QuivrHQ/quivr
Quivr
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.
Features
- Retrieval Augmented Generation Systems - Stores document embeddings in specialized databases to enable semantic similarity searches and context-aware information retrieval for generative models.
- Agentic Orchestrators - 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 - Answer specific questions by combining chat history and custom file lists to produce structured and context-aware responses through retrieval-augmented generation.
- Retrieval-Augmented Generation Frameworks - Define and execute retrieval-augmented generation workflows by configuring node sequences, reranking parameters, and language model settings within a structured configuration file.
- Retrieval Augmented Generation Platforms - Create chatbots that process uploaded text files to answer user questions using retrieval systems and streaming responses for real-time interaction.
- Vector Databases - Store and retrieve vector embeddings in relational databases to enable efficient similarity searches and semantic data retrieval for large knowledge bases.
- Document Ingestion Pipelines - A structured process that handles file parsing, text chunking, and vector embedding management to transform raw documents into searchable knowledge bases.
- Agentic Tooling - Access specific tools associated with a workflow node to perform custom data processing or information retrieval tasks during execution.
- Knowledge Bases - A unified layer for managing file storage across different backends while maintaining chat history to support ongoing and context-aware interactions.
- Knowledge Base Storage - Upload, retrieve, and delete files through a unified interface that supports both local and memory-based storage implementations for knowledge management.
- Document & Data Assistants - Private, local-first alternative to OpenAI GPTs for document retrieval.
- End-User Applications - Personal second brain and AI assistant.
- Knowledge Retrieval - Personal productivity assistant for chatting with local documents.
- Local AI Search - Second brain interface for chatting with local files and embeddings.
- RAG Applications - GenAI second brain for document interaction.
- Retrieval Augmented Generation - Personal productivity assistant for chatting with local documents.
- Monitoring and Observability - Framework for deploying and using RAG-based AI agents.
- Data Ingestion Pipelines - Add new files to the storage system using an asynchronous method that must be implemented by all storage subclasses.
- Storage Abstraction Layers - Provides a common interface for managing file ingestion and retrieval across diverse local and remote storage backends.
- Conversation Memory Managers - Store and retrieve sequences of messages between users and language models to maintain context for accurate and relevant responses during ongoing interactions.
- Multimodal Conversational Interfaces - Develop chatbots that process text files and answer user queries through both text and audio by integrating speech-to-text and streaming response capabilities.
- Data Retrieval Interfaces - Fetch lists of file objects currently held in the system using an asynchronous interface compatible with various storage backends.
- Document Parsers - Convert PDF files into smaller manageable text chunks using dedicated processors to facilitate efficient indexing and retrieval within the system.
- Configuration Pipelines - Uses structured schemas to define document ingestion, chunking parameters, and model settings to ensure consistent data processing across the system.
