[](./examples/Youtube-Search-QA-Bot/main.py) [](./examples/Youtube-Search-QA-Bot/index.js) [](#)||
-| [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | [](./examples/Code-Documentation-QA-Bot/main.py) [](./examples/Code-Documentation-QA-Bot/index.js)[](#)||
-| [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | [](./examples/reducing_hallucinations_ai_agents/main.py) [](./examples/reducing_hallucinations_ai_agents/index.js)[](#)| [](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f)|
-| [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) | [](./examples/multimodal_clip_diffusiondb/main.py) [](#)| [](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939)|
-| [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | [](./examples/multimodal_video_search/main.py) [](#) |[](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939)|
-| [Multimodal Image + Text Search](/examples/multimodal_search/) | [](./examples/multimodal_search/main.py) [](#)| [](https://medium.com/etoai/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939) |
-| [TransformersJS Embedding example](./examples/js-transformers/) |[](./examples/js-transformers/index.js) [](#) | |
-| [Movie Recommender](/examples/movie-recommender/) | [](./examples/movie-recommender/main.py)| |
-| [Product Recommender](./examples/product-recommender/) | [](./examples/product-recommender/main.py) | |
-| [Audio Search](./examples/audio_search/) | [](./examples/audio_search/main.py) [](#) | |
-| [Arxiv paper recommender](/examples/arxiv-recommender) | [](./examples/arxiv-recommender/main.py) [](#) | |
-| [Multi-lingual search](/examples/multi-lingual-wiki-qa) | [](.examples/multi-lingual-wiki-qa/main.py) [](#) | |
-| [Instruct-Multitask](./examples/instruct-multitask) | [](.examples/instruct-multitask/main.py) [](#) | [](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)|
-| [Improve RAG with Re-ranking](/examples/RAG_re_ranking/) | [](#) |[](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)|
-| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | [](.examples/instruct-multitask/main.py) [](#) | [](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)|
-| [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | [](#)|[](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)|
-| [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | [](#) |[](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)|
-| [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | [](#)|[](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)|
-| [RAG Fusion](/examples/RAG_Fusion/) | [](#)|
-| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | [](#)|[](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/) | [](#) [](#)| |
-| [NER powered with Semantic Search](/tutorials/NER-powered-Semantic-Search/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [](#) | [](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493)|
-[Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [](#) | [](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)|
-| [Facial Recognition](./examples/facial_recognition) | |
-| [Accelerate Vector Search Applications Using OpenVINO](/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [](#)| [](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)|
-| [Search Within Images](/examples/Contextual-Compression-with-RAG/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [](#) |[](https://blog.lancedb.com/search-within-an-image-331b54e4285e)|
-| [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [](#) |[](https://medium.com/etoai/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301) |
-| [Imagebind demo app](/examples/imagebind_demo/) | 
|
+| [Youtube transcript search bot](/examples/Youtube-Search-QA-Bot/) | [](./examples/Youtube-Search-QA-Bot/main.py) [](./examples/Youtube-Search-QA-Bot/index.js) [](#) [](#)||
+| [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) | [](./examples/Code-Documentation-QA-Bot/main.py) [](./examples/Code-Documentation-QA-Bot/index.js) [](#) [](#)||
+| [Databricks DBRX Website Bot](./examples/databricks_DBRX_website_bot/) | [](./examples/databricks_DBRX_website_bot/main.py) [](#) [](#)|
+| [CLI-based SDK Manual Chatbot with Phidata](/examples/CLI-SDK-Manual-Chatbot-Locally/) | [](./examples/CLI-SDK-Manual-Chatbot-Locally/assistant.py) [](#) [](#)|
+| [TransformersJS Embedding example](./examples/js-transformers/) |[](./examples/js-transformers/index.js) [](#) [](#)| |
+| [Inbuilt Hybrid Search](/examples/Inbuilt-Hybrid-Search) | [](#) [](#)||
+| [Audio Search](./examples/audio_search/) | [](./examples/audio_search/main.py) [](#) [](#)| |
+| [Multi-lingual search](/examples/multi-lingual-wiki-qa) | [](./examples/multi-lingual-wiki-qa/main.py) [](#) [](#)| |
+| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_lancedb/) | [](#) [](#)|[](https://blog.lancedb.com/hybrid-search-combining-bm25-and-semantic-search-for-better-results-with-lan-1358038fe7e6)|
+| [Search Within Images](/examples/search-within-images-with-sam-and-clip/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/search-within-images-with-sam-and-clip/main.ipynb) [](#) [](#)| [](https://blog.lancedb.com/search-within-an-image-331b54e4285e)|
+| [Accelerate Vector Search Applications Using OpenVINO](/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [](#) [](#)| [](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-lancedb/)|
+| [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) | [](./examples/multimodal_clip_diffusiondb/main.py) [](#) [](#)| [](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)|
+| [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) | [](./examples/multimodal_video_search/main.py) [](#) [](#)|[](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)|
+| [Multimodal Image + Text Search](/examples/multimodal_search/) | [](./examples/multimodal_search/main.py) [](#) [](#)| [](https://blog.lancedb.com/multi-modal-ai-made-easy-with-lancedb-clip-5aaf8801c939/)|
+| [Movie Recommender](/examples/movie-recommender/) | [](./examples/movie-recommender/main.py) [](#)| |
+| [Product Recommender](./examples/product-recommender/) | [](./examples/product-recommender/main.py)[](#)| |
+| [Arxiv paper recommender](/examples/arxiv-recommender) | [](./examples/arxiv-recommender/main.py) [](#) [](#)| |
+| [Improve RAG with Re-ranking](/examples/RAG_Reranking/) | [](#) [](#)|[](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)|
+| [Improve RAG with FLARE](/examples/Advanced-RAG-with-FLARE) | [](./examples/Advanced-RAG-with-FLARE/app.py) [](#) [](#)| [](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f)|
+| [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) | [](#) [](#)|[](https://blog.lancedb.com/advanced-rag-precise-zero-shot-dense-retrieval-with-hyde-0946c54dfdcb)|
+| [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) | [](#) [](#)|[](https://blog.lancedb.com/better-rag-with-lotr-lord-of-retriever-23c8336b9a35)|
+| [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) | [](#) [](#)|[](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)|
+| [Query Expansion and Reranker ](/examples/QueryExpansion&Reranker/) | [](#) [](#)|[](https://blog.lancedb.com/improving-rag-with-query-expansion-reranking-models/)|
+| [RAG Fusion](/examples/RAG_Fusion/) | [](#) [](#)|
+| [Contextual-Compression-with-RAG](/examples/Contextual-Compression-with-RAG/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Contextual-Compression-with-RAG/main.ipynb) [](#) [](#)|[](https://blog.lancedb.com/enhance-rag-integrate-contextual-compression-and-filtering-for-precision-a29d4a810301/) |
+| [Instruct-Multitask](./examples/instruct-multitask) | [](./examples/instruct-multitask/main.py) [](#) [](#)|[](https://blog.lancedb.com/multitask-embedding-with-lancedb-be18ec397543)|
+| [Evaluating Prompts with Prompttools](/examples/prompttools-eval-prompts/) | [](#) [](#) [](#)| |
+| [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) | [](./examples/reducing_hallucinations_ai_agents/main.py) [](./examples/reducing_hallucinations_ai_agents/index.js) [](#) [](#) |[](https://blog.lancedb.com/how-to-reduce-hallucinations-from-llm-powered-agents-using-long-term-memory-72f262c3cc1f/)|
+| [AI Trends Searcher with CrewAI](./examples/AI-Trends-with-CrewAI/) | [](#) [](#)|[](https://blog.lancedb.com/track-ai-trends-crewai-agents-rag/)|
+| [SuperAgent Autogen](/examples/SuperAgent_Autogen) | [](#) [](#)||
+[Sentiment Analysis : Analysing Hotel Reviews](/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Sentiment-Analysis-Analyse-Hotel-Reviews/Sentiment_Analysis_using_LanceDB.ipynb) [](#) [](#)| [](https://blog.lancedb.com/sentiment-analysis-using-lancedb-2da3cb1e3fa6)|
+| [Facial Recognition](./examples/facial_recognition) | [](#)|
+| [Imagebind demo app](/examples/imagebind_demo/) | [](#)|
@@ -69,32 +75,40 @@ These are ready to use applications built using LanceDB serverless vector databa
|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
| [YOLOExplorer](https://github.com/lancedb/yoloexplorer) | Iterate on your YOLO / CV datasets using SQL, Vector semantic search, and more within seconds |  |
| [Website Chatbot (Deployable Vercel Template)](https://github.com/lancedb/lancedb-vercel-chatbot) | Create a chatbot from the sitemap of any website/docs of your choice. Built using vectorDB serverless native javascript package. |  |
-| [Multi-Modal Search Engine](https://github.com/lancedb/vectordb-recipes/tree/rf/applications/multimodal-search) | Create a Multi-modal search engine app, to search images using both images or text | |
| [ Chat with multiple URL/website ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/chat_with_anywebsite/) | Conversational AI for Any Website with Mistral,Bge Embedding & LanceDB | |
-| [ Hr chatbot ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/HR_chatbot/) | Hr chatbot - ask your personal query using zero-shot React agent & tools ||
| [ Talk with Youtube Video using GPT4 Vision API ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-youtube-gpt4-vision-api/) | Talk with Youtube Video using GPT4 Vision API and Langchain | |
| [ 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 | |
| [ Talk with Wikipedia ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-wikipedia) | Talk with Wikipedia Pages | |
| [ Talk with Github ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/talk-with-github) | Talk with Github Codespaces using Qwen1.5 | |
-| [ Document Chat with Langroid ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/docchat-with-langroid) | Talk with your Documents using Langroid | |
-| [ Fastapi RAG template ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_RAG_with_FASTAPI) | FastAPI based RAG template with Websocket support | |
-| [ 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 | |
+| [ Document Chat with Langroid ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/docchat-with-langroid) | Talk with your Documents using Langroid | |
+| [ Hr chatbot ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/HR_chatbot/) | Hr chatbot - ask your personal query using zero-shot React agent & tools ||
+| [Advanced Chatbot with Parler TTS ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_with_Parler_TTS) | This Chatbot app uses Lancedb Hybrid search, FTS & reranker method with Parlers TTS library.||
+| [Multi-Modal Search Engine](https://github.com/lancedb/vectordb-recipes/tree/rf/applications/multimodal-search) | Create a Multi-modal search engine app, to search images using both images or text | |
+| [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.||
| [Multilingual-RAG](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Multilingual_RAG/) | Multilingual RAG with cohere embedding & support 100+ languages||
+| [ Fastapi RAG template ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Chatbot_RAG_with_FASTAPI) | FastAPI based RAG template with Websocket support | |
+| [ GTE MLX RAG ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/GTE_mlx_RAG) | mlx based RAG model using lancedb api support | |
+| [ Healthcare Chatbot ](https://github.com/lancedb/vectordb-recipes/tree/main/applications/Healthcare_chatbot/) | Healthcare chatbot using domain specific LLM & Embedding model | |
+
+
## Tutorials
Looking to get started with LLMs, vectorDBs, and the world of Generative AI? These in-depth tutorials and courses cover these concepts with practical follow along colabs where possible.
| Tutorial | Interactive Environment | Blog Link |
| --------- | -------------------------- | ----------- |
| | | |
-| [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [](#) | |
-| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/product-quantization-compress-high-dimensional-vectors-dfcba98fab47) | | [](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) | | [](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) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [](#)| [](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) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [](#)| [](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) | [](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) | [](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) |
-| [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [](#)| [](https://medium.com/etoai/ner-powered-semantic-search-using-lancedb-51051dc3e493) |
-| [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [](#) [](#)|[](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) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Accelerate-Vector-Search-Applications-Using-OpenVINO/clip_text_image_search.ipynb) [](#)| [](https://blog.lancedb.com/accelerate-vector-search-applications-using-openvino-51366eabf866)|
+| [Build RAG from Scratch](./tutorials/RAG-from-Scratch) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/RAG-from-Scratch/RAG_from_Scratch.ipynb) [](#) [](#)| |
+| [Local RAG from Scratch with Llama3](./tutorials/Local-RAG-from-Scratch) | [](./tutorials/Local-RAG-from-Scratch/rag.py) [](#) [](#)| |
+| [A Primer on Text Chunking and its Types](./tutorials/different-types-text-chunking-in-RAG) | [](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) [](#)| [](https://blog.lancedb.com/a-primer-on-text-chunking-and-its-types-a420efc96a13) |
+| [Langchain LlamaIndex Chunking](./tutorials/Langchain-LlamaIndex-Chunking) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb) [](#)| [](https://blog.lancedb.com/chunking-techniques-with-langchain-and-llamaindex/) |
+| [Comparing Cohere Rerankers with LanceDB](./tutorials/cohere-reranker) | [](#)| [](https://blog.lancedb.com/benchmarking-cohere-reranker-with-lancedb/) |
+| [NER powered Semantic Search](./tutorials/NER-powered-Semantic-Search) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/NER-powered-Semantic-Search/NER_powered_Semantic_Search_with_LanceDB.ipynb) [](#) [](#)| [](https://blog.lancedb.com/ner-powered-semantic-search-using-lancedb-51051dc3e493) |
+| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) |[](#) | [](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) |
+| [Corrective RAG with Langgraph](./tutorials/Corrective-RAG-with_Langgraph/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Corrective-RAG-with_Langgraph/CRAG_with_Langgraph.ipynb) [](#) [](#)| [](https://blog.lancedb.com/implementing-corrective-rag-in-the-easiest-way-2/)|
+| [LLMs, RAG, & the missing storage layer for AI](https://blog.lancedb.com/llms-rag-the-missing-storage-layer-for-ai-28ded35fa984) | [](#)| [](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) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/fine-tuning_LLM_with_PEFT_QLoRA/main.ipynb) [](#) [](#)| [](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) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/chatbot_using_Llama2_&_lanceDB/main.ipynb) [](#) [](#)| [](https://blog.lancedb.com/context-aware-chatbot-using-llama-2-lancedb-as-vector-database-4d771d95c755) |
+| [Better RAG with FLARE](./tutorials/better-rag-FLAIR) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/better-rag-FLAIR/main.ipynb) [](#) [](#) [](#)|[](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) |
diff --git a/applications/Chatbot_with_Parler_TTS/.env-example b/applications/Chatbot_with_Parler_TTS/.env-example
new file mode 100644
index 00000000..a6f7e3f7
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/.env-example
@@ -0,0 +1 @@
+OPENAI_API_KEY = 'sk-youekey'
diff --git a/applications/Chatbot_with_Parler_TTS/README.md b/applications/Chatbot_with_Parler_TTS/README.md
new file mode 100644
index 00000000..e80719b4
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/README.md
@@ -0,0 +1,39 @@
+# Chat with PDF using LanceDB and Paler TTS
+This application integrates a PDF chat functionality using LanceDB with advanced RAG (Retrieval-Augmented Generation) methods and
+leverages the Paler Text-to-Speech (TTS) model for audio output.
+
+It is designed to enable high-quality text and speech interaction with PDF documents.
+
+
+## Features
+
+Hybrid Search: Combines vector-based and keyword searches to improve result relevance.
+
+Full-Text Search (FTS): Utilizes Tavity for enhanced text search capabilities within documents.
+
+Colbert Reranker: Improves the accuracy of search results by reranking them based on relevance.
+
+Langchain Prompts: Controls LLM (Large Language Model) outputs using customized prompts for more tailored interactions.
+
+Paler Text-to-Speech (TTS): A lightweight, high-quality TTS model that mimics various speech styles and attributes.
+
+## Installation
+Clone the repository and install the required packages:
+```
+pip install -r requirements.txt
+```
+
+## Running the Application
+Start the application by running the main script. This will launch a Gradio interface accessible via a web browser:
+
+create ```.env ``` file & pass the openai_api_key. or simply rename the ```.env-example ``` file to ```.env```
+
+```
+python3 main.py # Gradio app will run
+```
+## Outputs
+The application provides two types of outputs from the processed PDF documents:
+
+Text: Extracted and processed text displayed in a user-friendly format.
+Audio: Natural sounding speech generated from the text, customizable by speaker characteristics such as gender and pitch.
+
diff --git a/applications/Chatbot_with_Parler_TTS/constants.py b/applications/Chatbot_with_Parler_TTS/constants.py
new file mode 100644
index 00000000..d73cadfc
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/constants.py
@@ -0,0 +1,5 @@
+input_pdf = "https://d18rn0p25nwr6d.cloudfront.net/CIK-0001559720/8a9ebed0-815a-469a-87eb-1767d21d8cec.pdf"
+
+parler_tts_description = """ Utilize a male voice with an Indian English
+accent for the chatbot. The speech should be clear, ensuring each word is
+distinctly articulated in a crisp and confined audio environment. """
diff --git a/applications/Chatbot_with_Parler_TTS/main.py b/applications/Chatbot_with_Parler_TTS/main.py
new file mode 100644
index 00000000..54dca71d
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/main.py
@@ -0,0 +1,38 @@
+import gradio as gr
+from rag_lance import get_rag_output
+from tts_module import text_to_speech
+
+
+def process_question(question, include_audio):
+ generated_text = get_rag_output(question)
+ if include_audio:
+ audio_file_path = text_to_speech(generated_text)
+ return generated_text, audio_file_path
+ else:
+ return generated_text, None # Return None for the audio part
+
+
+iface = gr.Interface(
+ fn=process_question,
+ inputs=[
+ gr.Textbox(lines=2, placeholder="Enter a question..."),
+ gr.Checkbox(
+ label="Include audio", value=True
+ ), # Default to True, can be unchecked by user
+ ],
+ outputs=[
+ gr.Textbox(label="Generated Text"),
+ gr.Audio(label="Generated Audio", type="filepath"), # No optional keyword
+ ],
+ title="Advance RAG chatbot with TTS support",
+ description="Ask a question and get a text response along with its audio representation. Optionally, include the audio response.",
+ examples=[
+ ["What is net profit of Airbnb ?"],
+ [
+ "What are the specific factors contributing to Airbnb's increased operational expenses in the last fiscal year"
+ ],
+ ],
+)
+
+if __name__ == "__main__":
+ iface.launch(debug=True, share=True)
diff --git a/applications/Chatbot_with_Parler_TTS/prompt.py b/applications/Chatbot_with_Parler_TTS/prompt.py
new file mode 100644
index 00000000..6723f64b
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/prompt.py
@@ -0,0 +1,19 @@
+rag_prompt = """
+As an AI Assistant, your role is to provide authentic and accurate responses. Analyze the question and its context thoroughly to determine the most appropriate answer.
+
+**Instructions:**
+- Understand the context and nuances of the question to identify relevant and precise information.
+- if its general greeting then answer should be hellow how can i help you,please ask related quetions so i can help
+- If an answer cannot be conclusively determined from the provided information, inform the user rather than making up an answer.
+- When multiple interpretations of a question exist, briefly present these viewpoints, then provide the most plausible answer based on the context.
+- Focus on providing concise and factual responses, excluding irrelevant details.
+- For sensitive or potentially harmful topics, advise users to seek professional advice or consult authoritative sources.
+- Keep your answer clear and within 500 words.
+
+**Context:**
+{context}
+
+**Question:**
+{question}
+
+"""
diff --git a/applications/Chatbot_with_Parler_TTS/rag_lance.py b/applications/Chatbot_with_Parler_TTS/rag_lance.py
new file mode 100644
index 00000000..cd4794d0
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/rag_lance.py
@@ -0,0 +1,113 @@
+import os
+import torch
+import lancedb
+from dotenv import load_dotenv
+from constants import input_pdf
+from prompts import rag_prompt
+from langchain_community.vectorstores import LanceDB
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_openai import ChatOpenAI, OpenAIEmbeddings
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.document_loaders import PyPDFLoader
+from langchain_core.messages import HumanMessage, SystemMessage
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from lancedb.embeddings import get_registry
+from lancedb.pydantic import Vector, LanceModel
+from lancedb.rerankers import ColbertReranker
+
+
+load_dotenv()
+
+
+class Document:
+ def __init__(self, page_content, metadata=None):
+ self.page_content = page_content
+ self.metadata = metadata if metadata is not None else {}
+
+ def __repr__(self):
+ return f"Document(page_content='{self.page_content}', metadata={self.metadata})"
+
+
+def get_rag_output(question):
+ input_pdf_file = input_pdf
+
+ # Create your PDF loader
+ loader = PyPDFLoader(input_pdf_file)
+
+ # Load the PDF document
+ documents = loader.load()
+
+ # Chunk the financial report
+ text_splitter = RecursiveCharacterTextSplitter(chunk_size=1024, chunk_overlap=0)
+ docs = text_splitter.split_documents(documents)
+
+ openai = get_registry().get("openai").create()
+
+ class Schema(LanceModel):
+ text: str = openai.SourceField()
+ vector: Vector(openai.ndims()) = openai.VectorField()
+
+ embedding_function = OpenAIEmbeddings()
+
+ db = lancedb.connect("~/langchain")
+ table = db.create_table(
+ "airbnb",
+ schema=Schema,
+ mode="overwrite",
+ )
+
+ # Load the document into LanceDB
+ db = LanceDB.from_documents(docs, embedding_function, connection=table)
+ table.create_fts_index("text", replace=True)
+
+ reranker = ColbertReranker()
+ docs_n = (
+ table.search(question, query_type="hybrid")
+ .limit(5)
+ .rerank(reranker=reranker)
+ .to_pandas()["text"]
+ .to_list()
+ )
+
+ metadata = {"source": input_pdf_file}
+ docs_with_metadata = [
+ Document(page_content=text, metadata=metadata) for text in docs_n
+ ]
+
+ vectorstore = LanceDB.from_documents(
+ documents=docs_with_metadata,
+ embedding=OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"]),
+ )
+
+ retriever = vectorstore.as_retriever()
+
+ rag_prompt_template = rag_prompt
+
+ prompt = PromptTemplate(
+ template=rag_prompt_template,
+ input_variables=[
+ "context",
+ "question",
+ ],
+ )
+
+ def format_docs(docs):
+ return "\n\n".join(doc.page_content for doc in docs)
+
+ llm = ChatOpenAI(
+ model="gpt-3.5-turbo",
+ temperature=0,
+ openai_api_key=os.environ["OPENAI_API_KEY"],
+ )
+
+ rag_chain = (
+ {"context": retriever | format_docs, "question": RunnablePassthrough()}
+ | prompt
+ | llm
+ | StrOutputParser()
+ )
+
+ output = rag_chain.invoke(question)
+ return output
diff --git a/applications/Chatbot_with_Parler_TTS/requirements.txt b/applications/Chatbot_with_Parler_TTS/requirements.txt
new file mode 100644
index 00000000..f5673ae3
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/requirements.txt
@@ -0,0 +1,10 @@
+pip install langchain
+langchain-community
+langchain-openai
+lancedb
+bs4
+tantivy==0.20.1
+pypdf
+gradio
+torch
+git+https://github.com/huggingface/parler-tts.git
diff --git a/applications/Chatbot_with_Parler_TTS/tts_module.py b/applications/Chatbot_with_Parler_TTS/tts_module.py
new file mode 100644
index 00000000..94d49be4
--- /dev/null
+++ b/applications/Chatbot_with_Parler_TTS/tts_module.py
@@ -0,0 +1,23 @@
+import torch
+import soundfile as sf
+from transformers import AutoTokenizer
+from parler_tts import ParlerTTSForConditionalGeneration
+
+
+def text_to_speech(text, filename="output_audio.wav"):
+ device = "cuda:0" if torch.cuda.is_available() else "cpu"
+ model = ParlerTTSForConditionalGeneration.from_pretrained(
+ "parler-tts/parler_tts_mini_v0.1"
+ ).to(device)
+ tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")
+
+ # description = "A clear and articulate Indian English male voice with a medium pitch and neutral accent with a friendly and engaging tone. The audio quality is high, ensuring that each word is easily understandable without any background noise."
+ description = "Utilize a male voice with a low pitch and an Indian English accent for the chatbot. The speech should be fast yet clear, ensuring each word is distinctly articulated in a crisp and confined audio environment."
+ input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
+ prompt_input_ids = tokenizer(text, return_tensors="pt").input_ids.to(device)
+
+ generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+ audio_arr = generation.cpu().numpy().squeeze()
+ sf.write(filename, audio_arr, model.config.sampling_rate)
+
+ return filename
diff --git a/applications/Healthcare_chatbot/README.md b/applications/Healthcare_chatbot/README.md
new file mode 100644
index 00000000..a1b60132
--- /dev/null
+++ b/applications/Healthcare_chatbot/README.md
@@ -0,0 +1,47 @@
+## Overview
+This project introduces a healthcare-related Retrieval-Augmented Generation (RAG) chatbot, designed to deliver quick responses to medical inquiries. Utilizing OpenBioLLM-Llama3 / openai llm and the NeuML's PubMedBERT for embedding,
+this chatbot is adept at processing and responding to medical data queries.
+
+
+
+## Key Features
+### Language Model:
+
+To utilize OpenBioLLM-Llama3 .download model in the local system & pass the path of it
+link for downloading gguf version model https://huggingface.co/PrunaAI/OpenBioLLM-Llama3-8B-GGUF-smashed
+change this model based on requirements & performance
+
+### Embeddings:
+Uses NeuML's PubMedBERT (https://huggingface.co/NeuML/pubmedbert-base-embeddings), which is fine-tuned on PubMed data with the BERT architecture to ensure high relevance and contextual accuracy in responses.
+
+### Database and Framework:
+Incorporates the LanceDB vector database and Cohere reranker within the LangChain framework to enhance efficient query processing and response generation.
+
+## Installation
+Follow these steps to set up the chatbot on your local machine:
+
+Clone the repository & install
+
+```pip install -r requirements.txt```
+
+## Start the application:
+```
+uvicorn main:app --reload
+```
+
+
+After launching the server, open the ```index.html ```
+file in any web browser to start interacting with the chatbot.
+
+
+## Usage
+
+Use the chatbot via the provided web interface. Enter your medical-related questions into the chat input box, and receive responses generated from the integrated language models and databases.
+
+
+
+## Note
+Please be advised that while the chatbot provides information based on learned data, it can occasionally deliver incorrect information or miss critical nuances. Always consult with a healthcare professional before making any medical decisions based on advice received from the chatbot.
+
+## Disclaimer
+This chatbot is intended for informational purposes only and should not be used as a substitute for professional medical advice, diagnosis, or treatment. Always seek the advice of your physician or other qualified health provider with any questions you may have regarding a medical condition
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diff --git a/applications/Healthcare_chatbot/data/data.txt b/applications/Healthcare_chatbot/data/data.txt
new file mode 100644
index 00000000..8b137891
--- /dev/null
+++ b/applications/Healthcare_chatbot/data/data.txt
@@ -0,0 +1 @@
+
diff --git a/applications/Healthcare_chatbot/index.html b/applications/Healthcare_chatbot/index.html
new file mode 100644
index 00000000..60139efd
--- /dev/null
+++ b/applications/Healthcare_chatbot/index.html
@@ -0,0 +1,181 @@
+
+
+
+
+
+ | \n", + " | _id | \n", + "title | \n", + "text | \n", + "num_words | \n", + "
|---|---|---|---|---|
| 35 | \n", + "9ea16bc34448ca9d713f4501f1a6215a26746372 | \n", + "A survey of software testing practices in alberta | \n", + "Software organizations have typically de-empha... | \n", + "236 | \n", + "
| 42 | \n", + "9d1940f843c448cc378214ff6bad3c1279b1911a | \n", + "Shape-aware Instance Segmentation | \n", + "We address the problem of instance-level seman... | \n", + "187 | \n", + "
| 31 | \n", + "4b53f660eb6cfe9180f9e609ad94df8606724a3d | \n", + "Text mining of news-headlines for FOREX market... | \n", + "In this paper a novel approach is proposed to ... | \n", + "621 | \n", + "
| 21 | \n", + "b579366db457216b0548220bf369ab9eb183a0cc | \n", + "An analysis on the significance of ticket anal... | \n", + "Software even though intangible should undergo... | \n", + "232 | \n", + "
| 29 | \n", + "8e508720cdb495b7821bf6e43c740eeb5f3a444a | \n", + "Learning Scalable Deep Kernels with Recurrent\\... | \n", + "Many applications in speech, robotics, finance... | \n", + "138 | \n", + "
| \n", + " | vector | \n", + "text | \n", + "title | \n", + "num_words | \n", + "score | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "[-0.0123116635, -0.003029472, 0.022851666, -0.... | \n", + "Sociological and technical difficulties, such ... | \n", + "Hipikat: a project memory for software develop... | \n", + "210 | \n", + "6.811104 | \n", + "
| 1 | \n", + "[-0.013957698, 0.02428029, 0.0018158188, -0.03... | \n", + "Hierarchical Pitman-Yor Process priors are com... | \n", + "Compressed Nonparametric Language Modelling | \n", + "134 | \n", + "6.782823 | \n", + "
| 2 | \n", + "[-0.035857946, -0.00020074612, 0.009314451, -0... | \n", + "We introduce a long short-term memory recurren... | \n", + "Speech-driven 3 D Facial Animation with Implic... | \n", + "171 | \n", + "6.157275 | \n", + "
| 3 | \n", + "[-0.03081366, -0.0026948636, 0.010220175, -0.0... | \n", + "This report summarizes the objectives and eval... | \n", + "SemEval-2015 Task 11: Sentiment Analysis of Fi... | \n", + "112 | \n", + "6.050443 | \n", + "
| 4 | \n", + "[0.0012136719, -0.022606706, 0.0142465895, -0.... | \n", + "Software organizations have typically de-empha... | \n", + "A survey of software testing practices in alberta | \n", + "236 | \n", + "6.037307 | \n", + "
| \n", + " | vector | \n", + "text | \n", + "title | \n", + "num_words | \n", + "_distance | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "[-0.029037403, -0.0019001433, 0.01014971, -0.0... | \n", + "Classifying short texts to one category or clu... | \n", + "Using deep learning for short text understanding | \n", + "187 | \n", + "0.354801 | \n", + "
| 1 | \n", + "[-0.013545574, -0.0019434954, 0.009623939, -0.... | \n", + "a r t i c l e i n f o a b s t r a c t We prese... | \n", + "BabelNet: The automatic construction, evaluati... | \n", + "133 | \n", + "0.402469 | \n", + "
| 2 | \n", + "[-0.027462298, -0.00018946378, 0.017612722, -0... | \n", + "We introduce a technique for augmenting neural... | \n", + "Deep Voice 2 : Multi-Speaker Neural Text-to-Sp... | \n", + "151 | \n", + "0.417934 | \n", + "
| 3 | \n", + "[-0.035857946, -0.00020074612, 0.009314451, -0... | \n", + "We introduce a long short-term memory recurren... | \n", + "Speech-driven 3 D Facial Animation with Implic... | \n", + "171 | \n", + "0.421723 | \n", + "
| 4 | \n", + "[-0.010440272, -0.0065525724, 0.018469144, -0.... | \n", + "The presented work aims at generating a system... | \n", + "BCSAT : A Benchmark Corpus for Sentiment Analy... | \n", + "126 | \n", + "0.430030 | \n", + "
| 5 | \n", + "[-0.037621364, 0.011013092, 0.02912708, -0.034... | \n", + "This paper proposes quiz-style information pre... | \n", + "Effects of quiz-style information presentation... | \n", + "141 | \n", + "0.441784 | \n", + "
| 6 | \n", + "[-0.03081366, -0.0026948636, 0.010220175, -0.0... | \n", + "This report summarizes the objectives and eval... | \n", + "SemEval-2015 Task 11: Sentiment Analysis of Fi... | \n", + "112 | \n", + "0.445057 | \n", + "
| 7 | \n", + "[-0.009847619, 0.007252175, 0.00984077, -0.031... | \n", + "This paper presents a simple unsupervised lear... | \n", + "Thumbs Up or Thumbs Down? Semantic Orientation... | \n", + "163 | \n", + "0.448393 | \n", + "
| 8 | \n", + "[-0.02750998, 0.017241215, 0.016468005, -0.014... | \n", + "With the development of online advertisements,... | \n", + "Clickbait Convolutional Neural Network | \n", + "160 | \n", + "0.448454 | \n", + "
| 9 | \n", + "[-0.016168512, 0.010419708, 0.029975908, -0.01... | \n", + "Theories on the functions of the hippocampal s... | \n", + "Memory, navigation and theta rhythm in the hip... | \n", + "130 | \n", + "0.453170 | \n", + "
| \n", + " | vector | \n", + "text | \n", + "title | \n", + "num_words | \n", + "_relevance_score | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "[-0.029037403, -0.0019001433, 0.01014971, -0.0... | \n", + "Classifying short texts to one category or clu... | \n", + "Using deep learning for short text understanding | \n", + "187 | \n", + "0.700000 | \n", + "
| 1 | \n", + "[-0.013957698, 0.02428029, 0.0018158188, -0.03... | \n", + "Hierarchical Pitman-Yor Process priors are com... | \n", + "Compressed Nonparametric Language Modelling | \n", + "134 | \n", + "0.674416 | \n", + "
| 2 | \n", + "[-0.013545574, -0.0019434954, 0.009623939, -0.... | \n", + "a r t i c l e i n f o a b s t r a c t We prese... | \n", + "BabelNet: The automatic construction, evaluati... | \n", + "133 | \n", + "0.256456 | \n", + "
| 3 | \n", + "[-0.035857946, -0.00020074612, 0.009314451, -0... | \n", + "We introduce a long short-term memory recurren... | \n", + "Speech-driven 3 D Facial Animation with Implic... | \n", + "171 | \n", + "0.123807 | \n", + "
| 4 | \n", + "[-0.027462298, -0.00018946378, 0.017612722, -0... | \n", + "We introduce a technique for augmenting neural... | \n", + "Deep Voice 2 : Multi-Speaker Neural Text-to-Sp... | \n", + "151 | \n", + "0.112552 | \n", + "
| \n", + " | vector | \n", + "text | \n", + "title | \n", + "num_words | \n", + "_relevance_score | \n", + "
|---|---|---|---|---|---|
| 0 | \n", + "[-0.029037403, -0.0019001433, 0.01014971, -0.0... | \n", + "Classifying short texts to one category or clu... | \n", + "Using deep learning for short text understanding | \n", + "187 | \n", + "0.700000 | \n", + "
| 3 | \n", + "[-0.035857946, -0.00020074612, 0.009314451, -0... | \n", + "We introduce a long short-term memory recurren... | \n", + "Speech-driven 3 D Facial Animation with Implic... | \n", + "171 | \n", + "0.298656 | \n", + "
| 4 | \n", + "[-0.027462298, -0.00018946378, 0.017612722, -0... | \n", + "We introduce a technique for augmenting neural... | \n", + "Deep Voice 2 : Multi-Speaker Neural Text-to-Sp... | \n", + "151 | \n", + "0.209681 | \n", + "
| 5 | \n", + "[0.0012136719, -0.022606706, 0.0142465895, -0.... | \n", + "Software organizations have typically de-empha... | \n", + "A survey of software testing practices in alberta | \n", + "236 | \n", + "0.198860 | \n", + "
| 7 | \n", + "[-0.027905056, -4.77228e-05, 0.0129647385, -0.... | \n", + "In this paper a novel approach is proposed to ... | \n", + "Text mining of news-headlines for FOREX market... | \n", + "621 | \n", + "0.100694 | \n", + "
+
+## Enhancing RAG with Query Expansion & Reranking Models
+
+
+
+### 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.
+
+
+
+### 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/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": {
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+ "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 (\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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+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# sample of retrieved_documents\n",
+ "print(retrieved_documents[0].page_content)"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "C3InbcrAjyWE",
+ "outputId": "0e277a08-619c-4a18-b0f3-e500eb695b41"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "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": "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",
+ " | \n", + " | vector | \n", + "text | \n", + "_relevance_score | \n", + "
|---|---|---|---|
| 0 | \n", + "[-0.0043462967, -0.022663297, -0.014687485, -0... | \n", + "35 \\nReportable Segments \\nFiscal Year 2022... | \n", + "0.580270 | \n", + "
| 1 | \n", + "[0.0073030023, -0.018824346, 0.001970756, -0.0... | \n", + "82 \\nIn addition, certain costs incurred at ... | \n", + "0.580032 | \n", + "
| 2 | \n", + "[-0.0026444048, -0.023148028, -0.008980748, -0... | \n", + "33 Dynamics products and cloud services revenu... | \n", + "0.579247 | \n", + "
| 3 | \n", + "[-0.014430126, -0.01449687, 0.0075287614, -0.0... | \n", + "34 net income tax benefit related to an India ... | \n", + "0.526167 | \n", + "
+
+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.
+
+
+
+
+
+[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 94%
rename from examples/RAG_re_ranking/lancedb_cloud/README.md
rename to examples/RAG_Reranking/lancedb_cloud/README.md
index 96a2d95b..53a36feb 100644
--- a/examples/RAG_re_ranking/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/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/SuperAgent_Autogen/README.md b/examples/SuperAgent_Autogen/README.md
new file mode 100644
index 00000000..75bf77e9
--- /dev/null
+++ b/examples/SuperAgent_Autogen/README.md
@@ -0,0 +1,52 @@
+
+
+# Super Agent: Integrating Autogen Technologies and Vector Databases
+
+## Overview
+
+
+
+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:
+
+
+
+
+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/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/).
+
diff --git a/examples/SuperAgent_Autogen/main.ipynb b/examples/SuperAgent_Autogen/main.ipynb
new file mode 100644
index 00000000..9278654b
--- /dev/null
+++ b/examples/SuperAgent_Autogen/main.ipynb
@@ -0,0 +1,693 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "lHFbPb4JU9xF"
+ },
+ "source": [
+ "This notebook illustrates how you can use the langchain with custom pdf data & chat it using autogen agent"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "3poVgyh-bZJ-",
+ "outputId": "ad799a6e-7eec-4e14-dae3-f7e86c9e67cc"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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",
+ "execution_count": null,
+ "metadata": {
+ "id": "0tLTTT9ucFEb"
+ },
+ "outputs": [],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ObNypsHRV3mz"
+ },
+ "source": [
+ "Requirements\n",
+ "AutoGen requires Python>=3.8. To run this notebook example, please install pyautogen:\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "6RuVu12whCG0"
+ },
+ "outputs": [],
+ "source": [
+ "!pip install pyautogen"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "sUFdvTyVh8xF"
+ },
+ "outputs": [],
+ "source": [
+ "import lancedb\n",
+ "\n",
+ "embeddings = OpenAIEmbeddings(openai_api_key=\"sk-yourapikey\")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "kztlyFIXU8m-"
+ },
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "ODKg12trdhX-",
+ "outputId": "a7041322-f633-496c-a8e8-126a81cbb5d9"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "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"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "1oC3NAFyd4Kb"
+ },
+ "source": [
+ "create OAI_CONFIG_LIST.json file in pwd & upload\n",
+ "in it\n",
+ "\n",
+ "\n",
+ "[\n",
+ " {\n",
+ " \"model\": \"gpt-4\",\n",
+ " \"api_key\": \"sk-yourapikey\"\n",
+ " }\n",
+ "]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "H1bRXWu-cE_C"
+ },
+ "outputs": [],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "yV0pNPiRPy8h"
+ },
+ "source": [
+ "# create file name with OAI_CONFIG_LIT.json & put below authentications code"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "yWDhjTDMcFBi"
+ },
+ "outputs": [],
+ "source": [
+ "# create file name with OAI_CONFIG_LIT.\n",
+ "[{\"model\": \"gpt-4\", \"api_key\": \"sk-yourapikey\"}]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "5gapqmsscFG-"
+ },
+ "outputs": [],
+ "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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "5dLkCqa0dLXV",
+ "outputId": "28ab5984-c875-4281-95e5-d48bfdd12e99"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "import lancedb\n",
+ "\n",
+ "embeddings = OpenAIEmbeddings(openai_api_key=\"sk-yourapikey\")\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)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "YMBoF5kucFMJ",
+ "outputId": "13d7edab-5f3d-4698-fe6f-40f33dcd865a"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "qa = ConversationalRetrievalChain.from_llm(\n",
+ " OpenAI(\n",
+ " temperature=0,\n",
+ " openai_api_key=\"sk-yourapikey\",\n",
+ " ),\n",
+ " vectorstore.as_retriever(),\n",
+ " memory=ConversationBufferMemory(memory_key=\"chat_history\", return_messages=True),\n",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "HjSVygLIcSEX"
+ },
+ "outputs": [],
+ "source": [
+ "def answer_food_question(question):\n",
+ " response = qa({\"question\": question})\n",
+ " return response[\"answer\"]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 160
+ },
+ "id": "XCqxSaQSepsW",
+ "outputId": "c1fc1bdc-9f2e-467b-cb51-fdde3fc964ae"
+ },
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "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"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.google.colaboratory.intrinsic+json": {
+ "type": "string"
+ },
+ "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.'"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "question = \"what is good food\"\n",
+ "answer_food_question(question)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "BY4Fz-l7cUCA",
+ "outputId": "3a10b926-3659-46d3-d76b-7e083daf8fca"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install pyautogen"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "anvuLAIycaqb"
+ },
+ "source": [
+ "#### 4. Set up AutoGen user agent and assistant agent with function calling enabled."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "Vca8Y_khcUID"
+ },
+ "outputs": [],
+ "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",
+ "}"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "1XHjzIYAcfE7"
+ },
+ "outputs": [],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "hOZKxakHchZ4",
+ "outputId": "7ca36fe0-a211-409a-abb0-57e3cd05e429"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "UDXo2V06fNjz",
+ "outputId": "37cf6766-9b68-4a81-e0c6-245d2af28a30"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "UrlFGYW0g0sJ",
+ "outputId": "e82396e5-3dca-402c-889c-394557aeea0d"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "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"
+ ]
+ }
+ ],
+ "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",
+ ")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "En6-kvjcjaid"
+ },
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "display_name": "Python 3",
+ "name": "python3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/examples/databricks_DBRX_website_bot/README.md b/examples/databricks_DBRX_website_bot/README.md
new file mode 100644
index 00000000..e99db29e
--- /dev/null
+++ b/examples/databricks_DBRX_website_bot/README.md
@@ -0,0 +1,27 @@
+## Hogwarts chatbot with Open source RAG using DBRX, LanceDB, and LLama-index with Huggingface Embeddings
+
+This application is a website chatbot that uses the Open source RAG model with DBRX, LanceDB, and LLama-index with Hugginface Embeddings.
+
+### Steps to Run the Application
+
+1. Install Dependencies
+```
+pip install -r requirements.txt
+```
+
+2. Setup Databricks Serving Endpoint and token as environment variables for using databricks serving endpoint. You can also use the dbrx model locally as it is open source.
+```
+export DATABRICKS_TOKEN=
+
# 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.
-
+
[Read full blog](https://blog.lancedb.com/modified-rag-parent-document-bigger-chunk-retriever-62b3d1e79bc6)
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
diff --git a/examples/reducing_hallucinations_ai_agents/README.md b/examples/reducing_hallucinations_ai_agents/README.md
index 86265eb7..a144a59b 100644
--- a/examples/reducing_hallucinations_ai_agents/README.md
+++ b/examples/reducing_hallucinations_ai_agents/README.md
@@ -4,7 +4,7 @@ AI agents can help simplify and automate tedious workflows. By going through thi
Colab walkthrough -
-
+
### Setup
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
- [](https://medium.com/etoai/search-within-an-image-331b54e4285e)
-
+ [](https://blog.lancedb.com/search-within-an-image-331b54e4285e)
+
### 🚀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**
+
+
+ Read the Full [blog post](https://blog.lancedb.com/search-within-an-image-331b54e4285e)
diff --git a/tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb b/tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb
new file mode 100644
index 00000000..badab977
--- /dev/null
+++ b/tutorials/Langchain-LlamaIndex-Chunking/Langchain_Llamaindex_chunking.ipynb
@@ -0,0 +1,1148 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# Llama Index Text Chunking Strategies\n"
+ ],
+ "metadata": {
+ "id": "1o6oVH_fNA0N"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "lCgSu4wS5L02",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "c9cef73c-2e7b-4415-f1e0-3dad04cb76c6"
+ },
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m496.7/496.7 kB\u001b[0m \u001b[31m5.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m18.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.4/15.4 MB\u001b[0m \u001b[31m33.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.0/2.0 MB\u001b[0m \u001b[31m28.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.3/268.3 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m75.6/75.6 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m136.1/136.1 kB\u001b[0m \u001b[31m15.2 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[31m58.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m290.4/290.4 kB\u001b[0m \u001b[31m21.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m77.9/77.9 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[32m58.3/58.3 kB\u001b[0m \u001b[31m6.8 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[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+ "\u001b[?25h"
+ ]
+ }
+ ],
+ "source": [
+ "!pip install llama_index tree_sitter tree_sitter_languages -q"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Download for running any text file\n",
+ "!wget https://raw.githubusercontent.com/lancedb/vectordb-recipes/main/README.md\n",
+ "!wget https://frontiernerds.com/files/state_of_the_union.txt"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "xYx0PGZTD2xs",
+ "outputId": "042bb5d1-cb90-45c9-8183-0c986193cdb1"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "--2024-04-15 10:06:43-- https://raw.githubusercontent.com/lancedb/vectordb-recipes/main/README.md\n",
+ "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
+ "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 29701 (29K) [text/plain]\n",
+ "Saving to: ‘README.md’\n",
+ "\n",
+ "\rREADME.md 0%[ ] 0 --.-KB/s \rREADME.md 100%[===================>] 29.00K --.-KB/s in 0.002s \n",
+ "\n",
+ "2024-04-15 10:06:43 (12.1 MB/s) - ‘README.md’ saved [29701/29701]\n",
+ "\n",
+ "--2024-04-15 10:06:43-- https://frontiernerds.com/files/state_of_the_union.txt\n",
+ "Resolving frontiernerds.com (frontiernerds.com)... 172.67.180.189, 104.21.31.232, 2606:4700:3036::6815:1fe8, ...\n",
+ "Connecting to frontiernerds.com (frontiernerds.com)|172.67.180.189|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: unspecified [text/plain]\n",
+ "Saving to: ‘state_of_the_union.txt’\n",
+ "\n",
+ "state_of_the_union. [ <=> ] 39.91K --.-KB/s in 0.001s \n",
+ "\n",
+ "2024-04-15 10:06:43 (64.5 MB/s) - ‘state_of_the_union.txt’ saved [40864]\n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## File based Node Parsers"
+ ],
+ "metadata": {
+ "id": "2olIZB2unXwz"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "### Node Parser - Simple File\n",
+ "Covering all the files intelligently"
+ ],
+ "metadata": {
+ "id": "HtW5uzownkVP"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Simple File\n",
+ "from llama_index.core.node_parser import SimpleFileNodeParser\n",
+ "from llama_index.readers.file import FlatReader\n",
+ "from pathlib import Path\n",
+ "\n",
+ "md_docs = FlatReader().load_data(Path(\"README.md\"))\n",
+ "\n",
+ "parser = SimpleFileNodeParser()\n",
+ "\n",
+ "# Additionally, you can augment this with a text-based parser to accurately handle text length\n",
+ "md_nodes = parser.get_nodes_from_documents(md_docs)\n",
+ "md_nodes[0].text"
+ ],
+ "metadata": {
+ "id": "GqWdmhBdWrB4",
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 105
+ },
+ "outputId": "172b7873-06cc-4a71-fd5b-acdcd24aeb0d"
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ "'VectorDB-recipes\\n