Fast_Api-Rag-Chatbot

conversational PDF chatbot using Open AI and FastApi

PDF Chatbot

Description

PDF Chatbot is an intelligent conversational AI system that allows users to upload PDF documents and engage in question-answering interactions based on the document's content. Built with FastAPI and leveraging OpenAI's powerful language models, this application demonstrates the practical implementation of Retrieval-Augmented Generation (RAG) in a real-world scenario.

Features

• PDF Upload: Users can upload PDF documents to the system.
• Text Extraction: Automatically extracts text content from uploaded PDFs.
• Intelligent Chunking: Splits extracted text into manageable chunks for processing.
• Vector Embedding: Creates and stores vector embeddings of text chunks for efficient retrieval.
• Conversational AI: Enables users to ask questions about the uploaded document and receive contextually relevant answers.
• RAG Implementation: Utilizes Retrieval-Augmented Generation to provide accurate and context-aware responses.

Technology Stack

• FastAPI: For creating robust and high-performance API endpoints.
• OpenAI API: Leverages GPT-3.5-turbo for natural language understanding and generation.
• LangChain: Facilitates the creation of the conversational retrieval chain.
• FAISS: Efficient similarity search and clustering of dense vectors.
• PyPDF: For extracting text from PDF documents.
• Pydantic: Data validation and settings management using Python type annotations.
• Python-dotenv: Management of environment variables.

Installation

1. Clone this repository:
2. Install the required dependencies: pip install -r requirements.txt
3. Set up your OpenAI API key in a .env file: OPENAI_API_KEY=your_api_key_here

Usage

1. Start the FastAPI server:

2. Access the API documentation at http://localhost:8000/docs

3. Use the /upload endpoint to upload a PDF file.

4. Use the /query endpoint to ask questions about the uploaded PDF.

API Endpoints

• GET /: Root endpoint, returns a welcome message.
• POST /upload: Uploads a PDF file and processes it for querying.
• POST /query: Accepts a query about the uploaded PDF and returns an AI-generated response.

Testing

Run the unit tests using pytest: