-b5b5-b72b32ec0ef5" + }, + "outputs": [ + { + "data": { + "application/vnd.google.colaboratory.intrinsic+json": { + "summary": "{\n \"name\": \" to_pandas()\",\n \"rows\": 5,\n \"fields\": [\n {\n \"column\": \"vector\",\n \"properties\": {\n \"dtype\": \"object\",\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"text\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"We introduce a long short-term memory recurrent neural network (LSTM-RNN) approach for real-time facial animation, which automatically estimates head rotation and facial action unit activations of a speaker from just her speech. Specifically, the time-varying contextual non-linear mapping between audio stream and visual facial movements is realized by training a LSTM neural network on a large audio-visual data corpus. In this work, we extract a set of acoustic features from input audio, including Mel-scaled spectrogram, Mel frequency cepstral coefficients and chromagram that can effectively represent both contextual progression and emotional intensity of the speech. Output facial movements are characterized by 3D rotation and blending expression weights of a blendshape model, which can be used directly for animation. Thus, even though our model does not explicitly predict the affective states of the target speaker, her emotional manifestation is recreated via expression weights of the face model. Experiments on an evaluation dataset of different speakers across a wide range of affective states demonstrate promising results of our approach in real-time speech-driven facial animation.\",\n \"In this paper a novel approach is proposed to predict intraday directional-movements of a currency-pair in the foreign exchange market based on the text of breaking financial news-headlines. The motivation behind this work is twofold: First, although market-prediction through text-mining is shown to be a promising area of work in the literature, the text-mining approaches utilized in it at this stage are not much beyond basic ones as it is still an emerging field. This work is an effort to put more emphasis on the text-mining methods and tackle some specific aspects thereof that are weak in previous works, namely: the problem of high dimensionality as well as the problem of ignoring sentiment and semantics in dealing with textual language. This research assumes that addressing these aspects of text-mining have an impact on the quality of the achieved results. The proposed system proves this assumption to be right. The second part of the motivation is to research a specific market, namely, the foreign exchange market, which seems not to have been researched in the previous works based on predictive text-mining. Therefore, results of this work also successfully demonstrate a predictive relationship between this specific market-type and the textual data of news. Besides the above two main components of the motivation, there are other specific aspects that make the setup of the proposed system and the conducted experiment unique, for example, the use of news article-headlines only and not news article-bodies, which enables usage of short pieces of text rather than long ones; or the use of general financial breaking news without any further filtration. In order to accomplish the above, this work produces a multi-layer algorithm that tackles each of the mentioned aspects of the text-mining problem at a designated layer. The first layer is termed the Semantic Abstraction Layer and addresses the problem of co-reference in text mining that is contributing to sparsity. Co-reference occurs when two or more words in a text corpus refer to the same concept. This work produces a custom approach by the name of Heuristic-Hypernyms Feature-Selection which creates a way to recognize words with the same parent-word to be regarded as one entity. As a result, prediction accuracy increases significantly at this layer which is attributed to appropriate noise-reduction from the feature-space. The second layer is termed Sentiment Integration Layer, which integrates sentiment analysis capability into the algorithm by proposing a sentiment weight by the name of SumScore that reflects investors\\u2019 sentiment. Additionally, this layer reduces the dimensions by eliminating those that are of zero value in terms of sentiment and thereby improves prediction accuracy. The third layer encompasses a dynamic model creation algorithm, termed Synchronous Targeted Feature Reduction (STFR). It is suitable for the challenge at hand whereby the mining of a stream of text is concerned. It updates the models with the most recent information available and, more importantly, it ensures that the dimensions are reduced to the absolute minimum. The algorithm and each of its layers are extensively evaluated using real market data and news content across multiple years and have proven to be solid and superior to any other comparable solution. The proposed techniques implemented in the system, result in significantly high directional-accuracies of up to 83.33%. On top of a well-rounded multifaceted algorithm, this work contributes a much needed research framework for this context with a test-bed of data that must make future research endeavors more convenient. The produced algorithm is scalable and its modular design allows improvement in each of its layers in future research. This paper provides ample details to reproduce the entire system and the conducted experiments. 2014 Elsevier Ltd. All rights reserved. A. Khadjeh Nassirtoussi et al. / Expert Systems with Applications 42 (2015) 306\\u2013324 307\",\n \"We introduce a technique for augmenting neural text-to-speech (TTS) with lowdimensional trainable speaker embeddings to generate different voices from a single model. As a starting point, we show improvements over the two state-ofthe-art approaches for single-speaker neural TTS: Deep Voice 1 and Tacotron. We introduce Deep Voice 2, which is based on a similar pipeline with Deep Voice 1, but constructed with higher performance building blocks and demonstrates a significant audio quality improvement over Deep Voice 1. We improve Tacotron by introducing a post-processing neural vocoder, and demonstrate a significant audio quality improvement. We then demonstrate our technique for multi-speaker speech synthesis for both Deep Voice 2 and Tacotron on two multi-speaker TTS datasets. We show that a single neural TTS system can learn hundreds of unique voices from less than half an hour of data per speaker, while achieving high audio quality synthesis and preserving the speaker identities almost perfectly.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"title\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 5,\n \"samples\": [\n \"Speech-driven 3 D Facial Animation with Implicit Emotional Awareness : A Deep Learning Approach\",\n \"Text mining of news-headlines for FOREX market prediction: A Multi-layer Dimension Reduction Algorithm with semantics and sentiment\",\n \"Deep Voice 2 : Multi-Speaker Neural Text-to-Speech\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"num_words\",\n \"properties\": {\n \"dtype\": \"number\",\n \"std\": 196,\n \"min\": 151,\n \"max\": 621,\n \"num_unique_values\": 5,\n \"samples\": [\n 171,\n 621,\n 151\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"_relevance_score\",\n \"properties\": {\n \"dtype\": \"float32\",\n \"num_unique_values\": 5,\n \"samples\": [\n 0.2986561357975006,\n 0.10069400072097778,\n 0.20968052744865417\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}", + "type": "dataframe" + }, + "text/html": [ + "\n", + "
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You can hard code or pass dynamically too\n", + "\n", + " return pa.Table.from_pandas(df)\n", + "\n", + "\n", + "modified_reranker = MofidifiedLinearReranker(filters=[\"dual-band\"])\n", + "\n", + "table.search(\n", + " \"To confuse the AI and DNN embedding, let's put random terms from other sentences- automation training test memory?\",\n", + " query_type=\"hybrid\",\n", + ").rerank(reranker=modified_reranker).limit(7).to_pandas()" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/examples/Query_expansion_&_Reranker/README.md b/examples/QueryExpansion&Reranker/README.md similarity index 67% rename from examples/Query_expansion_&_Reranker/README.md rename to examples/QueryExpansion&Reranker/README.md index c10f77d3..b89b35d8 100644 --- a/examples/Query_expansion_&_Reranker/README.md +++ b/examples/QueryExpansion&Reranker/README.md @@ -1,5 +1,7 @@ +Open In Colab + ## Enhancing RAG with Query Expansion & Reranking Models -![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/8d1c28a3-4129-43f6-a76c-b633348e61a7) +![image](../../assets/query-expansion.png) ### Overview @@ -10,11 +12,10 @@ Our focus is on improving the precision and recall of document retrieval process ### Key Features - Query Expansion: Utilizes Large Language Models (LLMs) to generate meaningful expansions of search queries, addressing issues like query ambiguity and improving document matching. - Reranking Methods: Implements advanced reranking models including Cross-Encoder reranking, ColBERT v2, and FlashRank to refine search results and prioritize relevant documents. + + Open In Colab -### code -Google Colab walkthrough Open In Colab - -### Learn deeper in Our Blog +### Blog For a detailed exploration of the concepts and methodologies discussed in this project, visit our blog diff --git a/examples/QueryExpansion&Reranker/main.ipynb b/examples/QueryExpansion&Reranker/main.ipynb new file mode 100644 index 00000000..7a010038 --- /dev/null +++ b/examples/QueryExpansion&Reranker/main.ipynb @@ -0,0 +1,5266 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + }, + "widgets": { + "application/vnd.jupyter.widget-state+json": { + "512a4a1ac0a54087b19b3ab709d408a5": { + "model_module": "@jupyter-widgets/controls", + "model_name": "HBoxModel", + "model_module_version": "1.5.0", + "state": { + "_dom_classes": [], + "_model_module": "@jupyter-widgets/controls", + "_model_module_version": "1.5.0", + "_model_name": "HBoxModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/controls", + "_view_module_version": "1.5.0", + "_view_name": "HBoxView", + 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"Requirement already satisfied: mypy-extensions>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from typing-inspect<1,>=0.4.0->dataclasses-json<0.7,>=0.5.7->langchain) (1.0.0)\n" + ] + } + ], + "source": [ + "!pip install lancedb langchain openai tiktoken pypdf tantivy sentence-transformers" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# download your choice pdf file & upload here\n", + "\n", + " https://github.com/akashAD98/dummy_data/blob/main/microsoft_annual_report_2022.pdf" + ], + "metadata": { + "id": "je__kyDIyI62" + } + }, + { + "cell_type": "code", + "source": [ + "import os\n", + "import time\n", + "import lancedb\n", + "import getpass\n", + "import pandas as pd\n", + "from lancedb.embeddings import EmbeddingFunctionRegistry\n", + "from lancedb.pydantic import LanceModel, Vector\n", + "from lancedb.embeddings import get_registry\n", + "from langchain.text_splitter import CharacterTextSplitter\n", + "from langchain.document_loaders import PyPDFLoader\n", + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.embeddings import HuggingFaceEmbeddings\n", + "from langchain_community.vectorstores import LanceDB\n", + "\n", + "\n", + "# Set the OPENAI_API_KEY environment variable\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-yourkey\"" + ], + "metadata": { + "id": "QcfZ_lr6FrEA" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def word_wrap(string, n_chars=72):\n", + " # Wrap a string at the next space after n_chars\n", + " if len(string) < n_chars:\n", + " return string\n", + " else:\n", + " return (\n", + " string[:n_chars].rsplit(\" \", 1)[0]\n", + " + \"\\n\"\n", + " + word_wrap(string[len(string[:n_chars].rsplit(\" \", 1)[0]) + 1 :], n_chars)\n", + " )" + ], + "metadata": { + "id": "NtbIb3QPjfuW" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We are using langchain fro loading the pdf file & doing prprocessing" + ], + "metadata": { + "id": "7JSJy4AROGOM" + } + }, + { + "cell_type": "code", + "source": [ + "# your pdf file\n", + "loader = PyPDFLoader(\"/content/microsoft_annual_report_2022.pdf\")\n", + "documents = loader.load_and_split()\n", + "\n", + "# split it into chunks\n", + "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", + "docsn = text_splitter.split_documents(documents)\n", + "\n", + "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n", + "\n", + "docsearch = LanceDB.from_documents(docsn, embeddings)\n", + "\n", + "query = \"What were the most important factors that contributed to increases in revenue?\"\n", + "docsnew = docsearch.similarity_search(query)\n", + "print(docsnew[0].page_content)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0TUtpO0_jkUY", + "outputId": "919b95f7-1a6e-4f1c-c063-fde2d978f57e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", + " warn_deprecated(\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "35 \n", + "Reportable Segments \n", + "Fiscal Year 2022 Compared with Fiscal Year 2021 \n", + "Productivity and Business Processes \n", + "Revenue increased $9.4 billion or 18%. \n", + "• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \n", + "Commercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \n", + "medium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \n", + "products revenue declined 22% driven by continued customer shift to cloud offerings. \n", + "• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \n", + "Consumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \n", + "• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \n", + "and advertising demand in our Marketing Solutions business. \n", + "• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \n", + "Operating income increased $5.3 billion or 22%. \n", + "• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \n", + "margin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \n", + "gross margin percentage increased 2 points driven by improvement across all cloud services. \n", + "• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \n", + "Gross margin and operating income both included an unfavorable foreign currency impact of 2%. \n", + "Intelligent Cloud \n", + "Revenue increased $15.2 billion or 25%. \n", + "• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \n", + "services. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \n", + "services. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \n", + "SQL Server running in multi -cloud environments. \n", + "• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \n", + "Operating income increased $6.6 billion or 25%. \n", + "• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \n", + "percentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \n", + "was relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \n", + "mix shift to Azure and other cloud services. \n", + "• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \n", + "Revenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \n", + "More Personal Computing \n", + "Revenue increased $5.6 billion or 10%. \n", + "• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \n", + "Commercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \n", + "market, which has higher revenue per license. Windows Commercial products and cloud services revenue \n", + "increased 11% driven by demand for Microsoft 365.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "L9y74fpPfHag" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Query expansion" + ], + "metadata": { + "id": "nr7_uISgfHo4" + } + }, + { + "cell_type": "code", + "source": [ + "# we are using openai for generating query\n", + "import os\n", + "import openai\n", + "from openai import OpenAI\n", + "\n", + "openai.api_key = os.environ[\"OPENAI_API_KEY\"]\n", + "openai_client = OpenAI()\n", + "\n", + "\n", + "def augment_query_generated(query, model=\"gpt-3.5-turbo\"):\n", + " messages = [\n", + " {\n", + " \"role\": \"system\",\n", + " \"content\": \"You are a helpful expert financial research assistant. Provide an example answer to the given question, that might be found in a document like an annual report. \",\n", + " },\n", + " {\"role\": \"user\", \"content\": query},\n", + " ]\n", + "\n", + " response = openai_client.chat.completions.create(\n", + " model=model,\n", + " messages=messages,\n", + " )\n", + " content = response.choices[0].message.content\n", + " return content" + ], + "metadata": { + "id": "B7B0XA3ajsFY" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### its generating query & hypothetica answer" + ], + "metadata": { + "id": "-2v8mN9XQy7Y" + } + }, + { + "cell_type": "code", + "source": [ + "original_query = (\n", + " \"What were the most important factors that contributed to increases in revenue?\"\n", + ")\n", + "hypothetical_answer = augment_query_generated(original_query)\n", + "# we are combining our orignal query + hypothetical_answer\n", + "joint_query = f\"{original_query} {hypothetical_answer}\"\n", + "print(word_wrap(joint_query))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "IPBqpky3jtwM", + "outputId": "51c6b216-d80d-458a-f058-79df1c69b37f" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "What were the most important factors that contributed to increases in\n", + "revenue? \"In the fiscal year 2021, our company experienced significant\n", + "growth in revenue due to several key factors. Firstly, our successful\n", + "market expansion efforts into emerging markets led to a substantial\n", + "increase in sales volume from new customer segments. Additionally,\n", + "strategic partnerships with key industry players resulted in lucrative\n", + "collaborations that boosted our revenue streams. Furthermore, our focus\n", + "on innovation and product development led to the launch of highly\n", + "successful new product lines that resonated well with our target\n", + "audience. Lastly, our robust marketing campaigns and enhanced digital\n", + "presence played a crucial role in driving higher sales and revenue\n", + "growth for the year.\"\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# we are passing the joint_query & fining the similar docs for this query\n", + "retrieved_documents = docsearch.similarity_search(joint_query, k=4)\n", + "print(retrieved_documents)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "daOypkNqjwIh", + "outputId": "830749bc-36bc-4d27-cc42-dced1fdfd0b2" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[Document(page_content='35 \\nReportable Segments \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nProductivity and Business Processes \\nRevenue increased $9.4 billion or 18%. \\n• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \\nCommercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \\nmedium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \\nproducts revenue declined 22% driven by continued customer shift to cloud offerings. \\n• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \\nConsumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \\n• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \\nand advertising demand in our Marketing Solutions business. \\n• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \\nOperating income increased $5.3 billion or 22%. \\n• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \\nmargin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \\ngross margin percentage increased 2 points driven by improvement across all cloud services. \\n• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nIntelligent Cloud \\nRevenue increased $15.2 billion or 25%. \\n• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \\nservices. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \\nservices. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \\nSQL Server running in multi -cloud environments. \\n• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \\nOperating income increased $6.6 billion or 25%. \\n• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \\npercentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \\nwas relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \\nmix shift to Azure and other cloud services. \\n• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \\nRevenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \\nMore Personal Computing \\nRevenue increased $5.6 billion or 10%. \\n• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \\nCommercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \\nmarket, which has higher revenue per license. Windows Commercial products and cloud services revenue \\nincreased 11% driven by demand for Microsoft 365.', metadata={'vector': [0.027368593961000443, -0.014953545294702053, -0.0041684047318995, 0.03653769567608833, -0.009155231527984142, -0.01824110560119152, -0.034734394401311874, 0.045415498316287994, -0.014287710189819336, 0.0345679335296154, 0.05373843386769295, -0.019212115556001663, 0.00569427665323019, -0.04400059953331947, -0.07263150066137314, 0.03933975100517273, -0.02145930752158165, -0.005413377657532692, 0.023595528677105904, 0.009189910255372524, -0.03531700000166893, 0.004102514591068029, -0.008537947200238705, 0.034734394401311874, -0.016035526990890503, -0.005881542805582285, 0.014273838140070438, 0.013753654435276985, 0.02448330819606781, 0.015799710527062416, 0.002777780406177044, -0.003655156819149852, -0.026758244261145592, -0.009828002192080021, 0.05662371963262558, 0.001376752625219524, 0.016243599355220795, -0.05432103946805, 0.05512559041380882, 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competitive, with frequent changes in both technologies and business models. Each \\nindustry shift is an opportunity to conceive new products, new technologies, or new ideas that can further transform the \\nindustry and our business. At Microsoft, we push the boundaries of what is possible through a broad range of research \\nand development activities that seek to identify and address the changing demands of customers and users, industry \\ntrends, and competitive forces. \\nEconomic Conditions, Challenges, and Risks \\nThe markets for software, devices, and cloud -based services are dynamic and highly competitive. Our competitors are \\ndeveloping new software and devices, while also deploying competing cloud -based services for consumers and \\nbusinesses. The devices and form factors customers prefer evolve rapidly, and influence how users access services in \\nthe cloud, and in some cases, the user’s choice of which suite of cloud -based services to use. We must continue to evolve \\nand adapt over an extended time in pace with this changing environment. The investments we are making in infrastructure \\nand devices will continue to increase our operating costs and may decrease our operating margins. \\nOur success is highly dependent on our ability to attract and retain qualified employees. We hire a mix of university and \\nindustry talent worldwide. We compete for talented individuals globally by offering an exceptional working environment, \\nbroad customer reach, scale in resources, the ability to grow one’s career across many different products and businesses, \\nand competitive compensation and benefits. Aggregate demand for our software, services, and devices is correlated to \\nglobal macroeconomic and geopolitical factors, which remain dynamic. \\nOur devices are primarily manufactured by third -party contract manufacturers, some of which contain certain components \\nfor which there are very few qualified suppliers. For these components, we have limited near -term flexibility to use other \\nmanufacturers if a current vendor becomes unavailable or is unable to meet our requirements. Extended disruptions at \\nthese suppliers and/or manufacturers could lead to a similar disruption in our ability to manufacture devices on time to \\nmeet consumer demand. \\nOur international operations provide a significant portion of our total revenue and expenses. Many of these revenue and \\nexpenses are denominated in currencies other than the U.S. dollar. As a result, changes in foreign exchange rates may \\nsignificantly affect revenue and expenses. Fluctuations in the U.S. dollar relative to certain foreign currencies did not hav e \\na material impact on reported revenue or expenses from our international operations in fiscal year 2022. \\nRefer to Risk Factors in our fiscal year 2022 Form 10 -K for a discussion of these factors and other risks. \\nSeasonality \\nOur revenue fluctuates quarterly and is generally higher in the second and fourth quarters of our fiscal year. Second \\nquarter revenue is driven by corporate year -end spending trends in our major markets and holiday season spending by \\nconsumers, and fourth quarter revenue is driven by the volume of multi -year on -premises contracts executed during the \\nperiod. \\nReportable Segments \\nWe report our financial performance based on the following segments: Productivity and Business Processes, Intelligent \\nCloud, and More Personal Computing. The segment amounts included in MD&A are presented on a basis consistent with \\nour internal management reporting. Additional information on our reportable segments is contained in Note 19 – Segment \\nInformation and Geographic Data of the Notes to Financial Statements in our fiscal year 2022 Form 10 -K. \\nMetrics \\nWe use metrics in assessing the performance of our business and to make informed decisions regarding the allocation of', metadata={'vector': [0.015208840370178223, -0.03496319428086281, -4.293437450542115e-05, 0.023085245862603188, 0.012839212082326412, -0.040924523025751114, -0.04849541187286377, 0.029419157654047012, -0.026960110291838646, 0.009553029201924801, 0.06366699188947678, -0.019195478409528732, 0.017287854105234146, -0.03162484988570213, -0.07803379744291306, 0.036542944610118866, -0.023651571944355965, -0.02155020460486412, 0.02842063643038273, 0.017466694116592407, -0.0331449881196022, 0.011527719907462597, -0.025961587205529213, 0.0442032553255558, -0.0012230038410052657, -0.0024534594267606735, 0.011758721433579922, 0.005100661888718605, 0.019463738426566124, 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Document(page_content='33 Dynamics products and cloud services revenue growth Revenue from Dynamics products and cloud services, \\nincluding Dynamics 365, comprising a set of intelligent, \\ncloud -based applications across ERP, CRM, Customer \\nInsights, Power Apps, and Power Automate; and on -\\npremises ERP and CRM applications \\n \\nLinkedIn revenue growth Revenue from LinkedIn, including Talent Solutions, \\nMarketing Solutions, Premium Subscriptions, and Sales \\nSolutions \\n \\nServer products and cloud services revenue growth Revenue from Server products and cloud services, \\nincluding Azure and other cloud services; SQL Server, \\nWindows Server, Visual Studio, System Center, and \\nrelated Client Access Licenses (“CALs”); and Nuance and \\nGitHub \\nMore Personal Computing \\nMetrics related to our More Personal Computing segment assess the performance of key lines of business within this \\nsegment. These metrics provide strategic product insights which allow us to assess the performance across our \\ncommercial and consumer businesses. As we have diversity of target audiences and sales motions within the Windows \\nbusiness, we monitor metrics that are reflective of those varying motions. \\n \\nWindows OEM revenue growth Revenue from sales of Windows Pro and non -Pro licenses sold \\nthrough the OEM channel \\n \\nWindows Commercial products and cloud \\nservices revenue growth Revenue from Windows Commercial products and cloud services, \\ncomprising volume licensing of the Windows operating system, \\nWindows cloud services, and other Windows commercial offerings \\n \\nSurface revenue growth Revenue from Surface devices and accessories \\n \\nXbox content and services revenue growth Revenue from Xbox content and services, comprising first - and third -\\nparty content (including games and in -game content), Xbox Game \\nPass and other subscriptions, Xbox Cloud Gaming, third -party disc \\nroyalties, advertising, and other cloud services \\n \\nSearch and news advertising revenue, \\nexcluding TAC, growth Revenue from search and news advertising excluding traffic \\nacquisition costs (“TAC”) paid to Bing Ads network publishers and \\nnews partners \\nSUMMARY RESULTS OF OPERATIONS \\n \\n(In millions, except percentages and per share amounts) 2022 2021 Percentage \\nChange \\n \\nRevenue $ 198,270 $ 168,088 18% \\nGross margin 135,620 115,856 17% \\nOperating income 83,383 69,916 19% \\nNet income 72,738 61,271 19% \\nDiluted earnings per share 9.65 8.05 20% \\n \\nAdjusted net income (non -GAAP) 69,447 60,651 15% \\nAdjusted diluted earnings per share (non -GAAP) 9.21 7.97 16% \\nAdjusted net income and adjusted diluted earnings per share (“EPS”) are non -GAAP financial measures which exclude \\nthe net income tax benefit related to transfer of intangible properties in the first quarter of fiscal year 2022 and the', metadata={'vector': [0.04153840243816376, -0.017145821824669838, -0.008022157475352287, 0.0258274357765913, 0.0013270620256662369, -0.009420781396329403, -0.06156843528151512, 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Refer to the Non -GAAP Financial Measures section below for a reconciliation of our financial results reported in \\naccordance with GAAP to non -GAAP financial results. See Note 12 – Income Taxes of the Notes to Financial Statements \\nin our fiscal year 2022 Form 10 -K for further discussion. \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nRevenue increased $30.2 billion or 18% driven by growth across each of our segments. Intelligent Cloud revenue \\nincreased driven by Azure and other cloud services. Productivity and Business Processes revenue increased driven by \\nOffice 365 Commercial and LinkedIn. More Personal Computing revenue increased driven by Search and news \\nadvertising and Windows. \\nCost of revenue increased $10.4 billion or 20% driven by growth in Microsoft Cloud. \\nGross margin increased $19.8 billion or 17% driven by growth across each of our segments. \\n• Gross margin percentage decreased slightly. Excluding the impact of the fiscal year 2021 change in \\naccounting estimate for the useful lives of our server and network equipment, gross margin percentage \\nincreased 1 point driven by improvement in Productivity and Business Processes. \\n• Microsoft Cloud gross margin percentage decreased slightly to 70%. Excluding the impact of the change in \\naccounting estimate, Microsoft Cloud gross margin percentage increased 3 points driven by improvement \\nacross our cloud services, offset in part by sales mix shift to Azure and other cloud services. \\nOperating expenses increased $6.3 billion or 14% driven by investments in cloud engineering, LinkedIn, Gaming, and \\ncommercial sales. \\nKey changes in operating expenses were: \\n• Research and development expenses increased $3.8 billion or 18% driven by investments in cloud \\nengineering, Gaming, and LinkedIn. \\n• Sales and marketing expenses increased $1.7 billion or 8% driven by investments in commercial sales and \\nLinkedIn. Sales and marketing included a favorable foreign currency impact of 2%. \\n• General and administrative expenses increased $793 million or 16% driven by investments in corporate \\nfunctions. \\nOperating income increased $13.5 billion or 19% driven by growth across each of our segments. \\nCurrent year net income and diluted EPS were positively impacted by the net tax benefit related to the transfer of \\nintangible properties, which resulted in an increase to net income and diluted EPS of $3.3 billion and $0.44, respectively. \\nPrior year net income and diluted EPS were positively impacted by the net tax benefit related to the India Supreme Court \\ndecision on withholding taxes, which resulted in an increase to net income and diluted EPS of $620 million and $0.08, \\nrespectively. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nSEGMENT RESULTS OF OPERATIONS \\n \\n(In millions, except percentages) 2022 2021 Percentage \\nChange \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 18% \\nIntelligent Cloud 75,251 60,080 25% \\nMore Personal Computing 59,655 54,093 10% \\nTotal $ 198,270 $ 168,088 18% \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 22% \\nIntelligent Cloud 32,721 26,126 25% \\nMore Personal Computing 20,975 19,439 8% \\nTotal $ 83,383 $ 69,916 19%', metadata={'vector': [0.04216254875063896, -0.013476958498358727, -0.004202566109597683, 0.03570127487182617, 0.0005548379267565906, -0.016125807538628578, -0.0518270842730999, 0.045393187552690506, -0.034113336354494095, 0.015482417307794094, 0.05869903042912483, -0.022833485156297684, 0.035098955035209656, -0.02413395419716835, -0.06264150142669678, 0.023162024095654488, -0.01989031955599785, -0.0056091248989105225, 0.01709773577749729, -0.0009077611030079424, -0.051224760711193085, 0.02058846689760685, -0.012730900198221207, 0.022614458575844765, -0.014962229877710342, 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Office 365 \n", + "Commercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \n", + "medium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \n", + "products revenue declined 22% driven by continued customer shift to cloud offerings. \n", + "• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \n", + "Consumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \n", + "• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \n", + "and advertising demand in our Marketing Solutions business. \n", + "• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \n", + "Operating income increased $5.3 billion or 22%. \n", + "• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \n", + "margin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \n", + "gross margin percentage increased 2 points driven by improvement across all cloud services. \n", + "• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \n", + "Gross margin and operating income both included an unfavorable foreign currency impact of 2%. \n", + "Intelligent Cloud \n", + "Revenue increased $15.2 billion or 25%. \n", + "• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \n", + "services. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \n", + "services. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \n", + "SQL Server running in multi -cloud environments. \n", + "• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \n", + "Operating income increased $6.6 billion or 25%. \n", + "• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \n", + "percentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \n", + "was relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \n", + "mix shift to Azure and other cloud services. \n", + "• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \n", + "Revenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \n", + "More Personal Computing \n", + "Revenue increased $5.6 billion or 10%. \n", + "• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \n", + "Commercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \n", + "market, which has higher revenue per license. Windows Commercial products and cloud services revenue \n", + "increased 11% driven by demand for Microsoft 365.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Reranker model" + ], + "metadata": { + "id": "slsNEwLV-NWS" + } + }, + { + "cell_type": "markdown", + "source": [ + "we are using landb for colbertv2 implementation\n", + "COlbertv2 is reranker method helpful for improving the ranking of results its returning top ranking\n", + "results as the most relevant to your particular query.\n", + "\n", + "for more technical details please check the blog :" + ], + "metadata": { + "id": "nRDEYibze8xA" + } + }, + { + "cell_type": "code", + "source": [ + "db = lancedb.connect(\"/tmp/db\")\n", + "registry = EmbeddingFunctionRegistry.get_instance()\n", + "func = registry.get(\"openai\").create()\n", + "\n", + "\n", + "class Words(LanceModel):\n", + " text: str = func.SourceField()\n", + " vector: Vector(func.ndims()) = func.VectorField()\n", + "\n", + "\n", + "table = db.create_table(\"wordsllm\", schema=Words, mode=\"overwrite\")\n", + "\n", + "# data from retriver\n", + "formatted_data = [{\"text\": doc.page_content} for doc in retrieved_documents]\n", + "\n", + "\n", + "# ingest docs with auto-vectorization\n", + "table.add(formatted_data)\n", + "# Create the FTS index on the 'text' field\n", + "table.create_fts_index([\"text\"], replace=True)\n", + "\n", + "\n", + "# normal search\n", + "query = \"technologies and business models\"\n", + "actual = table.search(query).limit(1).to_pydantic(Words)[0]\n", + "print(actual.text)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ZHjbtdCpkB-r", + "outputId": "a33b3216-7764-4fc7-8f43-66371c954dc6" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "82 \n", + "In addition, certain costs incurred at a corporate level that are identifiable and that benefit our segments are allocated to \n", + "them. These allocated costs include legal, including settlements and fines, information technology, human resources, \n", + "finance, excise taxes, field selling, shared facilities services, and customer service and support. Each allocation is \n", + "measured differently based on the specific facts and circumstances of the costs being allocated. \n", + "Segment revenue and operating income were as follows during the periods presented: \n", + " \n", + "(In millions) \n", + " \n", + "Year Ended June 30, 2022 2021 2020 \n", + " \n", + "Revenue \n", + " \n", + "Productivity and Business Processes $ 63,364 $ 53,915 $ 46,398 \n", + "Intelligent Cloud 75,251 60,080 48,366 \n", + "More Personal Computing 59,655 54,093 48,251 \n", + "Total $ 198,270 $ 168,088 $ 143,015 \n", + " \n", + "Operating Income \n", + " \n", + "Productivity and Business Processes $ 29,687 $ 24,351 $ 18,724 \n", + "Intelligent Cloud 32,721 26,126 18,324 \n", + "More Personal Computing 20,975 19,439 15,911 \n", + "Total $ 83,383 $ 69,916 $ 52,959 \n", + "No sales to an individual customer or country other than the United States accounted for more than 10% of revenue for \n", + "fiscal years 2022, 2021, or 2020. Revenue, classified by the major geographic areas in which our customers were located, \n", + "was as follows: \n", + " \n", + "(In millions) \n", + " \n", + "Year Ended June 30, 2022 2021 2020 \n", + " \n", + "United States (a) $ 100,218 $ 83,953 $ 73,160 \n", + "Other countries 98,052 84,135 69,855 \n", + "Total $ 198,270 $ 168,088 $ 143,015 \n", + "(a) Includes billings to OEMs and certain multinational organizations because of the nature of these businesses and the \n", + "impracticability of determining the geographic source of the revenue. \n", + "Revenue, classified by significant product and service offerings, was as follows: \n", + " \n", + "(In millions) \n", + " \n", + "Year Ended June 30, 2022 2021 2020 \n", + " \n", + "Server products and cloud services $ 67,321 $ 52,589 $ 41,379 \n", + "Office products and cloud services 44,862 39,872 35,316 \n", + "Windows 24,761 22,488 21,510 \n", + "Gaming 16,230 15,370 11,575 \n", + "LinkedIn 13,816 10,289 8,077 \n", + "Search and news advertising 11,591 9,267 8,524 \n", + "Enterprise Services 7,407 6,943 6,409 \n", + "Devices 6,991 6,791 6,457 \n", + "Other 5,291 4,479 3,768 \n", + "Total $ 198,270 $ 168,088 $ 143,015 \n", + "We have recast certain previously reported amounts in the table above to conform to the way we internally manage and \n", + "monitor our business.\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Colbertv2 reranker model" + ], + "metadata": { + "id": "DWriE9uWMgPb" + } + }, + { + "cell_type": "code", + "source": [ + "from lancedb.rerankers import ColbertReranker\n", + "\n", + "reranker_colbert = ColbertReranker()\n", + "results_colbert = (\n", + " table.search(\"technologies and business models\", query_type=\"hybrid\")\n", + " .rerank(reranker=reranker_colbert)\n", + " .to_pandas()\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 368, + "referenced_widgets": [ + "512a4a1ac0a54087b19b3ab709d408a5", + "e4fffd410f144690a30509cd6ebfe3d0", + "e1d7a067149048cdb0c96c7b8fc49d3a", + "6cf906c96d934cef8bd3236d011335c3", + "9ccc93963e214e8ebbcec70fc8380004", + "5c103a7e49654f9fa544614faa53266d", + "094b771f8f664daba18dce94377a3b82", + "8904ec0f7c5e4c84ab091ec0b41c21f7", + "43340646831643f588fc7242cb77e335", + "9e35f34b4314464b88ae8d056a9ff0c9", + "ce328d74dd624c86bfb3564ced691433", + "f6e58bbf74fa4f72847eefbfdb88f5a5", + "cb16b243e4ed44cbaa44aae6dff804df", + "3da764b62956470bbe525b0f211d1aba", + "6b6d018ac1464f718de54d590a05d812", + "44cfb577ac064a5791edb3f985e4319e", + "626fbd41136f4695ad87b950940eee2a", + "7ee096ddd2e0430a90cd70d9038bbed8", + "ad12c139c01645588babd5847828338f", + "f9235562c819445c9dd8dbd470339686", + "d0c9b80dbda54445a9d11db9d78483b9", + "24b143e06316458eb749bf5205a6b50b", + "6dbe42d65d32487db6384bad62f61ea0", + "b09df7fdc4d848c68445f097eabb26ff", + "d71d780bb8fb426998037943f21a57e7", + "d8dea6d0dc4248bc901541fa430aad4f", + "29a9346c94aa404ea618e57e47109880", + "d66c89fc9dfb4826b3cb4dfda31cf007", + "eb07c72549434e3bbac0c78daf97341f", + "e8a29343ae5a49d4b71c0cee39903eba", + "71457dc5c8ea4b69b0118a3972fc7f5f", + "8aa6071ab59b4e1b867d6538261decc9", + "34f4a5a1851a4268a0ab749c967066d9", + "edc4db29c82744b1929358793c22cecb", + "22a7472d547a48d684e208aa80a48daa", + "98185bbf7b794e3189ca9868a0a8c548", + "79ecc557cfba4409876c543ad9fd4d1d", + "d51840e06afa471ca5d09e63d02d7af5", + "5323184c79864acfb798ee3d0903e5e3", + "c17a522cbf9341d796fa7cacacded1cd", + "0d67cc434d514e7fbb844462fe613936", + "dc06c0ec5c7e40d98a8008d03b162a40", + "ad17228a04184934a4fd107307c59c9c", + "bf1598b78a01422c99a77b0c00704f24", + "240ed6a8d0df400d9b5b8a86dbb89d03", + "1cc8951a55584143a4f24b11d4221d0b", + "f52c290c9f5d4c7d830578bd139ab4a9", + "0ec0c7c3998d41e6890903c713f518bc", + "6b3936ecd33a41f2a61c368c86498865", + "93bd0eca9ace4bf4a10bd0467223ed90", + "ac8d646d12e6417ca32eb45e478a9cfe", + "ba1f368d05fd40578d7bca97269fffab", + "9a5d372096cb4f759778026b9b57b376", + "81d022b3239647509d245b7ab890badb", + "832bc71baafb44118b338d3f124b9927", + "9fa2d08290ea419a8455e06ea92db3e8", + "ce808eccce44475a9628cf45c7cf7e56", + "67ca36dd2e844fa6bb17ab776cb25b1f", + "f586fe78b39244d097f774a380f976fc", + "e668274af14544148104c94f1a1cfc6a", + "6588d853f27c4cc795e41e146eab356f", + "3a1134aa7e19470bbac3d88c0ad63c0c", + "e5bd14e45ee04cfaaa0028f2b3f03341", + "3302a4bb82ff4d5989c01c0acfae4997", + "f13df4048fc04e3394cab184b2fbadb0", + "df6656e2d170478ca0078d65677871f8" + ] + }, + "id": "-JCKr0_n6jjb", + "outputId": "dc215c45-5d2c-4014-a5c6-b8bfc1076fea" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/content/lancedb/python/lancedb/rerankers/colbert.py:40: FutureWarning: promote has been superseded by promote_options='default'.\n", + " combined_results = self.merge_results(vector_results, fts_results)\n", + "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", + "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", + "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", + "You will be able to reuse this secret in all of your notebooks.\n", + "Please note that authentication is recommended but still optional to access public models or datasets.\n", + " warnings.warn(\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/405 [00:00\n", + "
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vectortext_relevance_score
0[-0.0043462967, -0.022663297, -0.014687485, -0...35 \\nReportable Segments \\nFiscal Year 2022...0.580270
1[0.0073030023, -0.018824346, 0.001970756, -0.0...82 \\nIn addition, certain costs incurred at ...0.580032
2[-0.0026444048, -0.023148028, -0.008980748, -0...33 Dynamics products and cloud services revenu...0.579247
3[-0.014430126, -0.01449687, 0.0075287614, -0.0...34 net income tax benefit related to an India ...0.526167
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Another way to think about this is\n", + "your re-ranking model scores each of the results conditioned on the\n", + "query, and those with the highest score are the most\n", + "relevant.\n", + "Then you can just select the top ranking\n", + "results as the most relevant to your particular query.\n", + "So let's take a look at how to do this in practice" + ], + "metadata": { + "id": "hn9bxtkQeQBf" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "\n", + "# cross encoder reranker\n", + "from sentence_transformers import CrossEncoder\n", + "\n", + "cross_encoder = CrossEncoder(\"cross-encoder/ms-marco-MiniLM-L-6-v2\")\n", + "\n", + "# Extract text content from Document objects and convert to strings\n", + "document_texts = [doc.page_content for doc in retrieved_documents]\n", + "query_text = (\n", + " \"What were the most important factors that contributed to increases in revenue?\"\n", + ")\n", + "# Create pairs as strings\n", + "pairs = [[query_text, doc_text] for doc_text in document_texts]\n", + "# Predict scores for pairs\n", + "scores = cross_encoder.predict(pairs)\n", + "# Print scores\n", + "print(\"Scores:\")\n", + "for score in scores:\n", + " print(score)\n", + "\n", + "\n", + "print(\"New Ordering:\")\n", + "for o in np.argsort(scores)[::-1]:\n", + " print(o + 1)" + ], + "metadata": { + "id": "Bhg8NKLzk716", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 351, + "referenced_widgets": [ + "9607864fc48046f18b029ee3fc460998", + "a66b8790b1d649dfbe2b8b23810f8b08", + "4830ede53f4e481a844fceed058d3709", + "035029d168a945c6825e95d7e640a649", + "3b84205b280940b7b5910a3a1bc59428", + "f80e6d5c5324449aae87bcb498060ad6", + "e66b07711d8145d0837735c52ac6ae5b", + "fd47bda4836b4c47a6eab7f4adad746c", + "a10be6ba5286402390e7e800df2773c5", + "86ceb09a7cea4cbebdc833b18c06eee1", + "80d0de24b4974c56944acee7dac4cd3c", + "cfa8b359cbe841c590022567b2912936", + "cfbbebe5f3834beba9860b664b14263b", + "607a9a07aead4d1ba536b9a037746307", + "8989a92722094eaa87b78e03a3006ee5", + "4c4724a005d94c948c9df8accc4bd9be", + "f0e02ea03aff47b3b86b31e856f6f979", + "ec1eaf8260da4275a77ee827a3eee933", + "3d98c4e94ef54414a1aabf4b57ca8fad", + "0aadb5b86e344bd482a973ce507fcb9b", + "7ad7b106ae1b4104aea6faaa4451e12b", + "5228d72006e6422ab0fdc39241008622", + "371783f879314d9f97372bc35d73b43a", + "d6a4461d6f004844ac3e939d232d4ea4", + "beb93bd7017649269a9725173bfe095a", + "363f2fe905e24d7db098c85e0e6cac0d", + "49d9663243e94c9485839bbc9c0fdd6d", + "0e14830f5dcf4fb4af8d5a2762cac409", + "b12276a43b47439f8bc21b36a16c83bd", + "907b6225eef84efbadfb53b4f749cfa7", + "9632d557ac41490abee15f80f89d1e8c", + "f9052f766c6644349111e72f9579a09b", + "1e49b79aaa654886b6c06544deb160f0", + "d6ee90511c1843b1b680df60afc66d95", + "61f362cebf6e4f259d7cb1e20b6f1aea", + "06e798bf472645fc807917f7320b7ca0", + "679e1bd10e79449a9240872fccc4d819", + "1cca0d4e0444459da89c234b54f25ff9", + "6d98a47526f6447ea853aa2a17a07414", + "292c462527c54d35b04807262a35bd71", + "2aeb4c955a5748459179106ea10bb1a5", + "c6bd6b236f4e42b997d502dc27b95bba", + "6cdc965edc9648aebd9f3d89cae3955d", + "ea381193a01b4d85a457a70362182454", + "00c97c56f748495db23e999b1daa823b", + "e5766214355249e1a00ca05c9cd72e5f", + "7e2cca6135484b578c9a822d8f14ad0a", + "9b7b7a7a9684476aac925a44e9b391b7", + "fba748349bac44eab84f9e9361ef076d", + "1f68f5e201fd4a86ba134a39bf4a1727", + "a2ffb345d1ea467a92f952dbacf80b27", + "004e0f18fe604aa3974ea4dfd8421b31", + "4cc07587af674134a9e644eb3eb1674f", + "a349191878374a2282ab194818e80d31", + "9f6f8810125e4a12ba9d00274bce61dc" + ] + }, + "outputId": "05e40b97-832b-4ff3-f043-986269ed529e" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/794 [00:00flashrank) (15.0.1)\n", + "Requirement already satisfied: flatbuffers in /usr/local/lib/python3.10/dist-packages (from onnxruntime->flashrank) (23.5.26)\n", + "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from onnxruntime->flashrank) (23.2)\n", + "Requirement already satisfied: protobuf in /usr/local/lib/python3.10/dist-packages (from onnxruntime->flashrank) (3.20.3)\n", + "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from onnxruntime->flashrank) (1.12)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->flashrank) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->flashrank) (3.6)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->flashrank) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->flashrank) (2024.2.2)\n", + "Requirement already satisfied: huggingface_hub<1.0,>=0.16.4 in /usr/local/lib/python3.10/dist-packages (from tokenizers->flashrank) (0.20.3)\n", + "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (3.13.1)\n", + "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (2023.6.0)\n", + "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (6.0.1)\n", + "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub<1.0,>=0.16.4->tokenizers->flashrank) (4.9.0)\n", + "Requirement already satisfied: humanfriendly>=9.1 in /usr/local/lib/python3.10/dist-packages (from coloredlogs->onnxruntime->flashrank) (10.0)\n", + "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->onnxruntime->flashrank) (1.3.0)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Metadata is optional, Id can be your DB ids from your retrieval stage or simple numeric indices.\n", + "from flashrank import Ranker, RerankRequest\n", + "\n", + "# query = \"How to speedup LLMs?\"\n", + "\n", + "query = \"What were the most important factors that contributed to increases in revenue?\"\n", + "\n", + "ranker = Ranker(model_name=\"ms-marco-MiniLM-L-12-v2\", cache_dir=\"/opt\")\n", + "\n", + "rerankrequest = RerankRequest(query=query, passages=formatted_data)\n", + "results = ranker.rerank(rerankrequest)\n", + "print(results)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "4NzbLIaRZ9lo", + "outputId": "3a7f2b44-4d28-4976-e7de-2403034f9736" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "[{'text': '35 \\nReportable Segments \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nProductivity and Business Processes \\nRevenue increased $9.4 billion or 18%. \\n• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \\nCommercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \\nmedium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \\nproducts revenue declined 22% driven by continued customer shift to cloud offerings. \\n• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \\nConsumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \\n• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \\nand advertising demand in our Marketing Solutions business. \\n• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \\nOperating income increased $5.3 billion or 22%. \\n• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \\nmargin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \\ngross margin percentage increased 2 points driven by improvement across all cloud services. \\n• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nIntelligent Cloud \\nRevenue increased $15.2 billion or 25%. \\n• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \\nservices. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \\nservices. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \\nSQL Server running in multi -cloud environments. \\n• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \\nOperating income increased $6.6 billion or 25%. \\n• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \\npercentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \\nwas relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \\nmix shift to Azure and other cloud services. \\n• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \\nRevenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \\nMore Personal Computing \\nRevenue increased $5.6 billion or 10%. \\n• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \\nCommercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \\nmarket, which has higher revenue per license. Windows Commercial products and cloud services revenue \\nincreased 11% driven by demand for Microsoft 365.', 'score': 0.14885372}, {'text': '34 net income tax benefit related to an India Supreme Court decision on withholding taxes in the third quarter of fiscal year \\n2021. Refer to the Non -GAAP Financial Measures section below for a reconciliation of our financial results reported in \\naccordance with GAAP to non -GAAP financial results. See Note 12 – Income Taxes of the Notes to Financial Statements \\nin our fiscal year 2022 Form 10 -K for further discussion. \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nRevenue increased $30.2 billion or 18% driven by growth across each of our segments. Intelligent Cloud revenue \\nincreased driven by Azure and other cloud services. Productivity and Business Processes revenue increased driven by \\nOffice 365 Commercial and LinkedIn. More Personal Computing revenue increased driven by Search and news \\nadvertising and Windows. \\nCost of revenue increased $10.4 billion or 20% driven by growth in Microsoft Cloud. \\nGross margin increased $19.8 billion or 17% driven by growth across each of our segments. \\n• Gross margin percentage decreased slightly. Excluding the impact of the fiscal year 2021 change in \\naccounting estimate for the useful lives of our server and network equipment, gross margin percentage \\nincreased 1 point driven by improvement in Productivity and Business Processes. \\n• Microsoft Cloud gross margin percentage decreased slightly to 70%. Excluding the impact of the change in \\naccounting estimate, Microsoft Cloud gross margin percentage increased 3 points driven by improvement \\nacross our cloud services, offset in part by sales mix shift to Azure and other cloud services. \\nOperating expenses increased $6.3 billion or 14% driven by investments in cloud engineering, LinkedIn, Gaming, and \\ncommercial sales. \\nKey changes in operating expenses were: \\n• Research and development expenses increased $3.8 billion or 18% driven by investments in cloud \\nengineering, Gaming, and LinkedIn. \\n• Sales and marketing expenses increased $1.7 billion or 8% driven by investments in commercial sales and \\nLinkedIn. Sales and marketing included a favorable foreign currency impact of 2%. \\n• General and administrative expenses increased $793 million or 16% driven by investments in corporate \\nfunctions. \\nOperating income increased $13.5 billion or 19% driven by growth across each of our segments. \\nCurrent year net income and diluted EPS were positively impacted by the net tax benefit related to the transfer of \\nintangible properties, which resulted in an increase to net income and diluted EPS of $3.3 billion and $0.44, respectively. \\nPrior year net income and diluted EPS were positively impacted by the net tax benefit related to the India Supreme Court \\ndecision on withholding taxes, which resulted in an increase to net income and diluted EPS of $620 million and $0.08, \\nrespectively. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nSEGMENT RESULTS OF OPERATIONS \\n \\n(In millions, except percentages) 2022 2021 Percentage \\nChange \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 18% \\nIntelligent Cloud 75,251 60,080 25% \\nMore Personal Computing 59,655 54,093 10% \\nTotal $ 198,270 $ 168,088 18% \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 22% \\nIntelligent Cloud 32,721 26,126 25% \\nMore Personal Computing 20,975 19,439 8% \\nTotal $ 83,383 $ 69,916 19%', 'score': 0.04124021}, {'text': '33 Dynamics products and cloud services revenue growth Revenue from Dynamics products and cloud services, \\nincluding Dynamics 365, comprising a set of intelligent, \\ncloud -based applications across ERP, CRM, Customer \\nInsights, Power Apps, and Power Automate; and on -\\npremises ERP and CRM applications \\n \\nLinkedIn revenue growth Revenue from LinkedIn, including Talent Solutions, \\nMarketing Solutions, Premium Subscriptions, and Sales \\nSolutions \\n \\nServer products and cloud services revenue growth Revenue from Server products and cloud services, \\nincluding Azure and other cloud services; SQL Server, \\nWindows Server, Visual Studio, System Center, and \\nrelated Client Access Licenses (“CALs”); and Nuance and \\nGitHub \\nMore Personal Computing \\nMetrics related to our More Personal Computing segment assess the performance of key lines of business within this \\nsegment. These metrics provide strategic product insights which allow us to assess the performance across our \\ncommercial and consumer businesses. As we have diversity of target audiences and sales motions within the Windows \\nbusiness, we monitor metrics that are reflective of those varying motions. \\n \\nWindows OEM revenue growth Revenue from sales of Windows Pro and non -Pro licenses sold \\nthrough the OEM channel \\n \\nWindows Commercial products and cloud \\nservices revenue growth Revenue from Windows Commercial products and cloud services, \\ncomprising volume licensing of the Windows operating system, \\nWindows cloud services, and other Windows commercial offerings \\n \\nSurface revenue growth Revenue from Surface devices and accessories \\n \\nXbox content and services revenue growth Revenue from Xbox content and services, comprising first - and third -\\nparty content (including games and in -game content), Xbox Game \\nPass and other subscriptions, Xbox Cloud Gaming, third -party disc \\nroyalties, advertising, and other cloud services \\n \\nSearch and news advertising revenue, \\nexcluding TAC, growth Revenue from search and news advertising excluding traffic \\nacquisition costs (“TAC”) paid to Bing Ads network publishers and \\nnews partners \\nSUMMARY RESULTS OF OPERATIONS \\n \\n(In millions, except percentages and per share amounts) 2022 2021 Percentage \\nChange \\n \\nRevenue $ 198,270 $ 168,088 18% \\nGross margin 135,620 115,856 17% \\nOperating income 83,383 69,916 19% \\nNet income 72,738 61,271 19% \\nDiluted earnings per share 9.65 8.05 20% \\n \\nAdjusted net income (non -GAAP) 69,447 60,651 15% \\nAdjusted diluted earnings per share (non -GAAP) 9.21 7.97 16% \\nAdjusted net income and adjusted diluted earnings per share (“EPS”) are non -GAAP financial measures which exclude \\nthe net income tax benefit related to transfer of intangible properties in the first quarter of fiscal year 2022 and the', 'score': 0.00040568286}, {'text': '82 \\nIn addition, certain costs incurred at a corporate level that are identifiable and that benefit our segments are allocated to \\nthem. These allocated costs include legal, including settlements and fines, information technology, human resources, \\nfinance, excise taxes, field selling, shared facilities services, and customer service and support. Each allocation is \\nmeasured differently based on the specific facts and circumstances of the costs being allocated. \\nSegment revenue and operating income were as follows during the periods presented: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 $ 46,398 \\nIntelligent Cloud 75,251 60,080 48,366 \\nMore Personal Computing 59,655 54,093 48,251 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 $ 18,724 \\nIntelligent Cloud 32,721 26,126 18,324 \\nMore Personal Computing 20,975 19,439 15,911 \\nTotal $ 83,383 $ 69,916 $ 52,959 \\nNo sales to an individual customer or country other than the United States accounted for more than 10% of revenue for \\nfiscal years 2022, 2021, or 2020. Revenue, classified by the major geographic areas in which our customers were located, \\nwas as follows: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nUnited States (a) $ 100,218 $ 83,953 $ 73,160 \\nOther countries 98,052 84,135 69,855 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\n(a) Includes billings to OEMs and certain multinational organizations because of the nature of these businesses and the \\nimpracticability of determining the geographic source of the revenue. \\nRevenue, classified by significant product and service offerings, was as follows: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nServer products and cloud services $ 67,321 $ 52,589 $ 41,379 \\nOffice products and cloud services 44,862 39,872 35,316 \\nWindows 24,761 22,488 21,510 \\nGaming 16,230 15,370 11,575 \\nLinkedIn 13,816 10,289 8,077 \\nSearch and news advertising 11,591 9,267 8,524 \\nEnterprise Services 7,407 6,943 6,409 \\nDevices 6,991 6,791 6,457 \\nOther 5,291 4,479 3,768 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\nWe have recast certain previously reported amounts in the table above to conform to the way we internally manage and \\nmonitor our business.', 'score': 0.00026307348}]\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# this how you can use this methods & pass this data to llm & get better results fro your RAG applications" + ], + "metadata": { + "id": "bzHa6zld2wQa" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "GowqlU-y2wda" + }, + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/examples/Query_expansion_&_Reranker/main.ipynb b/examples/Query_expansion_&_Reranker/main.ipynb deleted file mode 100644 index 842bc3a4..00000000 --- a/examples/Query_expansion_&_Reranker/main.ipynb +++ /dev/null @@ -1,5251 +0,0 @@ -{ - 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] - }, - { - "cell_type": "markdown", - "source": [ - "# download your choice pdf file & upload here\n", - "\n", - " https://github.com/akashAD98/dummy_data/blob/main/microsoft_annual_report_2022.pdf" - ], - "metadata": { - "id": "je__kyDIyI62" - } - }, - { - "cell_type": "code", - "source": [ - "import os\n", - "import time\n", - "import lancedb\n", - "import getpass\n", - "import pandas as pd\n", - "from lancedb.embeddings import EmbeddingFunctionRegistry\n", - "from lancedb.pydantic import LanceModel, Vector\n", - "from lancedb.embeddings import get_registry\n", - "from langchain.text_splitter import CharacterTextSplitter\n", - "from langchain.document_loaders import PyPDFLoader\n", - "from langchain.embeddings import OpenAIEmbeddings\n", - "from langchain.embeddings import HuggingFaceEmbeddings\n", - "from langchain_community.vectorstores import LanceDB\n", - "\n", - "\n", - "# Set the OPENAI_API_KEY environment variable\n", - "os.environ['OPENAI_API_KEY'] = 'sk-yourkey'" - ], - "metadata": { - "id": "QcfZ_lr6FrEA" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "def word_wrap(string, n_chars=72):\n", - " # Wrap a string at the next space after n_chars\n", - " if len(string) < n_chars:\n", - " return string\n", - " else:\n", - " return string[:n_chars].rsplit(' ', 1)[0] + '\\n' + word_wrap(string[len(string[:n_chars].rsplit(' ', 1)[0])+1:], n_chars)" - ], - "metadata": { - "id": "NtbIb3QPjfuW" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "We are using langchain fro loading the pdf file & doing prprocessing" - ], - "metadata": { - "id": "7JSJy4AROGOM" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "#your pdf file\n", - "loader = PyPDFLoader(\"/content/microsoft_annual_report_2022.pdf\")\n", - "documents = loader.load_and_split()\n", - "\n", - "# split it into chunks\n", - "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)\n", - "docsn = text_splitter.split_documents(documents)\n", - "\n", - "embeddings = OpenAIEmbeddings(model=\"text-embedding-3-large\")\n", - "\n", - "docsearch = LanceDB.from_documents(docsn, embeddings)\n", - "\n", - "query = \"What were the most important factors that contributed to increases in revenue?\"\n", - "docsnew = docsearch.similarity_search(query)\n", - "print(docsnew[0].page_content)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "0TUtpO0_jkUY", - "outputId": "919b95f7-1a6e-4f1c-c063-fde2d978f57e" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", - " warn_deprecated(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "35 \n", - "Reportable Segments \n", - "Fiscal Year 2022 Compared with Fiscal Year 2021 \n", - "Productivity and Business Processes \n", - "Revenue increased $9.4 billion or 18%. \n", - "• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \n", - "Commercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \n", - "medium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \n", - "products revenue declined 22% driven by continued customer shift to cloud offerings. \n", - "• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \n", - "Consumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \n", - "• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \n", - "and advertising demand in our Marketing Solutions business. \n", - "• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \n", - "Operating income increased $5.3 billion or 22%. \n", - "• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \n", - "margin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \n", - "gross margin percentage increased 2 points driven by improvement across all cloud services. \n", - "• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \n", - "Gross margin and operating income both included an unfavorable foreign currency impact of 2%. \n", - "Intelligent Cloud \n", - "Revenue increased $15.2 billion or 25%. \n", - "• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \n", - "services. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \n", - "services. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \n", - "SQL Server running in multi -cloud environments. \n", - "• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \n", - "Operating income increased $6.6 billion or 25%. \n", - "• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \n", - "percentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \n", - "was relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \n", - "mix shift to Azure and other cloud services. \n", - "• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \n", - "Revenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \n", - "More Personal Computing \n", - "Revenue increased $5.6 billion or 10%. \n", - "• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \n", - "Commercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \n", - "market, which has higher revenue per license. Windows Commercial products and cloud services revenue \n", - "increased 11% driven by demand for Microsoft 365.\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "L9y74fpPfHag" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### Query expansion" - ], - "metadata": { - "id": "nr7_uISgfHo4" - } - }, - { - "cell_type": "code", - "source": [ - "#we are using openai for generating query\n", - "import os\n", - "import openai\n", - "from openai import OpenAI\n", - "openai.api_key = os.environ['OPENAI_API_KEY']\n", - "openai_client = OpenAI()\n", - "\n", - "\n", - "def augment_query_generated(query, model=\"gpt-3.5-turbo\"):\n", - " messages = [\n", - " {\n", - " \"role\": \"system\",\n", - " \"content\": \"You are a helpful expert financial research assistant. Provide an example answer to the given question, that might be found in a document like an annual report. \"\n", - " },\n", - " {\"role\": \"user\", \"content\": query}\n", - " ]\n", - "\n", - " response = openai_client.chat.completions.create(\n", - " model=model,\n", - " messages=messages,\n", - " )\n", - " content = response.choices[0].message.content\n", - " return content" - ], - "metadata": { - "id": "B7B0XA3ajsFY" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "### its generating query & hypothetica answer" - ], - "metadata": { - "id": "-2v8mN9XQy7Y" - } - }, - { - "cell_type": "code", - "source": [ - "original_query = \"What were the most important factors that contributed to increases in revenue?\"\n", - "hypothetical_answer = augment_query_generated(original_query)\n", - "# we are combining our orignal query + hypothetical_answer\n", - "joint_query = f\"{original_query} {hypothetical_answer}\"\n", - "print(word_wrap(joint_query))" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "IPBqpky3jtwM", - "outputId": "51c6b216-d80d-458a-f058-79df1c69b37f" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "What were the most important factors that contributed to increases in\n", - "revenue? \"In the fiscal year 2021, our company experienced significant\n", - "growth in revenue due to several key factors. Firstly, our successful\n", - "market expansion efforts into emerging markets led to a substantial\n", - "increase in sales volume from new customer segments. Additionally,\n", - "strategic partnerships with key industry players resulted in lucrative\n", - "collaborations that boosted our revenue streams. Furthermore, our focus\n", - "on innovation and product development led to the launch of highly\n", - "successful new product lines that resonated well with our target\n", - "audience. Lastly, our robust marketing campaigns and enhanced digital\n", - "presence played a crucial role in driving higher sales and revenue\n", - "growth for the year.\"\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# we are passing the joint_query & fining the similar docs for this query\n", - "retrieved_documents = docsearch.similarity_search(joint_query,k=4)\n", - "print(retrieved_documents)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "daOypkNqjwIh", - "outputId": "830749bc-36bc-4d27-cc42-dced1fdfd0b2" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[Document(page_content='35 \\nReportable Segments \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nProductivity and Business Processes \\nRevenue increased $9.4 billion or 18%. \\n• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \\nCommercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \\nmedium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \\nproducts revenue declined 22% driven by continued customer shift to cloud offerings. \\n• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \\nConsumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \\n• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \\nand advertising demand in our Marketing Solutions business. \\n• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \\nOperating income increased $5.3 billion or 22%. \\n• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \\nmargin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \\ngross margin percentage increased 2 points driven by improvement across all cloud services. \\n• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nIntelligent Cloud \\nRevenue increased $15.2 billion or 25%. \\n• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \\nservices. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \\nservices. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \\nSQL Server running in multi -cloud environments. \\n• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \\nOperating income increased $6.6 billion or 25%. \\n• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \\npercentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \\nwas relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \\nmix shift to Azure and other cloud services. \\n• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \\nRevenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \\nMore Personal Computing \\nRevenue increased $5.6 billion or 10%. \\n• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \\nCommercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \\nmarket, which has higher revenue per license. Windows Commercial products and cloud services revenue \\nincreased 11% driven by demand for Microsoft 365.', metadata={'vector': [0.027368593961000443, -0.014953545294702053, -0.0041684047318995, 0.03653769567608833, -0.009155231527984142, -0.01824110560119152, -0.034734394401311874, 0.045415498316287994, -0.014287710189819336, 0.0345679335296154, 0.05373843386769295, -0.019212115556001663, 0.00569427665323019, -0.04400059953331947, -0.07263150066137314, 0.03933975100517273, -0.02145930752158165, -0.005413377657532692, 0.023595528677105904, 0.009189910255372524, -0.03531700000166893, 0.004102514591068029, -0.008537947200238705, 0.034734394401311874, -0.016035526990890503, -0.005881542805582285, 0.014273838140070438, 0.013753654435276985, 0.02448330819606781, 0.015799710527062416, 0.002777780406177044, -0.003655156819149852, -0.026758244261145592, -0.009828002192080021, 0.05662371963262558, 0.001376752625219524, 0.016243599355220795, -0.05432103946805, 0.05512559041380882, 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competitive, with frequent changes in both technologies and business models. Each \\nindustry shift is an opportunity to conceive new products, new technologies, or new ideas that can further transform the \\nindustry and our business. At Microsoft, we push the boundaries of what is possible through a broad range of research \\nand development activities that seek to identify and address the changing demands of customers and users, industry \\ntrends, and competitive forces. \\nEconomic Conditions, Challenges, and Risks \\nThe markets for software, devices, and cloud -based services are dynamic and highly competitive. Our competitors are \\ndeveloping new software and devices, while also deploying competing cloud -based services for consumers and \\nbusinesses. The devices and form factors customers prefer evolve rapidly, and influence how users access services in \\nthe cloud, and in some cases, the user’s choice of which suite of cloud -based services to use. We must continue to evolve \\nand adapt over an extended time in pace with this changing environment. The investments we are making in infrastructure \\nand devices will continue to increase our operating costs and may decrease our operating margins. \\nOur success is highly dependent on our ability to attract and retain qualified employees. We hire a mix of university and \\nindustry talent worldwide. We compete for talented individuals globally by offering an exceptional working environment, \\nbroad customer reach, scale in resources, the ability to grow one’s career across many different products and businesses, \\nand competitive compensation and benefits. Aggregate demand for our software, services, and devices is correlated to \\nglobal macroeconomic and geopolitical factors, which remain dynamic. \\nOur devices are primarily manufactured by third -party contract manufacturers, some of which contain certain components \\nfor which there are very few qualified suppliers. For these components, we have limited near -term flexibility to use other \\nmanufacturers if a current vendor becomes unavailable or is unable to meet our requirements. Extended disruptions at \\nthese suppliers and/or manufacturers could lead to a similar disruption in our ability to manufacture devices on time to \\nmeet consumer demand. \\nOur international operations provide a significant portion of our total revenue and expenses. Many of these revenue and \\nexpenses are denominated in currencies other than the U.S. dollar. As a result, changes in foreign exchange rates may \\nsignificantly affect revenue and expenses. Fluctuations in the U.S. dollar relative to certain foreign currencies did not hav e \\na material impact on reported revenue or expenses from our international operations in fiscal year 2022. \\nRefer to Risk Factors in our fiscal year 2022 Form 10 -K for a discussion of these factors and other risks. \\nSeasonality \\nOur revenue fluctuates quarterly and is generally higher in the second and fourth quarters of our fiscal year. Second \\nquarter revenue is driven by corporate year -end spending trends in our major markets and holiday season spending by \\nconsumers, and fourth quarter revenue is driven by the volume of multi -year on -premises contracts executed during the \\nperiod. \\nReportable Segments \\nWe report our financial performance based on the following segments: Productivity and Business Processes, Intelligent \\nCloud, and More Personal Computing. The segment amounts included in MD&A are presented on a basis consistent with \\nour internal management reporting. Additional information on our reportable segments is contained in Note 19 – Segment \\nInformation and Geographic Data of the Notes to Financial Statements in our fiscal year 2022 Form 10 -K. \\nMetrics \\nWe use metrics in assessing the performance of our business and to make informed decisions regarding the allocation of', metadata={'vector': [0.015208840370178223, -0.03496319428086281, -4.293437450542115e-05, 0.023085245862603188, 0.012839212082326412, -0.040924523025751114, -0.04849541187286377, 0.029419157654047012, -0.026960110291838646, 0.009553029201924801, 0.06366699188947678, -0.019195478409528732, 0.017287854105234146, -0.03162484988570213, -0.07803379744291306, 0.036542944610118866, -0.023651571944355965, -0.02155020460486412, 0.02842063643038273, 0.017466694116592407, -0.0331449881196022, 0.011527719907462597, -0.025961587205529213, 0.0442032553255558, -0.0012230038410052657, -0.0024534594267606735, 0.011758721433579922, 0.005100661888718605, 0.019463738426566124, 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Document(page_content='33 Dynamics products and cloud services revenue growth Revenue from Dynamics products and cloud services, \\nincluding Dynamics 365, comprising a set of intelligent, \\ncloud -based applications across ERP, CRM, Customer \\nInsights, Power Apps, and Power Automate; and on -\\npremises ERP and CRM applications \\n \\nLinkedIn revenue growth Revenue from LinkedIn, including Talent Solutions, \\nMarketing Solutions, Premium Subscriptions, and Sales \\nSolutions \\n \\nServer products and cloud services revenue growth Revenue from Server products and cloud services, \\nincluding Azure and other cloud services; SQL Server, \\nWindows Server, Visual Studio, System Center, and \\nrelated Client Access Licenses (“CALs”); and Nuance and \\nGitHub \\nMore Personal Computing \\nMetrics related to our More Personal Computing segment assess the performance of key lines of business within this \\nsegment. These metrics provide strategic product insights which allow us to assess the performance across our \\ncommercial and consumer businesses. As we have diversity of target audiences and sales motions within the Windows \\nbusiness, we monitor metrics that are reflective of those varying motions. \\n \\nWindows OEM revenue growth Revenue from sales of Windows Pro and non -Pro licenses sold \\nthrough the OEM channel \\n \\nWindows Commercial products and cloud \\nservices revenue growth Revenue from Windows Commercial products and cloud services, \\ncomprising volume licensing of the Windows operating system, \\nWindows cloud services, and other Windows commercial offerings \\n \\nSurface revenue growth Revenue from Surface devices and accessories \\n \\nXbox content and services revenue growth Revenue from Xbox content and services, comprising first - and third -\\nparty content (including games and in -game content), Xbox Game \\nPass and other subscriptions, Xbox Cloud Gaming, third -party disc \\nroyalties, advertising, and other cloud services \\n \\nSearch and news advertising revenue, \\nexcluding TAC, growth Revenue from search and news advertising excluding traffic \\nacquisition costs (“TAC”) paid to Bing Ads network publishers and \\nnews partners \\nSUMMARY RESULTS OF OPERATIONS \\n \\n(In millions, except percentages and per share amounts) 2022 2021 Percentage \\nChange \\n \\nRevenue $ 198,270 $ 168,088 18% \\nGross margin 135,620 115,856 17% \\nOperating income 83,383 69,916 19% \\nNet income 72,738 61,271 19% \\nDiluted earnings per share 9.65 8.05 20% \\n \\nAdjusted net income (non -GAAP) 69,447 60,651 15% \\nAdjusted diluted earnings per share (non -GAAP) 9.21 7.97 16% \\nAdjusted net income and adjusted diluted earnings per share (“EPS”) are non -GAAP financial measures which exclude \\nthe net income tax benefit related to transfer of intangible properties in the first quarter of fiscal year 2022 and the', metadata={'vector': [0.04153840243816376, -0.017145821824669838, -0.008022157475352287, 0.0258274357765913, 0.0013270620256662369, -0.009420781396329403, -0.06156843528151512, 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Refer to the Non -GAAP Financial Measures section below for a reconciliation of our financial results reported in \\naccordance with GAAP to non -GAAP financial results. See Note 12 – Income Taxes of the Notes to Financial Statements \\nin our fiscal year 2022 Form 10 -K for further discussion. \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nRevenue increased $30.2 billion or 18% driven by growth across each of our segments. Intelligent Cloud revenue \\nincreased driven by Azure and other cloud services. Productivity and Business Processes revenue increased driven by \\nOffice 365 Commercial and LinkedIn. More Personal Computing revenue increased driven by Search and news \\nadvertising and Windows. \\nCost of revenue increased $10.4 billion or 20% driven by growth in Microsoft Cloud. \\nGross margin increased $19.8 billion or 17% driven by growth across each of our segments. \\n• Gross margin percentage decreased slightly. Excluding the impact of the fiscal year 2021 change in \\naccounting estimate for the useful lives of our server and network equipment, gross margin percentage \\nincreased 1 point driven by improvement in Productivity and Business Processes. \\n• Microsoft Cloud gross margin percentage decreased slightly to 70%. Excluding the impact of the change in \\naccounting estimate, Microsoft Cloud gross margin percentage increased 3 points driven by improvement \\nacross our cloud services, offset in part by sales mix shift to Azure and other cloud services. \\nOperating expenses increased $6.3 billion or 14% driven by investments in cloud engineering, LinkedIn, Gaming, and \\ncommercial sales. \\nKey changes in operating expenses were: \\n• Research and development expenses increased $3.8 billion or 18% driven by investments in cloud \\nengineering, Gaming, and LinkedIn. \\n• Sales and marketing expenses increased $1.7 billion or 8% driven by investments in commercial sales and \\nLinkedIn. Sales and marketing included a favorable foreign currency impact of 2%. \\n• General and administrative expenses increased $793 million or 16% driven by investments in corporate \\nfunctions. \\nOperating income increased $13.5 billion or 19% driven by growth across each of our segments. \\nCurrent year net income and diluted EPS were positively impacted by the net tax benefit related to the transfer of \\nintangible properties, which resulted in an increase to net income and diluted EPS of $3.3 billion and $0.44, respectively. \\nPrior year net income and diluted EPS were positively impacted by the net tax benefit related to the India Supreme Court \\ndecision on withholding taxes, which resulted in an increase to net income and diluted EPS of $620 million and $0.08, \\nrespectively. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nSEGMENT RESULTS OF OPERATIONS \\n \\n(In millions, except percentages) 2022 2021 Percentage \\nChange \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 18% \\nIntelligent Cloud 75,251 60,080 25% \\nMore Personal Computing 59,655 54,093 10% \\nTotal $ 198,270 $ 168,088 18% \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 22% \\nIntelligent Cloud 32,721 26,126 25% \\nMore Personal Computing 20,975 19,439 8% \\nTotal $ 83,383 $ 69,916 19%', metadata={'vector': [0.04216254875063896, -0.013476958498358727, -0.004202566109597683, 0.03570127487182617, 0.0005548379267565906, -0.016125807538628578, -0.0518270842730999, 0.045393187552690506, -0.034113336354494095, 0.015482417307794094, 0.05869903042912483, -0.022833485156297684, 0.035098955035209656, -0.02413395419716835, -0.06264150142669678, 0.023162024095654488, -0.01989031955599785, -0.0056091248989105225, 0.01709773577749729, -0.0009077611030079424, -0.051224760711193085, 0.02058846689760685, -0.012730900198221207, 0.022614458575844765, -0.014962229877710342, 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Office 365 \n", - "Commercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \n", - "medium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \n", - "products revenue declined 22% driven by continued customer shift to cloud offerings. \n", - "• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \n", - "Consumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \n", - "• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \n", - "and advertising demand in our Marketing Solutions business. \n", - "• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \n", - "Operating income increased $5.3 billion or 22%. \n", - "• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \n", - "margin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \n", - "gross margin percentage increased 2 points driven by improvement across all cloud services. \n", - "• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \n", - "Gross margin and operating income both included an unfavorable foreign currency impact of 2%. \n", - "Intelligent Cloud \n", - "Revenue increased $15.2 billion or 25%. \n", - "• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \n", - "services. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \n", - "services. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \n", - "SQL Server running in multi -cloud environments. \n", - "• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \n", - "Operating income increased $6.6 billion or 25%. \n", - "• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \n", - "percentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \n", - "was relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \n", - "mix shift to Azure and other cloud services. \n", - "• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \n", - "Revenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \n", - "More Personal Computing \n", - "Revenue increased $5.6 billion or 10%. \n", - "• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \n", - "Commercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \n", - "market, which has higher revenue per license. Windows Commercial products and cloud services revenue \n", - "increased 11% driven by demand for Microsoft 365.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#Reranker model" - ], - "metadata": { - "id": "slsNEwLV-NWS" - } - }, - { - "cell_type": "markdown", - "source": [ - "we are using landb for colbertv2 implementation\n", - "COlbertv2 is reranker method helpful for improving the ranking of results its returning top ranking\n", - "results as the most relevant to your particular query.\n", - "\n", - "for more technical details please check the blog :" - ], - "metadata": { - "id": "nRDEYibze8xA" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "\n", - "db = lancedb.connect(\"/tmp/db\")\n", - "registry = EmbeddingFunctionRegistry.get_instance()\n", - "func = registry.get(\"openai\").create()\n", - "\n", - "class Words(LanceModel):\n", - " text: str = func.SourceField()\n", - " vector: Vector(func.ndims()) = func.VectorField()\n", - "\n", - "table = db.create_table(\"wordsllm\", schema=Words,mode=\"overwrite\")\n", - "\n", - "# data from retriver\n", - "formatted_data = [{\"text\": doc.page_content} for doc in retrieved_documents]\n", - "\n", - "\n", - "# ingest docs with auto-vectorization\n", - "table.add(formatted_data)\n", - "# Create the FTS index on the 'text' field\n", - "table.create_fts_index(['text'],replace=True)\n", - "\n", - "\n", - "# normal search\n", - "query = \"technologies and business models\"\n", - "actual = table.search(query).limit(1).to_pydantic(Words)[0]\n", - "print(actual.text)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ZHjbtdCpkB-r", - "outputId": "a33b3216-7764-4fc7-8f43-66371c954dc6" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "82 \n", - "In addition, certain costs incurred at a corporate level that are identifiable and that benefit our segments are allocated to \n", - "them. These allocated costs include legal, including settlements and fines, information technology, human resources, \n", - "finance, excise taxes, field selling, shared facilities services, and customer service and support. Each allocation is \n", - "measured differently based on the specific facts and circumstances of the costs being allocated. \n", - "Segment revenue and operating income were as follows during the periods presented: \n", - " \n", - "(In millions) \n", - " \n", - "Year Ended June 30, 2022 2021 2020 \n", - " \n", - "Revenue \n", - " \n", - "Productivity and Business Processes $ 63,364 $ 53,915 $ 46,398 \n", - "Intelligent Cloud 75,251 60,080 48,366 \n", - "More Personal Computing 59,655 54,093 48,251 \n", - "Total $ 198,270 $ 168,088 $ 143,015 \n", - " \n", - "Operating Income \n", - " \n", - "Productivity and Business Processes $ 29,687 $ 24,351 $ 18,724 \n", - "Intelligent Cloud 32,721 26,126 18,324 \n", - "More Personal Computing 20,975 19,439 15,911 \n", - "Total $ 83,383 $ 69,916 $ 52,959 \n", - "No sales to an individual customer or country other than the United States accounted for more than 10% of revenue for \n", - "fiscal years 2022, 2021, or 2020. Revenue, classified by the major geographic areas in which our customers were located, \n", - "was as follows: \n", - " \n", - "(In millions) \n", - " \n", - "Year Ended June 30, 2022 2021 2020 \n", - " \n", - "United States (a) $ 100,218 $ 83,953 $ 73,160 \n", - "Other countries 98,052 84,135 69,855 \n", - "Total $ 198,270 $ 168,088 $ 143,015 \n", - "(a) Includes billings to OEMs and certain multinational organizations because of the nature of these businesses and the \n", - "impracticability of determining the geographic source of the revenue. \n", - "Revenue, classified by significant product and service offerings, was as follows: \n", - " \n", - "(In millions) \n", - " \n", - "Year Ended June 30, 2022 2021 2020 \n", - " \n", - "Server products and cloud services $ 67,321 $ 52,589 $ 41,379 \n", - "Office products and cloud services 44,862 39,872 35,316 \n", - "Windows 24,761 22,488 21,510 \n", - "Gaming 16,230 15,370 11,575 \n", - "LinkedIn 13,816 10,289 8,077 \n", - "Search and news advertising 11,591 9,267 8,524 \n", - "Enterprise Services 7,407 6,943 6,409 \n", - "Devices 6,991 6,791 6,457 \n", - "Other 5,291 4,479 3,768 \n", - "Total $ 198,270 $ 168,088 $ 143,015 \n", - "We have recast certain previously reported amounts in the table above to conform to the way we internally manage and \n", - "monitor our business.\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#Colbertv2 reranker model" - ], - "metadata": { - "id": "DWriE9uWMgPb" - } - }, - { - "cell_type": "code", - "source": [ - "from lancedb.rerankers import ColbertReranker\n", - "reranker_colbert = ColbertReranker()\n", - "results_colbert = table.search(\"technologies and business models\", query_type=\"hybrid\").rerank(reranker=reranker_colbert).to_pandas()" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 368, - "referenced_widgets": [ - "512a4a1ac0a54087b19b3ab709d408a5", - "e4fffd410f144690a30509cd6ebfe3d0", - "e1d7a067149048cdb0c96c7b8fc49d3a", - "6cf906c96d934cef8bd3236d011335c3", - 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"f586fe78b39244d097f774a380f976fc", - "e668274af14544148104c94f1a1cfc6a", - "6588d853f27c4cc795e41e146eab356f", - "3a1134aa7e19470bbac3d88c0ad63c0c", - "e5bd14e45ee04cfaaa0028f2b3f03341", - "3302a4bb82ff4d5989c01c0acfae4997", - "f13df4048fc04e3394cab184b2fbadb0", - "df6656e2d170478ca0078d65677871f8" - ] - }, - "id": "-JCKr0_n6jjb", - "outputId": "dc215c45-5d2c-4014-a5c6-b8bfc1076fea" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/content/lancedb/python/lancedb/rerankers/colbert.py:40: FutureWarning: promote has been superseded by promote_options='default'.\n", - " combined_results = self.merge_results(vector_results, fts_results)\n", - "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n", - "The secret `HF_TOKEN` does not exist in your Colab secrets.\n", - "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n", - "You will be able to reuse this secret in all of your notebooks.\n", - "Please note that authentication is recommended but still optional to access public models or datasets.\n", - " warnings.warn(\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer_config.json: 0%| | 0.00/405 [00:00\n", - "
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0[-0.0043462967, -0.022663297, -0.014687485, -0...35 \\nReportable Segments \\nFiscal Year 2022...0.580270
1[0.0073030023, -0.018824346, 0.001970756, -0.0...82 \\nIn addition, certain costs incurred at ...0.580032
2[-0.0026444048, -0.023148028, -0.008980748, -0...33 Dynamics products and cloud services revenu...0.579247
3[-0.014430126, -0.01449687, 0.0075287614, -0.0...34 net income tax benefit related to an India ...0.526167
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Another way to think about this is\n", - "your re-ranking model scores each of the results conditioned on the\n", - "query, and those with the highest score are the most\n", - "relevant.\n", - "Then you can just select the top ranking\n", - "results as the most relevant to your particular query.\n", - "So let's take a look at how to do this in practice" - ], - "metadata": { - "id": "hn9bxtkQeQBf" - } - }, - { - "cell_type": "code", - "source": [ - "\n", - "import numpy as np\n", - "#cross encoder reranker\n", - "from sentence_transformers import CrossEncoder\n", - "cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')\n", - "\n", - "# Extract text content from Document objects and convert to strings\n", - "document_texts = [doc.page_content for doc in retrieved_documents]\n", - "query_text = \"What were the most important factors that contributed to increases in revenue?\"\n", - "# Create pairs as strings\n", - "pairs = [[query_text, doc_text] for doc_text in document_texts]\n", - "# Predict scores for pairs\n", - "scores = cross_encoder.predict(pairs)\n", - "# Print scores\n", - "print(\"Scores:\")\n", - "for score in scores:\n", - " print(score)\n", - "\n", - "\n", - "print(\"New Ordering:\")\n", - "for o in np.argsort(scores)[::-1]:\n", - " print(o+1)" - ], - "metadata": { - "id": "Bhg8NKLzk716", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 351, - "referenced_widgets": [ - "9607864fc48046f18b029ee3fc460998", - "a66b8790b1d649dfbe2b8b23810f8b08", - "4830ede53f4e481a844fceed058d3709", - "035029d168a945c6825e95d7e640a649", - "3b84205b280940b7b5910a3a1bc59428", - "f80e6d5c5324449aae87bcb498060ad6", - "e66b07711d8145d0837735c52ac6ae5b", - "fd47bda4836b4c47a6eab7f4adad746c", - "a10be6ba5286402390e7e800df2773c5", - "86ceb09a7cea4cbebdc833b18c06eee1", - "80d0de24b4974c56944acee7dac4cd3c", - "cfa8b359cbe841c590022567b2912936", - "cfbbebe5f3834beba9860b664b14263b", - "607a9a07aead4d1ba536b9a037746307", - "8989a92722094eaa87b78e03a3006ee5", - "4c4724a005d94c948c9df8accc4bd9be", - "f0e02ea03aff47b3b86b31e856f6f979", - "ec1eaf8260da4275a77ee827a3eee933", - "3d98c4e94ef54414a1aabf4b57ca8fad", - "0aadb5b86e344bd482a973ce507fcb9b", - "7ad7b106ae1b4104aea6faaa4451e12b", - "5228d72006e6422ab0fdc39241008622", - "371783f879314d9f97372bc35d73b43a", - "d6a4461d6f004844ac3e939d232d4ea4", - "beb93bd7017649269a9725173bfe095a", - "363f2fe905e24d7db098c85e0e6cac0d", - "49d9663243e94c9485839bbc9c0fdd6d", - "0e14830f5dcf4fb4af8d5a2762cac409", - "b12276a43b47439f8bc21b36a16c83bd", - "907b6225eef84efbadfb53b4f749cfa7", - "9632d557ac41490abee15f80f89d1e8c", - "f9052f766c6644349111e72f9579a09b", - "1e49b79aaa654886b6c06544deb160f0", - "d6ee90511c1843b1b680df60afc66d95", - "61f362cebf6e4f259d7cb1e20b6f1aea", - "06e798bf472645fc807917f7320b7ca0", - "679e1bd10e79449a9240872fccc4d819", - "1cca0d4e0444459da89c234b54f25ff9", - "6d98a47526f6447ea853aa2a17a07414", - "292c462527c54d35b04807262a35bd71", - "2aeb4c955a5748459179106ea10bb1a5", - "c6bd6b236f4e42b997d502dc27b95bba", - "6cdc965edc9648aebd9f3d89cae3955d", - 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"\n", - "rerankrequest = RerankRequest(query=query, passages=formatted_data)\n", - "results = ranker.rerank(rerankrequest)\n", - "print(results)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "4NzbLIaRZ9lo", - "outputId": "3a7f2b44-4d28-4976-e7de-2403034f9736" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "[{'text': '35 \\nReportable Segments \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nProductivity and Business Processes \\nRevenue increased $9.4 billion or 18%. \\n• Office Commercial products and cloud services revenue increased $4.4 billion or 13%. Office 365 \\nCommercial revenue grew 18% driven by seat growth of 14%, with continued momentum in small and \\nmedium business and frontline worker offerings, as well as growth in revenue per user. Office Commercial \\nproducts revenue declined 22% driven by continued customer shift to cloud offerings. \\n• Office Consumer products and cloud services revenue increased $641 million or 11% driven by Microsoft 365 \\nConsumer subscription revenue. Microsoft 365 Consumer subscribers grew 15% to 59.7 million. \\n• LinkedIn revenue increased $3.5 billion or 34% driven by a strong job market in our Talent Solutions business \\nand advertising demand in our Marketing Solutions business. \\n• Dynamics products and cloud services revenue increased 25% driven by Dynamics 365 growth of 39%. \\nOperating income increased $5.3 billion or 22%. \\n• Gross margin increased $7.3 billion or 17% driven by growth in Office 365 Commercial and LinkedIn. Gross \\nmargin percentage was relatively unchanged. Excluding the impact of the change in accounting estimate, \\ngross margin percentage increased 2 points driven by improvement across all cloud services. \\n• Operating expenses increased $2.0 billion or 11% driven by investments in LinkedIn and cloud engineering. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nIntelligent Cloud \\nRevenue increased $15.2 billion or 25%. \\n• Server products and cloud services revenue increased $14.7 billion or 28% driven by Azure and other cloud \\nservices. Azure and other cloud services revenue grew 45% driven by growth in our consumption -based \\nservices. Server products revenue increased 5% driven by hybrid solutions, including Windows Server and \\nSQL Server running in multi -cloud environments. \\n• Enterprise Services revenue increased $464 million or 7% driven by growth in Enterprise Support Services. \\nOperating income increased $6.6 billion or 25%. \\n• Gross margin increased $9.4 billion or 22% driven by growth in Azure and other cloud services. Gross margin \\npercentage decreased. Excluding the impact of the change in accounting estimate, gross margin percentage \\nwas relatively unchanged driven by improvement in Azure and other cloud services, offset in part by sales \\nmix shift to Azure and other cloud services. \\n• Operating expenses increased $2.8 billion or 16% driven by investments in Azure and other cloud services. \\nRevenue and operating income included an unfavorable foreign currency impact of 2% and 3%, respectively. \\nMore Personal Computing \\nRevenue increased $5.6 billion or 10%. \\n• Windows revenue increased $2.3 billion or 10% driven by growth in Windows OEM and Windows \\nCommercial. Windows OEM revenue increased 11% driven by continued strength in the commercial PC \\nmarket, which has higher revenue per license. Windows Commercial products and cloud services revenue \\nincreased 11% driven by demand for Microsoft 365.', 'score': 0.14885372}, {'text': '34 net income tax benefit related to an India Supreme Court decision on withholding taxes in the third quarter of fiscal year \\n2021. Refer to the Non -GAAP Financial Measures section below for a reconciliation of our financial results reported in \\naccordance with GAAP to non -GAAP financial results. See Note 12 – Income Taxes of the Notes to Financial Statements \\nin our fiscal year 2022 Form 10 -K for further discussion. \\nFiscal Year 2022 Compared with Fiscal Year 2021 \\nRevenue increased $30.2 billion or 18% driven by growth across each of our segments. Intelligent Cloud revenue \\nincreased driven by Azure and other cloud services. Productivity and Business Processes revenue increased driven by \\nOffice 365 Commercial and LinkedIn. More Personal Computing revenue increased driven by Search and news \\nadvertising and Windows. \\nCost of revenue increased $10.4 billion or 20% driven by growth in Microsoft Cloud. \\nGross margin increased $19.8 billion or 17% driven by growth across each of our segments. \\n• Gross margin percentage decreased slightly. Excluding the impact of the fiscal year 2021 change in \\naccounting estimate for the useful lives of our server and network equipment, gross margin percentage \\nincreased 1 point driven by improvement in Productivity and Business Processes. \\n• Microsoft Cloud gross margin percentage decreased slightly to 70%. Excluding the impact of the change in \\naccounting estimate, Microsoft Cloud gross margin percentage increased 3 points driven by improvement \\nacross our cloud services, offset in part by sales mix shift to Azure and other cloud services. \\nOperating expenses increased $6.3 billion or 14% driven by investments in cloud engineering, LinkedIn, Gaming, and \\ncommercial sales. \\nKey changes in operating expenses were: \\n• Research and development expenses increased $3.8 billion or 18% driven by investments in cloud \\nengineering, Gaming, and LinkedIn. \\n• Sales and marketing expenses increased $1.7 billion or 8% driven by investments in commercial sales and \\nLinkedIn. Sales and marketing included a favorable foreign currency impact of 2%. \\n• General and administrative expenses increased $793 million or 16% driven by investments in corporate \\nfunctions. \\nOperating income increased $13.5 billion or 19% driven by growth across each of our segments. \\nCurrent year net income and diluted EPS were positively impacted by the net tax benefit related to the transfer of \\nintangible properties, which resulted in an increase to net income and diluted EPS of $3.3 billion and $0.44, respectively. \\nPrior year net income and diluted EPS were positively impacted by the net tax benefit related to the India Supreme Court \\ndecision on withholding taxes, which resulted in an increase to net income and diluted EPS of $620 million and $0.08, \\nrespectively. \\nGross margin and operating income both included an unfavorable foreign currency impact of 2%. \\nSEGMENT RESULTS OF OPERATIONS \\n \\n(In millions, except percentages) 2022 2021 Percentage \\nChange \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 18% \\nIntelligent Cloud 75,251 60,080 25% \\nMore Personal Computing 59,655 54,093 10% \\nTotal $ 198,270 $ 168,088 18% \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 22% \\nIntelligent Cloud 32,721 26,126 25% \\nMore Personal Computing 20,975 19,439 8% \\nTotal $ 83,383 $ 69,916 19%', 'score': 0.04124021}, {'text': '33 Dynamics products and cloud services revenue growth Revenue from Dynamics products and cloud services, \\nincluding Dynamics 365, comprising a set of intelligent, \\ncloud -based applications across ERP, CRM, Customer \\nInsights, Power Apps, and Power Automate; and on -\\npremises ERP and CRM applications \\n \\nLinkedIn revenue growth Revenue from LinkedIn, including Talent Solutions, \\nMarketing Solutions, Premium Subscriptions, and Sales \\nSolutions \\n \\nServer products and cloud services revenue growth Revenue from Server products and cloud services, \\nincluding Azure and other cloud services; SQL Server, \\nWindows Server, Visual Studio, System Center, and \\nrelated Client Access Licenses (“CALs”); and Nuance and \\nGitHub \\nMore Personal Computing \\nMetrics related to our More Personal Computing segment assess the performance of key lines of business within this \\nsegment. These metrics provide strategic product insights which allow us to assess the performance across our \\ncommercial and consumer businesses. As we have diversity of target audiences and sales motions within the Windows \\nbusiness, we monitor metrics that are reflective of those varying motions. \\n \\nWindows OEM revenue growth Revenue from sales of Windows Pro and non -Pro licenses sold \\nthrough the OEM channel \\n \\nWindows Commercial products and cloud \\nservices revenue growth Revenue from Windows Commercial products and cloud services, \\ncomprising volume licensing of the Windows operating system, \\nWindows cloud services, and other Windows commercial offerings \\n \\nSurface revenue growth Revenue from Surface devices and accessories \\n \\nXbox content and services revenue growth Revenue from Xbox content and services, comprising first - and third -\\nparty content (including games and in -game content), Xbox Game \\nPass and other subscriptions, Xbox Cloud Gaming, third -party disc \\nroyalties, advertising, and other cloud services \\n \\nSearch and news advertising revenue, \\nexcluding TAC, growth Revenue from search and news advertising excluding traffic \\nacquisition costs (“TAC”) paid to Bing Ads network publishers and \\nnews partners \\nSUMMARY RESULTS OF OPERATIONS \\n \\n(In millions, except percentages and per share amounts) 2022 2021 Percentage \\nChange \\n \\nRevenue $ 198,270 $ 168,088 18% \\nGross margin 135,620 115,856 17% \\nOperating income 83,383 69,916 19% \\nNet income 72,738 61,271 19% \\nDiluted earnings per share 9.65 8.05 20% \\n \\nAdjusted net income (non -GAAP) 69,447 60,651 15% \\nAdjusted diluted earnings per share (non -GAAP) 9.21 7.97 16% \\nAdjusted net income and adjusted diluted earnings per share (“EPS”) are non -GAAP financial measures which exclude \\nthe net income tax benefit related to transfer of intangible properties in the first quarter of fiscal year 2022 and the', 'score': 0.00040568286}, {'text': '82 \\nIn addition, certain costs incurred at a corporate level that are identifiable and that benefit our segments are allocated to \\nthem. These allocated costs include legal, including settlements and fines, information technology, human resources, \\nfinance, excise taxes, field selling, shared facilities services, and customer service and support. Each allocation is \\nmeasured differently based on the specific facts and circumstances of the costs being allocated. \\nSegment revenue and operating income were as follows during the periods presented: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nRevenue \\n \\nProductivity and Business Processes $ 63,364 $ 53,915 $ 46,398 \\nIntelligent Cloud 75,251 60,080 48,366 \\nMore Personal Computing 59,655 54,093 48,251 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\n \\nOperating Income \\n \\nProductivity and Business Processes $ 29,687 $ 24,351 $ 18,724 \\nIntelligent Cloud 32,721 26,126 18,324 \\nMore Personal Computing 20,975 19,439 15,911 \\nTotal $ 83,383 $ 69,916 $ 52,959 \\nNo sales to an individual customer or country other than the United States accounted for more than 10% of revenue for \\nfiscal years 2022, 2021, or 2020. Revenue, classified by the major geographic areas in which our customers were located, \\nwas as follows: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nUnited States (a) $ 100,218 $ 83,953 $ 73,160 \\nOther countries 98,052 84,135 69,855 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\n(a) Includes billings to OEMs and certain multinational organizations because of the nature of these businesses and the \\nimpracticability of determining the geographic source of the revenue. \\nRevenue, classified by significant product and service offerings, was as follows: \\n \\n(In millions) \\n \\nYear Ended June 30, 2022 2021 2020 \\n \\nServer products and cloud services $ 67,321 $ 52,589 $ 41,379 \\nOffice products and cloud services 44,862 39,872 35,316 \\nWindows 24,761 22,488 21,510 \\nGaming 16,230 15,370 11,575 \\nLinkedIn 13,816 10,289 8,077 \\nSearch and news advertising 11,591 9,267 8,524 \\nEnterprise Services 7,407 6,943 6,409 \\nDevices 6,991 6,791 6,457 \\nOther 5,291 4,479 3,768 \\nTotal $ 198,270 $ 168,088 $ 143,015 \\nWe have recast certain previously reported amounts in the table above to conform to the way we internally manage and \\nmonitor our business.', 'score': 0.00026307348}]\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "# this how you can use this methods & pass this data to llm & get better results fro your RAG applications" - ], - "metadata": { - "id": "bzHa6zld2wQa" - } - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "GowqlU-y2wda" - }, - "execution_count": null, - "outputs": [] - } - ] -} diff --git a/examples/SuperAgent_Autogen_with_lancedb/README.md b/examples/SuperAgent_Autogen/README.md similarity index 80% rename from examples/SuperAgent_Autogen_with_lancedb/README.md rename to examples/SuperAgent_Autogen/README.md index 29b3653c..75bf77e9 100644 --- a/examples/SuperAgent_Autogen_with_lancedb/README.md +++ b/examples/SuperAgent_Autogen/README.md @@ -1,7 +1,9 @@ + Open In Colab + # Super Agent: Integrating Autogen Technologies and Vector Databases ## Overview -![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/dafa79aa-2eb8-4fc9-8e01-048a08ab0319) +![image](../../assets/superagent-autogen.png) The Super Agent project showcases the integration of cutting-edge technologies including Ollama, LiteLLM, Autogen, LanceDB, and LangChain to create a powerful AI agent. This agent leverages the strengths of vector databases and conversational AI to provide sophisticated data management and context-aware interactions. The goal is to harness the capabilities of LanceDB's vector database and Autogen's conversational AI framework to build a Super Agent that excels in understanding and processing complex queries. @@ -10,7 +12,7 @@ The Super Agent project showcases the integration of cutting-edge technologies i For detailed installation instructions, please refer to our comprehensive guide available in the Google Colaboratory notebook: -Google Colab Notebook Open In Colab + Open In Colab This guide will walk you through setting up each component of the Super Agent, ensuring a seamless integration process. @@ -44,7 +46,7 @@ The Super Agent's implementation involves several key steps: ## Resources -- **Google Colab**: For hands-on examples and detailed code snippets for implementing the Super Agent, visit our [Google Colab Notebook](https://colab.research.google.com/drive/12LjrteGKfFOgJL8gB7KTZmJGvXda4KsJ?usp=sharing). +- **Google Colab**: For hands-on examples and detailed code snippets for implementing the Super Agent, visit our [Google Colab Notebook](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/SuperAgent_Autogen/main.ipynb). -- **Blog Post**: For a more in-depth exploration of the concepts and methodologies discussed in this project, please visit our [blog post](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases/). +- **Blog Post**: For a more in-depth exploration of the concepts and methodologies discussed in this project, please visit our [Blog Post](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases/). diff --git a/examples/SuperAgent_Autogen/main.ipynb b/examples/SuperAgent_Autogen/main.ipynb new file mode 100644 index 00000000..d7136f27 --- /dev/null +++ b/examples/SuperAgent_Autogen/main.ipynb @@ -0,0 +1,697 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "This notebook illustrates how you can use the langchain with custom pdf data & chat it using autogen agent" + ], + "metadata": { + "id": "lHFbPb4JU9xF" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3poVgyh-bZJ-", + "outputId": "ad799a6e-7eec-4e14-dae3-f7e86c9e67cc" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.8/88.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m811.8/811.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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This behaviour is the source of the following dependency conflicts.\n", + "llmx 0.0.15a0 requires cohere, which is not installed.\n", + "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n", + "\u001b[0m" + ] + } + ], + "source": [ + "%pip install pyautogen~=0.1.0 langchain openai tiktoken lancedb pypdf -q -U" + ] + }, + { + "cell_type": "code", + "source": [ + "from langchain.embeddings import OpenAIEmbeddings\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain.document_loaders import PyPDFLoader\n", + "from langchain.memory import ConversationBufferMemory\n", + "from langchain.llms import OpenAI\n", + "from langchain.chains import ConversationalRetrievalChain\n", + "from langchain_community.vectorstores import LanceDB" + ], + "metadata": { + "id": "0tLTTT9ucFEb" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Requirements\n", + "AutoGen requires Python>=3.8. To run this notebook example, please install pyautogen:\n" + ], + "metadata": { + "id": "ObNypsHRV3mz" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install pyautogen" + ], + "metadata": { + "id": "6RuVu12whCG0" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import lancedb\n", + "\n", + "embeddings = OpenAIEmbeddings(\n", + " openai_api_key=\"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\"\n", + ")" + ], + "metadata": { + "id": "sUFdvTyVh8xF" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [], + "metadata": { + "id": "kztlyFIXU8m-" + } + }, + { + "cell_type": "code", + "source": [ + "# !wget -O uniswap_v3.pdf https://uniswap.org/whitepaper-v3.pdf\n", + "!wget -O food.pdf https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "ODKg12trdhX-", + "outputId": "a7041322-f633-496c-a8e8-126a81cbb5d9" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "--2024-02-11 04:40:16-- https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf\n", + "Resolving pdf.usaid.gov (pdf.usaid.gov)... 23.7.61.67, 2600:1408:ec00:380::1923, 2600:1408:ec00:38f::1923\n", + "Connecting to pdf.usaid.gov (pdf.usaid.gov)|23.7.61.67|:443... connected.\n", + "HTTP request sent, awaiting response... 200 OK\n", + "Length: 6419525 (6.1M) [application/pdf]\n", + "Saving to: ‘food.pdf’\n", + "\n", + "food.pdf 100%[===================>] 6.12M --.-KB/s in 0.1s \n", + "\n", + "2024-02-11 04:40:16 (42.7 MB/s) - ‘food.pdf’ saved [6419525/6419525]\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "create OAI_CONFIG_LIST.json file in pwd & upload\n", + "in it\n", + "\n", + "\n", + "[\n", + " {\n", + " \"model\": \"gpt-4\",\n", + " \"api_key\": \"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\"\n", + " }\n", + "]" + ], + "metadata": { + "id": "1oC3NAFyd4Kb" + } + }, + { + "cell_type": "code", + "source": [ + "import autogen\n", + "\n", + "config_list = autogen.config_list_from_json(\n", + " \"OAI_CONFIG_LIST.json\",\n", + " filter_dict={\n", + " \"model\": [\"gpt-4\"],\n", + " },\n", + ")" + ], + "metadata": { + "id": "H1bRXWu-cE_C" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# create file name with OAI_CONFIG_LIT.json & put below authentications code" + ], + "metadata": { + "id": "yV0pNPiRPy8h" + } + }, + { + "cell_type": "code", + "source": [ + "# create file name with OAI_CONFIG_LIT.\n", + "[{\"model\": \"gpt-4\", \"api_key\": \"sk-yourapikey\"}]" + ], + "metadata": { + "id": "yWDhjTDMcFBi" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "loaders = [PyPDFLoader(\"./food.pdf\")]\n", + "docs = []\n", + "for l in loaders:\n", + " docs.extend(l.load())\n", + "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\n", + "docs = text_splitter.split_documents(docs)" + ], + "metadata": { + "id": "5gapqmsscFG-" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "import lancedb\n", + "\n", + "embeddings = OpenAIEmbeddings(\n", + " openai_api_key=\"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\"\n", + ")\n", + "\n", + "db = lancedb.connect(\"/tmp/lancedb\")\n", + "table = db.create_table(\n", + " \"my_table\",\n", + " data=[\n", + " {\n", + " \"vector\": embeddings.embed_query(\"Hello food\"),\n", + " \"text\": \"Hello food\",\n", + " \"id\": \"1\",\n", + " }\n", + " ],\n", + " mode=\"overwrite\",\n", + ")\n", + "\n", + "vectorstore = LanceDB.from_documents(docs, embeddings, connection=table)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "5dLkCqa0dLXV", + "outputId": "28ab5984-c875-4281-95e5-d48bfdd12e99" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", + " warn_deprecated(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "qa = ConversationalRetrievalChain.from_llm(\n", + " OpenAI(\n", + " temperature=0,\n", + " openai_api_key=\"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\",\n", + " ),\n", + " vectorstore.as_retriever(),\n", + " memory=ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True),\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "YMBoF5kucFMJ", + "outputId": "13d7edab-5f3d-4698-fe6f-40f33dcd865a" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.llms.openai.OpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.\n", + " warn_deprecated(\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def answer_food_question(question):\n", + " response = qa({\"question\": question})\n", + " return response[\"answer\"]" + ], + "metadata": { + "id": "HjSVygLIcSEX" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "question = \"what is good food\"\n", + "answer_food_question(question)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 160 + }, + "id": "XCqxSaQSepsW", + "outputId": "c1fc1bdc-9f2e-467b-cb51-fdde3fc964ae" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", + " warn_deprecated(\n" + ] + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "' Good food is food that provides the recommended amounts of nutrients for the body to perform all its physiological activities. It is important to eat the right food, at the right time, in the right amounts, and prepared correctly in order to maintain a balanced diet and promote good nutrition. Good food is essential for physical and cognitive development and can improve overall health and quality of life.'" + ], + "application/vnd.google.colaboratory.intrinsic+json": { + "type": "string" + } + }, + "metadata": {}, + "execution_count": 18 + } + ] + }, + { + "cell_type": "code", + "source": [ + "!pip install pyautogen" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "BY4Fz-l7cUCA", + "outputId": "3a10b926-3659-46d3-d76b-7e083daf8fca" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Requirement already satisfied: pyautogen in /usr/local/lib/python3.10/dist-packages (0.1.14)\n", + "Requirement already satisfied: diskcache in /usr/local/lib/python3.10/dist-packages (from pyautogen) (5.6.3)\n", + "Requirement already satisfied: flaml in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.1.1)\n", + "Requirement already satisfied: openai<1 in /usr/local/lib/python3.10/dist-packages (from pyautogen) (0.28.1)\n", + "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from pyautogen) (1.0.1)\n", + "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.4.0)\n", + "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (2.31.0)\n", + "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (4.66.1)\n", + "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (3.9.3)\n", + "Requirement already satisfied: NumPy>=1.17.0rc1 in /usr/local/lib/python3.10/dist-packages (from flaml->pyautogen) (1.23.5)\n", + "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.3.2)\n", + "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.6)\n", + "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2.0.7)\n", + "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2024.2.2)\n", + "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.3.1)\n", + "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (23.2.0)\n", + "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.4.1)\n", + "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (6.0.5)\n", + "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.9.4)\n", + "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (4.0.3)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#### 4. Set up AutoGen user agent and assistant agent with function calling enabled." + ], + "metadata": { + "id": "anvuLAIycaqb" + } + }, + { + "cell_type": "code", + "source": [ + "llm_config = {\n", + " \"request_timeout\": 600,\n", + " \"seed\": 42,\n", + " \"config_list\": config_list,\n", + " \"temperature\": 0,\n", + " \"functions\": [\n", + " {\n", + " \"name\": \"answer_food_question\",\n", + " \"description\": \"Answer to any quetion related to food & provided answer_food_question \",\n", + " \"parameters\": {\n", + " \"type\": \"object\",\n", + " \"properties\": {\n", + " \"question\": {\n", + " \"type\": \"string\",\n", + " \"description\": \"The question to ask in relation to food\",\n", + " }\n", + " },\n", + " \"required\": [\"question\"],\n", + " },\n", + " }\n", + " ],\n", + "}" + ], + "metadata": { + "id": "Vca8Y_khcUID" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# create an AssistantAgent instance named \"assistant\"\n", + "assistant = autogen.AssistantAgent(\n", + " name=\"assistant\",\n", + " llm_config=llm_config,\n", + ")\n", + "# create a UserProxyAgent instance named \"user_proxy\"\n", + "user_proxy = autogen.UserProxyAgent(\n", + " name=\"user_proxy\",\n", + " human_input_mode=\"NEVER\",\n", + " max_consecutive_auto_reply=10,\n", + " code_execution_config={\"work_dir\": \".\"},\n", + " llm_config=llm_config,\n", + " system_message=\"\"\"Reply TERMINATE if the task has been solved at full satisfaction.\n", + "Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\"\"\",\n", + " function_map={\"answer_food_question\": answer_food_question},\n", + ")" + ], + "metadata": { + "id": "1XHjzIYAcfE7" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# the assistant receives a message from the user, which contains the task description\n", + "user_proxy.initiate_chat(\n", + " assistant,\n", + " message=\"\"\"\n", + "what is good food?\n", + "\"\"\",\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "hOZKxakHchZ4", + "outputId": "7ca36fe0-a211-409a-abb0-57e3cd05e429" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user_proxy (to assistant):\n", + "\n", + "\n", + "what is good food?\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "***** Suggested function Call: answer_food_question *****\n", + "Arguments: \n", + "\n", + "{\n", + " \"question\": \"what is good food?\"\n", + "}\n", + "*********************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + ">>>>>>>> EXECUTING FUNCTION answer_food_question...\n", + "user_proxy (to assistant):\n", + "\n", + "***** Response from calling function \"answer_food_question\" *****\n", + " Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", + "*****************************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n", + "user_proxy (to assistant):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# the assistant receives a message from the user, which contains the task description\n", + "user_proxy.initiate_chat(\n", + " assistant,\n", + " message=\"\"\"\n", + "please explain me essential minerals, sources, functions and symptoms of\n", + "deficiency?\n", + "\"\"\",\n", + ")" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "UDXo2V06fNjz", + "outputId": "37cf6766-9b68-4a81-e0c6-245d2af28a30" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user_proxy (to assistant):\n", + "\n", + "\n", + "please explain me essential minerals, sources, functions and symptoms of\n", + "deficiency?\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "Sure, here are some essential minerals, their sources, functions, and symptoms of deficiency:\n", + "\n", + "1. Calcium:\n", + " - Sources: Dairy products, leafy green vegetables, fish with edible bones (like sardines and salmon), fortified foods.\n", + " - Functions: Necessary for bone health, muscle function, nerve transmission, blood clotting.\n", + " - Deficiency Symptoms: Osteoporosis, rickets in children, muscle cramps, dental problems.\n", + "\n", + "2. Iron:\n", + " - Sources: Red meat, poultry, eggs, fruits, green vegetables, fortified bread.\n", + " - Functions: Essential for the production of red blood cells, helps in oxygen transport.\n", + " - Deficiency Symptoms: Anemia, fatigue, weakness, immune system problems.\n", + "\n", + "3. Magnesium:\n", + " - Sources: Nuts, seeds, whole grains, green leafy vegetables, fish, beans, yogurt.\n", + " - Functions: Helps in over 300 enzyme reactions, including regulation of blood pressure, supports immune system.\n", + " - Deficiency Symptoms: Loss of appetite, nausea, fatigue, weakness, muscle cramps, numbness.\n", + "\n", + "4. Potassium:\n", + " - Sources: Bananas, oranges, cantaloupe, honeydew, apricots, grapefruit, cooked spinach, cooked broccoli, potatoes, sweet potatoes, mushrooms, peas, cucumbers, zucchini, eggplant, pumpkins, leafy greens.\n", + " - Functions: Maintains fluid balance, helps in nerve transmission and muscle contraction.\n", + " - Deficiency Symptoms: Fatigue, weakness, constipation, muscle cramps.\n", + "\n", + "5. Zinc:\n", + " - Sources: Meat, shellfish, legumes, seeds, nuts, dairy, eggs, whole grains.\n", + " - Functions: Necessary for immune function, protein synthesis, DNA synthesis, cell division, wound healing.\n", + " - Deficiency Symptoms: Growth retardation, loss of appetite, impaired immune function, hair loss, diarrhea, delayed sexual maturation.\n", + "\n", + "Please note that this is not an exhaustive list and there are other essential minerals as well. Also, the symptoms of deficiency can vary from person to person and can often be symptoms of other conditions as well. Always consult with a healthcare provider for accurate information.\n", + "\n", + "--------------------------------------------------------------------------------\n", + "user_proxy (to assistant):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# the assistant receives a message from the user, which contains the task description\n", + "user_proxy.initiate_chat(\n", + " assistant,\n", + " message=\"\"\"\n", + "which food Keeps eyes healthy ?\n", + "\"\"\",\n", + ")" + ], + "metadata": { + "id": "UrlFGYW0g0sJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e82396e5-3dca-402c-889c-394557aeea0d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "user_proxy (to assistant):\n", + "\n", + "\n", + "which food Keeps eyes healthy ?\n", + "\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "***** Suggested function Call: answer_food_question *****\n", + "Arguments: \n", + "\n", + "{\n", + " \"question\": \"which food Keeps eyes healthy ?\"\n", + "}\n", + "*********************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "\n", + ">>>>>>>> EXECUTING FUNCTION answer_food_question...\n", + "user_proxy (to assistant):\n", + "\n", + "***** Response from calling function \"answer_food_question\" *****\n", + " Foods that are rich in Vitamin A, such as yellow/orange fruits and vegetables, dark green and deep yellow fruits and vegetables, liver, egg yolk, dairy products, and margarine can help maintain healthy eyes.\n", + "*****************************************************************\n", + "\n", + "--------------------------------------------------------------------------------\n", + "assistant (to user_proxy):\n", + "\n", + "Foods that are rich in Vitamin A can help maintain healthy eyes. These include:\n", + "\n", + "1. Yellow/orange fruits and vegetables: These include carrots, sweet potatoes, pumpkins, and apricots.\n", + "2. Dark green and deep yellow fruits and vegetables: These include spinach, kale, and other leafy greens.\n", + "3. Liver: This is a great source of Vitamin A.\n", + "4. Egg yolk: This is another good source of Vitamin A.\n", + "5. Dairy products: These include milk, cheese, and yogurt.\n", + "6. Margarine: This is also a good source of Vitamin A.\n", + "\n", + "Including these foods in your diet can help keep your eyes healthy.\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n", + "user_proxy (to assistant):\n", + "\n", + "TERMINATE\n", + "\n", + "--------------------------------------------------------------------------------\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "En6-kvjcjaid" + }, + "execution_count": null, + "outputs": [] + } + ] +} diff --git a/examples/SuperAgent_Autogen_with_lancedb/main.ipynb b/examples/SuperAgent_Autogen_with_lancedb/main.ipynb deleted file mode 100644 index 7446400f..00000000 --- a/examples/SuperAgent_Autogen_with_lancedb/main.ipynb +++ /dev/null @@ -1,698 +0,0 @@ -{ - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "provenance": [] - }, - "kernelspec": { - "name": "python3", - "display_name": "Python 3" - }, - "language_info": { - "name": "python" - } - }, - "cells": [ - { - "cell_type": "markdown", - "source": [ - "This notebook illustrates how you can use the langchain with custom pdf data & chat it using autogen agent" - ], - "metadata": { - "id": "lHFbPb4JU9xF" - } - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "3poVgyh-bZJ-", - "outputId": "ad799a6e-7eec-4e14-dae3-f7e86c9e67cc" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m88.8/88.8 kB\u001b[0m \u001b[31m2.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m811.8/811.8 kB\u001b[0m \u001b[31m9.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m226.7/226.7 kB\u001b[0m \u001b[31m9.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m111.9/111.9 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m284.0/284.0 kB\u001b[0m \u001b[31m13.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m295.2/295.2 kB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.0/77.0 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m21.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m239.4/239.4 kB\u001b[0m \u001b[31m23.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.7/55.7 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.6/21.6 MB\u001b[0m \u001b[31m34.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m38.3/38.3 MB\u001b[0m \u001b[31m12.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m55.4/55.4 kB\u001b[0m \u001b[31m4.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "llmx 0.0.15a0 requires cohere, which is not installed.\n", - "ibis-framework 7.1.0 requires pyarrow<15,>=2, but you have pyarrow 15.0.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0m" - ] - } - ], - "source": [ - "%pip install pyautogen~=0.1.0 langchain openai tiktoken lancedb pypdf -q -U" - ] - }, - { - "cell_type": "code", - "source": [ - "from langchain.embeddings import OpenAIEmbeddings\n", - "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", - "from langchain.document_loaders import PyPDFLoader\n", - "from langchain.memory import ConversationBufferMemory\n", - "from langchain.llms import OpenAI\n", - "from langchain.chains import ConversationalRetrievalChain\n", - "from langchain_community.vectorstores import LanceDB\n" - ], - "metadata": { - "id": "0tLTTT9ucFEb" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "Requirements\n", - "AutoGen requires Python>=3.8. To run this notebook example, please install pyautogen:\n" - ], - "metadata": { - "id": "ObNypsHRV3mz" - } - }, - { - "cell_type": "code", - "source": [ - "!pip install pyautogen" - ], - "metadata": { - "id": "6RuVu12whCG0" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import lancedb\n", - "\n", - "embeddings = OpenAIEmbeddings(openai_api_key='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')" - ], - "metadata": { - "id": "sUFdvTyVh8xF" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [], - "metadata": { - "id": "kztlyFIXU8m-" - } - }, - { - "cell_type": "code", - "source": [ - "# !wget -O uniswap_v3.pdf https://uniswap.org/whitepaper-v3.pdf\n", - "!wget -O food.pdf https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "ODKg12trdhX-", - "outputId": "a7041322-f633-496c-a8e8-126a81cbb5d9" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "--2024-02-11 04:40:16-- https://pdf.usaid.gov/pdf_docs/PA00TBCT.pdf\n", - "Resolving pdf.usaid.gov (pdf.usaid.gov)... 23.7.61.67, 2600:1408:ec00:380::1923, 2600:1408:ec00:38f::1923\n", - "Connecting to pdf.usaid.gov (pdf.usaid.gov)|23.7.61.67|:443... connected.\n", - "HTTP request sent, awaiting response... 200 OK\n", - "Length: 6419525 (6.1M) [application/pdf]\n", - "Saving to: ‘food.pdf’\n", - "\n", - "food.pdf 100%[===================>] 6.12M --.-KB/s in 0.1s \n", - "\n", - "2024-02-11 04:40:16 (42.7 MB/s) - ‘food.pdf’ saved [6419525/6419525]\n", - "\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "create OAI_CONFIG_LIST.json file in pwd & upload\n", - "in it\n", - "\n", - "\n", - "[\n", - " {\n", - " \"model\": \"gpt-4\",\n", - " \"api_key\": \"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\"\n", - " }\n", - "]" - ], - "metadata": { - "id": "1oC3NAFyd4Kb" - } - }, - { - "cell_type": "code", - "source": [ - "import autogen\n", - "\n", - "config_list = autogen.config_list_from_json(\n", - " \"OAI_CONFIG_LIST.json\",\n", - " filter_dict={\n", - " \"model\": [\"gpt-4\"],\n", - " },\n", - ")" - ], - "metadata": { - "id": "H1bRXWu-cE_C" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "markdown", - "source": [ - "# create file name with OAI_CONFIG_LIT.json & put below authentications code" - ], - "metadata": { - "id": "yV0pNPiRPy8h" - } - }, - { - "cell_type": "code", - "source": [ - "# create file name with OAI_CONFIG_LIT.\n", - "[\n", - " {\n", - " \"model\": \"gpt-4\",\n", - " \"api_key\": \"sk-yourapikey\"\n", - " }\n", - "]" - ], - "metadata": { - "id": "yWDhjTDMcFBi" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "loaders = [ PyPDFLoader('./food.pdf') ]\n", - "docs = []\n", - "for l in loaders:\n", - " docs.extend(l.load())\n", - "text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000)\n", - "docs = text_splitter.split_documents(docs)" - ], - "metadata": { - "id": "5gapqmsscFG-" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "import lancedb\n", - "\n", - "embeddings = OpenAIEmbeddings(openai_api_key='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')\n", - "\n", - "db = lancedb.connect(\"/tmp/lancedb\")\n", - "table = db.create_table(\n", - " \"my_table\",\n", - " data=[\n", - " {\n", - " \"vector\": embeddings.embed_query(\"Hello food\"),\n", - " \"text\": \"Hello food\",\n", - " \"id\": \"1\",\n", - " }\n", - " ],\n", - " mode=\"overwrite\",\n", - ")\n", - "\n", - "vectorstore = LanceDB.from_documents(docs, embeddings, connection=table)\n" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "5dLkCqa0dLXV", - "outputId": "28ab5984-c875-4281-95e5-d48bfdd12e99" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.embeddings.openai.OpenAIEmbeddings` was deprecated in langchain-community 0.1.0 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAIEmbeddings`.\n", - " warn_deprecated(\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "qa = ConversationalRetrievalChain.from_llm(\n", - " OpenAI(temperature=0,openai_api_key='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'),\n", - " vectorstore.as_retriever(),\n", - " memory=ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True)\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "YMBoF5kucFMJ", - "outputId": "13d7edab-5f3d-4698-fe6f-40f33dcd865a" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.llms.openai.OpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import OpenAI`.\n", - " warn_deprecated(\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "def answer_food_question(question):\n", - " response = qa({\"question\": question})\n", - " return response[\"answer\"]" - ], - "metadata": { - "id": "HjSVygLIcSEX" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "question = 'what is good food'\n", - "answer_food_question(question)" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 160 - }, - "id": "XCqxSaQSepsW", - "outputId": "c1fc1bdc-9f2e-467b-cb51-fdde3fc964ae" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n", - " warn_deprecated(\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "' Good food is food that provides the recommended amounts of nutrients for the body to perform all its physiological activities. It is important to eat the right food, at the right time, in the right amounts, and prepared correctly in order to maintain a balanced diet and promote good nutrition. Good food is essential for physical and cognitive development and can improve overall health and quality of life.'" - ], - "application/vnd.google.colaboratory.intrinsic+json": { - "type": "string" - } - }, - "metadata": {}, - "execution_count": 18 - } - ] - }, - { - "cell_type": "code", - "source": [ - "!pip install pyautogen" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "BY4Fz-l7cUCA", - "outputId": "3a10b926-3659-46d3-d76b-7e083daf8fca" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: pyautogen in /usr/local/lib/python3.10/dist-packages (0.1.14)\n", - "Requirement already satisfied: diskcache in /usr/local/lib/python3.10/dist-packages (from pyautogen) (5.6.3)\n", - "Requirement already satisfied: flaml in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.1.1)\n", - "Requirement already satisfied: openai<1 in /usr/local/lib/python3.10/dist-packages (from pyautogen) (0.28.1)\n", - "Requirement already satisfied: python-dotenv in /usr/local/lib/python3.10/dist-packages (from pyautogen) (1.0.1)\n", - "Requirement already satisfied: termcolor in /usr/local/lib/python3.10/dist-packages (from pyautogen) (2.4.0)\n", - "Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (2.31.0)\n", - "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (4.66.1)\n", - "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from openai<1->pyautogen) (3.9.3)\n", - "Requirement already satisfied: NumPy>=1.17.0rc1 in /usr/local/lib/python3.10/dist-packages (from flaml->pyautogen) (1.23.5)\n", - "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.3.2)\n", - "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (3.6)\n", - "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2.0.7)\n", - "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.20->openai<1->pyautogen) (2024.2.2)\n", - "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.3.1)\n", - "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (23.2.0)\n", - "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.4.1)\n", - "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (6.0.5)\n", - "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (1.9.4)\n", - "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->openai<1->pyautogen) (4.0.3)\n" - ] - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#### 4. Set up AutoGen user agent and assistant agent with function calling enabled." - ], - "metadata": { - "id": "anvuLAIycaqb" - } - }, - { - "cell_type": "code", - "source": [ - "llm_config={\n", - " \"request_timeout\": 600,\n", - " \"seed\": 42,\n", - " \"config_list\": config_list,\n", - " \"temperature\": 0,\n", - " \"functions\": [\n", - " {\n", - " \"name\": \"answer_food_question\",\n", - " \"description\": \"Answer to any quetion related to food & provided answer_food_question \",\n", - " \"parameters\": {\n", - " \"type\": \"object\",\n", - " \"properties\": {\n", - " \"question\": {\n", - " \"type\": \"string\",\n", - " \"description\": \"The question to ask in relation to food\",\n", - " }\n", - " },\n", - " \"required\": [\"question\"],\n", - " },\n", - " }\n", - " ],\n", - "}" - ], - "metadata": { - "id": "Vca8Y_khcUID" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# create an AssistantAgent instance named \"assistant\"\n", - "assistant = autogen.AssistantAgent(\n", - " name=\"assistant\",\n", - " llm_config=llm_config,\n", - ")\n", - "# create a UserProxyAgent instance named \"user_proxy\"\n", - "user_proxy = autogen.UserProxyAgent(\n", - " name=\"user_proxy\",\n", - " human_input_mode=\"NEVER\",\n", - " max_consecutive_auto_reply=10,\n", - " code_execution_config={\"work_dir\": \".\"},\n", - " llm_config=llm_config,\n", - " system_message=\"\"\"Reply TERMINATE if the task has been solved at full satisfaction.\n", - "Otherwise, reply CONTINUE, or the reason why the task is not solved yet.\"\"\",\n", - " function_map={\"answer_food_question\": answer_food_question}\n", - ")\n", - "\n", - "\n", - "\n" - ], - "metadata": { - "id": "1XHjzIYAcfE7" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "# the assistant receives a message from the user, which contains the task description\n", - "user_proxy.initiate_chat(\n", - " assistant,\n", - " message=\"\"\"\n", - "what is good food?\n", - "\"\"\"\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "hOZKxakHchZ4", - "outputId": "7ca36fe0-a211-409a-abb0-57e3cd05e429" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "user_proxy (to assistant):\n", - "\n", - "\n", - "what is good food?\n", - "\n", - "\n", - "--------------------------------------------------------------------------------\n", - "assistant (to user_proxy):\n", - "\n", - "***** Suggested function Call: answer_food_question *****\n", - "Arguments: \n", - "\n", - "{\n", - " \"question\": \"what is good food?\"\n", - "}\n", - "*********************************************************\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\n", - ">>>>>>>> EXECUTING FUNCTION answer_food_question...\n", - "user_proxy (to assistant):\n", - "\n", - "***** Response from calling function \"answer_food_question\" *****\n", - " Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", - "*****************************************************************\n", - "\n", - "--------------------------------------------------------------------------------\n", - "assistant (to user_proxy):\n", - "\n", - "Good food is food that is able to provide the recommended amounts of nutrients for the body to perform all its physiological activities. It is important for our health and well-being because it helps us maintain a balanced diet, promotes physical and cognitive development, and protects us from foodborne illnesses. Good food also ensures that we have enough energy for physical activity and basic body functions, and it helps us maintain a healthy weight. Additionally, good food can improve our overall quality of life and productivity.\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n", - "user_proxy (to assistant):\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# the assistant receives a message from the user, which contains the task description\n", - "user_proxy.initiate_chat(\n", - " assistant,\n", - " message=\"\"\"\n", - "please explain me essential minerals, sources, functions and symptoms of\n", - "deficiency?\n", - "\"\"\"\n", - ")" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "UDXo2V06fNjz", - "outputId": "37cf6766-9b68-4a81-e0c6-245d2af28a30" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "user_proxy (to assistant):\n", - "\n", - "\n", - "please explain me essential minerals, sources, functions and symptoms of\n", - "deficiency?\n", - "\n", - "\n", - "--------------------------------------------------------------------------------\n", - "assistant (to user_proxy):\n", - "\n", - "Sure, here are some essential minerals, their sources, functions, and symptoms of deficiency:\n", - "\n", - "1. Calcium:\n", - " - Sources: Dairy products, leafy green vegetables, fish with edible bones (like sardines and salmon), fortified foods.\n", - " - Functions: Necessary for bone health, muscle function, nerve transmission, blood clotting.\n", - " - Deficiency Symptoms: Osteoporosis, rickets in children, muscle cramps, dental problems.\n", - "\n", - "2. Iron:\n", - " - Sources: Red meat, poultry, eggs, fruits, green vegetables, fortified bread.\n", - " - Functions: Essential for the production of red blood cells, helps in oxygen transport.\n", - " - Deficiency Symptoms: Anemia, fatigue, weakness, immune system problems.\n", - "\n", - "3. Magnesium:\n", - " - Sources: Nuts, seeds, whole grains, green leafy vegetables, fish, beans, yogurt.\n", - " - Functions: Helps in over 300 enzyme reactions, including regulation of blood pressure, supports immune system.\n", - " - Deficiency Symptoms: Loss of appetite, nausea, fatigue, weakness, muscle cramps, numbness.\n", - "\n", - "4. Potassium:\n", - " - Sources: Bananas, oranges, cantaloupe, honeydew, apricots, grapefruit, cooked spinach, cooked broccoli, potatoes, sweet potatoes, mushrooms, peas, cucumbers, zucchini, eggplant, pumpkins, leafy greens.\n", - " - Functions: Maintains fluid balance, helps in nerve transmission and muscle contraction.\n", - " - Deficiency Symptoms: Fatigue, weakness, constipation, muscle cramps.\n", - "\n", - "5. Zinc:\n", - " - Sources: Meat, shellfish, legumes, seeds, nuts, dairy, eggs, whole grains.\n", - " - Functions: Necessary for immune function, protein synthesis, DNA synthesis, cell division, wound healing.\n", - " - Deficiency Symptoms: Growth retardation, loss of appetite, impaired immune function, hair loss, diarrhea, delayed sexual maturation.\n", - "\n", - "Please note that this is not an exhaustive list and there are other essential minerals as well. Also, the symptoms of deficiency can vary from person to person and can often be symptoms of other conditions as well. Always consult with a healthcare provider for accurate information.\n", - "\n", - "--------------------------------------------------------------------------------\n", - "user_proxy (to assistant):\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "# the assistant receives a message from the user, which contains the task description\n", - "user_proxy.initiate_chat(\n", - " assistant,\n", - " message=\"\"\"\n", - "which food Keeps eyes healthy ?\n", - "\"\"\"\n", - ")" - ], - "metadata": { - "id": "UrlFGYW0g0sJ", - "colab": { - "base_uri": "https://localhost:8080/" - }, - "outputId": "e82396e5-3dca-402c-889c-394557aeea0d" - }, - "execution_count": null, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "user_proxy (to assistant):\n", - "\n", - "\n", - "which food Keeps eyes healthy ?\n", - "\n", - "\n", - "--------------------------------------------------------------------------------\n", - "assistant (to user_proxy):\n", - "\n", - "***** Suggested function Call: answer_food_question *****\n", - "Arguments: \n", - "\n", - "{\n", - " \"question\": \"which food Keeps eyes healthy ?\"\n", - "}\n", - "*********************************************************\n", - "\n", - "--------------------------------------------------------------------------------\n", - "\n", - ">>>>>>>> EXECUTING FUNCTION answer_food_question...\n", - "user_proxy (to assistant):\n", - "\n", - "***** Response from calling function \"answer_food_question\" *****\n", - " Foods that are rich in Vitamin A, such as yellow/orange fruits and vegetables, dark green and deep yellow fruits and vegetables, liver, egg yolk, dairy products, and margarine can help maintain healthy eyes.\n", - "*****************************************************************\n", - "\n", - "--------------------------------------------------------------------------------\n", - "assistant (to user_proxy):\n", - "\n", - "Foods that are rich in Vitamin A can help maintain healthy eyes. These include:\n", - "\n", - "1. Yellow/orange fruits and vegetables: These include carrots, sweet potatoes, pumpkins, and apricots.\n", - "2. Dark green and deep yellow fruits and vegetables: These include spinach, kale, and other leafy greens.\n", - "3. Liver: This is a great source of Vitamin A.\n", - "4. Egg yolk: This is another good source of Vitamin A.\n", - "5. Dairy products: These include milk, cheese, and yogurt.\n", - "6. Margarine: This is also a good source of Vitamin A.\n", - "\n", - "Including these foods in your diet can help keep your eyes healthy.\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n", - "user_proxy (to assistant):\n", - "\n", - "TERMINATE\n", - "\n", - "--------------------------------------------------------------------------------\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "En6-kvjcjaid" - }, - "execution_count": null, - "outputs": [] - } - ] -} diff --git a/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb b/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb new file mode 100644 index 00000000..3eb81d46 --- /dev/null +++ b/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb @@ -0,0 +1,2341 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + 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"_model_name": "DescriptionStyleModel", + "_view_count": null, + "_view_module": "@jupyter-widgets/base", + "_view_module_version": "1.2.0", + "_view_name": "StyleView", + "description_width": "" + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "# Retrieval-Augmented Generation(RAG) from Scratch" + ], + "metadata": { + "id": "CVjHcUsRyM7I" + } + }, + { + "cell_type": "markdown", + "source": [ + "In this notebook, we will build a Retrieval-Augmented Generation(RAG) pipeline from scratch without using any popular libraries such as Langchain or Llamaindex.\n", + "\n", + "RAG is a technique that retrieves related documents to the user's question, combines them with LLM-base prompt, and sends them to LLMs like GPT to produce more factually accurate generation." + ], + "metadata": { + "id": "WjJnK8VHx4r1" + } + }, + { + "cell_type": "markdown", + "source": [ + "Lets Split RAG Pipeline into 5 parts:\n", + "\n", + "1. Data loading\n", + "2. Chunking and Embedding\n", + "3. Vector Store\n", + "4. Retrieval & Prompt preparation\n", + "5. Answer Generation" + ], + "metadata": { + "id": "pvGgyVYb7JNs" + } + }, + { + "cell_type": "markdown", + "source": [ + "Here is an image illustrating the RAG process\n", + "\n", + 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+ ], + "metadata": { + "id": "DTE0cKVh7oES" + } + }, + { + "cell_type": "markdown", + "source": [ + "## Install Dependencies" + ], + "metadata": { + "id": "rmiiI22M4aPK" + } + }, + { + "cell_type": "code", + "source": [ + "# Install\n", + "!pip install transformers scikit-learn docx2txt datasets nltk lancedb openai==0.28 -q" + ], + "metadata": { + "id": "aGP9H97ghb9-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7b3c4d2a-c2ab-4b4d-8019-b83d0af518bf" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m510.5/510.5 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.3/21.3 MB\u001b[0m \u001b[31m43.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.5/76.5 kB\u001b[0m \u001b[31m10.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m116.3/116.3 kB\u001b[0m \u001b[31m14.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m21.5/21.5 MB\u001b[0m \u001b[31m51.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m98.7/98.7 kB\u001b[0m \u001b[31m14.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[?25h Building wheel for docx2txt (setup.py) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Set OPENAI API KEY as env variable" + ], + "metadata": { + "id": "ygbayCQH6tlr" + } + }, + { + "cell_type": "code", + "source": [ + "# Set OPENAI_API_KEY\n", + "\n", + "import os\n", + "\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-...\"" + ], + "metadata": { + "id": "hOP0kua_q2lp" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Loading" + ], + "metadata": { + "id": "BcX8B04yCp7x" + } + }, + { + "cell_type": "code", + "source": [ + "# Load text\n", + "\n", + "# !wget link\n", + "with open(\"lease.txt\", \"r\") as file:\n", + " text_data = file.read()" + ], + "metadata": { + "id": "GNdKH6GuOuo3" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Data Chunking and Embedding" + ], + "metadata": { + "id": "J9ZADS66CuK9" + } + }, + { + "cell_type": "code", + "source": [ + "# Recursive Text Splitter\n", + "\n", + "import nltk\n", + "\n", + "nltk.download(\"punkt\")\n", + "from nltk.tokenize import sent_tokenize\n", + "import re\n", + "\n", + "\n", + "def recursive_text_splitter(text, max_chunk_length=1000, overlap=100):\n", + " \"\"\"\n", + " Helper function for chunking text recursively\n", + " \"\"\"\n", + " # Initialize result\n", + " result = []\n", + "\n", + " current_chunk_count = 0\n", + " separator = [\"\\n\", \" \"]\n", + " _splits = re.split(f\"({separator})\", text)\n", + " splits = [_splits[i] + _splits[i + 1] for i in range(1, len(_splits), 2)]\n", + "\n", + " for i in range(len(splits)):\n", + " if current_chunk_count != 0:\n", + " chunk = \"\".join(\n", + " splits[\n", + " current_chunk_count\n", + " - overlap : current_chunk_count\n", + " + max_chunk_length\n", + " ]\n", + " )\n", + " else:\n", + " chunk = \"\".join(splits[0:max_chunk_length])\n", + "\n", + " if len(chunk) > 0:\n", + " result.append(\"\".join(chunk))\n", + " current_chunk_count += max_chunk_length\n", + "\n", + " return result" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "n_O8DxuqGPKx", + "outputId": "db3b9321-7ecf-401b-e54e-3e962c041bd5" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "[nltk_data] Downloading package punkt to /root/nltk_data...\n", + "[nltk_data] Unzipping tokenizers/punkt.zip.\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Split the text using the recursive character text splitter\n", + "chunks = recursive_text_splitter(text_data, max_chunk_length=100, overlap=10)\n", + "print(\"Number of Chunks: \", len(chunks))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "kQ9MggTd1nQU", + "outputId": "791d6cae-73fb-4b2a-9536-592b49346835" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number of Chunks: 11\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Embedder" + ], + "metadata": { + "id": "HoGDypAnC5ZS" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import AutoTokenizer, AutoModel\n", + "import torch\n", + "\n", + "# Choose a pre-trained model (e.g., BERT, RoBERTa, etc.)\n", + "# Load the tokenizer and model\n", + "model_name = \"bert-base-uncased\"\n", + "tokenizer = AutoTokenizer.from_pretrained(model_name)\n", + "model = AutoModel.from_pretrained(model_name)\n", + "\n", + "\n", + "def embedder(chunk):\n", + " \"\"\"\n", + " Helper function to embed chunk of text\n", + " \"\"\"\n", + " # Tokenize the input text\n", + " tokens = tokenizer(chunk, return_tensors=\"pt\", padding=True, truncation=True)\n", + "\n", + " # Get the model's output (including embeddings)\n", + " with torch.no_grad():\n", + " model_output = model(**tokens)\n", + "\n", + " # Extract the embeddings\n", + " embeddings = model_output.last_hidden_state[:, 0, :]\n", + " embed = embeddings[0].numpy()\n", + " return embed" + ], + "metadata": { + "colab": { + "base_uri": 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+ "text/plain": [ + "tokenizer_config.json: 0%| | 0.00/48.0 [00:00 Date: Sun, 17 Mar 2024 22:00:04 +0530 Subject: [PATCH 04/32] Updated Blog Link (#157) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update --- README.md | 56 +++++++++++++++++++++++++++---------------------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index fd1959b3..6b9ceea8 100644 --- a/README.md +++ b/README.md @@ -34,34 +34,34 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Youtube transcript search bot](/examples/Youtube-Search-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Youtube-Search-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Youtube-Search-QA-Bot/index.js) [![LLM](https://img.shields.io/badge/openai-api-white)](#)|| | [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Code-Documentation-QA-Bot/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/Code-Documentation-QA-Bot/index.js)[![LLM](https://img.shields.io/badge/openai-api-white)](#)|| | [Inbuilt Hybrid Search](/examples/Inbuilt-Hybrid-Search) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) || -| [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/reducing_hallucinations_ai_agents/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/reducing_hallucinations_ai_agents/index.js)[![LLM](https://img.shields.io/badge/openai-api-white)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f)| -| [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_clip_diffusiondb/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939)| -| [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_video_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939)| -| [Multimodal Image + Text Search](/examples/multimodal_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939) | +| [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/reducing_hallucinations_ai_agents/main.py) [![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/reducing_hallucinations_ai_agents/index.js)[![LLM](https://img.shields.io/badge/openai-api-white)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/)| +| [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_clip_diffusiondb/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| +| [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_video_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| +| [Multimodal Image + Text Search](/examples/multimodal_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multimodal_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)| | [TransformersJS Embedding example](./examples/js-transformers/) |[![JS](https://img.shields.io/badge/javascript-%23323330.svg?style=for-the-badge&logo=javascript&logoColor=%23F7DF1E)](./examples/js-transformers/index.js) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | | | [Movie Recommender](/examples/movie-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/movie-recommender/main.py)| | | [Product Recommender](./examples/product-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/product-recommender/main.py) | | | [Audio Search](./examples/audio_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/audio_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | | | [Arxiv paper recommender](/examples/arxiv-recommender) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/arxiv-recommender/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | | | [Multi-lingual search](/examples/multi-lingual-wiki-qa) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/multi-lingual-wiki-qa/main.py) [![LLM](https://img.shields.io/badge/cohere-api-pink)](#) | | -| [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| -| [Improve RAG with Re-ranking](/examples/RAG_re_ranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| -| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)| -| [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)| -| [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)| -| [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)| -| [Query Expansion and Reranker ](/examples/QueryExpansion&Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| +| [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| +| [Improve RAG with Re-ranking](/examples/RAG_re_ranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| +| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)| +| [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)| +| [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)| +| [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)| +| [Query Expansion and Reranker ](/examples/QueryExpansion&Reranker/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)| | [RAG Fusion](/examples/RAG_Fusion/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)| -| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)| +| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)| | [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| | -| [NER powered with Semantic Search](/tutorials/NER-powered-Semantic-Search/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493)| -[Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)| +| [NER powered with Semantic Search](/tutorials/NER-powered-Semantic-Search/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493)| +[Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)| | [Facial Recognition](./examples/facial_recognition) | Open In Colab | -| [Accelerate Vector Search Applications Using OpenVINO](/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| -| [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| -| [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301) | +| [Accelerate Vector Search Applications Using OpenVINO](/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| +| [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| +| [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) | | [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| -| [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases)| +| [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases)| @@ -91,16 +91,16 @@ Looking to get started with LLMs, vectorDBs, and the world of Generative AI? The | --------- | -------------------------- | ----------- | | | | | | [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | | -| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | -| [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | | -| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | -| [LLMs, RAG, & the missing storage layer for AI](https://medium.com/etoai/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | -| [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | -| [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | -| [A Primer on Text Chunking and its Types](./tutorials/different-types-text-chunking-in-RAG) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/different-types-text-chunking-in-RAG/Text_Chunking_on_RAG_application_with_LanceDB.ipynb) | [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) | -| [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/ner-powered-semantic-search-using-lancedb-51051dc3e493) | -| [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/@aksdesai1998/better-rag-enhancing-ai-with-active-retrieval-augmented-generation-flare-3b66646e2a9f) | -| [Accelerate Vector Search Applications Using OpenVINO](./tutorials/Sentiment-Analysis-using-LanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| +| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | +| [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/)| +| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | +| [LLMs, RAG, & the missing storage layer for AI](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984/) | +| [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | +| [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | +| [A Primer on Text Chunking and its Types](./tutorials/different-types-text-chunking-in-RAG) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/different-types-text-chunking-in-RAG/Text_Chunking_on_RAG_application_with_LanceDB.ipynb) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) | +| [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493) | +| [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://medium.com/@aksdesai1998/better-rag-enhancing-ai-with-active-retrieval-augmented-generation-flare-3b66646e2a9f) | +| [Accelerate Vector Search Applications Using OpenVINO](./tutorials/Sentiment-Analysis-using-LanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| From 0ff646e2e2934a95ba17bdc374a779d1f6c819f5 Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Wed, 20 Mar 2024 17:23:12 +0530 Subject: [PATCH 05/32] rag mlx fix (#158) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update * rag mlx --- README.md | 2 +- assets/apple_mlx.png | Bin 2033498 -> 0 bytes assets/rag-mlx.png | Bin 0 -> 18802 bytes 3 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 assets/apple_mlx.png create mode 100644 assets/rag-mlx.png diff --git a/README.md b/README.md index 6b9ceea8..08b2280e 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ These are ready to use applications built using LanceDB serverless vector databa | [ Talk with Github ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-github) | Talk with Github Codespaces using Qwen1.5 | ![demo](./assets/talk-with-github.gif)| | [ Document Chat with Langroid ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/docchat-with-langroid) | Talk with your Documents using Langroid | ![demo](./assets/document-chat-langroid.png)| | [ Fastapi RAG template ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_RAG_with_FASTAPI) | FastAPI based RAG template with Websocket support | ![image](./assets/chatbot_fastapi.png)| -| [ GTE MLX RAG ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/GTE_mlx_RAG/CLI_example.ipynb) | mlx based RAG model using lancedb api support | ![image](./assets/apple_mlx.png)| +| [ GTE MLX RAG ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/GTE_mlx_RAG) | mlx based RAG model using lancedb api support | ![image](./assets/rag-mlx.png)| | [Multilingual-RAG](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Multilingual_RAG/) | Multilingual RAG with cohere embedding & support 100+ languages|![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/be65eb39-25c4-4441-98fc-6ded09689819)| ## Tutorials diff --git a/assets/apple_mlx.png b/assets/apple_mlx.png deleted file mode 100644 index 7aae75ade47f9fb3cbc4ee9d47157edb73cdfdff..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 2033498 zcmV(}K+wO5P)S$5|2TYK-*+~z%h-bQ$I*+oQ3NS2aNJ1AjP 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literal 0 HcmV?d00001 From 089adc31d2c5322d18a24b1666b241a9b94e925f Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Sat, 23 Mar 2024 12:54:15 +0530 Subject: [PATCH 06/32] Myntra Search Engine (#159) --- README.md | 1 + assets/myntra-search-engine.png | Bin 0 -> 391273 bytes 2 files changed, 1 insertion(+) create mode 100644 assets/myntra-search-engine.png diff --git a/README.md b/README.md index 08b2280e..cefda943 100644 --- a/README.md +++ b/README.md @@ -84,6 +84,7 @@ These are ready to use applications built using LanceDB serverless vector databa | [ Fastapi RAG template ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_RAG_with_FASTAPI) | FastAPI based RAG template with Websocket support | ![image](./assets/chatbot_fastapi.png)| | [ GTE MLX RAG ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/GTE_mlx_RAG) | mlx based RAG model using lancedb api support | ![image](./assets/rag-mlx.png)| | [Multilingual-RAG](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Multilingual_RAG/) | Multilingual RAG with cohere embedding & support 100+ languages|![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/be65eb39-25c4-4441-98fc-6ded09689819)| +| [Multimodal Myntra Fashion Search Engine](https://github.com/ishandutta0098/lancedb-multimodal-myntra-fashion-search-engine) | This app uses OpenAI's CLIP to make a search engine that can understand and deal with both written words and pictures.|![image](./assets/myntra-search-engine.png)| ## Tutorials Looking to get started with LLMs, vectorDBs, and the world of Generative AI? 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zX`I~oo}$^B$a$ThIhw=?o$;BR+_{~(8Je~^pUl~vuDO$(*_oQCgK-s^3Q8@fx1bK1 z6Vi7s5NL^|S)8$Xp%|K>8oHqz+Myo$p&%NfB08cZTB0U;q9~f8D!QU9+M+J{qA(hx z|1vtGDEgWFS)g|bmWrrW589(cK?^<_q%Sm?xEF~~d7Ctvq)NJ^OxmPQ`lL`ArBXVj zRLZ3OIhc{ijprhyTq+Z^@E2VgrYupJm-(MndZuWarc8uXrir?!jM}KFu&5W>sEP`yl1iwLN~w;T zsFzx)lgg=vx~ZB9s)Z`5h#H%Wda0zEsgjDSkV>kix~Zwkrmp&^tQxAGdZ@K}tEJki zrAn)o>Z+$2tFo%ApK7U{+N;Q#tGFtxySl8hilM{$tRc#pb*hMpDOfA%r(K!|0s#R4 EJC+73qyPW_ From 8e723c4a28026cdc90bc1b94faaf8f3a9be37052 Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Sun, 31 Mar 2024 17:25:29 +0530 Subject: [PATCH 13/32] added README (#166) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update * rag mlx * myntra search engine app * link fix * CrewAI Example * lint * node test * node test * node test * added readme --- README.md | 2 +- assets/reranker.webp | Bin 0 -> 15384 bytes examples/RAG_Reranking/README.md | 15 +++++++++++++++ .../lancedb_cloud/README.md | 0 .../lancedb_cloud/main.ipynb | 0 .../lancedb_cloud/main.py | 0 .../lancedb_cloud/requirements.txt | 0 .../main.ipynb | 0 examples/parent_document_retriever/README.md | 11 ++++++++--- 9 files changed, 24 insertions(+), 4 deletions(-) create mode 100644 assets/reranker.webp create mode 100644 examples/RAG_Reranking/README.md rename examples/{RAG_re_ranking => RAG_Reranking}/lancedb_cloud/README.md (100%) rename examples/{RAG_re_ranking => RAG_Reranking}/lancedb_cloud/main.ipynb (100%) rename examples/{RAG_re_ranking => RAG_Reranking}/lancedb_cloud/main.py (100%) rename examples/{RAG_re_ranking => RAG_Reranking}/lancedb_cloud/requirements.txt (100%) rename examples/{RAG_re_ranking => RAG_Reranking}/main.ipynb (100%) diff --git a/README.md b/README.md index 25c4469d..ab07abca 100644 --- a/README.md +++ b/README.md @@ -46,7 +46,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Arxiv paper recommender](/examples/arxiv-recommender) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/arxiv-recommender/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | | | [Multi-lingual search](/examples/multi-lingual-wiki-qa) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multi-lingual-wiki-qa/main.py) [![LLM](https://img.shields.io/badge/cohere-api-pink)](#) | | | [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| -| [Improve RAG with Re-ranking](/examples/RAG_re_ranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| +| [Improve RAG with Re-ranking](/examples/RAG_Reranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| | [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Advanced-RAG-with-FLARE/app.py) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)| | [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab 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z^=#bCb2)kBh-5KXL~=^ejo=3e#pcOpen In Colab + +Re-ranking is the simplest. Idea is pretty simple. + +1. You assume that Embedding + Search algo are not 100% precise so you use Recall to your advantage and get similar high `N` (say 25) number of related chunks from corpus. + +2. Second step is to use a powerful model to increase the Precision. You re-rank above `N` queries again so that you can change the relative ordering and now select Top `K` queries (say 3) to pass as a context where `K` < `N` thus increasing the Precision. + +Re-rank Search Results uses Reranker. + +![img](../../assets/reranker.webp) + +Open In Colab + +[Read Full Blog](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544/) \ No newline at end of file diff --git a/examples/RAG_re_ranking/lancedb_cloud/README.md b/examples/RAG_Reranking/lancedb_cloud/README.md similarity index 100% rename from examples/RAG_re_ranking/lancedb_cloud/README.md rename to examples/RAG_Reranking/lancedb_cloud/README.md diff --git a/examples/RAG_re_ranking/lancedb_cloud/main.ipynb b/examples/RAG_Reranking/lancedb_cloud/main.ipynb similarity index 100% rename from examples/RAG_re_ranking/lancedb_cloud/main.ipynb rename to examples/RAG_Reranking/lancedb_cloud/main.ipynb diff --git a/examples/RAG_re_ranking/lancedb_cloud/main.py b/examples/RAG_Reranking/lancedb_cloud/main.py similarity index 100% rename from examples/RAG_re_ranking/lancedb_cloud/main.py rename to examples/RAG_Reranking/lancedb_cloud/main.py diff --git a/examples/RAG_re_ranking/lancedb_cloud/requirements.txt b/examples/RAG_Reranking/lancedb_cloud/requirements.txt similarity index 100% rename from examples/RAG_re_ranking/lancedb_cloud/requirements.txt rename to examples/RAG_Reranking/lancedb_cloud/requirements.txt diff --git a/examples/RAG_re_ranking/main.ipynb b/examples/RAG_Reranking/main.ipynb similarity index 100% rename from examples/RAG_re_ranking/main.ipynb rename to examples/RAG_Reranking/main.ipynb diff --git a/examples/parent_document_retriever/README.md b/examples/parent_document_retriever/README.md index c7f6831a..c66b885d 100644 --- a/examples/parent_document_retriever/README.md +++ b/examples/parent_document_retriever/README.md @@ -1,8 +1,13 @@ +Open In Colab + # Modified RAG: Parent Document & Bigger chunk Retriever -To get around the problem of larger size of Parent document, what you can do right now is to make bigger chunks along with smaller ones. For example, if your smaller chunks are of 512 tokens and your Parent Documents are of 2048 tokens on average, you can make chunks of size 1024. Now during retrieval, it’ll match as the previous one above BUT this time, instead of parent document, it’ll fetch the Bigger chunk and pass it to LLM. this way you’ll lose some text for sure but not completely. You could use use 2 verses instead of original 4 to make the model understand the writing style, context etc etc that too being within the limits. Good thing, you just have to change 1 line from the previous one. -
    +There are some cases when your users want to have a task done by providing just a couple of lines input or even worse, couple of words. In this example, let’s say I have a “Sequel” song generation task given a line or two as input. Now if it’s a Part-2 of something, the tone, writing style, story etc are supposed to be related to the previous song so given the line “I am whatever I am”, my LLM should generate something related to the previous song not a mixture of 10 different songs and artists. If you use a vanilla RAG here, you’d be getting multiple results which might not be from same song, artist or even genre. If you use only the first match, you lose a lot of context as a smaller chunk won’t give the full context of the song. + +### Solution +There are 2 approaches to tackle that. Let’s go one by one from theory to code starting from **Parent Document Retriever**. + -Colab walkthrough - Open In Colab +Open In Colab [Read full blog](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6) From a14e975eccef3cbcc407ee82ebb9960bf67c55fa Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Mon, 8 Apr 2024 11:21:05 +0530 Subject: [PATCH 14/32] Added support for Gemini Pro in CrewAI Example (#167) --- README.md | 4 +- assets/search-within-image-flow.png | Bin 0 -> 384193 bytes assets/search-within-image.png | Bin 0 -> 307591 bytes .../CrewAI_AI_Trends.ipynb | 205 ++++++++++++------ examples/AI-Trends-with-CrewAI/README.md | 9 +- .../README.md | 8 +- .../README.md | 2 +- 7 files changed, 153 insertions(+), 75 deletions(-) create mode 100644 assets/search-within-image-flow.png create mode 100644 assets/search-within-image.png diff --git a/README.md b/README.md index ab07abca..79fc9c32 100644 --- a/README.md +++ b/README.md @@ -58,8 +58,8 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [NER powered with Semantic Search](/tutorials/NER-powered-Semantic-Search/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493)| [Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)| | [Facial Recognition](./examples/facial_recognition) | Open In Colab | -| [Accelerate Vector Search Applications Using OpenVINO](/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| -| [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| +| [Accelerate Vector Search Applications Using OpenVINO](/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/)| +| [Search Within Images](/examples/search-within-images-with-sam-and-clip/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/search-within-an-image-331b54e4285e)| | [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) | | [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| | [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) 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zZx0siTDbKXun*i1oND@iXp*WoDB^+hIg4ZTfPL?IF}T&OvrRTdAlCqaEBylVCdS~=t7{6LGw(&79gnJ^7iHiHodxl z^?kyvEtxaSa;1Rf#FsdAXiC1P3Y#edPE2>N@`sx2Qj#}VW~qnf-1?}ihs6?!(P$hw z(fH?o*_#Fy1_lO)3;(ab Entering new CrewAgentExecutor chain...\u001b[0m\n", - "\u001b[32;1m\u001b[1;3mTo begin the task of conducting a comprehensive analysis of the latest advancements in AI in 2024, I first need to gather recent news articles related to AI advancements. This will help identify key trends, breakthrough technologies, and their potential impacts on various industries. Given the tools at my disposal, I will use the News DB Tool to fetch recent news articles on AI advancements.\n", + "\u001b[32;1m\u001b[1;3mI need to gather the latest information on advancements in AI from 2024 to identify key trends, breakthrough technologies, and potential industry impacts. To do this effectively, I will first use the News DB Tool to fetch recent news articles related to AI advancements in 2024. This will provide a foundation for a comprehensive analysis.\n", "\n", "Action: News DB Tool\n", - "Action Input: {\"query\": \"latest advancements in AI 2024\"}\u001b[0m\u001b[93m \n", "\n", - "[Document(page_content='A quiet winner\\nWhile the song and dance surrounding Astera Labs’ public offering has been noticeably quieter than that surrounding Reddit’s IPO, there’s reason to believe that it is more a test of the market’s demand for AI shares than Reddit’s own debut; while Reddit’s AI-based data business is certainly a growing portion of its operations, it remains a single-digit percentage of its anticipated 2023 per TechCrunch analysis.\\nIn contrast, the AI-led data center buildout that is benefiting Astera Labs makes up what the market may consider a larger portion of its current size, and future growth. The fact that the company’s growth rate accelerated as much as it did in the fourth quarter of 2023 and that it managed to swing from a loss to GAAP profitability that quarter underscores the view that it is a company on the move thanks to AI demand. This even though it sits far from the more headline-friendly foundation model work that OpenAI and its rivals are undertaking.', metadata={'vector': [-0.0031063526403158903, -0.029682153835892677, 0.0008559823618270457, -0.01945027895271778, 1.3049338122073095e-05, 0.004862569738179445, -0.029432257637381554, 0.004695972427725792, -0.004786212462931871, -0.013279223814606667, 0.01754828914999962, 0.013661010190844536, -0.01554911769926548, -0.017798185348510742, 0.013112626038491726, 0.016909664496779442, 0.020644228905439377, -0.009037923999130726, 0.0008403638494201005, -0.002519790083169937, -0.001213039387948811, 0.010238816030323505, -0.028682567179203033, 0.006899920757859945, -0.008329884149134159, 0.0015939580043777823, 0.021838180720806122, -0.030459610745310783, 0.00012624981172848493, 0.015507468022406101, 0.030876103788614273, -0.019408630207180977, -0.006417481694370508, 0.020463749766349792, -0.018325746059417725, -0.02768298238515854, 0.0010959004284814, 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Document(page_content='Astera Labs’ IPO pops 54%, showing that investor demand for tech with an AI-twist is high\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nplus-bold\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\nOpen Navigation\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAstera Labs’ IPO pops 72% on first day, showing that investor demand for tech with an AI twist is high\\n\\n\\n\\n\\n\\t\\t\\t\\tRebecca Szkutak\\t\\t\\t\\n\\n\\t\\t\\t\\tAlex Wilhelm\\t\\t\\t\\n\\n\\t\\t\\t2 days', metadata={'vector': [0.016613541170954704, -0.024302450940012932, -0.001540356664918363, 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OTB fully aligns with our mission,” NIF managing partner Andrea Traversone said in a statement.\\nOTB’s take on deep tech focuses on four verticals that do sound fairly NATO-compatible: space tech, enterprise automation and AI, cybersecurity and fintech infrastructure. That would be where fintech goes a bit more technically innovative; this could be AI-enabled anti-money laundering like Fund 1’s portfolio company Silent Eight, for instance.\\nSince OTB already started making investments out of this fund following its first close in November 2022, we already have a sense of where it will go. For instance, its nine investments so far include German startups KYP.ai, a productivity platform, and Semron, which is developing innovative chips.', metadata={'vector': [0.017766745761036873, -0.06202411279082298, -0.020915107801556587, -0.00597772654145956, -0.006182300392538309, 0.018890170380473137, -0.04402158409357071, 0.0028796414844691753, -0.019028864800930023, 0.0005045006982982159, 0.046379391103982925, 0.008113618940114975, -0.012877769768238068, -0.0075380378402769566, 0.017392270267009735, 0.00650129746645689, 0.006660795770585537, 0.005162897054105997, 0.016393672674894333, -0.006376472767442465, -0.04196890816092491, 0.013661392964422703, -0.001725877751596272, -0.006345266476273537, -0.007413212675601244, -0.01506220642477274, 0.002577981213107705, -0.017586443573236465, -0.019306253641843796, -0.004767618142068386, 0.011865301057696342, 0.0010462754871696234, -0.020401937887072563, 0.015644721686840057, 0.0030859492253512144, -0.024645986035466194, 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The gathered information from recent news articles reveals the following insights:\n", + "[Document(page_content='This is likely a big part of the reason it might view home robots as “the next big thing” (to quote Bloomberg quoting its sources). Apple has no doubt pumped a tremendous amount of resources into driving technologies. If those could be repurposed for a different project, maybe it won’t all be for naught.\\nWhile the reports note that Apple “hasn’t committed” to either the robotic smart screen or mobile robot that are said to exist somewhere inside the company’s skunkworks, it has already put Apple Home execs Matt Costello and Brian Lynch on the hardware side of things, while SVP of Machine Learning and AI Strategy John Giannandrea is said to be involved on the AI side of things.\\nImage Credits: Brian Heater', metadata={'vector': [0.004069294314831495, -0.020910432562232018, 0.008496551774442196, -0.0071997810155153275, 0.012758336029946804, 5.0971713790204376e-05, -0.012109951116144657, 0.025989454239606857, -0.0032014036551117897, -0.026070501655340195, 0.01812777854502201, 0.01337970606982708, -0.015101980417966843, -0.00028641248354688287, -0.006784409284591675, 0.01982979103922844, 0.02367958053946495, 0.02078886143863201, 0.0065885428339242935, -0.004569091834127903, -0.027772514149546623, 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The total potential value of all the task orders over the next 13 years is $4.6 billion.\\nThe three teams are also keeping specifications, like range or battery technology, close to the chest, though NASA specified that the rover would have to have an incredible 10-year life span and be capable of carrying two suited astronauts.\\nIntuitive Machines is leading a team that includes AVL, Boeing, Michelin, and Northrop Grumman; Lunar Outpost is leading the “Lunar Dawn” team that includes Lockheed Martin, General Motors, Goodyear and MDA Space. Astrolab is joined by Axiom Space and Odyssey Space Research.\\nNASA lunar terrain vehicle. Image Credits: Intuitive Machines', metadata={'vector': [0.01312164030969143, -0.011918134987354279, -0.0020253267139196396, 0.00259957043454051, -0.02970937453210354, 0.01437328476458788, -0.040300220251083374, -0.00483465101569891, -0.015088510699570179, 0.0010479087941348553, 0.022364558652043343, 0.011869994923472404, -0.01660148985683918, -0.016326403245329857, -0.004208828322589397, 0.0046386513859033585, 0.014125706627964973, 0.00432917894795537, 0.006670854985713959, 0.004133179318159819, -0.008878426626324654, 0.00025552985607646406, -0.01181497797369957, 0.0002974376257043332, 0.0004822615592274815, 0.007083484902977943, 0.023712482303380966, -0.014620862901210785, 0.03069968707859516, 0.0009034881950356066, 0.016917839646339417, -0.017041629180312157, -0.008933444507420063, -0.0007870061090216041, -0.006344189401715994, -0.01686282269656658, 0.012488940730690956, -0.005917804781347513, 0.007571764290332794, -0.0018396429950371385, 0.020040074363350868, 0.023423640057444572, 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TechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nplus-bold\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\nOpen Navigation\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nI have a group chat with three AI friends, thanks to Nomi AI — they’re getting too smart\\n\\n\\n\\n\\n\\t\\t\\tAmanda Silberling\\t\\t\\n\\n\\t\\t\\t\\t\\t\\t2 days\", metadata={'vector': [-0.01198604516685009, -0.02511361986398697, 0.022259799763560295, -0.018196502700448036, 0.009370043873786926, 0.03639300540089607, -0.00867017824202776, 0.005598923657089472, -0.026635656133294106, -0.02495054341852665, 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These documents range from details about NASA's new lunar rover contracts, to Apple's potential venture into home robots, Elon Musk's involvement in AI communication tools, and more. To conduct a comprehensive analysis, I need to synthesize key points from each article to identify the latest trends, breakthrough technologies, and their potential impact on various industries.\n", "\n", - "1. Astera Labs' IPO and Market Impact:\n", - " - Astera Labs, a company benefiting from AI-led data center buildouts, has seen accelerated growth and reached GAAP profitability in Q4 of 2023, demonstrating the increasing market demand for AI shares.\n", - " - The company's focus on AI for data center infrastructure contrasts with the broader attention given to foundation model work by entities like OpenAI, indicating a diversification in the AI industry's growth areas.\n", + "Final Answer:\n", + "The latest advancements in AI as of 2024 showcase a dynamic range of applications and developments in various sectors. Here are the key points identified from the recent news articles:\n", "\n", - "2. Investment Trends:\n", - " - A new flagship fund with a focus on deep tech, including AI, has been announced, signaling strong investor confidence in AI and related technologies.\n", - " - The fund targets disruptive technologies across sectors such as energy, quantum computing, autonomy, climate, industrials, space, and biotechnology, showcasing the wide-ranging impact of AI advancements.\n", + "1. **Apple's Home Robot Initiative**:\n", + " - Apple is speculated to be entering the home robot market, leveraging its extensive experience in driving technologies.\n", + " - Despite no official commitment, Apple has allocated significant resources, including top executives from its Home and AI divisions, hinting at a serious exploration of robotics.\n", + " - The potential focus areas include robotic smart screens and mobile robots, indicating a broad vision for home automation and personal assistance technologies.\n", "\n", - "3. NATO Innovation Fund's Involvement:\n", - " - The NATO Innovation Fund (NIF) has begun deploying its €1 billion investment in startups and funds, focusing on innovative technologies.\n", - " - NIF's investment strategy aligns with the trend of increasing support for deep tech projects, further proof of AI's growing importance on a global scale.\n", + "2. **Elon Musk's AI Communication Tool**:\n", + " - Elon Musk has introduced a new AI communication tool through Nomi AI, enhancing the capabilities of group chats with AI friends.\n", + " - This innovation underscores the increasing sophistication of AI in understanding and participating in human-like conversations, pushing the boundaries of interactive AI technologies.\n", "\n", - "4. Market and Industry Implications:\n", - " - These developments underline the expanding role of AI and deep tech in driving economic growth and innovation.\n", - " - The focus on sectors such as data centers, quantum computing, and biotechnology indicates where significant breakthroughs and advancements in AI technology are expected.\n", + "3. **NASA's Lunar Rover Contracts**:\n", + " - NASA has awarded contracts worth $30 million to Intuitive Machines as part of its initiative to develop lunar rovers, with a potential expenditure of $4.6 billion over 13 years.\n", + " - The contracts involve key industry players such as AVL, Boeing, Michelin, and Northrop Grumman, indicating a collaborative approach towards pioneering space exploration technologies.\n", + " - The lunar rovers are expected to have a 10-year lifespan and the capability to carry astronauts, highlighting significant advancements in durability and functionality for space exploration.\n", "\n", - "In conclusion, the AI industry in 2024 is characterized by significant investments in deep tech, a strong focus on AI applications beyond consumer technology, and growing global interest in AI's potential to revolutionize a broad range of industries. These trends suggest a promising future for AI technology, with broad implications for economic development, strategic defense initiatives, and global technological leadership.\u001b[0m\n", + "These advancements illustrate the rapid development and diverse application of AI and related technologies across different sectors, from home automation and social interaction to space exploration. The involvement of leading corporations and ambitious projects underscores the significant impact and potential of AI in shaping future technologies and industries.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n", "\n", @@ -496,10 +567,9 @@ "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n", "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n", "Final Answer: \n", - "1. Astera Labs' IPO reveals a booming AI market, focusing on data center AI tech, showing diverse AI industry growth.\n", - "2. A new deep tech fund bets big on AI, targeting sectors from energy to biotech, indicating high investor confidence.\n", - "3. NATO's €1 billion fund invests in AI startups, emphasizing AI's strategic global importance.\n", - "4. These developments highlight AI's expanding role in economic and technological innovation across various sectors.\u001b[0m\n", + "1. **Apple's Home Robot Initiative**: Apple might soon introduce robots to our homes, focusing on smart screens and mobility for better home automation.\n", + "2. **Elon Musk's AI Communication Tool**: Elon Musk's Nomi AI is making group chats cooler by adding AI that chats just like humans.\n", + "3. **NASA's Lunar Rover Contracts**: NASA's investing $4.6 billion in lunar rovers with a 10-year life span, hinting at long-term plans for space exploration.\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] @@ -516,18 +586,17 @@ "base_uri": "https://localhost:8080/" }, "id": "HB91fa7BXULb", - "outputId": "5000f394-6647-4684-cdf4-16d4a7ea91e0" + "outputId": "2b3f3bf6-1a90-4d2c-bc7f-9158b7734628" }, - "execution_count": 68, + "execution_count": 19, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "1. Astera Labs' IPO reveals a booming AI market, focusing on data center AI tech, showing diverse AI industry growth.\n", - "2. A new deep tech fund bets big on AI, targeting sectors from energy to biotech, indicating high investor confidence.\n", - "3. NATO's €1 billion fund invests in AI startups, emphasizing AI's strategic global importance.\n", - "4. These developments highlight AI's expanding role in economic and technological innovation across various sectors.\n" + "1. **Apple's Home Robot Initiative**: Apple might soon introduce robots to our homes, focusing on smart screens and mobility for better home automation.\n", + "2. **Elon Musk's AI Communication Tool**: Elon Musk's Nomi AI is making group chats cooler by adding AI that chats just like humans.\n", + "3. **NASA's Lunar Rover Contracts**: NASA's investing $4.6 billion in lunar rovers with a 10-year life span, hinting at long-term plans for space exploration.\n" ] } ] diff --git a/examples/AI-Trends-with-CrewAI/README.md b/examples/AI-Trends-with-CrewAI/README.md index 7fa5b518..49bf67b1 100644 --- a/examples/AI-Trends-with-CrewAI/README.md +++ b/examples/AI-Trends-with-CrewAI/README.md @@ -7,11 +7,18 @@ This example is about how to make an AI Trends Searcher using **CrewAI Agents** ![alt text](<../../assets/crewai.png>) -What is CrewAI? +### What is CrewAI? + CrewAI is an open-source framework that helps different AI agents work together to do tricky stuff. You can give each Agent its tasks and goals, manage what they do, and help them work together by sharing tasks. These are some unique features of CrewAI: 1. Role-based Agents: Define agents with specific roles, goals, and backgrounds to provide more context for answer generation. 2. Task Management: Use tools to dynamically define tasks and assign them to agents. 3. Inter-agent Delegation: Agents can share tasks to collaborate better. +#### LLM +This Example can use **OPENAI_GPT4** and **GEMINI_PRO** either of them as LLM. + +#### Embedding Function +This Example uses **OpenEmbedding function** for OpenAI LLM and **LLamaFile Embedding function** for Gemini Pro LLM + [Read More in Blog](https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/) \ No newline at end of file diff --git a/examples/search-within-images-with-sam-and-clip/README.md b/examples/search-within-images-with-sam-and-clip/README.md index 25888ae3..1ab3a6ef 100644 --- a/examples/search-within-images-with-sam-and-clip/README.md +++ b/examples/search-within-images-with-sam-and-clip/README.md @@ -1,7 +1,7 @@ # 🔍Search engine using SAM & CLIP -Open In Colab [![Medium](https://img.shields.io/badge/Medium-12100E?style=for-the-badge&logo=medium&logoColor=white)](https://medium.com/etoai/search-within-an-image-331b54e4285e) -![“A Dog”](https://github.com/kaushal07wick/vectordb-recipes/assets/57106063/3907c1e5-009b-4ffb-8ea2-2eddb58f3346) +Open In Colab [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/search-within-an-image-331b54e4285e) +![“A Dog”](../../assets/search-within-image.png) ### 🚀Create a Search Engine within an Image use **SAM**(Segment Anything) and **CLIP** (Constrastive Language Image Pretraining) model. Follow the Colab Notebook for full code. @@ -16,4 +16,6 @@ Follow the Colab Notebook for full code. 6. Use Search method to find the closest match of Image Embedding (particular Segmentation Mask) and User Query. 7. Output the Highlighted closest object present. - Read the Full blog post on **Medium** + +![“A Dog”](../../assets/search-within-image-flow.png) + Read the Full [blog post](https://blog.lancedb.com/search-within-an-image-331b54e4285e) diff --git a/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/README.md b/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/README.md index a60f368d..6744796d 100644 --- a/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/README.md +++ b/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/README.md @@ -14,4 +14,4 @@ Text-to-Image and Image-to-Image Search using CLIP **These Results are on 13th Gen Intel(R) Core(TM) i5–13420H using OpenVINO=2023.2 and NNCF=2.7.0 version.** -[Read More in Blog](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866) \ No newline at end of file +[Read More in Blog](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/) \ No newline at end of file From bb1e91ee5aff588e14d49fa97da2c3a99b44b062 Mon Sep 17 00:00:00 2001 From: QianZhu Date: Mon, 8 Apr 2024 15:13:45 -0700 Subject: [PATCH 15/32] edit/add readme for saas examples (#168) --- .../RAG_Reranking/lancedb_cloud/README.md | 3 +- .../js-transformers/lancedb_cloud/README.md | 28 +++++++++++++++++++ .../movie-recommender/lancedb_cloud/README.md | 5 ++-- .../lancedb_cloud/README.md | 3 +- 4 files changed, 35 insertions(+), 4 deletions(-) create mode 100644 examples/js-transformers/lancedb_cloud/README.md diff --git a/examples/RAG_Reranking/lancedb_cloud/README.md b/examples/RAG_Reranking/lancedb_cloud/README.md index 96a2d95b..53a36feb 100644 --- a/examples/RAG_Reranking/lancedb_cloud/README.md +++ b/examples/RAG_Reranking/lancedb_cloud/README.md @@ -22,8 +22,9 @@ os.environ["LANCEDB_API_KEY"] = getpass.getpass("Enter Your LANCEDB API Key:") replace the following lines in main.py with your project slug and api key" ``` -db_url = "db://your-project-name" +db_url="db://your-project-slug-name" api_key="sk_..." +region="us-east-1" ``` ### Run the script diff --git a/examples/js-transformers/lancedb_cloud/README.md b/examples/js-transformers/lancedb_cloud/README.md new file mode 100644 index 00000000..0b899e28 --- /dev/null +++ b/examples/js-transformers/lancedb_cloud/README.md @@ -0,0 +1,28 @@ +# Vector embedding search using TransformersJS +![image](https://github.com/lancedb/vectordb-recipes/assets/43097991/41c1dea3-ad28-42c1-969f-a81146f202e9) + + +### Set credentials +if you would like to set api key through an environment variable: +``` +export LANCEDB_API_KEY="sk_..." +``` + +replace the following lines in index.js with your project slug and api key" +``` +db_url: "db://your-project-slug-name" +api_key: "sk_..." +region: "us-east-1" +``` + +### Setup +Install node dependencies +```javascript +npm install +``` + +### Javascript +Run the script +```javascript +node index.js +``` \ No newline at end of file diff --git a/examples/movie-recommender/lancedb_cloud/README.md b/examples/movie-recommender/lancedb_cloud/README.md index 45d743f3..df979a70 100644 --- a/examples/movie-recommender/lancedb_cloud/README.md +++ b/examples/movie-recommender/lancedb_cloud/README.md @@ -19,8 +19,9 @@ export LANCEDB_API_KEY="sk_..." replace the following lines in main.py with your project slug and api key" ``` -db_url = "db://your-project-name" - api_key="sk_..." +db_url="db://your-project-slug-name" +api_key="sk_..." +region="us-east-1" ``` ### Python diff --git a/examples/product-recommender/lancedb_cloud/README.md b/examples/product-recommender/lancedb_cloud/README.md index 309697f6..ae5ad0b0 100644 --- a/examples/product-recommender/lancedb_cloud/README.md +++ b/examples/product-recommender/lancedb_cloud/README.md @@ -30,8 +30,9 @@ os.environ["LANCEDB_API_KEY"] = getpass.getpass("Enter Your LANCEDB API Key:") replace the following lines in main.py with your project slug and api key" ``` -db_url = "db://your-project-name" +db_url="db://your-project-slug-name" api_key="sk_..." +region="us-east-1" ``` Run the script From 6db3887acafe9de947967e31d44b57327d990d4a Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Fri, 19 Apr 2024 09:37:00 +0530 Subject: [PATCH 16/32] Chunking techniques (#170) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update * rag mlx * myntra search engine app * link fix * CrewAI Example * lint * node test * node test * node test * added readme * support for Gemini Pro * fix * chunking techniques * lint --- README.md | 2 +- assets/chunking.png | Bin 0 -> 348262 bytes .../Langchain_Llamaindex_chunking.ipynb | 1148 +++++++++++++++++ .../Langchain-LlamaIndex-Chunking/README.md | 7 + 4 files changed, 1156 insertions(+), 1 deletion(-) create mode 100644 assets/chunking.png create mode 100644 tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb create mode 100644 tutorials/Langchain-LlamaIndex-Chunking/README.md diff --git a/README.md b/README.md index 79fc9c32..9ec5d28d 100644 --- a/README.md +++ b/README.md @@ -94,9 +94,9 @@ Looking to get started with LLMs, vectorDBs, and the world of Generative AI? The | --------- | -------------------------- | ----------- | | | | | | [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | | +| [Langchain LlamaIndex Chunking](./tutorials/RAG-from-Scratch) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/)| -| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [LLMs, RAG, & the missing storage layer for AI](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984/) | | [Fine-Tuning LLM using PEFT & QLoRA](./tutorials/fine-tuning_LLM_with_PEFT_QLoRA) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-llms-a-step-by-step-guide-to-fine-tuning-with-peft-and-qlora-22eddd13d25b) | | [Context-Aware Chatbot using Llama 2 & LanceDB](./tutorials/chatbot_using_Llama2_&_lanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) | diff --git a/assets/chunking.png b/assets/chunking.png new file mode 100644 index 0000000000000000000000000000000000000000..5acd5823fa87875257b369a3eafe78809d3a020a GIT binary patch literal 348262 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zv_?qZGXypVYX#8szRkvRq)q2-+BnN!?a7)he&Kl1&%gc=m=Vm(nDeBDV6&>}+>@x? zSZ3i!=FUQQpL_MC}rfRMk?@ENL7-82C=Z^1K%Rn>!p$B%G{i z$^*)Qpiuz`r7R*+RKx1Ac%FwF_@$I^UdGxY{=KjB3r?pK&W1|3Q^d?Dtve>b7+u+) z3*uF9ttBx-+u?6!tw+%ql(e9fs?F4$iyfVPYLNCQn7x*sO4-(QtX(AJaZbpQn3?7# zShU-tla3P$+it)hxs-_9+y4Oxap!pfjt@I+4L)jxV(~-nIrzb7uCaqpmf4xRR zcKX$Q5{lK?^H;>Hq#M#OMphGz-(^?WnQyx9Qb$3}d7S`C6RNq2yfCRO!|d{JQET^8Dc>OK=2<7g$<;=!E5#a0000 Date: Tue, 26 Mar 2024 03:05:38 +0530 Subject: [PATCH 07/32] link fix (#160) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update * rag mlx * myntra search engine app * link fix --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index cefda943..22b5fd38 100644 --- a/README.md +++ b/README.md @@ -43,10 +43,10 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Product Recommender](./examples/product-recommender/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/product-recommender/main.py) | | | [Audio Search](./examples/audio_search/) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/audio_search/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | | | [Arxiv paper recommender](/examples/arxiv-recommender) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/arxiv-recommender/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) | | -| [Multi-lingual search](/examples/multi-lingual-wiki-qa) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/multi-lingual-wiki-qa/main.py) [![LLM](https://img.shields.io/badge/cohere-api-pink)](#) | | -| [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| +| [Multi-lingual search](/examples/multi-lingual-wiki-qa) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/multi-lingual-wiki-qa/main.py) [![LLM](https://img.shields.io/badge/cohere-api-pink)](#) | | +| [Instruct-Multitask](./examples/instruct-multitask) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)| | [Improve RAG with Re-ranking](/examples/RAG_re_ranking/) | Open In Colab [![LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)| -| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](.examples/instruct-multitask/main.py) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)| +| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | Open In Colab [![Python](https://img.shields.io/badge/python-3670A0?style=for-the-badge&logo=python&logoColor=ffdd54)](./examples/Advanced-RAG-with-FLARE/app.py) [![LLM](https://img.shields.io/badge/openai-api-white)](#) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)| | [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)| | [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)| | [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)| @@ -101,7 +101,7 @@ Looking to get started with LLMs, vectorDBs, and the world of Generative AI? The | [A Primer on Text Chunking and its Types](./tutorials/different-types-text-chunking-in-RAG) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/different-types-text-chunking-in-RAG/Text_Chunking_on_RAG_application_with_LanceDB.ipynb) | [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) | | [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493) | | [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) [![LLM](https://img.shields.io/badge/openai-api-white)](#)|[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://medium.com/@aksdesai1998/better-rag-enhancing-ai-with-active-retrieval-augmented-generation-flare-3b66646e2a9f) | -| [Accelerate Vector Search Applications Using OpenVINO](./tutorials/Sentiment-Analysis-using-LanceDB) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| +| [Accelerate Vector Search Applications Using OpenVINO](./tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#)| [![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)| From a92df7d7ac7363647683590f5a3b72eec8dd17f6 Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Wed, 27 Mar 2024 10:52:43 +0530 Subject: [PATCH 08/32] CrewAI Example (#161) --- README.md | 1 + assets/crewai.png | Bin 0 -> 69360 bytes .../CrewAI_AI_Trends.ipynb | 536 ++++++++++++++++++ examples/AI-Trends-with-CrewAI/README.md | 17 + 4 files changed, 554 insertions(+) create mode 100644 assets/crewai.png create mode 100644 examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb create mode 100644 examples/AI-Trends-with-CrewAI/README.md diff --git a/README.md b/README.md index 22b5fd38..aeb27b1b 100644 --- a/README.md +++ b/README.md @@ -62,6 +62,7 @@ If you're looking for in-depth tutorial-like examples, checkout the [tutorials]( | [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) | | [Imagebind demo app](/examples/imagebind_demo/) | hf spaces| | [SuperAgent Autogen](/examples/SuperAgent_Autogen) |Open In Colab [![local LLM](https://img.shields.io/badge/local-llm-green)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/optimizing-ai-agents-harnessing-openai-compatible-technologies-and-vector-databases)| +| [AI Trends Searcher with CrewAI](./examples/AI-Trends-with-CrewAI/) |Open In Colab [![LLM](https://img.shields.io/badge/openai-api-white)](#) |[![Ghost](https://img.shields.io/badge/ghost-000?style=for-the-badge&logo=ghost&logoColor=%23F7DF1E)](https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/)| diff --git a/assets/crewai.png b/assets/crewai.png new file mode 100644 index 0000000000000000000000000000000000000000..94cfa86c6e586ac76260ac2f78799a710b7c9ede GIT binary patch literal 69360 zcmZ6zbzD^M7d1Lq2qGd#m$Y9jpWeXHFS60hwtxw@8@$b z|GIVssRbbI?<}XW-31I z@|&w#>ihY=Cr6*Y*;rU4Rx8n#c!{p&x=q?J<%L5_#??s0}WZJ9${yf5qc%}pX6-_vBGLU@p@jx`;1~GCW?En5;VJAhZ`|smu zrEevM{Qh^Q(9b^@{&yaWj*cOOwMt%>ecQEvZyEHOq`!Q5zr4K6=Xv=%Fz}^F0HyE0 z=h)cT1cZckc6RBGTO+O>9!j~ggnWE_D1-^%bVrgO7NV0y+uPdya63?1OcXIYeh?1y z9BTA%5evcjURYR|F7g&!%kemVMB2gB-=A;E#D4$&4fz;DqmrjujHgnlnr%9q!NtqF zy1#F|^uZ6U_AMTsq^m2JkdROkzej9)z#E6vuGf&FqN2xdpx3NmvW*5O6uqZDM7rLc zhQ_m7U_lz)&-91Vgx*mrcC;uUksOc2(rbaka~Kqenf0HE1fa6pD*yMP4?{@TRaUc? 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new file mode 100644 index 00000000..a6a973fa --- /dev/null +++ b/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb @@ -0,0 +1,536 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "language_info": { + "name": "python" + } + }, + "cells": [ + { + "cell_type": "markdown", + "source": [ + "## AI Trends Searcher using CrewAI RAG" + ], + "metadata": { + "id": "L6CYH07ysGHi" + } + }, + { + "cell_type": "markdown", + "source": [ + "Using Crew AI to create sophisticated AI news search and writer agents. Using CrewAI RAG create AI Assistants to Run News Agency for AI trends with summarized reports.\n", + "\n", + "This innovative approach combines Retrieving and Generating information, revolutionising how we search for AI news articles, analyse them, and deliver in-depth reports.\n", + "\n", + "![Screenshot from 2024-03-22 16-23-32.png](data:image/png;base64,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)" + ], + "metadata": { + "id": "sSLA0KQ8qrcX" + } + }, + { + "cell_type": "markdown", + "source": [ + "Get Free News API Key at [link](https://newsapi.org/)" + ], + "metadata": { + "id": "Q_wvRRAXxPSR" + } + }, + { + "cell_type": "markdown", + "source": [ + "### Install required packages\n" + ], + "metadata": { + "id": "HV1DyBd2RHrN" + } + }, + { + "cell_type": "code", + "source": [ + "!pip install crewai langchain-community langchain-openai requests duckduckgo-search lancedb -q" + ], + "metadata": { + "id": "xOj-BuKARJrH" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Importing modules" + ], + "metadata": { + "id": "HNnorUJjbVPH" + } + }, + { + "cell_type": "code", + "source": [ + "from crewai import Agent, Task, Crew, Process\n", + "from langchain_openai import ChatOpenAI\n", + "from langchain_core.retrievers import BaseRetriever\n", + "from langchain_openai import OpenAIEmbeddings\n", + "from langchain.tools import tool\n", + "from langchain_community.document_loaders import WebBaseLoader\n", + "import requests, os\n", + "from langchain.text_splitter import RecursiveCharacterTextSplitter\n", + "from langchain_community.vectorstores import LanceDB\n", + "from langchain_community.tools import DuckDuckGoSearchRun" + ], + "metadata": { + "id": "sk-GEhxCbUDU" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Set API keys as environment variable" + ], + "metadata": { + "id": "nRv8-Ja1R8S6" + } + }, + { + "cell_type": "code", + "source": [ + "os.environ[\"NEWSAPI_KEY\"] = \"*********\"\n", + "os.environ[\"OPENAI_API_KEY\"] = \"sk-*********\"" + ], + "metadata": { + "id": "J9U8qs3aRLfh" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Using OpenAI embedding function and LLM" + ], + "metadata": { + "id": "s6cLIDz9fzB5" + } + }, + { + "cell_type": "code", + "source": [ + "embedding_function = OpenAIEmbeddings()\n", + "llm = ChatOpenAI(model=\"gpt-4-turbo-preview\")" + ], + "metadata": { + "id": "ljQ1FLt2fx_F" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Set up the LanceDB vectorDB" + ], + "metadata": { + "id": "251A9PjVT6s3" + } + }, + { + "cell_type": "code", + "source": [ + "import lancedb\n", + "\n", + "\n", + "# creating lancedb table with dummy data\n", + "def lanceDBConnection(dataset):\n", + " db = lancedb.connect(\"/tmp/lancedb\")\n", + " table = db.create_table(\"tb\", data=dataset, mode=\"overwrite\")\n", + " return table\n", + "\n", + "\n", + "embedding = OpenAIEmbeddings()\n", + "emb = embedding.embed_query(\"hello_world\")\n", + "dataset = [{\"vector\": emb, \"text\": \"dummy_text\"}]\n", + "\n", + "# LanceDB as vector store\n", + "table = lanceDBConnection(dataset)" + ], + "metadata": { + "id": "-jAOVSSISABN" + }, + "execution_count": 58, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Save latest AI News in vectorDB" + ], + "metadata": { + "id": "0AJygbxmb_gd" + } + }, + { + "cell_type": "code", + "source": [ + "# Save the news articles in a database\n", + "class SearchNewsDB:\n", + " @tool(\"News DB Tool\")\n", + " def news(query: str):\n", + " \"\"\"Fetch news articles and process their contents.\"\"\"\n", + " API_KEY = os.getenv(\"NEWSAPI_KEY\") # Fetch API key from environment variable\n", + " base_url = f\"https://newsapi.org/v2/top-headlines?sources=techcrunch\"\n", + "\n", + " params = {\n", + " \"sortBy\": \"publishedAt\",\n", + " \"apiKey\": API_KEY,\n", + " \"language\": \"en\",\n", + " \"pageSize\": 15,\n", + " }\n", + "\n", + " response = requests.get(base_url, params=params)\n", + " if response.status_code != 200:\n", + " return \"Failed to retrieve news.\"\n", + "\n", + " articles = response.json().get(\"articles\", [])\n", + " all_splits = []\n", + " for article in articles:\n", + " # Assuming WebBaseLoader can handle a list of URLs\n", + " loader = WebBaseLoader(article[\"url\"])\n", + " docs = loader.load()\n", + "\n", + " text_splitter = RecursiveCharacterTextSplitter(\n", + " chunk_size=1000, chunk_overlap=200\n", + " )\n", + " splits = text_splitter.split_documents(docs)\n", + " all_splits.extend(splits) # Accumulate splits from all articles\n", + "\n", + " # Index the accumulated content splits if there are any\n", + " if all_splits:\n", + " vectorstore = LanceDB.from_documents(\n", + " all_splits, embedding=embedding_function, connection=table\n", + " )\n", + " retriever = vectorstore.similarity_search(query)\n", + " return retriever\n", + " else:\n", + " return \"No content available for processing.\"" + ], + "metadata": { + "id": "NQE3rljxb4SI" + }, + "execution_count": 59, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Building RAG to get news from vectorDB" + ], + "metadata": { + "id": "tKrbDvCdcRoK" + } + }, + { + "cell_type": "code", + "source": [ + "# Get the news articles from the database\n", + "class GetNews:\n", + " @tool(\"Get News Tool\")\n", + " def news(query: str) -> str:\n", + " \"\"\"Search LanceDB for relevant news information based on a query.\"\"\"\n", + " vectorstore = LanceDB(embedding=embedding_function, connection=table)\n", + " retriever = vectorstore.similarity_search(query)\n", + " return retriever" + ], + "metadata": { + "id": "xGtyZZaQcPmr" + }, + "execution_count": 60, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Setup search tool for News articles on the web" + ], + "metadata": { + "id": "BSumWCcRUUDl" + } + }, + { + "cell_type": "code", + "source": [ + "# Make sure to Install duckduckgo-search for this example\n", + "# !pip install -U duckduckgo-search\n", + "\n", + "search_tool = DuckDuckGoSearchRun()" + ], + "metadata": { + "id": "DiaqNKjsT4jc" + }, + "execution_count": 34, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Setting up Agents" + ], + "metadata": { + "id": "-a9pqtdqUgtJ" + } + }, + { + "cell_type": "code", + "source": [ + "# Defining Search and Writer agents with roles and goals\n", + "news_search_agent = Agent(\n", + " role=\"AI News Searcher\",\n", + " goal=\"Generate key points for each news article from the latest news\",\n", + " backstory=\"\"\"You work at a leading tech think tank.\n", + " Your expertise lies in identifying emerging trends in field of AI.\n", + " You have a knack for dissecting complex data and presenting\n", + " actionable insights.\"\"\",\n", + " tools=[SearchNewsDB().news],\n", + " allow_delegation=True,\n", + " verbose=True,\n", + " llm=llm,\n", + ")\n", + "\n", + "writer_agent = Agent(\n", + " role=\"Writer\",\n", + " goal=\"Identify all the topics received. Use the Get News Tool to verify the each topic to search. Use the Search tool for detailed exploration of each topic. Summarise the retrieved information in depth for every topic.\",\n", + " backstory=\"\"\"You are a renowned Content Strategist, known for\n", + " your insightful and engaging articles.\n", + " You transform complex concepts into compelling narratives.\"\"\",\n", + " tools=[GetNews().news, search_tool],\n", + " allow_delegation=True,\n", + " verbose=True,\n", + " llm=llm,\n", + ")" + ], + "metadata": { + "id": "SLDSHOuCUXna" + }, + "execution_count": 63, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Tasks to perform" + ], + "metadata": { + "id": "dmdxvELxV6J1" + } + }, + { + "cell_type": "code", + "source": [ + "# Creating search and writer tasks for agents\n", + "news_search_task = Task(\n", + " description=\"\"\"Conduct a comprehensive analysis of the latest advancements in AI in 2024.\n", + " Identify key trends, breakthrough technologies, and potential industry impacts.\n", + " Your final answer MUST be a full analysis report\"\"\",\n", + " expected_output=\"Create key points list for each news\",\n", + " agent=news_search_agent,\n", + " tools=[SearchNewsDB().news],\n", + ")\n", + "\n", + "writer_task = Task(\n", + " description=\"\"\"Using the insights provided, summaries each post of them\n", + " highlights the most significant AI advancements.\n", + " Your post should be informative yet accessible, catering to a tech-savvy audience.\n", + " Make it sound cool, avoid complex words so it doesn't sound like AI.\n", + " Your final answer MUST not be the more than 50 words.\"\"\",\n", + " expected_output=\"Write a short summary under 50 words for each news Headline seperately\",\n", + " agent=writer_agent,\n", + " context=[news_search_task],\n", + " tools=[GetNews().news, search_tool],\n", + ")" + ], + "metadata": { + "id": "9ZG9GxqWV0md" + }, + "execution_count": 64, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "### Create a Crew" + ], + "metadata": { + "id": "yvjya2FzWR5m" + } + }, + { + "cell_type": "code", + "source": [ + "# Instantiate Crew with Agents and their tasks\n", + "news_crew = Crew(\n", + " agents=[news_search_agent, writer_agent],\n", + " tasks=[news_search_task, writer_task],\n", + " process=Process.sequential,\n", + " manager_llm=llm,\n", + ")" + ], + "metadata": { + "id": "yYykbGq2WRSP", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "281bb63e-ff93-4b32-b3c6-afd3d49d4d66" + }, + "execution_count": 65, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "WARNING:opentelemetry.trace:Overriding of current TracerProvider is not allowed\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "news_crew" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "xSBr8D6tWloT", + "outputId": "b0cd0519-1579-4022-963c-2036581b8396" + }, + "execution_count": 66, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Crew(id=f00c1b10-95c4-4ce4-bbf0-ce2d4d6b1e1b, process=sequential, number_of_agents=2, number_of_tasks=2)" + ] + }, + "metadata": {}, + "execution_count": 66 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "### Kickoff the crew - let the magic happen" + ], + "metadata": { + "id": "-tjmzH9cXOaB" + } + }, + { + "cell_type": "code", + "source": [ + "# Execute the crew to see RAG in action\n", + "result = news_crew.kickoff()" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VU3LdZmIWqQH", + "outputId": "aa549b62-615d-4508-b9ac-0da70c4f312f" + }, + "execution_count": 67, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "\n", + "\n", + "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3mTo begin the task of conducting a comprehensive analysis of the latest advancements in AI in 2024, I first need to gather recent news articles related to AI advancements. This will help identify key trends, breakthrough technologies, and their potential impacts on various industries. Given the tools at my disposal, I will use the News DB Tool to fetch recent news articles on AI advancements.\n", + "\n", + "Action: News DB Tool\n", + "Action Input: {\"query\": \"latest advancements in AI 2024\"}\u001b[0m\u001b[93m \n", + "\n", + "[Document(page_content='A quiet winner\\nWhile the song and dance surrounding Astera Labs’ public offering has been noticeably quieter than that surrounding Reddit’s IPO, there’s reason to believe that it is more a test of the market’s demand for AI shares than Reddit’s own debut; while Reddit’s AI-based data business is certainly a growing portion of its operations, it remains a single-digit percentage of its anticipated 2023 per TechCrunch analysis.\\nIn contrast, the AI-led data center buildout that is benefiting Astera Labs makes up what the market may consider a larger portion of its current size, and future growth. The fact that the company’s growth rate accelerated as much as it did in the fourth quarter of 2023 and that it managed to swing from a loss to GAAP profitability that quarter underscores the view that it is a company on the move thanks to AI demand. This even though it sits far from the more headline-friendly foundation model work that OpenAI and its rivals are undertaking.', metadata={'vector': [-0.0031063526403158903, -0.029682153835892677, 0.0008559823618270457, -0.01945027895271778, 1.3049338122073095e-05, 0.004862569738179445, -0.029432257637381554, 0.004695972427725792, -0.004786212462931871, -0.013279223814606667, 0.01754828914999962, 0.013661010190844536, -0.01554911769926548, -0.017798185348510742, 0.013112626038491726, 0.016909664496779442, 0.020644228905439377, -0.009037923999130726, 0.0008403638494201005, -0.002519790083169937, -0.001213039387948811, 0.010238816030323505, -0.028682567179203033, 0.006899920757859945, -0.008329884149134159, 0.0015939580043777823, 0.021838180720806122, -0.030459610745310783, 0.00012624981172848493, 0.015507468022406101, 0.030876103788614273, -0.019408630207180977, -0.006417481694370508, 0.020463749766349792, -0.018325746059417725, -0.02768298238515854, 0.0010959004284814, 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Document(page_content='Astera Labs’ IPO pops 54%, showing that investor demand for tech with an AI-twist is high\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n \\n\\n\\n\\n \\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nplus-bold\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\nOpen Navigation\\n\\n\\n\\n\\n\\n\\nTechCrunch\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\n\\nAstera Labs’ IPO pops 72% on first day, showing that investor demand for tech with an AI twist is high\\n\\n\\n\\n\\n\\t\\t\\t\\tRebecca Szkutak\\t\\t\\t\\n\\n\\t\\t\\t\\tAlex Wilhelm\\t\\t\\t\\n\\n\\t\\t\\t2 days', metadata={'vector': [0.016613541170954704, -0.024302450940012932, -0.001540356664918363, 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OTB fully aligns with our mission,” NIF managing partner Andrea Traversone said in a statement.\\nOTB’s take on deep tech focuses on four verticals that do sound fairly NATO-compatible: space tech, enterprise automation and AI, cybersecurity and fintech infrastructure. That would be where fintech goes a bit more technically innovative; this could be AI-enabled anti-money laundering like Fund 1’s portfolio company Silent Eight, for instance.\\nSince OTB already started making investments out of this fund following its first close in November 2022, we already have a sense of where it will go. For instance, its nine investments so far include German startups KYP.ai, a productivity platform, and Semron, which is developing innovative chips.', metadata={'vector': [0.017766745761036873, -0.06202411279082298, -0.020915107801556587, -0.00597772654145956, -0.006182300392538309, 0.018890170380473137, -0.04402158409357071, 0.0028796414844691753, -0.019028864800930023, 0.0005045006982982159, 0.046379391103982925, 0.008113618940114975, -0.012877769768238068, -0.0075380378402769566, 0.017392270267009735, 0.00650129746645689, 0.006660795770585537, 0.005162897054105997, 0.016393672674894333, -0.006376472767442465, -0.04196890816092491, 0.013661392964422703, -0.001725877751596272, -0.006345266476273537, -0.007413212675601244, -0.01506220642477274, 0.002577981213107705, -0.017586443573236465, -0.019306253641843796, -0.004767618142068386, 0.011865301057696342, 0.0010462754871696234, -0.020401937887072563, 0.015644721686840057, 0.0030859492253512144, -0.024645986035466194, 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The gathered information from recent news articles reveals the following insights:\n", + "\n", + "1. Astera Labs' IPO and Market Impact:\n", + " - Astera Labs, a company benefiting from AI-led data center buildouts, has seen accelerated growth and reached GAAP profitability in Q4 of 2023, demonstrating the increasing market demand for AI shares.\n", + " - The company's focus on AI for data center infrastructure contrasts with the broader attention given to foundation model work by entities like OpenAI, indicating a diversification in the AI industry's growth areas.\n", + "\n", + "2. Investment Trends:\n", + " - A new flagship fund with a focus on deep tech, including AI, has been announced, signaling strong investor confidence in AI and related technologies.\n", + " - The fund targets disruptive technologies across sectors such as energy, quantum computing, autonomy, climate, industrials, space, and biotechnology, showcasing the wide-ranging impact of AI advancements.\n", + "\n", + "3. NATO Innovation Fund's Involvement:\n", + " - The NATO Innovation Fund (NIF) has begun deploying its €1 billion investment in startups and funds, focusing on innovative technologies.\n", + " - NIF's investment strategy aligns with the trend of increasing support for deep tech projects, further proof of AI's growing importance on a global scale.\n", + "\n", + "4. Market and Industry Implications:\n", + " - These developments underline the expanding role of AI and deep tech in driving economic growth and innovation.\n", + " - The focus on sectors such as data centers, quantum computing, and biotechnology indicates where significant breakthroughs and advancements in AI technology are expected.\n", + "\n", + "In conclusion, the AI industry in 2024 is characterized by significant investments in deep tech, a strong focus on AI applications beyond consumer technology, and growing global interest in AI's potential to revolutionize a broad range of industries. These trends suggest a promising future for AI technology, with broad implications for economic development, strategic defense initiatives, and global technological leadership.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n", + "\n", + "\n", + "\u001b[1m> Entering new CrewAgentExecutor chain...\u001b[0m\n", + "\u001b[32;1m\u001b[1;3m\u001b[0m\u001b[32;1m\u001b[1;3mThought: I now can give a great answer\n", + "Final Answer: \n", + "1. Astera Labs' IPO reveals a booming AI market, focusing on data center AI tech, showing diverse AI industry growth.\n", + "2. A new deep tech fund bets big on AI, targeting sectors from energy to biotech, indicating high investor confidence.\n", + "3. NATO's €1 billion fund invests in AI startups, emphasizing AI's strategic global importance.\n", + "4. These developments highlight AI's expanding role in economic and technological innovation across various sectors.\u001b[0m\n", + "\n", + "\u001b[1m> Finished chain.\u001b[0m\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(result)" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "HB91fa7BXULb", + "outputId": "5000f394-6647-4684-cdf4-16d4a7ea91e0" + }, + "execution_count": 68, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "1. Astera Labs' IPO reveals a booming AI market, focusing on data center AI tech, showing diverse AI industry growth.\n", + "2. A new deep tech fund bets big on AI, targeting sectors from energy to biotech, indicating high investor confidence.\n", + "3. NATO's €1 billion fund invests in AI startups, emphasizing AI's strategic global importance.\n", + "4. These developments highlight AI's expanding role in economic and technological innovation across various sectors.\n" + ] + } + ] + } + ] +} \ No newline at end of file diff --git a/examples/AI-Trends-with-CrewAI/README.md b/examples/AI-Trends-with-CrewAI/README.md new file mode 100644 index 00000000..7fa5b518 --- /dev/null +++ b/examples/AI-Trends-with-CrewAI/README.md @@ -0,0 +1,17 @@ +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/AI-Trends-with-CrewAI/CrewAI_AI_Trends.ipynb) + + + +## AI Trends Searcher using CrewAI Agents +This example is about how to make an AI Trends Searcher using **CrewAI Agents** and their Tasks. But before diving into that, let's first understand what CrewAI is and how we can use it for these applications. + +![alt text](<../../assets/crewai.png>) + +What is CrewAI? +CrewAI is an open-source framework that helps different AI agents work together to do tricky stuff. You can give each Agent its tasks and goals, manage what they do, and help them work together by sharing tasks. These are some unique features of CrewAI: + +1. Role-based Agents: Define agents with specific roles, goals, and backgrounds to provide more context for answer generation. +2. Task Management: Use tools to dynamically define tasks and assign them to agents. +3. Inter-agent Delegation: Agents can share tasks to collaborate better. + +[Read More in Blog](https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/) \ No newline at end of file From 3162dcb7c65311ee737baf01d6ffd66244b2d01b Mon Sep 17 00:00:00 2001 From: Prashant Dixit <54981696+PrashantDixit0@users.noreply.github.com> Date: Wed, 27 Mar 2024 23:38:00 +0530 Subject: [PATCH 09/32] node test ci fix (#163) * assests and app name * update README * demo gifs * talk with github codespaces * talk with github codespaces * gitignore * linted * added version * link fix * added local llm tag * crag * link fix * lint * llm tags * non-clickable badge * non-clickable badge * fix * tutorial llm tags * added instructions and fix * colab fix * fix * formatted * hybrid search and rag colab * colab format * python test * node test * python test * blog link update * rag mlx * myntra search engine app * link fix * CrewAI Example * lint * node test * node test * node test --- .github/workflows/examples-test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/examples-test.yml b/.github/workflows/examples-test.yml index 012a173e..31bb01e2 100644 --- a/.github/workflows/examples-test.yml +++ b/.github/workflows/examples-test.yml @@ -129,6 +129,7 @@ jobs: env: OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }} run: | + npm install @lancedb/vectordb-linux-x64-gnu for d in *; do if [[ $d == *.js ]]; then echo "$d"; From 04cd9ab02d28ef52c5f992a80cc3d133e257c03e Mon Sep 17 00:00:00 2001 From: Bert Date: Fri, 29 Mar 2024 09:48:30 -0400 Subject: [PATCH 10/32] update logo in README --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index aeb27b1b..7414bad9 100644 --- a/README.md +++ b/README.md @@ -7,7 +7,8 @@ This repository contains examples, applications, starter code, & tutorials to he - It **integrates into python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc. - LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions! - + +
    Join our community for support - Discord • Twitter From b51db24de7163232b8295b550abb42a05d54dd0f Mon Sep 17 00:00:00 2001 From: Bert Date: Fri, 29 Mar 2024 09:59:28 -0400 Subject: [PATCH 11/32] update Langroid photo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 7414bad9..25c4469d 100644 --- a/README.md +++ b/README.md @@ -82,7 +82,7 @@ These are ready to use applications built using LanceDB serverless vector databa | [ Talk with Podcast ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-podcast) | Talk with Youtube Podcast using Ollama and insanely-fast-whisper | ![demo](./assets/talk-with-podcast.gif)| | [ Talk with Wikipedia ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-wikipedia) | Talk with Wikipedia Pages | ![demo](./assets/talk-with-wikipedia.gif)| | [ Talk with Github ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-github) | Talk with Github Codespaces using Qwen1.5 | ![demo](./assets/talk-with-github.gif)| -| [ Document Chat with Langroid ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/docchat-with-langroid) | Talk with your Documents using Langroid | ![demo](./assets/document-chat-langroid.png)| +| [ Document Chat with Langroid ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/docchat-with-langroid) | Talk with your Documents using Langroid | ![demo](https://github.com/lancedb/vectordb-recipes/assets/5846846/e55c45a3-b5b0-478b-bf77-290c0d69daae)| | [ Fastapi RAG template ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_RAG_with_FASTAPI) | FastAPI based RAG template with Websocket support | ![image](./assets/chatbot_fastapi.png)| | [ GTE MLX RAG ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/GTE_mlx_RAG) | mlx based RAG model using lancedb api support | ![image](./assets/rag-mlx.png)| | [Multilingual-RAG](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Multilingual_RAG/) | Multilingual RAG with cohere embedding & support 100+ languages|![image](https://github.com/akashAD98/vectordb-recipes/assets/62583018/be65eb39-25c4-4441-98fc-6ded09689819)| From 7725cb145399ea44d69dc69dfba4ebaeb0453c75 Mon Sep 17 00:00:00 2001 From: albertlockett Date: Fri, 29 Mar 2024 10:23:08 -0400 Subject: [PATCH 12/32] update photos in recipe table --- assets/myntra-search-engine.png | Bin 391273 -> 443510 bytes assets/vercel-template.gif | Bin 209993 -> 55659 bytes 2 files changed, 0 insertions(+), 0 deletions(-) diff --git a/assets/myntra-search-engine.png b/assets/myntra-search-engine.png index 33e0d74efca462ee26fe4062815fde1b0251a853..ec355360208c66cd31c5ce0da82e2ba6df1eb579 100644 GIT binary patch literal 443510 zcmZsC1ymf((kQUFyW8UK&f*f>-3jh)32sYpclY4#!F6#61PzM^2*H8|$m6^B{_@^= zvuDoK)KpbhO?OrAR9An}P#ZBZqb4EJC30AMlTzhKy*e|V!PPrkiLpLWnO@HS9Y 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