# _llm

A Python package for interacting with LLM models through Ollama, supporting both remote API and local Ollama instances.

## Installation

Install directly from GitHub:

```bash
pip install git+https://github.com/lasseedfast/_llm.git
```

Or clone and install for development:

```bash
git clone https://github.com/lasseedfast/_llm.git
cd _llm
pip install -e .
```

## Dependencies

This package requires:

- env_manager: `pip install git+https://github.com/lasseedfast/env_manager.git`
- colorprinter: `pip install git+https://github.com/lasseedfast/colorprinter.git`
- ollama: For local model inference
- tiktoken: For token counting
- requests: For API communication

## Environment Variables

The package requires several environment variables to be set:

- `LLM_API_URL`: URL of the Ollama API
- `LLM_API_USER`: Username for API authentication
- `LLM_API_PWD_LASSE`: Password for API authentication
- `LLM_MODEL`: Standard model name
- `LLM_MODEL_SMALL`: Small model name
- `LLM_MODEL_VISION`: Vision model name
- `LLM_MODEL_LARGE`: Large context model name
- `LLM_MODEL_REASONING`: Reasoning model name
- `LLM_MODEL_TOOLS`: Tools model name

These can be set in a `.env` file in your project directory or in the ArangoDB environment document in the div database.

## Basic Usage

```python
from _llm import LLM

# Initialize the LLM
llm = LLM()

# Generate a response
result = llm.generate(
    query="I want to add 2 and 2",
)
print(result.content)
```

## Advanced Usage

### Working with Images

```python
from _llm import LLM

llm = LLM()
response = llm.generate(
    query="What's in this image?",
    images=["path/to/image.jpg"],
    model="vision"
)
```

### Streaming Responses

```python
from _llm import LLM

llm = LLM()
for chunk_type, chunk in llm.generate(
    query="Write a paragraph about AI",
    stream=True
):
    print(f"{chunk_type}: {chunk}")
```

### Using Async API

```python
import asyncio
from _llm import LLM

async def main():
    llm = LLM()
    response = await llm.async_generate(
        query="What is machine learning?",
        model="standard"
    )
    print(response)

asyncio.run(main())
```

## License

MIT