/Ai_Pdf_Reseach

Using LangChain to create a Streamlit web application for querying and analyzing content from a PDF document. The application allows users to input questions related to the PDF content, and it provides relevant answers along with additional analysis.

Primary LanguagePython

Ai Pdf Research using Open Ai Langchain

This project aims to develop a question-answering system capable of extracting relevant information from PDF documents and providing answers to user queries. The system utilizes language models and natural language processing techniques to comprehend the content of PDF files and generate responses to user questions.

Overview

The PDF Question-Answering System leverages the following components:

  • Text Extraction: PDF documents are parsed to extract text content using the PyPDF2 library, enabling the system to analyze textual information.

  • Language Models: OpenAI's language model, along with LangChain libraries, is employed to encode and understand the text extracted from PDF files. This facilitates the system's ability to comprehend the content and generate accurate responses.

  • Question-Answering Pipeline: The system incorporates a question-answering pipeline that processes user queries and matches them with relevant information extracted from PDF documents. This pipeline utilizes similarity search techniques and language model chains to generate responses.

Setup and Installation

To set up the PDF Question-Answering System locally, follow these steps:

  1. Clone the repository to your local machine:

    git clone <[repository-url](https://github.com/yllvar/Ai_Pdf_Reseach.git)>
cd BreadcrumbsAi_Pdf_Reseach

  2. Install the required dependencies listed in the requirements.txt file:

    pip install -r requirements.txt

  3. Ensure you have Python 3.x installed on your system.

  4. Create a .env file in the project directory and add your OpenAI API key:

    OPENAI_API_KEY=your-api-key-goes-here

  5. Run the Streamlit app to launch the question-answering interface:

    streamlit run app.py

  6. Upload a PDF file containing relevant information and start asking questions!

Usage

  1. Upload a PDF file: Use the provided interface to upload a PDF document containing the information you want to analyze and query.

  2. Enter your question: Input your question into the text field provided on the interface.

  3. Obtain answers and insights: After submitting your question, the system will process the PDF content, generate an answer based on the query, and provide additional analysis or insights related to the answer.

To elevate the functionality, usability and overall value question-answering app, here is the future improvements that will be implemented in the future:

  1. Improved Text Processing:

    • Implement more advanced text processing techniques to handle complex PDF structures, such as tables, images with captions, and footnotes.
    • Explore OCR (Optical Character Recognition) integration to extract text from scanned documents or images within PDFs.

  2. Enhanced User Interface:

    • Design a more intuitive and visually appealing user interface using Streamlit's built-in components or custom CSS styling.
    • Add interactive elements like sliders, dropdown menus, or buttons to enhance user experience and facilitate input.

  3. Multiple PDF Uploads:

    • Allow users to upload multiple PDF files simultaneously, enabling batch processing and comparison of information across different documents.

  4. PDF Preprocessing Options:

    • Provide options for PDF preprocessing, such as removing headers, footers, or other irrelevant content, to improve the accuracy of text extraction.

  5. Query Expansion Techniques:

    • Implement query expansion techniques to enhance the system's ability to interpret user questions and retrieve more relevant information from the PDF documents.

  6. Feedback Mechanism:

    • Introduce a feedback mechanism where users can provide feedback on the accuracy of answers, helping to refine and improve the question-answering system over time.

  7. Integration with External APIs:

    • Explore integration with external APIs or services for additional functionalities, such as language translation, summarization, or sentiment analysis.

  8. Error Handling and Logging:

    • Implement robust error handling mechanisms to gracefully handle unexpected errors and provide informative error messages to users.
    • Set up logging to track system usage, errors, and user interactions for monitoring and debugging purposes.

  9. Performance Optimization:

    • Optimize the performance of text processing and similarity search algorithms to reduce latency and improve responsiveness, especially for large PDF files.

  10. Documentation and Tutorials:

    • Create comprehensive documentation and tutorials to guide users on how to use the app effectively, interpret results, and understand its limitations.

  11. Community Engagement:

    • Foster a community around the project by creating forums, discussion groups, or social media channels where users can ask questions, share insights, and contribute to the development of the app.

  12. Continuous Integration and Deployment:

    • Set up continuous integration and deployment pipelines to automate testing, code deployment, and updates, ensuring the app remains stable and up-to-date.