RAGLight and ragctl
RAGLight provides the modular framework for building RAG systems while ragctl offers command-line tooling to test and optimize those pipelines—making them complements that work together rather than alternatives.
About RAGLight
Bessouat40/RAGLight
RAGLight is a modular framework for Retrieval-Augmented Generation (RAG). It makes it easy to plug in different LLMs, embeddings, and vector stores, and now includes seamless MCP integration to connect external tools and data sources.
Supports hybrid retrieval combining BM25 keyword search with semantic vector similarity using Reciprocal Rank Fusion, and offers agentic RAG capabilities with query reformulation for multi-turn conversations. Built on pluggable document processors and vector store backends (Chroma, Qdrant) with optional observability via Langfuse tracing. Provides both programmatic Python APIs and CLI/REST interfaces for rapid deployment, including a Docker Compose setup for production environments.
About ragctl
datallmhub/ragctl
A powerful CLI tool to manage, test, and optimize RAG pipelines. Streamline your Retrieval-Augmented Generation workflows from terminal.
Supports advanced document ingestion with OCR cascading (EasyOCR → PaddleOCR → pytesseract fallback) and intelligent semantic chunking via LangChain, outputting metadata-rich chunks across JSON/JSONL/CSV formats. Built for production with batch processing, automatic retry with exponential backoff, error recovery modes, and optional Qdrant vector store integration for complete RAG pipeline automation.
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