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Krish Dholakia 2023-12-25 06:09:55 +05:30 • committed by GitHub
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@ -30,6 +30,32 @@ LiteLLM manages
- Exception mapping - common exceptions across providers are mapped to the OpenAI exception types.
- Load-balance across multiple deployments (e.g. Azure/OpenAI) - `Router` **1k+ requests/second**
# OpenAI Proxy - ([Docs](https://docs.litellm.ai/docs/simple_proxy))
Track spend across multiple projects/people.
### Step 1: Start litellm proxy
```shell
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:8000
```
### Step 2: Replace openai base
```python
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
```
# Usage ([**Docs**](https://docs.litellm.ai/docs/))
> [!IMPORTANT]
@ -93,35 +119,6 @@ for part in response:
print(part.choices[0].delta.content or "")
```
## OpenAI Proxy - ([Docs](https://docs.litellm.ai/docs/simple_proxy))
LiteLLM Proxy manages:
* Calling 100+ LLMs Huggingface/Bedrock/TogetherAI/etc. in the OpenAI ChatCompletions & Completions format
* Load balancing - between Multiple Models + Deployments of the same model LiteLLM proxy can handle 1k+ requests/second during load tests
* Authentication & Spend Tracking Virtual Keys
### Step 1: Start litellm proxy
```shell
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:8000
```
### Step 2: Replace openai base
```python
import openai # openai v1.0.0+
client = openai.OpenAI(api_key="anything",base_url="http://0.0.0.0:8000") # set proxy to base_url
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
])
print(response)
```
## Logging Observability ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
LiteLLM exposes pre defined callbacks to send data to Langfuse, LLMonitor, Helicone, Promptlayer, Traceloop, Slack
```python