This repository provides a comprehensive collection of examples, applications, and tutorials for building Generative AI applications using LanceDB, a serverless vector database. It targets developers and researchers looking to quickly prototype and deploy solutions involving LLMs, multimodal models, and efficient vector search, offering ready-to-use code and step-by-step guides.

How It Works

The repository is structured around key GenAI application patterns, including Retrieval Augmented Generation (RAG), multimodal search, AI agents, and chatbots. It leverages LanceDB's Python and TypeScript SDKs, integrating seamlessly with data ecosystems like Pandas and Arrow, and enabling serverless deployments. The examples showcase various techniques for data retrieval, embedding generation, and LLM integration.

Quick Start & Requirements

• Installation: Primarily through Python package managers (pip) or by cloning the repository.
• Prerequisites: Python, and potentially specific libraries like LangChain, Hugging Face Transformers, or cloud provider SDKs depending on the example. GPU acceleration may be beneficial for embedding generation.
• Resources: Examples range from simple notebook executions to full-stack applications requiring backend services and potentially API keys for LLM providers.
• Links: Discord, Twitter, Udacity Course

Highlighted Details

• Extensive coverage of RAG techniques, including hybrid search, reranking, and advanced context retrieval.
• Multimodal examples demonstrating search across text, images, and audio using models like CLIP.
• AI Agent applications showcasing collaboration between multiple agents for complex tasks.
• Ready-to-use Node.js applications for web-based GenAI features.
• Concepts section provides deep dives into core AI techniques like text chunking and vector quantization.

Maintenance & Community

The project is actively maintained by the LanceDB team, with contributions encouraged via pull requests. Community support is available through Discord and Twitter.

Licensing & Compatibility

LanceDB is open-source. The repository content likely follows the LanceDB license, which is typically Apache 2.0, allowing for commercial use and integration into closed-source projects.

Limitations & Caveats

Some advanced applications may require specific cloud infrastructure, API keys, or substantial computational resources for training or inference. The breadth of examples means some might be experimental or have evolving dependencies.