ollama_pdf_rag and vector-search-nodejs
These are competitors: both implement RAG pipelines for PDF document chatting, with A using Ollama for local inference and B using LangChain with Couchbase for vector storage, offering alternative architectural approaches to the same use case.
About ollama_pdf_rag
tonykipkemboi/ollama_pdf_rag
A full-stack demo showcasing a local RAG (Retrieval Augmented Generation) pipeline to chat with your PDFs.
This tool helps you quickly get answers and insights from your PDF documents by having a natural conversation with them. You upload one or more PDFs, and then you can ask questions in plain language, receiving answers with citations back. Anyone who needs to extract information from documents or conduct research without relying on external AI services would find this useful.
About vector-search-nodejs
couchbase-examples/vector-search-nodejs
A RAG demo using LangChain that allows you to chat with your uploaded PDF documents
This tool helps you quickly get answers from your own collection of PDF documents. You upload your PDFs, and then you can ask questions in a chat interface to get answers relevant to the information within those documents. It's designed for anyone who needs to extract specific information or insights from their PDF archives without manually sifting through them.
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