# Flow Prompt

## Introduction

Flow Prompt is a dynamic, all-in-one library designed for managing and optimizing prompts for large language models (LLMs) in production and R&D settings. It facilitates budget-aware operations, dynamic data integration, latency and cost metrics visibility, and efficient load distribution across multiple AI models.

## Features

- **Dynamic Prompt Development**: Avoid budget exceptions with dynamic data.
- **Multi-Model Support**: Seamlessly integrate with various LLMs like OpenAI, Anthropic, and more.
- **Real-Time Insights**: Monitor interactions, request/response metrics in production.
- **Prompt Testing and Evolution**: Quickly test and iterate on prompts using historical data.

## Installation

Install Flow Prompt using pip:

```bash
pip install flow-prompt
```

## Authentication
### OpenAI Keys
```python
# setting as os.env
os.setenv('OPENAI_API_KEY', 'your_key_here')
# or creating flow_prompt obj
FlowPrompt(openai_key="your_key", openai_org="your_org")
```

### Azure Keys
Add Azure keys to accommodate multiple realms:
```python
# setting as os.env
os.setenv('AZURE_KEYS', '{"name_realm":{"url": "https://baseurl.azure.com/","key": "secret"}}')
# or creating flow_prompt obj
FlowPrompt(azure_keys={"realm_name":{"url": "https://baseurl.azure.com/", "key": "your_secret"}})
```

### FlowPrompt Keys
Obtain an API token from Flow Prompt and add it:

```python
# As an environment variable:
os.setenv('FLOW_PROMPT_API_TOKEN', 'your_token_here')
# Via code: 
FlowPrompt(api_token='your_api_token')
```

### Add Behavious:
- use OPENAI_BEHAVIOR
- or add your own Behaviour, you can set max count of attempts, if you have different AI Models, if the first attempt will fail because of retryable error, the second will be called, based on the weights.
```
from flow_prompt import OPENAI_GPT4_0125_PREVIEW_BEHAVIOUR
flow_behaviour = OPENAI_GPT4_0125_PREVIEW_BEHAVIOUR
```
or:
```
from flow_prompt import behaviour
flow_behaviour = behaviour.AIModelsBehaviour(
    attempts=[
        AttemptToCall(
            ai_model=AzureAIModel(
                realm='us-east-1',
                deployment_name="gpt-4-1106-preview",
                max_tokens=C_128K,
                support_functions=True,
            ),
            weight=100,
        ),
        AttemptToCall(
            ai_model=OpenAIModel(
                model="gpt-4-1106-preview",
                max_tokens=C_128K,
                support_functions=True,
            ),
            weight=100,
        ),
    ]
)
```

## Usage Examples:

```python
from flow_prompt import FlowPrompt, PipePrompt

# Initialize and configure FlowPrompt
flow = FlowPrompt(openai_key='your_api_key', openai_org='your_org')

# Create a prompt
prompt = PipePrompt('greet_user')
prompt.add("Hello {name}", role="system")

# Call AI model with FlowPrompt
context = {"name": "John Doe"}
response = flow.call(prompt.id, context, flow_behaviour)
print(response.content)
```
For more examples, visit Flow Prompt Usage Documentation.

## Best Security Practices
For production environments, it is recommended to store secrets securely and not directly in your codebase. Consider using a secret management service or encrypted environment variables.

## Contributing
We welcome contributions! Please see our Contribution Guidelines for more information on how to get involved.

## License
This project is licensed under the Apache2.0 License - see the [LICENSE](LICENSE.txt) file for details.

## Contact
For support or contributions, please contact us via GitHub Issues.