diff --git a/README.md b/README.md
index 0e7594bb..acf06192 100644
--- a/README.md
+++ b/README.md
@@ -29,6 +29,10 @@ Examples are available as:
The following examples are organized into different tables to make similar types of examples easily accessible.
+### Build from Scratch
+
+Build applications/examples using LanceDB for efficient vector-based document retrieval.
+
| Build from Scratch | Interactive Notebook & Scripts |
|-------- | -------------: |
|||
@@ -36,6 +40,9 @@ The following examples are organized into different tables to make similar types
| [Local RAG from Scratch with Llama3](./tutorials/Local-RAG-from-Scratch) | [](./tutorials/Local-RAG-from-Scratch/rag.py) [](#) [](#)| |
||||
+### MultiModal
+
+Create a multimodal search application using LanceDB for efficient vector-based retrieval of text and image data. Input text or image queries to find the most relevant documents and images from your corpus.
| Multimodal | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
@@ -45,7 +52,11 @@ The following examples are organized into different tables to make similar types
| [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/)|
||||
-| RAG | Interactive Notebook & Scripts | Blog |
+### RAG
+
+Develop a Retrieval-Augmented Generation (RAG) application using LanceDB for efficient vector-based information retrieval. Input text queries to retrieve relevant documents and generate comprehensive answers by combining retrieved information.
+
+| RAG | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
||||
| [Improve RAG with Re-ranking](/examples/RAG_Reranking/) | [](#) [](#)|[](https://blog.lancedb.com/simplest-method-to-improve-rag-pipeline-re-ranking-cf6eaec6d544)|
@@ -61,6 +72,9 @@ The following examples are organized into different tables to make similar types
| [Agentic RAG ](/tutorials/Agentic_RAG/) | [](#) [](#)|
||||
+### Vector Search
+
+Build a vector search application using LanceDB for efficient vector-based document retrieval. Input text queries to find the most relevant documents from your corpus.
| Vector Search | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
@@ -78,6 +92,10 @@ The following examples are organized into different tables to make similar types
| [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/)|
||||
+### Chatbot
+
+Create a chatbot application using LanceDB for efficient vector-based response generation. Input user queries to retrieve relevant context and generate coherent, context-aware replies.
+
| Chatbot | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
||||
@@ -88,6 +106,11 @@ The following examples are organized into different tables to make similar types
| [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) |
||||
+
+### Evaluation
+
+Develop an evaluation application. Input reference and candidate texts to measure their performance on various metrics.
+
| Evaluation | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
||||
@@ -95,6 +118,10 @@ The following examples are organized into different tables to make similar types
| [Evaluating RAG with RAGAs](./examples/Evaluating_RAG_with_RAGAs/) | [](#) [](#)| |
||||
+### AI Agents
+
+Design an AI agents coordination application with LanceDB for efficient vector-based communication and collaboration. Input queries to enable AI agents to exchange information, coordinate tasks, and achieve shared goals effectively.
+
| AI Agents | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
||||
@@ -103,8 +130,11 @@ The following examples are organized into different tables to make similar types
| [SuperAgent Autogen](/examples/SuperAgent_Autogen) | [](#) [](#)||
||||
+### Recommender Systems
+
+Create a recommender system application with LanceDB for efficient vector-based item recommendation. Input user preferences or item features to generate personalized recommendations and enhance user experience.
-| Recommender Systems | Interactive Notebook & Scripts | Blog |
+| Recommender Systems | Interactive Notebook & Scripts | Blog |
| --------- | -------------------------- | ----------- |
||||
| [Movie Recommender](/examples/movie-recommender/) | [](./examples/movie-recommender/main.py) [](#)| |
@@ -113,7 +143,11 @@ The following examples are organized into different tables to make similar types
| [Arxiv paper recommender](/examples/arxiv-recommender) | [](./examples/arxiv-recommender/main.py) [](#) [](#)| |
||||
-| Concepts | Interactive Notebook | Blog Link |
+### Concepts
+
+Checkout concepts of LLM applications pipeline to ensures accurate information retrieval.
+
+| Concepts | Interactive Notebook | Blog |
| --------- | -------------------------- | ----------- |
| | | |
| [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) |
diff --git a/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb b/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb
index c206a8c6..6a36bd18 100644
--- a/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb
+++ b/examples/LlamaIndex-demo/lancedb_cloud/main.ipynb
@@ -113,6 +113,7 @@
"import openai\n",
"import logging\n",
"import sys\n",
+ "\n",
"# Uncomment to see debug logs\n",
"# logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)\n",
"# logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))\n",
@@ -145,7 +146,7 @@
"source": [
"! mkdir -p 'data/paul_graham/'\n",
"! wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'\n",
- "! ls 'data/paul_graham/'\n"
+ "! ls 'data/paul_graham/'"
]
},
{
@@ -166,7 +167,7 @@
"outputs": [],
"source": [
"documents = SimpleDirectoryReader(\"data/paul_graham/\").load_data()\n",
- "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].hash)\n"
+ "print(\"Document ID:\", documents[0].doc_id, \"Document Hash:\", documents[0].hash)"
]
},
{
@@ -218,9 +219,7 @@
"source": [
"storage_context = StorageContext.from_defaults(vector_store=vector_store)\n",
"\n",
- "index = VectorStoreIndex.from_documents(\n",
- " documents, storage_context=storage_context\n",
- ")"
+ "index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)"
]
},
{
@@ -257,15 +256,15 @@
" MetadataFilter,\n",
")\n",
"\n",
- "date = datetime.today().strftime('%Y-%m-%d')\n",
+ "date = datetime.today().strftime(\"%Y-%m-%d\")\n",
"query_filters = MetadataFilters(\n",
" filters=[\n",
" MetadataFilter(\n",
- " key=\"creation_date\", operator=FilterOperator.EQ, value=date #using current date as the latest data is scraped\n",
- " ),\n",
- " MetadataFilter(\n",
- " key=\"file_size\", value=75040, operator=FilterOperator.GT\n",
+ " key=\"creation_date\",\n",
+ " operator=FilterOperator.EQ,\n",
+ " value=date, # using current date as the latest data is scraped\n",
" ),\n",
+ " MetadataFilter(key=\"file_size\", value=75040, operator=FilterOperator.GT),\n",
" ],\n",
" condition=FilterCondition.AND,\n",
")"