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Meta-Analysis data extraction

Supervised by Dr. M. Nguyen, Vy Nguyen

Steps

  • PDF Upload: Users can upload PDF files, which are then converted into text for further processing.
  • Semantic Search: The system creates embeddings for the text and uses them for efficient semantic search in a vector database.
  • Vector Store Generation: Learn how to generate a vector store from text chunks using embeddings and FAISS for efficient retrieval.
  • Conversational Chain: The system initializes a conversational chain for handling chat interactions.
  • Information Extraction: The application is capable of extracting information from documents based on user queries.
  • Results: Store the user questions, and the answers generated, into a CSV file

Prerequisites

Before getting started, ensure that you have the following prerequisites installed:

  • Python 3.x
  • LangChain framework
  • OpenAI GPT-3 or other large language models
  • FAISS library for efficient similarity search

Installation

  1. Clone the repository to your local machine:

    git clone https://github.com/PuchToTalk/Meta-Analysis-extraction
    
  2. Install the required dependencies:

    pip install -r requirements.txt
    

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Automating the meta-analysis process with NLP that can handle from literature review to data extraction

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