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docs(adding-litellm-proxy-config-to-docs): adding litellm proxy config to docs

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Krrish Dholakia 2023-10-07 14:14:05 -07:00
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docs/my-website/docs/proxy_server.md
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@ -6,46 +6,23 @@ import TabItem from '@theme/TabItem';
CLI Tool to create a LLM Proxy Server to translate openai api calls to any non-openai model (e.g. Huggingface, TogetherAI, Ollama, etc.) 100+ models [Provider List](https://docs.litellm.ai/docs/providers).
## Quick start
Call Huggingface models through your OpenAI proxy.
Call Ollama models through your OpenAI proxy.
### Start Proxy
```shell
$ pip install litellm
```
```shell
$ litellm --model huggingface/bigcode/starcoder
$ litellm --model ollama/llama2
#INFO: Uvicorn running on http://0.0.0.0:8000
```
This will host a local proxy api at: **http://0.0.0.0:8000**
### Test Proxy
Make a test ChatCompletion Request to your proxy
<Tabs>
<TabItem value="litellm" label="litellm cli">
```shell
litellm --test http://0.0.0.0:8000
```
</TabItem>
<TabItem value="openai" label="OpenAI">
```python
import openai
openai.api_base = "http://0.0.0.0:8000"
print(openai.ChatCompletion.create(model="test", messages=[{"role":"user", "content":"Hey!"}]))
```
</TabItem>
<TabItem value="curl" label="curl">
```curl
curl --location 'http://0.0.0.0:8000/chat/completions' \
Let's see if it works
```shell
$ curl --location 'http://0.0.0.0:8000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"messages": [
@ -56,20 +33,32 @@ curl --location 'http://0.0.0.0:8000/chat/completions' \
],
}'
```
</TabItem>
</Tabs>
### 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!"}]))
```
#### Other supported models:
<Tabs>
<TabItem value="anthropic" label="Anthropic">
<TabItem value="vllm-local" label="VLLM">
Assuming you're running vllm locally
```shell
$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1
$ litellm --model vllm/facebook/opt-125m
```
</TabItem>
<TabItem value="openai-proxy" label="OpenAI Compatible Server">
```shell
$ litellm --model openai/<model_name> --api_base <your-api-base>
```
</TabItem>
<TabItem value="huggingface" label="Huggingface">
```shell
@ -77,6 +66,14 @@ $ export HUGGINGFACE_API_KEY=my-api-key #[OPTIONAL]
$ litellm --model claude-instant-1
```
</TabItem>
<TabItem value="anthropic" label="Anthropic">
```shell
$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1
```
</TabItem>
<TabItem value="together_ai" label="TogetherAI">
@ -120,9 +117,8 @@ $ litellm --model palm/chat-bison
```shell
$ export AZURE_API_KEY=my-api-key
$ export AZURE_API_BASE=my-api-base
$ export AZURE_API_VERSION=my-api-version
$ litellm --model azure/my-deployment-id
$ litellm --model azure/my-deployment-name
```
</TabItem>
@ -149,8 +145,23 @@ $ litellm --model command-nightly
[**Jump to Code**](https://github.com/BerriAI/litellm/blob/fef4146396d5d87006259e00095a62e3900d6bb4/litellm/proxy.py#L36)
## Configure Model
### Deploy Proxy
To save api keys and/or customize model prompt, run:
```shell
$ litellm --config
```
This will open a .env file that will store these values locally.
To set api base, temperature, and max tokens, add it to your cli command
```shell
litellm --model ollama/llama2 \
--api_base http://localhost:11434 \
--max_tokens 250 \
--temperature 0.5
```
## Deploy Proxy
<Tabs>
<TabItem value="self-hosted" label="Self-Hosted">
@ -193,141 +204,9 @@ $ litellm --model claude-instant-1 --deploy
```
This will host a ChatCompletions API at: https://api.litellm.ai/44508ad4
#### Other supported models:
<Tabs>
<TabItem value="anthropic" label="Anthropic">
```shell
$ export ANTHROPIC_API_KEY=my-api-key
$ litellm --model claude-instant-1 --deploy
```
</TabItem>
<TabItem value="together_ai" label="TogetherAI">
```shell
$ export TOGETHERAI_API_KEY=my-api-key
$ litellm --model together_ai/lmsys/vicuna-13b-v1.5-16k --deploy
```
</TabItem>
<TabItem value="replicate" label="Replicate">
```shell
$ export REPLICATE_API_KEY=my-api-key
$ litellm \
--model replicate/meta/llama-2-70b-chat:02e509c789964a7ea8736978a43525956ef40397be9033abf9fd2badfe68c9e3
--deploy
```
</TabItem>
<TabItem value="petals" label="Petals">
```shell
$ litellm --model petals/meta-llama/Llama-2-70b-chat-hf --deploy
```
</TabItem>
<TabItem value="palm" label="Palm">
```shell
$ export PALM_API_KEY=my-palm-key
$ litellm --model palm/chat-bison --deploy
```
</TabItem>
<TabItem value="azure" label="Azure OpenAI">
```shell
$ export AZURE_API_KEY=my-api-key
$ export AZURE_API_BASE=my-api-base
$ export AZURE_API_VERSION=my-api-version
$ litellm --model azure/my-deployment-id --deploy
```
</TabItem>
<TabItem value="ai21" label="AI21">
```shell
$ export AI21_API_KEY=my-api-key
$ litellm --model j2-light --deploy
```
</TabItem>
<TabItem value="cohere" label="Cohere">
```shell
$ export COHERE_API_KEY=my-api-key
$ litellm --model command-nightly --deploy
```
</TabItem>
</Tabs>
### Test Deployed Proxy
Make a test ChatCompletion Request to your proxy
<Tabs>
<TabItem value="litellm" label="litellm cli">
```shell
litellm --test https://api.litellm.ai/44508ad4
```
</TabItem>
<TabItem value="openai" label="OpenAI">
```python
import openai
openai.api_base = "https://api.litellm.ai/44508ad4"
print(openai.ChatCompletion.create(model="test", messages=[{"role":"user", "content":"Hey!"}]))
```
</TabItem>
<TabItem value="curl" label="curl">
```curl
curl --location 'https://api.litellm.ai/44508ad4/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"messages": [
{
"role": "user",
"content": "what do you know?"
}
],
}'
```
</TabItem>
</Tabs>
</TabItem>
</Tabs>
## Setting api base, temperature, max tokens
```shell
litellm --model huggingface/bigcode/starcoder \
--api_base https://my-endpoint.huggingface.cloud \
--max_tokens 250 \
--temperature 0.5
```
**Ollama example**
```shell
$ litellm --model ollama/llama2 --api_base http://localhost:11434
```
## Tutorial - using HuggingFace LLMs with aider
[Aider](https://github.com/paul-gauthier/aider) is an AI pair programming in your terminal.