StanGirard/quiver
Quiver
Quiver is a framework for integrating retrieval augmented generation into applications. It provides a generative AI integration layer that connects large language models with vector stores to produce context-aware responses based on custom data.
The project features a knowledge base pipeline that parses diverse file types into searchable embeddings and a vector database orchestrator to manage data across different storage implementations. It utilizes a provider-agnostic model interface, allowing users to switch between various external AI providers or local models through a single unified system.
The system covers the orchestration of retrieval pipelines, including multi-format document parsing and the integration of custom tools and internet search functionality to enhance response accuracy.
Features
- RAG Frameworks - Provides a comprehensive development framework for integrating retrieval augmented generation by connecting LLMs with vector stores.
- Generative AI Integration Layers - Provides a backend integration layer that connects software products to language models via unified APIs.
- RAG Pipelines - Implements workflows that augment language model outputs by retrieving relevant external data from custom document sources.
- LLM Provider Integrations - Provides a unified connectivity system for interfacing with various external AI providers and local models.
- Provider Abstraction Layers - Implements an internal software architecture that standardizes requests and responses across different AI model providers.
- Model Provider Interfaces - Wraps various LLM APIs into a single unified interface to allow swapping providers without changing application logic.
- RAG Data Pipelines - Orchestrates the sequential data flow from document ingestion and embedding through to final response generation.
- Vector Database Abstractions - Decouples retrieval logic from specific database implementations like PGVector or Faiss through a common interface.
- Vector Database Orchestrators - Manages data ingestion, chunking, and retrieval across multiple vector store providers like PGVector and Faiss.
- Custom Retrieval Extensions - Provides the ability to integrate custom tools and real-time internet search to improve generative response accuracy.
- Search-Enhanced Generation - Enhances response accuracy by combining semantic vector search with real-time internet search functionality.
- Multi-Model AI Orchestrators - Integrates multiple AI providers and local models through a single interface to power application features.
- Retrieval Pipelines - Allows for the chaining of data fetching and filtering steps, including the integration of custom search modules.
- Document Parsing Pipelines - Processes diverse file types into standardized text chunks optimized for embedding and vector storage.
- File Ingestion Services - Facilitates the ingestion of various file formats to extract text and metadata for a searchable knowledge base.
- AI Knowledge Bases - Prepares and imports custom data from various file types to create high-quality knowledge bases for AI consumption.
- Ingestion Pipelines - Ships a pipeline for parsing diverse file types and converting them into searchable embeddings.
- Knowledge Management - Generative AI vault for personal knowledge storage.
Star history
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