Introducing VectorDB-recipes
The VectorDB-recipes project is a comprehensive hub for building Generative Artificial Intelligence (GenAI) applications. This repository is packed with examples, applications, starter code, and tutorials to help users swiftly embark on their GenAI projects.
Key Features
- LanceDB Integration: All projects and examples employ LanceDB, a free, open-source vector database. LanceDB is serverless, requiring no setup, making it a go-to choice for users looking to integrate vector search effortlessly into their applications.
- Python Ecosystem Compatibility: LanceDB is designed to seamlessly integrate into the Python data ecosystem. Users can utilize LanceDB in conjunction with popular tools such as pandas, Arrow, and Pydantic.
- Native TypeScript SDK: For those interested in implementing vector search in serverless functions, LanceDB offers a native TypeScript Software Development Kit (SDK).
Community and Support
VectorDB-recipes encourages collaboration and community engagement. Users seeking support or wishing to stay updated with the latest developments can join the project's dedicated Discord server or follow them on Twitter.
Repository Sections
VectorDB-recipes is organized into two main sections that cater to varying levels of expertise:
- Examples: This section provides users with hands-on code examples, specifically designed to take ideas from concept to proof-of-concept (PoC) in minutes.
- Applications: Aimed at those looking to dive deeper, this section includes ready-to-use Python and web applications built using applied Large Language Models (LLMs), VectorDB, and GenAI tools.
Exploring the Examples
The examples in the repository are further divided into specific categories to make navigation and learning more efficient:
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Build from Scratch: Guides users in developing applications or examples from the ground up using LanceDB, focusing on vector-based document retrieval.
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Multimodal: Teaches users to create search applications that handle both text and image queries, efficiently retrieving and displaying relevant documents and images.
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RAG (Retrieval-Augmented Generation): Demonstrates how to build RAG applications that retrieve information and generate detailed responses by overlaying retrieved content.
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Vector Search: Focuses on creating robust vector search applications using various search algorithms.
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Chatbot: Provides templates and examples for building chatbots that use user queries to retrieve and generate contextual replies.
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Evaluation: Offers tools for assessing the performance of text references against various metrics.
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AI Agents: Shows how to design applications with AI agents that exchange information, coordinate tasks, and achieve shared objectives.
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Recommender Systems: Helps users build systems that offer personalized recommendations to enhance user experience.
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Concepts: Discusses the key concepts related to LLM application pipelines, ensuring accurate information retrieval.
New Additions
- Social Media Posts Caption Generation: Now enhanced with Llama 3.2-11B Vision, allowing users to generate striking captions for social media posts.
- Advanced RAG Techniques: Innovative ways to improve context with the Context Enrichment Window feature.
Conclusion
VectorDB-recipes is an invaluable resource for anyone interested in diving into the world of GenAI. Whether you're a beginner or a seasoned developer, this repository provides the tools and knowledge required to effectively implement vector-based search and AI-driven applications. With its structured approach and extensive community support, users can confidently explore and master the intricacies of GenAI development.