kyopark2014/question-answering-chatbot-with-vector-store
It is a chatbot for question and answering using RAG based on LLM
This project helps you build a chatbot that answers questions based on your own documents. You upload various document types like PDFs, text files, or CSVs, and the chatbot then uses this information to provide accurate answers to user questions. This is perfect for businesses or individuals who need a smart assistant to extract information directly from their specific knowledge base.
No commits in the last 6 months.
Use this if you want to create a question-answering system that uses your private documents as its knowledge source, reducing 'hallucinations' often seen in general AI models.
Not ideal if you need a chatbot for general conversation without relying on a specific set of uploaded documents.
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15
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1
Language
Jupyter Notebook
License
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Category
Last pushed
Nov 27, 2023
Commits (30d)
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curl "https://pt-edge.onrender.com/api/v1/quality/rag/kyopark2014/question-answering-chatbot-with-vector-store"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
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