This Python application allows users to interact with multiple PDF documents using natural language queries. It's designed for individuals seeking to extract information from various PDF sources through a conversational interface, leveraging Langchain and an LLM for semantic understanding and response generation.

How It Works

The app processes PDFs by first extracting text, then segmenting it into manageable chunks. These chunks are converted into vector embeddings using a language model. When a user asks a question, the system finds the most semantically similar text chunks and feeds them to the LLM to generate a contextually relevant answer based solely on the loaded PDF content.

Quick Start & Requirements

• Install dependencies: pip install -r requirements.txt
• Requires an OpenAI API key set in a .env file (OPENAI_API_KEY=your_secrit_api_key).
• Run the app: streamlit run app.py
• Official tutorial available on YouTube.

Highlighted Details

• Utilizes Langchain for orchestrating the LLM interaction pipeline.
• Supports querying across multiple PDF documents simultaneously.
• Employs semantic similarity matching for question answering.
• Response generation is strictly limited to the content of the loaded PDFs.

Maintenance & Community

This repository is intended for educational purposes and does not accept contributions. It serves as supporting material for a YouTube tutorial.

Licensing & Compatibility

Released under the MIT License. This license permits commercial use and integration with closed-source projects.

Limitations & Caveats

The application is designed solely for educational purposes and does not accept contributions. Its functionality is tied to the availability and quality of the OpenAI API.