🚅 LiteLLM - A/B Testing LLMs in Production
Call all LLM APIs using the OpenAI format [Anthropic, Huggingface, Cohere, Azure OpenAI etc.]
LiteLLM allows you to call 100+ LLMs using completion
## This template server allows you to define LLMs with their A/B test ratios
```python
llm_dict = {
"gpt-4": 0.2,
"together_ai/togethercomputer/llama-2-70b-chat": 0.4,
"claude-2": 0.2,
"claude-1.2": 0.2
}
```
All models defined can be called with the same Input/Output format using litellm `completion`
```python
from litellm import completion
# SET API KEYS in .env
# openai call
response = completion(model="gpt-3.5-turbo", messages=messages)
# cohere call
response = completion(model="command-nightly", messages=messages)
# anthropic
response = completion(model="claude-2", messages=messages)
```
This server allows you to view responses, costs and latency on your LiteLLM dashboard
### LiteLLM Client UI
# Using LiteLLM A/B Testing Server
## Setup
### Install LiteLLM
```
pip install litellm
```
Stable version
```
pip install litellm==0.1.424
```
### Clone LiteLLM Git Repo
```
git clone https://github.com/BerriAI/litellm/
```
### Navigate to LiteLLM-A/B Test Server
```
cd litellm/cookbook/llm-ab-test-server
```
### Run the Server
```
python3 main.py
```
### Set your LLM Configs
Set your LLMs and LLM weights you want to run A/B testing with
In main.py set your selected LLMs you want to AB test in `llm_dict`
You can A/B test more than 100+ LLMs using LiteLLM https://docs.litellm.ai/docs/completion/supported
```python
llm_dict = {
"gpt-4": 0.2,
"together_ai/togethercomputer/llama-2-70b-chat": 0.4,
"claude-2": 0.2,
"claude-1.2": 0.2
}
```
#### Setting your API Keys
Set your LLM API keys in a .env file in the directory or set them as `os.environ` variables.
See https://docs.litellm.ai/docs/completion/supported for the format of API keys
LiteLLM generalizes api keys to follow the following format
`PROVIDER_API_KEY`
## Making Requests to the LiteLLM Server Locally
The server follows the Input/Output format set by the OpenAI Chat Completions API
Here is an example request made the LiteLLM Server
### Python
```python
import requests
import json
url = "http://localhost:5000/chat/completions"
payload = json.dumps({
"messages": [
{
"content": "who is CTO of litellm",
"role": "user"
}
]
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
```
### Curl Command
```
curl --location 'http://localhost:5000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"messages": [
{
"content": "who is CTO of litellm",
"role": "user"
}
]
}
'
```
## Viewing Logs
After running your first `completion()` call litellm autogenerates a new logs dashboard for you. Link to your Logs dashboard is generated in the terminal / console.
Example Terminal Output with Log Dashboard
# 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