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RAGFreeOpen Source
VERBA
The Golden RAGtriever — open-source RAG for everyone
BSD-3-Clause
ABOUT
Building a production-ready RAG application requires integrating vector databases, embedding models, LLM providers, document chunkers, and retrieval strategies — a complex, time-consuming process. Verba eliminates this complexity by providing a turnkey, modular open-source RAG tool with a built-in web UI and API. It supports multiple LLM providers (OpenAI, Anthropic, Cohere, Ollama, HuggingFace, Groq), data types (PDF, DOCX, CSV, audio, web pages), chunking strategies (token, semantic, recursive), and hybrid search — all configurable through an intuitive interface.
INSTALL
pip install goldenverbaINTEGRATION GUIDE
1. Query large collections of PDFs, DOCX, and CSV files to extract insights and answers from complex documents
2. Build a customer support knowledge base that provides citation-backed answers from internal documentation
3. Cross-reference multiple data sources with a single AI assistant that understands organizational context
4. Ingest academic research papers and perform semantic searches with summarized answers and source attribution
5. Deploy a private document Q&A system locally for privacy-sensitive data that cannot use external cloud services
TAGS
ragretrieval-augmented-generationweaviatevector-databasellmchatbotdocument-querysemantic-search