super-rag and rag_blueprint
These are complementary tools: Super-RAG provides the core pipeline infrastructure for retrieval, reranking, and summarization, while RAG Blueprint offers the evaluation and monitoring framework needed to assess and optimize those pipelines in production.
About super-rag
superagent-ai/super-rag
Super performant RAG pipelines for AI apps. Summarization, Retrieve/Rerank and Code Interpreters in one simple API.
Supports pluggable vector databases (Pinecone, Qdrant, Weaviate, PGVector) and multiple embedding providers (OpenAI, Cohere, HuggingFace, FastEmbed), with customizable semantic chunking and metadata filtering via REST API. Built on FastAPI with session-based caching and optional E2B.dev sandbox integration for executing computational queries safely. Handles diverse document formats through the Unstructured library with configurable parsing strategies and table processing.
About rag_blueprint
feld-m/rag_blueprint
A modular framework for building and deploying Retrieval-Augmented Generation (RAG) systems with built-in evaluation and monitoring.
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