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Ishaan Jaff 2023-11-08 13:30:39 -08:00 • committed by GitHub
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@ -74,24 +74,6 @@ result = completion('claude-2', messages, stream=True)
for chunk in result:
print(chunk['choices'][0]['delta'])
```
## OpenAI Proxy
Use LiteLLM in any OpenAI API compatible project
```shell
$ litellm --model huggingface/bigcode/starcoder
#INFO: Proxy running on http://0.0.0.0:8000
```
### Replace openai base
```python
import openai
openai.api_base = "http://0.0.0.0:8000"
print(openai.ChatCompletion.create(model="test", messages=[{"role":"user", "content":"Hey!"}]))
```
## Logging Observability - Log LLM Input/Output ([Docs](https://docs.litellm.ai/docs/observability/callbacks))
LiteLLM exposes pre defined callbacks to send data to LLMonitor, Langfuse, Helicone, Promptlayer, Traceloop, Slack
@ -111,6 +93,24 @@ litellm.success_callback = ["promptlayer", "llmonitor"] # log input/output to pr
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
```
## OpenAI Proxy
Use LiteLLM in any OpenAI API compatible project. Calling 100+ LLMs Huggingface/Bedrock/TogetherAI/etc. in the OpenAI ChatCompletions & Completions format
### 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.api_base = "http://0.0.0.0:8000"
print(openai.ChatCompletion.create(model="test", messages=[{"role":"user", "content":"Hey!"}]))
```
## Supported Provider ([Docs](https://docs.litellm.ai/docs/providers))
| Provider | [Completion](https://docs.litellm.ai/docs/#basic-usage) | [Streaming](https://docs.litellm.ai/docs/completion/stream#streaming-responses) | [Async Completion](https://docs.litellm.ai/docs/completion/stream#async-completion) | [Async Streaming](https://docs.litellm.ai/docs/completion/stream#async-streaming) |