Quivr is a framework for building retrieval-augmented generation pipelines that connect large language models to custom knowledge bases. It serves as a generative AI integration layer that abstracts the process of transforming diverse document sources into searchable context for AI responses.
The project orchestrates the end-to-end flow between document ingestion, vector storage management, and model provider interfaces. It features a vector-store-agnostic retrieval system and a modular API layer that allows for flexible switching between different generative model providers.
The system covers document parsing for various file formats, embedding-based semantic search, and the integration of external internet search results to augment retrieval accuracy. It provides the infrastructure to manage embeddings and perform semantic searches across different database backends.
