Awesome GitHub Repositories matching self-hosted chat with your own documents

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.

Open WebUI is a self-hosted, web-based platform designed for interacting with local and remote artificial intelligence models. It functions as a unified interface and orchestration suite, enabling users to build, deploy, and manage specialized AI agents equipped with custom instructions, external tool access, and private knowledge bases. The platform distinguishes itself through a modular architecture that supports complex AI workflows. It features a plugin-based framework for custom logic and pipeline-based request processing, allowing developers to filter or transform data streams before they reach a model. For enterprise environments, it provides centralized model management, role-based access control, and integration with standard identity providers like LDAP and SSO. It also includes sandboxed code execution and vector-database-based retrieval, enabling models to perform secure computations and semantic searches across private document collections. Beyond its core chat capabilities, the platform offers extensive administrative and operational tools. It supports multi-node deployments, horizontal scaling, and comprehensive system observability to ensure reliability in production settings. Users can further customize the interface, manage API access via personal tokens, and utilize persistent workspaces for collaborative knowledge management. The software is packaged for container-orchestrated deployment, allowing for consistent execution across diverse cloud and local infrastructure.

RAG-Anything is a retrieval-augmented generation framework designed to index diverse document formats and perform semantic search using local machine learning models. It functions as a local multimodal data processor, extracting and organizing information from various file types into a unified knowledge base to facilitate private document analysis. The system distinguishes itself through its high-throughput ingestion engine, which processes large batches of documents into searchable vector embeddings. By executing machine learning models directly on local hardware, the framework ensures that sensitive data remains private and independent of external cloud services. The platform supports comprehensive data management, including the ability to parse multimodal information and assemble context-aware windows for precise retrieval. It provides a structured pipeline for indexing high volumes of data and performing semantic similarity searches to generate accurate, context-specific responses.

SurfSense is a self-hosted platform designed for building retrieval-augmented generation pipelines and managing private knowledge bases. It functions as a containerized research stack that allows users to index diverse data sources and query them using language models, ensuring that all information retrieval is grounded in specific source citations. The platform distinguishes itself through its modular architecture, which supports the integration of custom tools and diverse language models via a unified abstraction layer. It facilitates secure, collaborative research environments by implementing role-based access control for shared knowledge bases, while also providing built-in text-to-speech capabilities to convert chat logs and documents into audio content. Beyond its core retrieval functions, the system includes comprehensive support for data ingestion from various file formats and web sources. It utilizes vector-database-backed indexing to maintain high-dimensional search capabilities and employs asynchronous background processing to handle resource-intensive tasks like media transcoding and document indexing without interrupting system responsiveness.

This platform serves as a comprehensive environment for managing private language models, document knowledge bases, and automated agent workflows within secure local infrastructure. It functions as a document-aware workspace that enables users to ingest diverse file formats into searchable repositories, ensuring that all data processing and model inference remain within private, local environments to maintain data sovereignty. The system distinguishes itself through a modular agentic engine that allows for the definition of custom skills and external tool execution. By utilizing a multi-model abstraction layer, it normalizes interactions across various local and cloud-based providers, while workspace-scoped management ensures that system prompts and knowledge bases remain isolated to meet specific operational requirements. Beyond core orchestration, the platform includes a document-parsing pipeline that converts files into structured text for semantic retrieval via local vector indexing. Users can further extend functionality through command-line triggers and persistent system instructions, standardizing how artificial intelligence behaves across different business contexts.

This project is a privacy-first backend service designed to facilitate retrieval-augmented generation by processing local documents into searchable vector representations. It provides a modular architecture that allows users to ingest diverse file formats, manage document metadata, and perform semantic searches to provide context-aware responses for chat and completion requests. The system distinguishes itself through a database-agnostic abstraction layer that supports various storage backends, ranging from local disk storage to enterprise-grade vector databases. It offers flexible deployment options, enabling users to run language models entirely on private hardware or connect to external cloud-based providers through a unified interface. To improve the quality of generated output, the engine incorporates reranking logic that refines retrieved document chunks before they are processed by the language model. The platform includes a comprehensive suite of tools for managing document intelligence pipelines, including automated parsing, text chunking, and embedding generation. Users can configure the system through environment-based profiles to match specific hardware capabilities, such as CPU or GPU-accelerated setups, and stream responses in real time to reduce latency. The application is configured via runtime settings files and environment variables, with support for building custom container images to suit specific deployment requirements.

Khoj is a self-hosted artificial intelligence platform designed for personal knowledge management and semantic information retrieval. It functions as a private assistant that indexes your local documents, notes, and external workspaces, allowing you to interact with your data through natural language queries and conversational chat. By maintaining a local-first architecture, the system ensures that your information remains under your control while providing context-aware responses grounded in your personal knowledge base. The platform distinguishes itself through a modular, cross-platform integration layer that embeds intelligent search and chat capabilities directly into your existing workflows. Whether you are working within text editors, web browsers, or mobile messaging applications, Khoj provides a unified interface to your data. It supports advanced retrieval strategies, such as dual-model architectures for semantic mapping and real-time internet grounding, which allow the assistant to synthesize private notes with external information while providing clear source citations. Beyond its core retrieval capabilities, the system offers a comprehensive suite of tools for data orchestration and research automation. It includes a pluggable ingestion pipeline for diverse file formats, automated query scheduling, and the ability to execute code or generate visual content directly within the chat interface. Users can configure custom agents, manage model routing, and secure their deployments with multi-user authentication, making it suitable for both individual use and enterprise-grade environments.

LibreChat is an artificial intelligence orchestration platform that provides a unified interface for interacting with multiple language models. It functions as a centralized workspace where users can switch between different intelligence engines, manage complex conversational workflows, and maintain persistent memory across sessions through a vector-database-backed storage system. The platform distinguishes itself through an extensible agent framework that supports autonomous task execution and the integration of external tools. It features a secure, containerized environment for executing code snippets and dynamically renders interactive artifacts, such as visual diagrams and functional user interface components, directly within the chat window. These capabilities allow for hands-on manipulation of generated content and the processing of multi-step tasks. Beyond core conversational features, the platform includes tools for dynamic knowledge retrieval, enabling the assistant to fetch and rerank live web data to provide up-to-date information. It also incorporates enterprise-grade security measures, including server-side session management and support for standard authentication protocols like OAuth and SAML, to ensure controlled access in multi-user environments.

NextChat is a self-hosted web application that provides a unified interface for interacting with multiple large language models. It functions as a conversational platform where users can manage and switch between diverse AI providers through configurable API backends, maintaining full control over their data and infrastructure. The platform features a persistent session layer designed to handle long-running dialogues by managing message history and context. It distinguishes itself through a structured prompt engineering environment that allows for the development and application of templates to refine model inputs. To ensure consistent performance during extended interactions, the application includes automated context window compression and dynamic prompt injection, which adjust historical message arrays to fit within model token limits. The software supports secure deployment via containerization, utilizing server-side proxying to manage sensitive API keys and authentication headers. It also incorporates local browser storage for low-latency access and offers options for synchronizing chat records across multiple sessions and devices. The application is configured through environment variables, allowing for flexible integration into private hosting environments.

GPT4All is a cross-platform runtime environment designed to execute large language models directly on local consumer hardware. By leveraging an optimized C++ inference backend, it enables private, offline AI interactions without requiring an internet connection or external cloud services. The project provides a comprehensive ecosystem for managing the entire model lifecycle, including discovery, downloading, and configuration of local weights. What distinguishes the platform is its integrated retrieval-augmented generation engine, which allows users to index local documents into semantic vector spaces. This capability enables context-aware chat sessions where the model can reference private files, notes, and spreadsheets to provide grounded, relevant responses. The system also features a local HTTP server that exposes an OpenAI-compatible API, allowing developers to integrate these private, self-hosted models into existing applications and workflows. Beyond its core inference and retrieval capabilities, the project includes a graphical desktop interface for end-user interaction and a Python software development kit for programmatic access. These tools support advanced configuration of model parameters, performance monitoring, and the management of local embedding pipelines for custom semantic search tasks. The software is distributed as a unified application package, with documentation available to guide users through installation and local environment setup.