“`BerriAI-litellm Product Overview
BerriAI-litellm, developed by BerriAI, is a versatile and lightweight solution designed to simplify the process of interacting with multiple Large Language Model (LLM) APIs. This tool is particularly useful for developers, Gen AI Enablement teams, and ML Platform Teams looking to streamline their workflow when working with various LLM providers.
What BerriAI-litellm Does
BerriAI-litellm acts as a unified interface to access over 100 different LLMs, including those from OpenAI, Azure, Cohere, Anthropic, VertexAI, HuggingFace, and more. It translates inputs into the respective formats required by these providers for endpoints such as `completion`, `embedding`, and `image_generation`, ensuring consistent output across different models.Key Features and Functionality
Unified Interface
- BerriAI-litellm provides a single interface to call multiple LLM APIs, eliminating the need to manage individual API calls and handle different response formats.
Consistent Output
- The tool ensures that text responses are consistently available, making it easier to integrate and parse responses.
Retry/Fallback Logic
- BerriAI-litellm includes retry and fallback logic across multiple deployments, such as Azure and OpenAI, to enhance reliability and reduce downtime.
Load Balancing and Cost Tracking
- The LiteLLM Proxy Server component allows for load balancing and cost tracking across multiple projects, helping in managing resources and budgets effectively.
LiteLLM Proxy Server (LLM Gateway)
- This server acts as a central service to access multiple LLMs, enabling features like tracking LLM usage, setting up guardrails, and customizing logging and caching per project. It is typically used by Gen AI Enablement and ML Platform Teams.
LiteLLM Python SDK
- For developers, the LiteLLM Python SDK provides a unified interface to access multiple LLMs directly within Python code. It supports various LLM providers and includes features like retry/fallback logic and cost tracking.
Installation and Usage
- The tool can be installed via `pip install litellm` for the Python SDK, or by running a Docker container for the LiteLLM Proxy Server. Detailed configuration and usage instructions are available, including setting up API keys and model configurations.
Benefits
- Simplicity: BerriAI-litellm simplifies the complex task of managing multiple LLM APIs, reducing the need for extensive code and individual API calls.
- Reliability: The tool ensures consistent output and includes retry/fallback logic to maintain service reliability.
- Flexibility: It offers both a Proxy Server for central management and a Python SDK for direct integration into development projects.
- Cost Management: Features for tracking spend and setting budgets help in managing resources efficiently.