# ReCallMe API

ReCallMe is a Flask-based REST API wrapper for interacting with various Large Language Models (LLMs) such as Gemini and Anthropic. This API provides endpoints for text and image-based inferences, as well as conversation handling with memory, allowing developers to integrate LLM capabilities into their applications.


## Table of Contents

* [Installation](#installation)
* [Environment Variables](#environment-variables)
* [API Documentation](#api-documentation)
* [Endpoints](#endpoints)
* [Usage](#usage)
* [Contributing](#contributing)
* [License](#license)


## Installation

1. Clone the repository:

```bash
git clone https://github.com/yourusername/recallme.git
cd recallme

2. Install dependencies using Poetry:

Make sure Poetry is installed.

poetry install

3. Set up the environment:

Ensure necessary environment variables are set up (see below).

4. Run the application:

poetry run flask run --host=0.0.0.0 --port=8080

5. Access the Swagger UI documentation:

Navigate to http://localhost:8080/recall-svc/api/v1 to view interactive API documentation.

Create a .env file in the project root with the following variables:

ANTHROPIC_KEY=your_anthropic_api_key
# Add other API keys as needed (e.g., for Gemini)

Replace your_anthropic_api_key with the actual API key for the Anthropic LLM service.

ReCallMe uses Flask-RESTx for the API framework and Swagger UI for interactive documentation. The documentation can be accessed at http://localhost:8080/recall-svc/api/v1/swagger.

• Description: Multiplies two numbers provided in the request.
• Method: POST
• Payload:

{
  "a": 2,
  "b": 3
}

• Response:

6

• Description: Makes a text inference call to the Gemini LLM.
• Method: POST
• Payload:

{
  "query": "What is the capital of France?"
}

• Response:

{
  "response": "The capital of France is Paris."
}

• Description: A conversational endpoint that maintains chat history and supports custom prompts.
• Method: POST
• Payload:

{
  "query": "Tell me a joke.",
  "custom_prompt": "Your message:",
  "history": []
}

• Response:

{
  "response": "Why did the chicken cross the road? To get to the other side!",
  "history": [...]
}

• Description: Makes a text inference call to the Anthropic LLM.
• Method: POST
• Payload:

{
  "query": "Summarize the history of AI."
}

• Response:

{
  "response": "AI has evolved significantly from early rule-based systems to today's deep learning models..."
}

• Description: Uses Gemini LLM to analyze images with optional text input and history.
• Method: POST
• Payload: (How the file parameter is structured depends on your implementation - likely multipart/form-data)

{
  "file": "<image_file>",
  "input_text": "Describe this image.",
  "mime_type": "image/png",
  "history": []
}

• Response:

{
  "response": "This is an image of a cat.",
  "history": [...]
}

To start the server and use the endpoints, follow these steps:

1. Ensure the server is running on 0.0.0.0 so it can be accessed externally.
2. Use tools like curl, Postman, or Swagger UI to interact with the API endpoints.

curl -X POST "http://localhost:8080/recall-svc/api/v1/multiply" -H "Content-Type: application/json" -d '{"a":5,"b":6}'

Contributions are welcome! To contribute:

1. Fork the repository.
2. Create a new branch with a descriptive name.
3. Make your changes and submit a pull request.

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

This improved README provides clearer formatting, more detailed explanations, and addresses potential issues like the file upload in the `/gemini-media-inference` endpoint.  Remember to replace `yourusername` with your actual GitHub username.  Also, create a LICENSE file (typically MIT License) in the project root.