https://github.com/lancedb/vectordb-recipes
High quality resources & applications for LLMs, multi-modal models and VectorDBs
https://github.com/lancedb/vectordb-recipes
agents ai deep-learning embeddings fine-tuning gpt gpt-4-vision langchain llama-index llms machine-learning multimodal openai rag vector-database
Last synced: 8 days ago
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High quality resources & applications for LLMs, multi-modal models and VectorDBs
- Host: GitHub
- URL: https://github.com/lancedb/vectordb-recipes
- Owner: lancedb
- License: apache-2.0
- Created: 2023-06-25T06:10:35.000Z (almost 2 years ago)
- Default Branch: main
- Last Pushed: 2025-03-30T06:21:58.000Z (23 days ago)
- Last Synced: 2025-04-06T21:05:59.722Z (15 days ago)
- Topics: agents, ai, deep-learning, embeddings, fine-tuning, gpt, gpt-4-vision, langchain, llama-index, llms, machine-learning, multimodal, openai, rag, vector-database
- Language: Jupyter Notebook
- Homepage: https://vectordb-recipes.vercel.app
- Size: 221 MB
- Stars: 744
- Watchers: 10
- Forks: 133
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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- awesome-ChatGPT-repositories - vectordb-recipes - High quality resources & applications for LLMs, multi-modal models and VectorDBs (Langchain)
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README
# VectorDB-recipes
Dive into building GenAI applications!
This repository contains examples, applications, starter code, & tutorials to help you kickstart your GenAI projects.- These are built using LanceDB, a free, open-source, serverless vectorDB that **requires no setup**.
- It **integrates into Python data ecosystem** so you can simply start using these in your existing data pipelines in pandas, arrow, pydantic etc.
- LanceDB has **native Typescript SDK** using which you can **run vector search** in serverless functions!
Join our community for support - Discord •---
This repository is divided into 2 sections:
- [Examples](#examples) - Get right into the code with minimal introduction, aimed at getting you from an idea to PoC within minutes!
- [Applications](#projects--applications) - Ready to use Python and web apps using applied LLMs, VectorDB and GenAI toolsThe following examples are organized into different tables to make similar types of examples easily accessible.
### Sections
- [Build from Scratch](#build-from-scratch) - Build applications/examples from scratch using LanceDB for efficient vector-based document retrieval.
- [Multimodal](#multimodal) - Build a multimodal search application with input text or image as queries.
- [RAG](#rag) - Build a variety of RAG by loading data from different formats and query with text.
- [Vector Search](#vector-search) - Build vector search application using different search algorithms.
- [Chatbot](#chatbot) - Build chatbot application where user input queries to retrieve relevant context and generate coherent, context-aware replies.
- [Evalution](#evaluation) - Evaluate reference and candidate texts to measure their performance on various metrics.
- [AI Agents](#ai-agents) - Design an application powered with AI agents to exchange information, coordinate tasks, and achieve shared goals effectively.
- [Recommender Systems](#recommender-systems) - Build Recommendation systems which generate personalized recommendations and enhance user experience.
- [Concepts](#concepts) - Concepts related to LLM applications pipeline to ensures accurate information retrieval.### 🌟 New 🌟
- Multimodal Vector Search: **Voyage AI x LanceDB** -
- Build autonomous Customer support agent using Langgraph -
- Comparing **ModernBERT** with series of Bert Models -
- Comparing **Deepseek's r1 VS OpenAI's o1** for RAG - [](./examples/Deepseek_R1_VS_GPT_4o/README.md)
- Testing **Deepseek Janus-Pro's** multimodality over Flickr 30K -### Build from Scratch
Build applications/examples using LanceDB for efficient vector-based document retrieval.
| Build from Scratch | Interactive Notebook & Scripts |
|-------- | -------------: |
|||
| [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) [](#) [](#)| |
| [Multi-Head RAG from Scratch](./tutorials/Multi-Head-RAG-from-Scratch/) | [](./tutorials/Multi-Head-RAG-from-Scratch/main.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 |
| --------- | -------------------------- | ----------- |
||||
| [Multimodal CLIP: DiffusionDB](/examples/multimodal_clip_diffusiondb/) |
| [Multimodal CLIP: Youtube videos](/examples/multimodal_video_search/) |
| [Cambrian-1: Vision centric exploration of images](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/) | [](https://www.kaggle.com/code/prasantdixit/cambrian-1-vision-centric-exploration-of-images/) [](#) [](#)| [](https://blog.lancedb.com/cambrian-1-vision-centric-exploration/)|
| [Multimodal Jina CLIP-V2 : Food Search ](/examples/multimodal_jina_clipv2/) |
| [Multimodal vector search: Voyage AI X LanceDB](./examples/voyagexlancedb/) |
||||### 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 |
| --------- | -------------------------- | ----------- |
||||
| [RAG using Deepseek R1 vs OpenAI o1](./examples/Deepseek_R1_VS_GPT_4o) | [](./examples/Deepseek_R1_VS_GPT_4o/README.md) [](#) |
| [RAG On PDF](/examples/RAG-On-PDF/) |
| [RAG with Contextual Retrieval and Hybrid search](./examples/Contextual-RAG/) |
| [RAG with Matryoshka Embeddings and LlamaIndex](./tutorials/RAG-with_MatryoshkaEmbed-Llamaindex/) |
| [RAG with IBM Watsonx](./examples/RAG-with-watsonx/) |
| [Cognee RAG](/examples/cognee-RAG/) |
| [Improve RAG with Re-ranking](/examples/RAG_Reranking/) |
| [Instruct-Multitask](./examples/instruct-multitask) |
| [Improve RAG with HyDE](/examples/Advance-RAG-with-HyDE/) |
| [Improve RAG with LOTR ](/examples/Advance_RAG_LOTR/) |
| [Advanced RAG: Context Enrichment Window](./examples/Advanced_RAG_Context_Enrichment_Window/) |
| [Advanced RAG: Late Chunking](./examples/Advanced_RAG_Late_Chunking/) |
| [Advanced RAG: Parent Document Retriever](/examples/parent_document_retriever/) |
| [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/)|
| [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/) |
| [Improve RAG with FLARE](./examples/better-rag-FLAIR) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/better-rag-FLAIR/main.ipynb) [](#) [](#) [](#)|[](https://blog.lancedb.com/better-rag-with-active-retrieval-augmented-generation-flare-3b66646e2a9f/) |
| [Agentic RAG ](/tutorials/Agentic_RAG/) |
| [GraphRAG ](/examples/Graphrag/) |
| [GraphRAG with CSV File ](/tutorials/GraphRAG_CSV/) |
| [GraphRAG with cognee - Multimedia ](/tutorials/GraphRAG_with_cognee/) |
||||### 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 |
| --------- | -------------------------- | ----------- |
||||
| [Inbuilt Hybrid Search](/examples/Inbuilt-Hybrid-Search) |
| [Hybrid search BM25 & lancedb ](./examples/Hybrid_search_bm25_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) |
| [Vector Arithmetic with LanceDB](./examples/Vector-Arithmetic-with-LanceDB/) |
| [Summarize and Search Reddit Posts](./examples/Reddit-summarization-and-search/) |
| [Imagebind demo app](./examples/imagebind_demo/) |[](#)|
| [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)|
| [Zero Shot Object Detection with CLIP](./examples/zero-shot-object-detection-CLIP/) |
| [Vector Search with TransformersJS](./examples/js-transformers/) |[](./examples/js-transformers/index.js) [](#) [](#)| |
| [Geospatial Recommendation System](./examples/Geospatial-Recommendation-System/) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/examples/Geospatial-Recommendation-System/geospatial-recommendation.ipynb) [](#) [](#)|
| [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 |
| --------- | -------------------------- | ----------- |
||||
| [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) [](#) [](#)|
| [Youtube transcript search bot](/examples/Youtube-Search-QA-Bot/) |
| [Langchain: Code Docs QA bot](/examples/Code-Documentation-QA-Bot/) |
| [Chatbot with any website using Crawl4AI ](/examples/CrawlerQ&A_website/) |
| [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 |
| --------- | -------------------------- | ----------- |
||||
| [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 |
| --------- | -------------------------- | ----------- |
||||
| [AI email assistant with Composio](/examples/AI-Email-Assistant-with-Composio/) |
| [Assitant Bot with OpenAI Swarm](./examples/assistance-bot-with-swarm/) |
| [AI Trends Searcher with CrewAI](./examples/AI-Trends-with-CrewAI/) |
| [SuperAgent Autogen](/examples/SuperAgent_Autogen) |
| [Build autonomous Customer support agent using Langgraph](./examples/customer_support_agent_langgraph/LangGraph_LanceDB.ipynb) |
| [AI Agents: Reducing Hallucination](/examples/reducing_hallucinations_ai_agents/) |
| [Multi Document Agentic RAG](./examples/multi-document-agentic-rag/) |
| [RASA: Customer Support Bot](./examples/RASA_Customer-support-bot) |
||||### 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 |
| --------- | -------------------------- | ----------- |
||||
| [Movie Recommender](/examples/movie-recommender/) |
| [Product Recommender](./examples/product-recommender/) |
| [Arxiv paper recommender](/examples/arxiv-recommender) |
| [Music Recommender](/applications/Music_Recommendation/) | [](./applications/Music_Recommendation/app_music.py) [](#)| |||||
||||### 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) |
| [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/) |
| [Create structured dataset using Instructor](./tutorials/NER-dataset-with-Instructor/) | [](./tutorials/NER-dataset-with-Instructor/main.py) [](#)| |
| [Comparing Cohere Rerankers with LanceDB](./tutorials/cohere-reranker) | [](#)| [](https://blog.lancedb.com/benchmarking-cohere-reranker-with-lancedb/) |
| [Product Quantization: Compress High Dimensional Vectors](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-2/) |[](#) | [](https://blog.lancedb.com/benchmarking-lancedb-92b01032874a-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) |
| [Extracting Complex tables-text from PDFs using LlamaParse ](./tutorials/Advace_RAG_LlamaParser) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/Advace_RAG_LlamaParser/main.ipynb) [](#) [](#) [](#)| |
| [Convert any Image dataset to lance Format](./tutorials/cli-sdk-to-convert-image-datasets-to-lance) | [](https://colab.research.google.com/github/lancedb/vectordb-recipes/blob/main/tutorials/cli-sdk-to-convert-image-datasets-to-lance/main.ipynb) [](#)| [](https://blog.lancedb.com/python-package-to-convert-image-datasets-to-lance-type/) |
||||## Projects & Applications
These are ready to use applications built using LanceDB serverless vector database. You can explore these open source projects, use parts of them in your projects or build your applications on top of these.### Node applications powered by LanceDB
| Project Name | Description | Screenshot |
|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
| [Writing assistant](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/lanchain_writing_assistant) | Writing assistant app using lanchain.js with LanceDB, allows you to get real time relevant suggestions and facts based on you written text to help you with your writing. |  |
| [Sentence Auto-Complete](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/sentance_auto_complete) | Sentance auto complete app using lanchain.js with LanceDB, allows you to get real time relevant auto complete suggestions and facts based on you written text to help you with your writing.You can also upload your data source in the form of a pdf file.You can switch between gpt models to get faster results. |  |
| [Article Recommendation](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/article_recommender) | Article Recommender: Explore vast data set of articles with Instant, Context-Aware Suggestions. Leveraging Advanced NLP, Vector Search, and Customizable Datasets, Our App Delivers Real-Time, Precise Article Recommendations. Perfect for Research, Content Curation, and Staying Informed. Unlock Smarter Insights with State-of-the-Art Technology in Content Retrieval and Discovery!". |  |
| [AI Powered Job Search](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/AI_powered_job_search) | Transform your job search experience with this AI-driven application. Powered by LangChain.js, LanceDB, and advanced semantic search, it provides real-time, highly accurate job listings tailored to your preferences. Featuring customizable datasets and advanced filtering options (e.g., skills, location, job type, and salary range), this app ensures you find the right opportunities quickly and effortlessly. Best suited for job seekers, recruiters, career platforms, custom job boards. |  |
| [AI Powered Multimodal meme search](https://github.com/lancedb/vectordb-recipes/tree/main/applications/node/multimodal_meme_finder) | An advanced AI-powered meme search engine that allows users to find memes using both text and image queries. By leveraging LanceDB as a high-performance vector database and Roboflow's CLIP model for embedding generation, the platform delivers fast and accurate meme retrieval. |  |
||||| Project Name | Description | Screenshot |
|-----------------------------------------------------|----------------------------------------------------------------------------------------------------------------------|-------------------------------------------|
| [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. |  |
| [Advanced Chatbot with Parler TTS ](./applications/Chatbot_with_Parler_TTS) | This Chatbot app uses Lancedb Hybrid search, FTS & reranker method with Parlers TTS library.||
| [Multi-Modal Search Engine](./applications/multimodal-search/) | Create a Multi-modal search engine app, to search images using both images or text | |
| [Evaluate RAG](./applications/evaluate_RAG/) | A working Streamlit RAG App designed to demonstrate end to to end production grade evaluation using 50+ scores and metrics which include guards, software metrics, traditional metrics and LLM as judge metrics. It uses mixture of specialised deep learning models and LLM as Judge models to do the evaluations ||
| [Multi-Agent Collaboration Chatbot](./applications/Multi_collabration_chatbot/) | Multi-Agent collabration chatbot using langgraph for share-market use case using Lancedb & tools such as Polygon ,Tavily ||
| [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](./applications/Multilingual_RAG/) | Multilingual RAG with cohere embedding & support 100+ languages||
| [Music Recommender](./applications/Music_Recommendation/) | Music Recommendation system using audio feature extraction and vector similarity search. By utilizing **LanceDB**, **PANNs** for audio tagging, and **Librosa** for audio feature extraction, the system finds and recommends tracks with similar audio characteristics based on a query song.||**🌟 New! 🌟 Applied GenAI and VectorDB course on Udacity**
Learn about GenAI and vectorDBs using LanceDB in the recently launched [Udacity Course](https://www.udacity.com/course/building-generative-ai-solutions-with-vector-databases--cd12952)
## Contributing Examples
If you're working on some cool applications that you'd like to add to this repo, please open a PR!
