A lightweight LLM-based tool for document analysis. Upload PDFs or text files and ask questions to get AI-powered insights, summaries, and key information.

• Upload & Analyze Documents: Easily upload PDF or text files for analysis.
• AI-powered Q&A & Summarization: Get answers to your questions and summaries generated by advanced AI models.
• Fast & User-friendly: Simple web interface for quick interactions.

• Python
• LangChain – for orchestrating document processing and LLM interactions.
• mistral API – for enabling advanced natural language processing and understanding.
• Streamlit – for building an interactive, web-based user interface.

Experience the application at ai-doc-reader.streamlit.app

1. Document Upload:
   Users upload PDF or plain text documents through the Streamlit interface.

2. Text Extraction:
   The backend parses and extracts text from the uploaded files.

3. LLM Processing:
   Extracted content is passed through LangChain pipelines, which interact with the OpenAI API to:

   • Summarize content
   • Answer user questions
   • Extract key information

4. User Interaction:
   Results (summaries, answers, highlights) are displayed immediately in the Streamlit app.

• Frontend:
  Built with Streamlit, providing a modern, reactive UI for uploads and queries.

• Backend:
  Python-based, using LangChain to structure LLM tasks and OpenAI for processing. The backend handles:

  • File parsing and validation
  • Query routing to LLM
  • Result formatting

• Extensibility:
  The project is modular, allowing easy integration of new LLM providers, custom analytics, or additional file formats.

• Support for analyzing multiple documents simultaneously.
• Enhanced NLP analytics (topic modeling, entity extraction).
• Exportable analysis reports.

1. Clone the Repository

   git clone https://github.com/Sayeem3051/document-analysis.git
   cd document-analysis

2. Install Dependencies

   • Requires Python 3.9+.
   • Install common dependencies (exact requirements file not detected, but typical packages are):

     pip install streamlit langchain openai

3. Configure OpenAI API Key

   • Obtain your API key from mistral.ai.
   • Set it as an environment variable:

     export mistral_API_KEY='your-api-key'

4. Run the Application

   streamlit run app.py

   (Replace app.py with the main script name if different.)

• Open the web interface.
• Upload a document (PDF or text).
• Ask questions or request a summary.
• Instantly view insights and answers.

Contributions are welcome! Please open issues or pull requests for new features, bug fixes, or improvements.

• Sayeem3051

This tool leverages cutting-edge LLMs and modern Python libraries to make document analysis easy and powerful for everyone!