LiteLLM: Simplifying Access to Multiple LLM APIs
LiteLLM is a remarkable Python SDK and Proxy Server (LLM Gateway) that has been designed to streamline the process of calling over 100 LLM APIs in the familiar OpenAI format. This includes popular platforms like Bedrock, Azure, OpenAI, VertexAI, Cohere, Anthropic, Sagemaker, HuggingFace, Replicate, and Groq.
Overview
LiteLLM offers a unified way to interact with various LLM providers. Instead of dealing with the intricacies of each individual API, developers can use LiteLLM's consistent interface. It takes care of translating inputs to the appropriate endpoints of different providers for completion, embedding, and image_generation tasks. This means that you can expect consistent output, with text responses always available in a standard format like ['choices'][0]['message']['content'].
Core Features
One of the standout features is its retry/fallback logic across multiple deployments. For example, if there are issues with an Azure or OpenAI deployment, LiteLLM can automatically switch to an alternative to ensure uninterrupted service. It also allows you to set budgets and rate limits per project, api key, and model, giving you better control over your usage and costs.
Another great aspect is its support for logging observability. LiteLLM exposes pre-defined callbacks that can send data to various tools like Lunary, Langfuse, DynamoDB, s3 Buckets, Helicone, Promptlayer, Traceloop, Athina, Slack, and MLflow. This enables you to keep a close eye on the performance and usage of your LLM calls.
Basic Usage
Getting started with LiteLLM is relatively straightforward. First, you need to install it using pip install litellm. Then, you can import the necessary functions. For instance, to make a simple completion call, you can do something like this:
from litellm import completion
import os
# set ENV variables
os.environ["OPENAI_API_KEY"] = "your-openai-key"
os.environ["COHERE_API_KEY"] = "your-cohere-key"
messages = [{
"content": "Hello, how are you?",
"role": "user"
}]
# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)
print(response)
You can also make asynchronous calls and even stream the model responses back for a more interactive experience. Overall, LiteLLM simplifies the complex world of interacting with multiple LLM APIs, making it accessible to both novice and experienced developers.
When compared to other existing solutions in the market, LiteLLM stands out for its comprehensive feature set and ease of use. While some tools might focus only on a specific set of providers or lack certain key features like the advanced logging and budget management capabilities of LiteLLM, it offers a holistic approach to working with LLMs.
In conclusion, LiteLLM is a valuable addition to the AI tool landscape, providing a seamless and efficient way to harness the power of multiple LLM APIs.
