Analyst Research API

An AI-powered API built with FastAPI for generating research reports using LangGraph for multi-step reasoning and OpenAI for response generation.

Features

• FastAPI Framework: Modern, high-performance web framework for building APIs
• AI Analyst Personas: Automatically creates analyst personas based on the research topic
• Simulated Interviews: Conducts interviews between analysts and AI experts
• Report Generation: Compiles insights into a comprehensive research report
• Multi-Step Reasoning: Uses LangGraph for structured reasoning workflows
• AI-Generated Summaries: OpenAI summarizes the retrieved data

Tech Stack

• Framework: FastAPI - Fast, modern Python web framework
• API Documentation: Automatic Swagger/OpenAPI documentation
• AI Model: OpenAI (GPT-3.5-turbo)
• Agentic Mechanism: LangGraph
• Dependency Management: Python virtual environments

Installation

1. Clone the repository:

git clone https://github.com/skanderkaroui/analyst-research-api.git
cd analyst-research-api

2. Create a virtual environment:

python -m venv venv
venv\Scripts\activate  # On Windows

3. Install dependencies:

pip install -r requirements.txt

4. Create a .env file and add your OpenAI API key:

OPENAI_API_KEY=your_openai_api_key

Version Compatibility

We've tested and confirmed that the following versions work well together:

fastapi==0.115.9
uvicorn==0.34.0
pydantic==2.10.6
pydantic-core==2.27.2
python-dotenv==1.0.1
langchain-core==0.3.40
langchain-openai==0.3.7
langgraph==0.3.1
openai==1.65.1
typing-extensions==4.12.2
langsmith==0.3.11

If you encounter dependency conflicts or errors like ForwardRef._evaluate() missing 1 required keyword-only argument: 'recursive_guard', try using these specific versions.

Usage

1. Start the FastAPI server:

# Activate the virtual environment
venv\Scripts\activate  # On Windows

# Start the server
uvicorn app.main:app --reload

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

3. Example API request:

curl -X POST "http://localhost:8000/research" \
     -H "Content-Type: application/json" \
     -d '{"topic": "The impact of artificial intelligence on healthcare", "max_analysts": 2}'

API Endpoints

Generate Research Report

POST /research

Request body:

{
    "topic": "The impact of artificial intelligence on healthcare",
    "max_analysts": 2
}

Response:

{
    "report": "# AI in Healthcare: A Comprehensive Analysis\n\n## Introduction\n..."
}

Running Test Scripts

Option 1: Run the Simple Test Script

python simple_test.py

Option 2: Run the Analyst Test Script

python analyst_test.py

Project Structure

• app.py: Original FastAPI application
• app/main.py: Modular FastAPI application
• simple_test.py: Simple test script for LangGraph
• analyst_test.py: Implementation of the analyst research system
• requirements.txt: Project dependencies

Troubleshooting

If you encounter any issues:

1. Make sure your OpenAI API key is correctly set in the .env file
2. Check that all dependencies are installed with the correct versions
3. Ensure you're using Python 3.8 or higher
4. If you see warnings about json_schema with GPT-3.5-turbo, these can be safely ignored

License

MIT

Author

Skander Karoui