GitHub-Documentation-Rag-Agent
Verified Safeby MandalAutomations
Overview
A Retrieval-Augmented Generation (RAG) system that provides intelligent answers to questions about GitHub documentation using local LLMs and vector embeddings.
Installation
No command providedEnvironment Variables
- OLLAMA_HOST
- PGDATABASE
- PGUSER
- PGPASSWORD
- PGHOST
- PGPORT
Security Notes
The code generally uses parameterized queries for database interactions and structured JSON for API calls, mitigating common injection risks. Configuration relies on environment variables, though default database credentials in `VectorDB` are weak for production but acceptable for a local dev setup. Network interactions are primarily with a local Ollama server, assumed to be trusted. No 'eval' or obfuscation observed.
Similar Servers
haiku.rag
Opinionated agentic RAG powered by LanceDB, Pydantic AI, and Docling to provide hybrid search, intelligent QA, and multi-agent research over user-provided documents, accessible via CLI, Python API, Web App, TUI, or as an MCP server for AI assistants.
Context-Engine
Self-improving code search and context engine for IDEs and AI agents, providing hybrid semantic/lexical search, symbol graph navigation, and persistent memory.
mcp-local-rag
Local RAG server for developers enabling private, offline semantic search with keyword boosting on personal or project documents (PDF, DOCX, TXT, MD, HTML).
vector-mcp
Provides a standardized API for AI agents to manage and interact with various vector database technologies for Retrieval Augmented Generation (RAG).