rag_api and rag-forge
These are complements: rag_api provides a production-ready FastAPI server with vector database integration, while rag-forge supplies the modular retrieval and chunking components that could power such a service.
About rag_api
danny-avila/rag_api
ID-based RAG FastAPI: Integration with Langchain and PostgreSQL/pgvector
Organizes embeddings by `file_id` to enable targeted, file-level vector retrieval with metadata filtering—particularly useful for multi-document RAG scenarios. Supports multiple embedding providers (OpenAI, Azure, Hugging Face, Bedrock, Ollama, Google) and vector backends beyond pgvector, with configurable chunking, batching, and async processing for scalability. Designed as a pluggable service for LibreChat but works as a standalone ID-based document indexing API with optional JWT authentication.
About rag-forge
kxgst228/rag-forge
Modular RAG framework with hybrid retrieval, intelligent chunking, and multi-provider LLM support
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