# QuivrHQ/quivr

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38,938 stars · 3,714 forks · Python · other

## Links

- GitHub: https://github.com/QuivrHQ/quivr
- Homepage: https://core.quivr.com
- awesome-repositories: https://awesome-repositories.com/repository/quivrhq-quivr.md

## Topics

`ai` `api` `chatbot` `chatgpt` `database` `docker` `framework` `frontend` `groq` `html` `javascript` `llm` `openai` `postgresql` `privacy` `rag` `react` `security` `typescript` `vector`

## Description

Quivr is a retrieval-augmented generation platform designed to transform raw documents into searchable knowledge bases. It functions as a centralized environment where users can ingest files, index them into vector databases, and interact with language models to receive contextually relevant, data-backed responses.

The platform distinguishes itself through an agentic workflow orchestrator that sequences retrieval tasks, tool execution, and model interactions to resolve complex, multi-step queries. This engine is entirely configuration-driven, allowing users to define document ingestion, chunking parameters, and workflow node sequences through structured schemas. By maintaining a unified knowledge management interface, the system tracks chat history alongside file storage, ensuring that interactions remain context-aware across diverse local and remote backends.

Beyond its core orchestration, the system provides a comprehensive pipeline for document processing, including parsing for various file formats and asynchronous task execution to maintain responsiveness during data ingestion. It supports the development of specialized chatbots, including voice-enabled interfaces, by integrating speech-to-text and text-to-speech capabilities with its underlying retrieval systems.

The project utilizes strict base classes to enforce configuration integrity, ensuring consistent data processing across all application settings.

## Tags

### Artificial Intelligence & ML

- [Retrieval Augmented Generation Systems](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-augmented-generation-systems.md) — Stores document embeddings in specialized databases to enable semantic similarity searches and context-aware information retrieval for generative models.
- [Agentic Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-orchestrators.md) — A configuration-driven engine that sequences retrieval tasks, tool execution, and language model interactions to resolve complex and multi-step user queries.
- [Agentic Workflow Orchestrators](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-workflow-orchestrators.md) — Answer specific questions by combining chat history and custom file lists to produce structured and context-aware responses through retrieval-augmented generation. ([source](https://core.quivr.com/en/latest/brain/brain/))
- [Retrieval-Augmented Generation Frameworks](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-augmented-generation-frameworks.md) — Define and execute retrieval-augmented generation workflows by configuring node sequences, reranking parameters, and language model settings within a structured configuration file. ([source](https://core.quivr.com/en/latest/workflows/examples/basic_rag/))
- [Retrieval Augmented Generation Platforms](https://awesome-repositories.com/f/artificial-intelligence-ml/retrieval-augmented-generation-platforms.md) — Create chatbots that process uploaded text files to answer user questions using retrieval systems and streaming responses for real-time interaction. ([source](https://core.quivr.com/en/latest/examples/chatbot/))
- [Vector Databases](https://awesome-repositories.com/f/artificial-intelligence-ml/vector-databases.md) — Store and retrieve vector embeddings in relational databases to enable efficient similarity searches and semantic data retrieval for large knowledge bases. ([source](https://core.quivr.com/en/latest/vectorstores/faiss/))
- [Agentic Tooling](https://awesome-repositories.com/f/artificial-intelligence-ml/agentic-tooling.md) — Access specific tools associated with a workflow node to perform custom data processing or information retrieval tasks during execution. ([source](https://core.quivr.com/en/latest/config/config/))
- [Conversation Memory Managers](https://awesome-repositories.com/f/artificial-intelligence-ml/conversation-memory-managers.md) — Store and retrieve sequences of messages between users and language models to maintain context for accurate and relevant responses during ongoing interactions. ([source](https://core.quivr.com/en/latest/brain/chat/))
- [Multimodal Conversational Interfaces](https://awesome-repositories.com/f/artificial-intelligence-ml/multimodal-conversational-interfaces.md) — Develop chatbots that process text files and answer user queries through both text and audio by integrating speech-to-text and streaming response capabilities. ([source](https://core.quivr.com/en/latest/examples/chatbot_voice/))

### Data & Databases

- [Document Ingestion Pipelines](https://awesome-repositories.com/f/data-databases/document-ingestion-pipelines.md) — A structured process that handles file parsing, text chunking, and vector embedding management to transform raw documents into searchable knowledge bases. ([source](https://core.quivr.com/en/latest/workflows/examples/basic_ingestion/))
- [Knowledge Base Storage](https://awesome-repositories.com/f/data-databases/knowledge-base-storage.md) — Upload, retrieve, and delete files through a unified interface that supports both local and memory-based storage implementations for knowledge management. ([source](https://core.quivr.com/en/latest/storage/))
- [Data Ingestion Pipelines](https://awesome-repositories.com/f/data-databases/data-ingestion-pipelines.md) — Add new files to the storage system using an asynchronous method that must be implemented by all storage subclasses. ([source](https://core.quivr.com/en/latest/storage/base/))
- [Storage Abstraction Layers](https://awesome-repositories.com/f/data-databases/storage-abstraction-layers.md) — Provides a common interface for managing file ingestion and retrieval across diverse local and remote storage backends.
- [Data Retrieval Interfaces](https://awesome-repositories.com/f/data-databases/data-retrieval-interfaces.md) — Fetch lists of file objects currently held in the system using an asynchronous interface compatible with various storage backends. ([source](https://core.quivr.com/en/latest/storage/base/))
- [Document Parsers](https://awesome-repositories.com/f/data-databases/document-parsers.md) — Convert PDF files into smaller manageable text chunks using dedicated processors to facilitate efficient indexing and retrieval within the system. ([source](https://core.quivr.com/en/latest/parsers/megaparse/))

### Content Management & Publishing

- [Knowledge Bases](https://awesome-repositories.com/f/content-management-publishing/documentation-knowledge-management/knowledge-bases.md) — A unified layer for managing file storage across different backends while maintaining chat history to support ongoing and context-aware interactions.

### Software Engineering & Architecture

- [Configuration Pipelines](https://awesome-repositories.com/f/software-engineering-architecture/configuration-pipelines.md) — Uses structured schemas to define document ingestion, chunking parameters, and model settings to ensure consistent data processing across the system.