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document-retrieval

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The "Questions" project, part of Harvard's CS50 AI course, develops an AI system for answering questions by retrieving documents and passages from a text corpus using tf-idf. It aids in understanding natural language processing (NLP) and information retrieval techniques.

  • Updated Jul 30, 2024
  • Jupyter Notebook

LLM_LangChain_ChatBot is a contextual document retrieval chatbot that leverages LangChain to process user queries and generate accurate responses based on the content of retrieved documents. Ideal for applications requiring precise information retrieval and context-aware interactions.

  • Updated Jun 14, 2024
  • Jupyter Notebook

The Intelligent "ASKDOC" project combines the power of Langchain, Azure, OpenAI models, and Python to deliver an intelligent question-answering system, that scans your PDF documents and answer queries based on its contents. It can be queried using Human Natural Language.

  • Updated Feb 4, 2024
  • Python

Dive into LangChain, a powerful platform that lets you interact with your data like never before. This guide offers insights on its unique capabilities, helping you tap into your data in conversational ways.

  • Updated Oct 22, 2023
  • Jupyter Notebook

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