ragapp/ragapp
Ragapp
Ce projet est une plateforme de génération augmentée par récupération (RAG) agentique et un framework d'orchestration conçu pour connecter les grands modèles de langage aux données privées d'entreprise. Il sert de passerelle IA auto-hébergée qui intègre des bases de données vectorielles et des outils externes pour automatiser des tâches complexes de récupération et de génération d'informations.
Le système se différencie par un constructeur de flux de travail d'agents IA qui orchestre plusieurs agents spécialisés avec des rôles distincts pour résoudre des problèmes en plusieurs étapes. Il inclut une interface d'intégration de base de données vectorielle dédiée pour l'indexation de documents privés et un bac à sable sécurisé pour exécuter du code dynamique et de l'analyse de données pendant les conversations.
La plateforme couvre un large éventail de capacités, y compris l'indexation de données d'entreprise, le reranking des résultats pour la précision, et la fourniture d'interfaces de chat interactives et d'API programmatiques. Elle fournit également des contrôles administratifs pour la gestion du comportement de l'IA, l'authentification des utilisateurs et la limitation de l'utilisation de l'API pour contrôler la consommation des ressources.
Le déploiement est géré via des outils d'orchestration de conteneurs tels que Docker Compose, avec une prise en charge du routage de trafic basé sur un reverse-proxy et du chiffrement TLS pour des environnements auto-hébergés sécurisés.
Features
- Agentic RAG Platforms - Serves as an end-to-end system integrating reasoning agents and retrieval-augmented generation for complex enterprise data fetching.
- Agentic Workflow Orchestration - Executes complex retrieval processes by delegating tasks to autonomous agents collaborating on defined goals.
- Role-Based Agent Orchestration - Coordinates multiple agents by assigning specific roles and operating procedures to complete complex retrieval tasks.
- Model Provider Integrations - Provides unified interfaces for connecting and configuring multiple remote or local language model providers.
- AI Provider Integrations - Includes configuration interfaces for connecting to various external and local large language model providers.
- AI Agent Orchestration - Implements a framework for coordinating specialized agents that combine custom instructions and document retrieval.
- AI Chat Interfaces - Provides conversational interfaces that allow direct interaction with application components and private data.
- AI Workflow Builders - Includes an interface for designing and executing agentic workflows with specialized roles for data analysis.
- LLM Orchestrators - Implements a framework that manages the connection and workflow between LLM deployments and external tools.
- AI Model Integrations - Provides adapters and interfaces for connecting both local and cloud-based AI models to the platform.
- Private Context Integration - Integrates proprietary documents and knowledge bases to ground language models in private enterprise data.
- Vector Store Retrievals - Queries vector databases to retrieve semantically relevant document segments to ground language model responses.
- Enterprise Source Indexing - Indexes enterprise documents and files to provide live context for AI agents.
- Code Execution Sandboxes - Provides secure, isolated environments to execute dynamic code and data analysis without compromising the host system.
- RAG Production Deployments - Deploys optimized RAG pipeline configurations into production-ready API servers.
- Interactive Chat Interfaces - Ships an interactive chat window allowing users to query private data models and receive generated responses.
- Web Chat Interfaces - Provides a web-based chat interface for users to interact directly with the retrieval-augmented generation system.
- Generative API Exposures - Exposes retrieval and generation capabilities via programmable endpoints for integration into external software applications.
- AI Model Configurations - Allows specifying the preferred language model and managing parameters like temperature and context limits.
- Local AI Endpoint Connections - Supports connections to OpenAI-compatible local servers for private AI deployments.
- LLM Provider Integrations - Manages connectivity and credential adapters for connecting to external and local large language model providers.
- Result Reranking - Applies reranking algorithms to improve the precision and relevance of retrieved documents passed to the model.
- AI Gateways - Acts as a containerized gateway serving interactive chat and programmatic APIs powered by LLMs.
- Reranking Retrieval Logics - Applies secondary scoring passes to rerank retrieved document chunks for improved accuracy and relevance.
- Integration User Interfaces - Provides a web interface for indexing private documents into vector stores and managing retrieval behavior.
- Container Deployment - Utilizes container-based deployment workflows to ensure consistent orchestration and lifecycle management.
- Docker Compose Deployments - Bootstraps the application and its dependencies using multi-service Docker Compose templates.
- Self-Hosted AI Platforms - Provides a deployable server environment for managing private data indexing and model orchestration on-premises.
- Reverse Proxy Authentication - Implements gateway-level authentication and TLS encryption by intercepting requests via a reverse proxy.
- Retrieval Configuration Interfaces - Offers user-facing tools to adjust indexing and retrieval strategies for the RAG pipeline.
- RAG Behavior Controls - Provides a dedicated administrative interface to oversee and configure the behavior of retrieval-augmented generation systems.
- Knowledge Retrieval - Enterprise-ready framework for agentic RAG workflows.
- Retrieval Augmented Generation - Enterprise-ready platform for agentic retrieval workflows.
