LiteLLM: A Comprehensive Analysis

LiteLLM

What is LiteLLM?

LiteLLM is a Python library that simplifies the process of integrating various Large Language Model (LLM) APIs, facilitating access to over 100 large language model services from different providers. With LiteLLM, you can seamlessly interact with LLM APIs using the standardized format of OpenAI.

These providers include well-known names like Azure, AWS Bedrock, Anthropic, HuggingFace, Cohere, OpenAI, Ollama, Sagemaker, HuggingFace, Replicate, and many more, giving you a wide range of options for language model capabilities.

Reference: Supported LLM providers

Features of LiteLLM

• Supports for various LLM providers- LiteLLM lets you work with multiple LLM providers by simply providing the API key, model name, and API endpoint.
• Open source- Since LiteLLM is open-source, anyone is welcome to use and contribute to it.
• Streaming support- ‘stream’ is one of the arguments in the completion() function. Using this LLM response can be streamed, much like Bing Chat or ChatGPT. It enhances user experience.
  Reference: LiteLLM Python SDK

response = completion(model=model_name, 
                      messages=[{"role": "user", "content": "Hey! how's it going?"}], 
                      stream=True)

• Consistent input and output format- It uses OpenAI format for all models. User requests are sent using the completion() function call. To get the response, you can access it as follows:

print(response['choices'][0]['message']['content'])

• Simplifies exception handling- Different LLM providers (E.g.: Azure / AWS) have their own way of reporting errors which can be difficult to handle. So, LiteLLM simplifies error handling by aligning exceptions from various providers with OpenAI’s exception types.

from openai import OpenAIError
from litellm import completion
import os

os.environ["ANTHROPIC_API_KEY"] = "bad-key"
try:
    completion(model=model_name, 
               messages=[{"role": "user", "content": "Hey, how's it going?"}])
except OpenAIError as e:
    print("Error: ", e)

• Cost tracking- You can seamlessly track streaming and non-streaming costs using the callback function. Implementing this callback function helps manage your budget efficiently.

litellm.success_callback = [track_cost_callback] 

Check out Version 1’s AI Webinar Series — Navigating the AI Landscape.

Litellm’s Error Handling and Fallback Mechanisms

Litellm offers error handling and fallback mechanisms to ensure that the application operates smoothly and without interruptions. LiteLLM employs 2 methods to mitigate failed requests: Retries and Fallbacks.

1. Retries- To retry failed requests, set the ‘num_retries’ parameter within the completion() function. This ensures improved robustness and reliability in handling request failures.

response = completion(model= model_name,
                      messages= messages,
                      num_retries= 2)

2. Fallbacks- This guarantees a response from the API call, even if the initial request fails.

• Context Window Fallbacks- The context_window_fallback_dict parameter is used to specify a fallback model when the context window of the primary model is exceeded. For example, in the GPT-3.5-turbo model, the maximum context window is 4096 tokens. If the total number of tokens in the messages exceeds this limit, an error will be thrown. To prevent this, use the context_window_fallback_dictyou parameter in the completion() function, specifying a fallback model like GPT-3.5-turbo-16k, which supports up to 16,000 tokens.
• Switch Models- Provide a list of models in the fallbacksparameter, including the main one you want to use and other backup models in case the main model doesn’t respond. So, if the main model fails, it automatically tries the fallback models listed in the order you specified.

response = completion(model="bad-model", 
                      messages=messages, 
                      fallbacks=["gpt-3.5-turbo" "gpt-4"])

• Also, there are other options available, such as switching API keys or API bases. For further information, visit: LiteLLM

Load Balancing Across Multiple Deployments

LiteLLM has multiple Advanced Routing Strategies and one of them is Least-Busy. The Least-Busy routing strategy works by keeping track of the number of active requests each model is handling. When a new request comes in, the router checks which model is currently handling the fewest requests and sends the new request to that model.

• This strategy is useful when you have multiple deployments of a model and you want to distribute the load evenly among them.
• It helps to maximize the utilization of resources and can lead to faster response times when there’s a high volume of requests.

Refer to this for more information: Advanced — Routing Strategies

Benefits

LiteLLM offers several benefits:

1. Reduced complexity: Integration of various language models becomes simpler with LiteLLM.
2. Increased flexibility: It translates the input requests into a specific format that matches each provider’s endpoint. It formats the response from the LLM into a standardized output structure.
3. Cost-effectiveness: Cost can be optimized by exploring different pricing models across different providers

Use cases:

1. Versatile Model Usage: You can easily move between different language models in LiteLLM inside a single application. For example, LiteLLM enables the system to dynamically transition to another model if one is lagging due to high latency, guaranteeing consumers a seamless and uninterrupted experience.

2. Effortless Cost Monitoring: With the callback function, effortlessly keep track of both streaming and non-streaming costs. Implementing this feature helps to manage the budget efficiently and effectively.

Check out the OpenAI Proxy Server for more information on tracking expenses & setting budgets per project.

Conclusion:

To sum up, LiteLLM proves to be an effective tool for streamlining the integration and utilization of over 100 language models from various leading providers. By offering seamless interaction through a standardized OpenAI format, LiteLLM simplifies the complexities of working with diverse APIs. Its support for streaming, error handling, load balancing mechanisms, and open-source nature contribute to its reliability and versatility.

With LiteLLM, businesses and developers can unlock a multitude of use cases, from dynamically switching between models to ensure optimal performance to effortlessly monitoring costs and usage. Overall, LiteLLM is a useful tool for anybody working with natural language processing.

About the Author
Vaishnavi R is a Data Scientist at the Version 1 AI Labs.