OneRAG and DocQuery

OneRAG

Established

DocQuery

Experimental

Maintenance 10/25

Adoption 9/25

Maturity 13/25

Community 21/25

Maintenance 10/25

Adoption 0/25

Maturity 11/25

Community 0/25

Stars: 113

Forks: 35

Downloads: —

Commits (30d): 0

Language: Python

License: MIT

Stars: —

Forks: —

Downloads: —

Commits (30d): 0

Language: Python

License: MIT

No Package No Dependents

About OneRAG

notadev-iamaura/OneRAG

Production-ready RAG Framework (Python/FastAPI). 1-line config swaps: 6 Vector DBs (Weaviate, Pinecone, Qdrant, ChromaDB, pgvector, MongoDB), 5 LLMs (Gemini, OpenAI, Claude, Ollama, OpenRouter). OpenAI-compatible API. 2100+ tests.

This project helps you quickly build and deploy a smart chatbot or question-answering system for your business using your own documents. You feed in unstructured text like PDFs, Word files, or Markdown, and it outputs intelligent, context-aware answers to user questions. This is ideal for product managers, innovation leads, or internal tool builders looking to create customer service bots, knowledge base assistants, or internal Q&A systems.

AI-powered-chatbots knowledge-management customer-support-automation document-intelligence internal-Q&A-systems

About DocQuery

Tanny1810/DocQuery

Production-grade document ingestion and Retrieval-Augmented Generation (RAG) system demonstrating scalable backend architecture using FastAPI, message queues, and dedicated workers. Supports async processing, S3-based storage, text chunking, embeddings, vector search, and clean separation of concerns.

Related comparisons

OneRAG and Advanced-RAG-monorepo OneRAG and raggo OneRAG and selfhosted-llm-rag-app OneRAG and rag-decision-support-system OneRAG and rag-studio OneRAG and minimal-rag-backend

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