ragatouille
Verified Safeby rykhalskyi
Overview
Locally deployable solution for Retrieval-Augmented Generation (RAG) and a Model Context Protocol (MCP) Server, enabling users to create and manage personalized knowledge bases from diverse data sources.
Installation
docker compose up -d --buildEnvironment Variables
- PYTHONUNBUFFERED
- ALLOWED_ORIGINS
- MCP_HOST
- MCP_PORT
Security Notes
Parameterized SQL queries are used to prevent SQL injection. Sensitive configurations like allowed origins and MCP server host/port are read from environment variables, which is a good practice. The simple_crawler module fetches content from external URLs; while 'requests' and 'BeautifulSoup' are generally robust, feeding untrusted external content into a processing pipeline always carries a marginal risk of unexpected behavior from malformed data, though no direct code execution vulnerabilities are apparent. No direct use of `eval` or `exec` was found.
Similar Servers
haiku.rag
An opinionated agentic RAG system that uses LanceDB for vector storage, Pydantic AI for multi-agent workflows, and Docling for document processing, exposing its capabilities as MCP tools for AI assistants.
Context-Engine
A Retrieval-Augmented Generation (RAG) stack for codebases, enabling context-aware AI agents for developers and IDEs through unified code indexing, hybrid search, and local LLM integration.
pageindex-mcp
Provides vectorless, reasoning-based RAG capabilities for LLMs to navigate and retrieve information from hierarchical document structures, primarily for long PDFs.
sparql-llm
This project provides tools for Large Language Model (LLM)-powered SPARQL query generation over specific knowledge graphs, including a chat interface for user interaction and a Model Context Protocol (MCP) server for tool exposure.