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.
772 stars. Actively maintained with 4 commits in the last 30 days.
Stars
772
Forks
344
Language
Python
License
MIT
Category
Last pushed
Mar 09, 2026
Commits (30d)
4
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/vector-db/danny-avila/rag_api"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Related tools
mburaksayici/smallevals
smallevals — CPU-fast, GPU-blazing fast offline retrieval evaluation for RAG systems with tiny QA models.
naxoc/riffrag
A local RAG builder for code with a Claude Code skills creator
GoparapukethaN/rag-forge
Modular RAG framework with hybrid retrieval, intelligent chunking, and multi-provider LLM support
kxgst228/rag-forge
Modular RAG framework with hybrid retrieval, intelligent chunking, and multi-provider LLM support
Dyinu/rag-forge
Benchmark multiple chunking, embedding, and retrieval combinations for RAG pipelines to find the...