Metadata-Version: 2.1
Name: litellm
Version: 0.1.689
Summary: Library to easily interface with LLM API providers
License: MIT
Author: BerriAI
Requires-Python: >=3.8,<4.0
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Dist: importlib-metadata (>=6.8.0,<7.0.0)
Requires-Dist: openai (>=0.27.0,<0.29.0)
Requires-Dist: python-dotenv (>=0.2.0)
Requires-Dist: tiktoken (>=0.4.0,<0.5.0)
Description-Content-Type: text/markdown

<h1 align="center">
        🚅 LiteLLM
    </h1>
    <p align="center">
        <p align="center">Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, TogetherAI, Azure, OpenAI, etc.]</p>
    </p>

<h4 align="center">
    <a href="https://pypi.org/project/litellm/" target="_blank">
        <img src="https://img.shields.io/pypi/v/litellm.svg" alt="PyPI Version">
    </a>
    <a href="https://pypi.org/project/litellm/0.1.1/" target="_blank">
        <img src="https://img.shields.io/badge/stable%20version-v0.1.424-blue?color=green&link=https://pypi.org/project/litellm/0.1.1/" alt="Stable Version">
    </a>
    <a href="https://dl.circleci.com/status-badge/redirect/gh/BerriAI/litellm/tree/main" target="_blank">
        <img src="https://dl.circleci.com/status-badge/img/gh/BerriAI/litellm/tree/main.svg?style=svg" alt="CircleCI">
    </a>
    <img src="https://img.shields.io/pypi/dm/litellm" alt="Downloads">
    <a href="https://discord.gg/wuPM9dRgDw" target="_blank">
        <img src="https://dcbadge.vercel.app/api/server/wuPM9dRgDw?style=flat">
    </a>
    <a href="https://www.ycombinator.com/companies/berriai">
        <img src="https://img.shields.io/badge/Y%20Combinator-W23-orange?style=flat-square" alt="Y Combinator W23">
    </a>
    <a href="https://github.com/BerriAI/litellm/issues">
      <img src="https://img.shields.io/github/commit-activity/m/BerriAI/litellm" alt="git commit activity" />
    </a>
</h4>

<h4 align="center">
<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_OpenAI.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>    
</h4>

<h4 align="center">
    <a href="https://docs.litellm.ai/docs/providers" target="_blank">100+ Supported Models</a> |
    <a href="https://docs.litellm.ai/docs/" target="_blank">Docs</a> |
    <a href="https://litellm.ai/playground" target="_blank">Demo Website</a>
</h4>
<a target="_blank" href="https://calendly.com/d/4mp-gd3-k5k/litellm-1-1-onboarding-chat">
  <p>📣<b>1-click deploy</b> your own LLM proxy server. Grab time, if you're interested!</p>
</a>

LiteLLM manages
- Translating inputs to the provider's completion and embedding endpoints
- Guarantees [consistent output](https://docs.litellm.ai/docs/completion/output), text responses will always be available at `['choices'][0]['message']['content']`
- Exception mapping - common exceptions across providers are mapped to the OpenAI exception types


# Usage

<a target="_blank" href="https://colab.research.google.com/github/BerriAI/litellm/blob/main/cookbook/liteLLM_OpenAI.ipynb">
  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>


```
pip install litellm
```

```python
from litellm import completion
import os
## set ENV variables
os.environ["OPENAI_API_KEY"] = "openai key"
os.environ["COHERE_API_KEY"] = "cohere key"

messages = [{ "content": "Hello, how are you?","role": "user"}]

# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)

# cohere call
response = completion(model="command-nightly", messages=messages)
```

Stable version
```
pip install litellm==0.1.424
```

## Streaming
liteLLM supports streaming the model response back, pass `stream=True` to get a streaming iterator in response.
Streaming is supported for OpenAI, Azure, Anthropic, Huggingface models
```python
response = completion(model="gpt-3.5-turbo", messages=messages, stream=True)
for chunk in response:
    print(chunk['choices'][0]['delta'])

# claude 2
result = completion('claude-2', messages, stream=True)
for chunk in result:
  print(chunk['choices'][0]['delta'])
```

# Support / talk with founders
- [Schedule Demo 👋](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version)
- [Community Discord 💭](https://discord.gg/wuPM9dRgDw)
- Our numbers 📞 +1 (770) 8783-106 / ‭+1 (412) 618-6238‬
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai

# Why did we build this 
- **Need for simplicity**: Our code started to get extremely complicated managing & translating calls between Azure, OpenAI, Cohere

# Contributors

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  <img src="https://contrib.rocks/image?repo=BerriAI/litellm" />
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