IELTS_writing_tutor

IELTS Writing Tutor API

Overview

This project is a FastAPI-based service designed to help users prepare for IELTS writing tasks. The system combines a CSV knowledge base, a PostgreSQL-backed vector database, and OpenAI GPT to provide personalized guidance, example essays, and feedback on writing.

Features

• Knowledge Base Integration: Uses a CSV knowledge base of sample IELTS writing essays, managed with a PostgreSQL-backed vector database (PgVector).
• Embedding Models: Employs OpenAI embeddings for efficient vector representation of essay data.
• Agent Functionality: A custom agent, "IELTS Writing Tutor," interacts with users, retrieves relevant essay examples, and provides tailored feedback and suggestions.
• API Endpoint: Accepts user queries via a /query endpoint and responds in JSON format.
• Guided Writing Assistance: Offers suggestions for writing styles, scores, and improvements, inspired by existing examples.

Technologies Used

• FastAPI: Framework for creating the REST API.
• PgVector: PostgreSQL extension for vector similarity searches.
• Phi Framework: Facilitates the creation of the custom agent and integration of knowledge bases.
• LangChain: Manages knowledge base and embeddings.
• OpenAI GPT: Utilizes the gpt-4o-mini model for natural language processing.
• Python Dotenv: For managing environment variables.

Directory Structure

.
├── app.py                   # Main API and agent implementation
├── sample_data/             # Directory containing sample IELTS writing essays
├── requirements.txt         # Python dependencies
├── .env                     # Environment variables (e.g., OpenAI API key)

Installation

1. Clone the repository:

   git clone https://github.com/your-repo/ielts-writing-tutor-api.git
   cd ielts-writing-tutor-api

2. Set up a Python virtual environment:

   python3 -m venv venv
   source venv/bin/activate  # For Linux/macOS
   venv\Scripts\activate     # For Windows

3. Install dependencies:

   pip install -r requirements.txt

4. Configure Environment Variables: Create a .env file in the project root with your OpenAI API key:

   OPENAI_API_KEY=your_openai_api_key

Usage

1. Run the FastAPI server:

   uvicorn app:app --host 0.0.0.0 --port 8000

2. Query the Agent: Interact with the agent using the /query endpoint:

   • Request Format:

     {
       "query": "Provide examples of band 7 essays on environmental topics."
     }

   • Response Format:

     {
       "response": "Here is an example of a band 7 essay on environmental protection: ..."
     }

3. Customize Data: Add or update IELTS writing examples in the sample_data/ directory. Ensure the file is a properly formatted CSV.

Configuration

• Knowledge Base Path: Update the path in CSVKnowledgeBase to point to your custom dataset.
• Database Configuration: Modify database connection details (e.g., table name, database URL) as required.
• Model and Embeddings: Adjust the OpenAIChat and OpenAIEmbeddings configurations if using alternative models or embeddings.

Dependencies

• faiss
• fastapi
• phi
• langchain_openai
• uvicorn
• python-dotenv

Install all dependencies with:

pip install -r requirements.txt

Additional Notes

• Ensure the PostgreSQL database with PgVector is set up and accessible.
• Use the agent's built-in instructions to customize user interactions further.
• This project is extensible for additional features, such as support for other writing tasks or exam formats.