<div style="text-align: left; margin-bottom: 20px;">
<img src="https://kiss-ai-stack.github.io/kissaistack.svg" alt="KISS AI Stack Banner" style="max-width: auto; height: 250px">
</div>
# KISS AI Stack - Core
**Effortless AI Stack Building**
Welcome to the core of the **KISS AI Stack**! This module helps you build a stack effortlessly using a simple YAML configuration file. Say goodbye to boilerplate code and embrace minimalism with the **KISS principle** (Keep It Simple, Stupid).
---
## Features
- **Centralized Stack Management**: Manage multiple session-based AI stacks with lifecycle support.
- **Minimal Dependencies**: Built using simple, vanilla vendor libraries.
- **Tool Classification**: Configure tools for your stack to handle specific tasks easily.
- **Supports RAG and Prompt-Based Models**: Choose the model type that suits your needs.
- **Thread-Safe**: Reliable operation in multi-threaded environments.
---
## Installation
Install the core module using pip:
```bash
pip install kiss-ai-stack-core
```
---
## Example Configuration
Here’s an example YAML configuration to set up an AI stack with different tools:
```yaml
stack:
decision_maker: # Required for tool classification
name: decision_maker
role: classify tools for given queries
kind: prompt # Choose from 'rag' or 'prompt'
ai_client:
provider: openai
model: gpt-4
api_key: <your-api-key>
tools:
- name: general_queries
role: process other queries if no suitable tool is found.
kind: prompt
ai_client:
provider: openai
model: gpt-4
api_key: <your-api-key>
- name: document_tool
role: process documents and provide answers based on them.
kind: rag # Retrieval-Augmented Generation
embeddings: text-embedding-ada-002
ai_client:
provider: openai
model: gpt-4
api_key: <your-api-key>
vector_db:
provider: chroma
kind: remote # Choose in-memory, storage, or remote options.
host: 0.0.0.0
port: 8000
secure: false
```
---
## Example Python Usage
Use the core module to build and interact with your AI stack:
```python
from kiss_ai_stack import Stacks
async def main():
try:
# Initialize a stack in the stack
await Stacks.bootstrap_stack(stack_id="my_stack", temporary=True)
# Process a query
response = await Stacks.generate_answer(stack_id="my_stack", query="What is Retrieval-Augmented Generation?")
print(response.answer)
except Exception as ex:
print(f"An error occurred: {ex}")
# Run the example
import asyncio
asyncio.run(main())
```
---
## How It Works
1. **Stack Initialization**: Use `Stack.bootstrap_stack` to initialize stacks with their configuration and resources.
2. **Query Processing**: Process queries with `Stack.generate_answer`, leveraging tools and AI clients defined in the YAML configuration.
3. **Tool Management**: Define tools to handle specific tasks like document processing or query classification.
4. **Vector Database**: Use the `vector_db` section to define how document embeddings are stored and retrieved for RAG-based tasks. Currently, only `Chroma` is supported.
---
## Documentation
### Key Methods
- `bootstrap_stack(stack_id: str, temporary: bool)`: Initialize a new stack session.
- `generate_answer(stack_id: str, query: Union[str, Dict, List])`: Process a query and return a response.
### Configuration Highlights
- **AI Client**: Configure the provider, model, and API key for supported services like OpenAI.
- **Tools**: Define tools such as general-purpose query handlers or document processors.
- **Vector Database**: Set up in-memory or persistent storage for RAG-based tasks.
---
## Contributing
We welcome contributions! Submit pull requests or open issues to improve this stack.
---
## License
This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.
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