FastAPI-Based QA Chatbot with Pinecone and OpenAI

https://frontendqa.netlify.app/

Overview

This project implements a Question Answering (QA) chatbot using FastAPI as the backend framework. It integrates Pinecone for vector-based similarity search and OpenAI for language model embeddings and responses. The bot is capable of:

• Extracting and embedding text from PDFs.
• Storing embeddings in a Pinecone vector database.
• Answering user queries by retrieving relevant context from the vector database.
• Supporting CORS to allow requests from different origins.

Technologies Used

• FastAPI: For building the REST API.
• Pinecone: Vector database for storing and retrieving embeddings.
• OpenAI API: For text embeddings and chat model (GPT-3.5-turbo).
• PyPDF2: For extracting text from PDF files.
• LangChain: For managing embeddings, text splitting, and retrieval workflows.
• Uvicorn: For running the FastAPI application.

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Features

1. PDF Text Extraction: Extracts and splits text into chunks for efficient embedding.
2. Vector Storage: Uses Pinecone to store and manage text embeddings.
3. Contextual Question Answering: Matches user queries to relevant chunks in the database and provides answers using GPT-3.5-turbo.
4. CORS Support: Configured to allow cross-origin requests, enabling integration with frontend applications.

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Setup Instructions

1. Prerequisites

• Python 3.8+
• API keys for OpenAI and Pinecone.
• Pip for dependency management.

2. Installation

1. Clone the repository:

   git clone <repository-url>
   cd <repository-folder>

2. Create a virtual environment:

   python -m venv venv
   source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install dependencies:

   pip install -r requirements.txt

4. Set up environment variables: Create a .env file in the project root:

   OPENAI_API_KEY=<paste your key here>
   PINECONE_API_KEY=<paste your key here>

3. Running the Application

Start the FastAPI server:

uvicorn main:app --reload

The API will be accessible at http://127.0.0.1:8000.

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API Endpoints

1. Home Page

Endpoint: GET /

• Description: Verifies that the server is running.
• Response:

  {
      "HomePage": "HomePage"
  }

2. Chatbot Endpoint

Endpoint: POST /chat

• Description: Fromate for handles user queries and provides answers.
• Request Body:

  {
      "query": "<Your question here>"
  }

• Response:

  {
      "query": "<Your question>",
      "answer": "<Generated answer>"
  }

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Frontend Integration

The backend includes CORS middleware configured for seamless integration with frontend applications. Ensure the origins list in the CORSMiddleware settings includes your frontend's URL:

origins = [
    "http://localhost:3000",  # Local frontend
]

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Known Issues

1. CORS Errors: Ensure proper configuration of CORS middleware to match frontend requests.
2. PDF Parsing Limitations: PyPDF2 may not extract text accurately from certain PDF formats.

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

For queries contact:

• Email: [sharadnaik001@gmail.com]