# stangirard/quivr

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39,167 stars · 3,726 forks · Python · NOASSERTION

## Links

- GitHub: https://github.com/StanGirard/quivr
- Homepage: https://core.quivr.com
- awesome-repositories: https://awesome-repositories.com/repository/stangirard-quivr.md

## Description

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.

## Tags

### Artificial Intelligence & ML

- [Retrieval Augmented Generation](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation.md) — Serves as a framework for retrieval-augmented generation, grounding AI responses in custom private datasets.
- [Knowledge Base Retrieval](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-rag-development/knowledge-base-retrieval.md) — Enables the storage and retrieval of custom knowledge bases to provide context-aware AI responses. ([source](https://github.com/stangirard/quivr#readme))
- [LLM Integration Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-systems-frameworks/model-integration-serving/ai-model-abstractions/llm-integration-layers.md) — Provides a standardized integration layer for swapping and configuring different large language model providers.
- [RAG Pipelines](https://awesome-repositories.com/f/artificial-intelligence-ml/language-model-orchestration/retrieval-augmented-generation/rag-pipelines.md) — Orchestrates workflows that integrate document ingestion and vector retrieval with language model generation. ([source](https://github.com/stangirard/quivr#readme))
- [LLM Integration Layers](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-integration-layers.md) — Acts as a middleware layer abstracting the complexity of connecting document sources to LLM providers.
- [LLM Provider Adapters](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-provider-adapters.md) — Implements provider adapters to connect applications to various LLMs using retrieved context.
- [LLM Provider Integrations](https://awesome-repositories.com/f/artificial-intelligence-ml/llm-provider-integrations.md) — Ships authentication and connectivity adapters for interfacing with multiple external LLM services. ([source](https://github.com/stangirard/quivr#readme))
- [RAG Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/rag-frameworks.md) — Provides a comprehensive framework specifically designed for building retrieval-augmented generation applications.
- [Semantic Search Engines](https://awesome-repositories.com/f/artificial-intelligence-ml/semantic-search-engines.md) — Implements semantic search engines to retrieve information based on conceptual meaning rather than keywords.
- [Vector Databases](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-databases.md) — Utilizes vector databases to store and query high-dimensional embeddings for semantic knowledge retrieval. ([source](https://github.com/stangirard/quivr#readme))
- [Vector Retrieval Abstractions](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-retrieval-abstractions.md) — Offers unified abstractions for performing semantic searches across various vector database backends.
- [Generative AI Development](https://awesome-repositories.com/f/artificial-intelligence-ml/generative-ai-resources/generative-ai-development.md) — Provides the architectural components necessary to build and maintain generative AI applications.

### Data & Databases

- [Ingestion Pipelines](https://awesome-repositories.com/f/data-databases/data-quality-frameworks/ai-knowledge-bases/ingestion-pipelines.md) — Provides pipelines for parsing and converting raw files into searchable embeddings for AI knowledge bases.
- [Multimodal Document Ingestion](https://awesome-repositories.com/f/data-databases/data-engineering-infrastructure/data-extraction-ingestion/data-ingestion/multimodal-document-ingestion.md) — Provides capabilities for ingesting various document formats, including complex layouts, for AI context. ([source](https://github.com/stangirard/quivr#readme))
- [Automated Document Ingestion](https://awesome-repositories.com/f/data-databases/data-engineering-infrastructure/data-extraction-ingestion/document-processing-tools/automated-document-ingestion.md) — Automates the ingestion and transformation of diverse file formats into structured text for AI processing.
- [Retrieval Augmentation](https://awesome-repositories.com/f/data-databases/retrieval-augmentation.md) — Augments the retrieval process by combining vector store data with external search toolsets. ([source](https://github.com/stangirard/quivr#readme))

### DevOps & Infrastructure

- [Vector Database Orchestrators](https://awesome-repositories.com/f/devops-infrastructure/automation-orchestration/vector-database-orchestrators.md) — Manages the ingestion, chunking, and storage of embeddings across multiple vector database providers.

### Web Development

- [Search Result Injection](https://awesome-repositories.com/f/web-development/search-result-management/search-result-injection.md) — Integrates live internet search results directly into model prompts to augment internal knowledge retrieval.

### Part of an Awesome List

- [Conversational Chatbots](https://awesome-repositories.com/f/awesome-lists/ai/conversational-chatbots.md) — Generative AI second brain for file storage and chat.
- [RAG Frameworks](https://awesome-repositories.com/f/awesome-lists/devtools/rag-frameworks.md) — Personal AI assistant framework acting as a second brain.