BerriAI

LiteLLM is an open-source Python library that simplifies the integration of various large language model (LLM) APIs from different providers. It offers a unified interface for developers to interact with multiple AI models, streamlining tasks such as text generation, translation, and conversational AI.

Key features:
- Unified Interface: Provides a standardized method for interacting with various LLM APIs, facilitating easy switching between different models and providers.
- Extensive Model Support: Accommodates a wide range of LLM models from providers including OpenAI, Huggingface, and others.
- Diverse Task Support: Enables various AI tasks such as text generation, translation, and conversational AI.
- Robust Error Handling: Ensures application stability and responsiveness in the event of errors or model failures.
- Comprehensive Logging: Allows developers to track and monitor application performance.
- Cost Calculation: Enables tracking and management of costs associated with using different LLM models.
- Proxy Integration: Integrates with OpenAI's proxy server for convenient request redirection to chosen models.
- Streaming Capability: Supports real-time display of text as it is generated by streaming model responses.
- Retry/Fallback Logic: Implements retry and fallback mechanisms across multiple deployments to maintain functionality during model failures or rate limit issues.

How it works:
1. Install the LiteLLM library using pip.
2. Import the library into your Python project.
3. Configure the desired LLM model and provider.
4. Use the unified interface to interact with the chosen model for various AI tasks.
5. Leverage additional features such as logging, cost calculation, and error handling as needed.

Use of AI:
LiteLLM utilizes large language models from various providers to offer a unified interface for AI-powered tasks. It acts as a wrapper around these models, simplifying their integration and use in applications.

AI foundation model:
The library is built on top of large language models from providers such as OpenAI, Huggingface, and others. It leverages these models to provide a standardized interface for interacting with various LLM APIs.

Target users:
- Developers working with AI models
- Software engineers integrating multiple LLM models into applications
- AI researchers requiring a standardized interface for model interaction

How to access:
LiteLLM is available as a Python library and can be installed using the command "pip install litellm". It is open-source software, free to use and distribute. However, some LLM providers may charge for their models or services.