README for FastAPI-Based Report GPT Generation Service

Overview

This repository contains the implementation of a FastAPI-based service designed to generate research reports. The service processes user-provided queries and report types, performing advanced research powered by GPTResearcher and responding with comprehensive results, including details, cost, context, images, and other associated metadata.

Features

• RESTful API to handle user queries and generate reports.
• Streaming responses to deliver research output in chunks.
• Secure API access with API Key authentication.
• Completely containerized setup with Docker.
• Built with modular design for easier scalability and maintenance.

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System Architecture

Core Components

1. FastAPI App (main.py):

   • Hosts the API endpoints.
   • Handles API Key authentication for secure use.
   • Accepts user inputs (query and report type) and generates a chunked streaming response.

2. Research Logic (deepresearch.py):

   • Encapsulates research and report generation.
   • Utilizes GPTResearcher to conduct research, generate reports, and retrieve extended data like images, contexts, or costs.

3. Docker Integration:

   • The application is containerized with a well-defined Dockerfile.
   • Includes dependency installation, environment setup, and FastAPI server configuration for rapid deployment.

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Prerequisites

Before running the application, ensure the following are installed on your system:

• Docker: Version 24.0+
• Python: Version 3.13+
• pip: Pre-installed Python package manager.

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Running the Application Locally

Cloning the Repository

Clone the repository to a directory of your choice:

git clone https://git.kvant.cloud/phoenix/gpt-researcher.git
cd gpt-researcher

Environment Variable Configuration

Create a .env file in the root of the project and define:

API_KEY=your_api_key # Replace "your_api_key" with your desired key
OPENAI_BASE_URL=
OPENAI_API_KEY=
EMBEDDING=
FAST_LLM=
SMART_LLM=
STRATEGIC_LLM=
OPENAI_API_VERSION=
SERPER_API_KEY=
RETRIEVER=serper

Installing Dependencies

Install the required Python modules based on the generated requirements.txt.

pip install --no-cache-dir -r requirements.txt

Running the App

Run the FastAPI app locally:

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

After running, your app will be available at http://127.0.0.1:8000.

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Using Docker for Deployment

Building the Docker Image

Build the Docker image using the Dockerfile provided:

docker build -t fastapi-report-service .

Running the Docker Container

Spin up a container and map FastAPI's default port, 8000:

docker run --env-file .env -p 8000:8000 fastapi-report-service

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API Usage

1. /get_report

• Method: POST
• Description: Generates a report based on user input.
• Headers:
  • X-API-KEY: API Key for authentication.
• Request Body (JSON):

{
  "query": "Research on AI in healthcare",
  "report_type": "research_report|resource_report|outline_report|custom_report|detailed_report|subtopic_report|deep"
}

• Streaming Response: Research and report are provided in chunks.

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Code Structure

├── Dockerfile                   # Configuration for Dockerizing the application
├── requirements.txt             # Python dependencies list
├── main.py                      # FastAPI server entry point
├── deepresearch.py              # Research-related logic and GPTResearcher integration
└── src/                         # Other project files and assets

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Features Under the Hood

1. Authentication:

   • An API key mechanism ensures that only authorized users can access endpoints.

2. Streaming Response:

   • Large research reports are sent incrementally using StreamingResponse for better experience and efficiency.

3. Modular Research Logic:

   • Research and generation tasks are handled by a dedicated class (ReportGenerator), making the application extensible.

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Future Enhancements

• Asynchronous Enhancements:

  • Improve async handling for long-running queries.

• Database Integration:

  • Save request history for auditing and reference purposes.

• Web Interface:

  • A user-friendly web application for interacting with the API.

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Contributing

Contributions are welcome! Feel free to fork the repository, make updates, and submit a pull request.