Giskard LLM Utils

A Python library providing utility functions and tools for working with Large Language Models (LLMs). This library is part of the Giskard ecosystem and provides various utilities for LLM operations, including model management, clustering, and more.

Purpose

This library aims to simplify working with LLMs by providing:

• A unified interface for different LLM providers through LiteLLM
• Support for both cloud-based and local embedding models
• Easy configuration through environment variables or direct initialization
• Synchronous and asynchronous operations for better performance

Installation

Standard Installation

pip install giskard-lmutils

Local Embedding Support

For local embedding capabilities, install with the local-embedding extra:

pip install "giskard-lmutils[local-embedding]"

This will install the required dependencies (torch and transformers) for running embedding models locally.

Development Installation

1. Install python, UV and make
2. Clone this repository
3. Setup the virtual environment using make setup

Using LiteLLMModel

The LiteLLMModel class provides a unified interface for working with various LLM providers through the LiteLLM library. It supports both completion and embedding operations, with both synchronous and asynchronous methods.

Configuration

You can configure the model in two ways:

1. Through environment variables:

# Required for OpenAI models
export OPENAI_API_KEY="your-api-key"

# Model configuration
export GSK_COMPLETION_MODEL="gpt-3.5-turbo"
export GSK_EMBEDDING_MODEL="text-embedding-ada-002"

from giskard_lmutils.model import LiteLLMModel

# This will use environment variables for model names
model = LiteLLMModel(
    completion_params={"temperature": 0.7},
    embedding_params={"is_local": False}  # Optional, defaults to False
)

Note: The environment variable prefix can be customized by passing an env_prefix parameter to the LiteLLMModel initialization. This allows you to use different models within the same application by setting different environment variables (e.g., CUSTOM_PREFIX_COMPLETION_MODEL).

2. Through specified model names:

model = LiteLLMModel(
    completion_model="gpt-3.5-turbo",
    embedding_model="text-embedding-ada-002",
    completion_params={"temperature": 0.7},
    embedding_params={"is_local": False}  # Optional, defaults to False
)

Note: When using OpenAI models, you must set the OPENAI_API_KEY environment variable. For other providers, refer to the LiteLLM documentation for their specific API key requirements.

Usage Examples

Text Completion

# Synchronous completion
response = model.complete([
    {"role": "user", "content": "What is the capital of France?"}
])

# Asynchronous completion
response = await model.acomplete([
    {"role": "user", "content": "What is the capital of France?"}
])

Text Embedding

# Synchronous embedding
embeddings = model.embed(["Hello, world!", "Another text"])

# Asynchronous embedding
embeddings = await model.aembed(["Hello, world!", "Another text"])

# Local embedding
model = LiteLLMModel(
    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
    embedding_params={"is_local": True}
)
embeddings = model.embed(["Hello, world!"])

Requirements

• Python >= 3.9, < 3.14
• Core dependencies:
  • numpy >= 2.2.2
  • litellm >= 1.59.3
• Optional dependencies (for local embedding):
  • torch >= 2.6.0
  • transformers >= 4.51.3

License

This project is licensed under the Apache Software License 2.0 - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Authors

• Kevin Messiaen ([email protected])