ecq-llmfactory 0.1.5

LLMFactory is a modular framework built on LangChain, offering a factory-based approach for seamless integration and management of Large Language Models (LLMs) from multiple providers, ensuring flexibility, scalability, and maintainability.

LLMFactory

LLMFactory is a modular and extensible framework built on top of the LangChain library, designed for streamlined integration and management of Large Language Models (LLMs). By leveraging LangChain's powerful abstractions and tools, LLMFactory provides a factory-based approach to initialize and manage LLMs from multiple providers, offering flexibility, maintainability, and scalability.

Key Features

1. LangChain Foundation

• LLMFactory utilizes LangChain as its core, ensuring compatibility with its powerful tools, chains, and agents.
• It is well-suited for applications requiring complex workflows and advanced LLM capabilities.

2. Abstract Factory Pattern

• The framework employs the abstract factory design pattern through the AbstractLLMFactory base class.
• This ensures a standardized interface for initializing and managing diverse LLMs.

3. Support for Multiple LLM Providers

• Pre-configured factories for leading LLM providers, including:
  • OpenAI (ChatOpenAI): Supporting models like GPT-3.5 and GPT-4.
  • DeepSeek: Extended functionality for OpenAI APIs.
  • Fireworks (ChatFireworks): Tailored models for specific workflows with additional parameters like top_p and retries.
  • Ollama (OllamaLLM): Lightweight models with customizable context size.
  • Anthropic (ChatAnthropic): Human-aligned conversational models (e.g., Claude).

5. Model Configuration

• The ModelConfig dataclass encapsulates essential initialization parameters, such as:
  • Model name
  • Temperature for response diversity
  • API keys and base URLs
  • Advanced parameters like top_p, max_tokens, timeouts, and retries

Installation

You can install just the base llmfactory package, or install a provider's package along with llmfactory.

This installs just the base package without installing any provider's SDK.

pip install llmfactory

This installs llmfactory along with anthropic's library.

pip install 'llmfactory[anthropic]'

This installs all the provider-specific libraries

pip install 'llmfactory[all]'

Set up

To get started, you will need API Keys for the providers you intend to use. You'll need to install the provider-specific library either separately or when installing aisuite.

The API Keys can be set as environment variables, or can be passed as config to the aisuite Client constructor. You can use tools like python-dotenv or direnv to set the environment variables manually.

Set the API keys.

export DEEPSEEK_API_KEY="your-deepseek-api-key"
export ANTHROPIC_API_KEY="your-anthropic-api-key"

Use the python client.

import os
import dotenv

from factory_llm import LLMFactoryProvider, ModelConfig

dotenv.load_dotenv()

ANTHROPIC_KEY = os.environ['ANTHROPIC_API_KEY']
model_config = ModelConfig(
    provider_name='Anthropic',
    model_name="claude-3-opus-20240229",
    api_key=ANTHROPIC_KEY,
    temperature=0.0,
)

factory = LLMFactoryProvider.get_factory(model_config)

# list supported providers
print(LLMFactoryProvider.get_supported_providers())

For a list of provider values, you can look at the directory - /providers/. The list of supported providers are of the format - <provider>_provider.py in that directory. We welcome providers adding support to this library by adding an implementation file in this directory. Please see section below for how to contribute.

Adding support for a provider

We have made easy for a provider or volunteer to add support for a new platform.

Naming Convention for Provider Modules

We follow a convention-based approach for loading providers, which relies on strict naming conventions for both the module name and the class name.

• The provider's module file must be named in the format <provider>_provider.py.
• The class inside this module must follow the format: the provider name with the first letter capitalized, followed by the suffix Provider.

Examples

• Hugging Face: The provider class should be defined as:

  class HuggingfaceProvider(AbstractLLMFactory)
  

  in providers/huggingface_provider.py.

• OpenAI: The provider class should be defined as:

  class OpenaiProvider(AbstractLLMFactory)
  

  in providers/openai_provider.py

This convention simplifies the addition of new providers and ensures consistency across provider implementations.