ragctl and rag_blueprint

A CLI tool for managing and testing RAG pipelines would complement a modular framework for building and deploying RAG systems with built-in evaluation and monitoring.

ragctl
50
Established
rag_blueprint
45
Emerging
Maintenance 6/25
Adoption 6/25
Maturity 22/25
Community 16/25
Maintenance 6/25
Adoption 6/25
Maturity 16/25
Community 17/25
Stars: 18
Forks: 7
Downloads:
Commits (30d): 0
Language: Python
License: MIT
Stars: 19
Forks: 10
Downloads:
Commits (30d): 0
Language: Python
License: MIT
No risk flags
No Package No Dependents

About ragctl

datallmhub/ragctl

A powerful CLI tool to manage, test, and optimize RAG pipelines. Streamline your Retrieval-Augmented Generation workflows from terminal.

This tool helps AI engineers and developers prepare various documents like PDFs, Word files, and images for use in Retrieval-Augmented Generation (RAG) applications. It takes raw documents, extracts text using advanced OCR, intelligently breaks them into meaningful chunks, and exports them in formats like JSON or directly into a vector store. This streamlines the crucial data preparation step for building robust RAG systems.

AI-engineering NLP-data-prep document-processing RAG-application-development vector-database-ingestion

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.

This project helps engineering and product teams build robust AI chatbots and question-answering systems that provide accurate information from internal documents. It takes existing knowledge bases like Confluence, Notion, or PDF files, processes them, and delivers an interactive chat interface where users can ask questions and get answers. The ideal user is a developer or technical lead creating a reliable AI knowledge agent for their organization.

AI-chatbot-development knowledge-base-automation enterprise-search-AI LLM-application-monitoring internal-documentation-Q&A

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