LiteLLM

LiteLLM simplifies LLM completion and embedding calls with an open-source library.

LLM completionLLM embeddingopen-source library

Introduction

LiteLLM is an open-source library that simplifies LLM completion and embedding calls. It provides a convenient and easy-to-use interface for calling different LLM models.

Key Features

The core features of LiteLLM include simplified LLM completion and embedding calls, support for multiple LLM models (such as GPT-3.5-turbo and Cohere's command-nightly), and a demo playground to compare LLM models.

Frequently Asked Questions

What is LiteLLM?

LiteLLM is an open-source library that simplifies LLM completion and embedding calls. It provides a convenient and easy-to-use interface for calling different LLM models.

How to use LiteLLM?

To use LiteLLM, you need to import the 'litellm' library and set the necessary environment variables for the LLM API keys (e.g., OPENAI_API_KEY and COHERE_API_KEY). Once the environment variables are set, you can create a Python function and make LLM completion calls using LiteLLM. LiteLLM allows you to compare different LLM models by providing a demo playground where you can write Python code and see the outputs.

What LLM models does LiteLLM support?

LiteLLM supports multiple LLM models, such as GPT-3.5-turbo and Cohere's command-nightly.

Can LiteLLM be used for research purposes?

Yes, LiteLLM can be used for research purposes as it simplifies LLM completion and embedding calls in Python.

Does LiteLLM have its own pricing?

No, LiteLLM is an open-source library and does not have its own pricing. The pricing of the underlying LLM models may vary and should be referred to their respective providers.

What is the demo playground in LiteLLM?

The demo playground in LiteLLM allows users to compare different LLM models by writing Python code and seeing the outputs.

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Use Cases

LiteLLM can be used for various natural language processing tasks, such as text generation, language understanding, chatbot development, and more. It is suitable for both research purposes and building applications that require LLM capabilities.