playgroundutils 0.1.1
Helper functions for the ConfidentialMind stack, primarily for Streamlit apps
License: Apache-2.0
Requires Python: >=3.10

# ConfidentialMind App / Playgroundutils

## Overview

The ConfidentialMind App package (also known as playgroundutils) is a Python library designed to simplify the development of secure generative AI applications on the ConfidentialMind stack. It provides utility functions and components for building Streamlit-based front-end applications that interact with Large Language Models (LLMs) and other services within the ConfidentialMind ecosystem.

## Features

- Streamlit application initialization and configuration
- Tool selector for managing multiple tools within a single application
- Chat UI component for interacting with LLMs
- Streaming handler for real-time LLM responses
- User authentication and token management
- LLM API interaction helpers

## Installation

To install the package, add the following to your `requirements.txt` file:

```
pip install playgroundutils
```



## Usage

### Initializing a Streamlit App

```python
from playgroundutils.app import init_streamlit_app

def main():
    APP_NAME = "My ConfidentialMind App"
    init_streamlit_app(APP_NAME)

    # Your app code here

if __name__ == "__main__":
    main()
```

### Creating a Tool Selector

```python
from playgroundutils.app import tool_selector
from confidentialmindserver.config_manager import BaseToolConfig

def parse_tools_config(tools):
    # Your tool parsing logic here
    pass

tools = [BaseToolConfig(...), BaseToolConfig(...)]
tool_selector(tools, parse_tools_config, APP_NAME)
```

### Implementing a Chat UI

```python
from playgroundutils.components import chat_ui

LLM_CONFIG_ID = "your_llm_config_id"
chat_ui(LLM_CONFIG_ID, system_prompt="You are a helpful assistant.")
```

### Handling LLM API Requests

```python
from playgroundutils.llm_api_helpers import LLMAPIHandler, ChatMessage
from playgroundutils.streaming import StreamHandler

llm_api_handler = LLMAPIHandler(LLM_CONFIG_ID)
stream_handler = StreamHandler(st.empty())

messages = [ChatMessage(role="user", content="Hello, AI!")]
response = llm_api_handler.handle_query(messages, stream_handler, temperature=0.7, stream=True)
```

## Components

- `app.py`: Contains functions for initializing Streamlit apps and managing tool selection.
- `components.py`: Provides UI components like the chat interface.
- `llm_api_helpers.py`: Handles interactions with LLM APIs.
- `streaming.py`: Manages streaming responses from LLMs.
- `user.py`: Handles user authentication and token management.

## Configuration

The package works in conjunction with the ConfidentialMind server package to manage configurations and connectors. Make sure to set up your `ConfigManager` and define necessary connectors as described in the main ConfidentialMind SDK documentation.

## Local Development

To run your Streamlit application locally using this package:

```bash
export CONFIDENTIAL_MIND_LOCAL_CONFIG="True"
export CONFIDENTIAL_MIND_LOCAL_DEV="True"
streamlit run your_app.py
```

## Note

This package is designed to work within the ConfidentialMind ecosystem. Make sure you have the necessary permissions and access to the ConfidentialMind stack before using this package.

For more detailed information on the ConfidentialMind SDK and its capabilities, please refer to the main SDK documentation.