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This beginner-friendly project implements a Retrieval-Augmented Generation (RAG) chatbot using Python and LangChain. It utilizes the OpenAI embedding model for text vectorization, ChromaDB for efficient retrieval, and ChatOpenAI for response generation. By combining retrieval with AI-generated responses

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binjojoy/AI-chatbot-using-langchain

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Chatbot with ChromaDB and OpenAI

This project is a simple chatbot that finds relevant answers from stored knowledge using ChromaDB and OpenAI embeddings.

How It Works

  1. You provide a query (a question or text input).
  2. The chatbot searches its database (ChromaDB) for similar content.
  3. If a relevant match is found (with a confidence score of 0.7 or higher), it returns the best results.
  4. If no good match is found, it tells you that no relevant answers are available.

How to Run

Intitally create a database by running the create_database script.. put the required data in the data/books directory Run the script with a query:

python chatbot.py "Your query here"

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This beginner-friendly project implements a Retrieval-Augmented Generation (RAG) chatbot using Python and LangChain. It utilizes the OpenAI embedding model for text vectorization, ChromaDB for efficient retrieval, and ChatOpenAI for response generation. By combining retrieval with AI-generated responses

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