llm-interface 0.1.1

A flexible interface for working with various LLM providers

LLM Interface

A flexible Python interface for working with various Language Model providers, including OpenAI, Anthropic, and Ollama. This library provides a unified way to interact with different LLM providers while supporting features like structured outputs, tool execution, and response caching.

Features

• Multiple Provider Support

  • OpenAI (GPT models)
  • Anthropic (Claude models)
  • Ollama (local and remote)
  • Remote Ollama via SSH

• Advanced Capabilities

  • Structured output parsing with Pydantic models
  • Function/tool calling support
  • Response caching
  • Comprehensive logging
  • JSON mode support
  • System prompt handling

• Developer-Friendly

  • Type hints throughout
  • Extensive test coverage
  • Flexible configuration options
  • Error handling and retries

Installation

Install using pip:

pip install llm-interface

Or using Poetry:

poetry add llm-interface

Basic Usage

Simple Chat Completion

from llm_interface import llm_from_config

# Create an OpenAI interface
llm = llm_from_config(
    provider="openai",
    model_name="gpt-4",
)

# Simple chat
response = llm.chat([
    {"role": "user", "content": "What is the capital of France?"}
])

Structured Output with Pydantic

from pydantic import BaseModel

class LocationInfo(BaseModel):
    city: str
    country: str
    population: int

response = llm.generate_pydantic(
    prompt_template="Provide information about Paris",
    output_schema=LocationInfo,
    system="You are a helpful geography assistant"
)

Tool/Function Calling

from llm_interface.llm_tool import tool

@tool(name="get_weather")
def get_weather(location: str, units: str = "celsius") -> str:
    """Get weather information for a location.
    
    Args:
        location: City or location name
        units: Temperature units (celsius/fahrenheit)
    """
    # Implementation here
    return f"Weather in {location}"

response = llm.chat(
    messages=[{"role": "user", "content": "What's the weather in Paris?"}],
    tools=[get_weather]
)

Remote Ollama Setup

llm = llm_from_config(
    provider="remote_ollama",
    model_name="llama2",
    hostname="example.com",
    username="user"
)

Configuration

The library supports various configuration options through the llm_from_config function:

llm = llm_from_config(
    provider="openai",          # "openai", "anthropic", "ollama", or "remote_ollama"
    model_name="gpt-4",        # Model name
    max_tokens=4096,           # Maximum tokens in response
    host=None,                 # Local Ollama host
    hostname=None,             # Remote SSH hostname
    username=None,             # Remote SSH username
    log_dir="logs",           # Directory for logs
    use_cache=True            # Enable response caching
)

Environment Variables

Required environment variables based on provider:

• OpenAI: OPENAI_API_KEY
• Anthropic: ANTHROPIC_API_KEY
• Remote Ollama: SSH key loaded in SSH agent

Development

This project uses Poetry for dependency management:

# Install dependencies
poetry install

# Run tests
poetry run pytest

# Format code
poetry run black .

# Run linter
poetry run flake8

License

Apache License 2.0 - See LICENSE file for details.