Financial Document Analyzer

Project Description

A brief description of what this project does. (e.g., An application that analyzes financial documents using AI agents, built with CrewAI and FastAPI.)

Status

The core application infrastructure is now stable. All initial dependency and code bugs have been resolved, and the application is ready for LLM configuration.

Bugs Found and How They Were Fixed

This section details the issues I encountered and the steps taken to resolve them.

Dependency Conflicts

• OpenAI version conflict: The openai library version (1.30.5) conflicted with the litellm dependency.

  • Fix: Updated requirements.txt from openai==1.30.5 to openai>=1.68.2.

• Pydantic-core version conflict: The pydantic-core version (2.8.0) caused compatibility issues with pydantic.

  • Fix: Updated requirements.txt from pydantic_core==2.8.0 to pydantic_core>=2.14.1.

Code Bugs

• CrewAI Import Errors: The application used a deprecated agent_builder module.

  • Fix: Updated agents.py to use the correct import statement: from crewai import Agent.

• Tool Validation Errors: The FinancialDocumentTool was not properly defined for use with CrewAI.

  • Fix: Updated tools.py to ensure FinancialDocumentTool properly inherits from crewai.tools.base_tool.BaseTool and correctly defines the path parameter with a Pydantic field.

• Function vs. Tool Confusion: The main application was calling the tool's raw function instead of a tool instance.

  • Fix: Updated main.py to correctly instantiate and use FinancialDocumentTool objects.

Setup Instructions

Follow these steps to set up the project locally.

1. Install Python 3.12: This project is tested with and requires Python 3.12. You can download it from the official Python website.

2. Clone the repository:

   git clone [Your Repository Link Here]
   cd [Your Repository Name]

3. Create a virtual environment:

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

4. Install dependencies:

   pip install -r requirements.txt

5. Configure your LLM: Create a .env file in the project root and add your API key. (e.g., GEMINI_API_KEY=your_key_here). Note: This project uses litellm which may require a specific format for API keys.

Usage Instructions

To start the FastAPI application:

uvicorn main:app --reload


I understand. You want to update the README.md file to explicitly state that the project requires Python 3.12. This is a good practice to ensure consistency and avoid compatibility issues for other users.

Here is the updated README.md template with the Python version requirement added to the setup instructions.

Markdown

# Financial Document Analyzer

## Project Description
A brief description of what this project does. (e.g., An application that analyzes financial documents using AI agents, built with CrewAI and FastAPI.)

## Status
The core application infrastructure is now stable. All initial dependency and code bugs have been resolved, and the application is ready for LLM configuration.

## Bugs Found and How They Were Fixed
This section details the issues I encountered and the steps taken to resolve them.

### Dependency Conflicts
- **OpenAI version conflict:** The `openai` library version (`1.30.5`) conflicted with the `litellm` dependency.
  - **Fix:** Updated `requirements.txt` from `openai==1.30.5` to `openai>=1.68.2`.

- **Pydantic-core version conflict:** The `pydantic-core` version (`2.8.0`) caused compatibility issues with `pydantic`.
  - **Fix:** Updated `requirements.txt` from `pydantic_core==2.8.0` to `pydantic_core>=2.14.1`.

### Code Bugs
- **CrewAI Import Errors:** The application used a deprecated `agent_builder` module.
  - **Fix:** Updated `agents.py` to use the correct import statement: `from crewai import Agent`.

- **Tool Validation Errors:** The `FinancialDocumentTool` was not properly defined for use with CrewAI.
  - **Fix:** Updated `tools.py` to ensure `FinancialDocumentTool` properly inherits from `crewai.tools.base_tool.BaseTool` and correctly defines the `path` parameter with a Pydantic field.

- **Function vs. Tool Confusion:** The main application was calling the tool's raw function instead of a tool instance.
  - **Fix:** Updated `main.py` to correctly instantiate and use `FinancialDocumentTool` objects.

## Setup Instructions
Follow these steps to set up the project locally.

1.  **Install Python 3.12:**
    This project is tested with and requires **Python 3.12**. You can download it from the [official Python website](https://www.python.org/downloads/).

2.  **Clone the repository:**
    ```bash
    git clone [Your Repository Link Here]
    cd [Your Repository Name]
    ```

3.  **Create a virtual environment:**
    ```bash
    python -m venv venv
    source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
    ```

4.  **Install dependencies:**
    ```bash
    pip install -r requirements.txt
    ```

5.  **Configure your LLM:**
    Create a `.env` file in the project root and add your API key. (e.g., `GEMINI_API_KEY=your_key_here`). Note: This project uses `litellm` which may require a specific format for API keys.

## Usage Instructions
To start the FastAPI application:

```bash
uvicorn main:app --reload
The server will run on http://127.0.0.1:8000.

API Documentation
The API documentation is automatically generated by FastAPI. Once the server is running, you can access the interactive docs at:

Swagger UI: http://127.0.0.1:8000/docs

ReDoc: http://127.0.0.1:8000/redoc

The primary endpoint is:

POST /process_document

Description: Initiates the financial document analysis process.

Request Body:

JSON

{
  "file_path": "path/to/your/document.pdf"
}
Response:
Returns a JSON object with the analysis results.