forked from phoenix/litellm-mirror
docs(configs.md): add aws routing to proxy docs
This commit is contained in:
Krrish Dholakia
parent
48bd58f007
commit
ee50e97c98
2 changed files with 125 additions and 113 deletions
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@ -22,18 +22,22 @@ Set a model alias for your deployments.
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In the `config.yaml` the model_name parameter is the user-facing name to use for your deployment.
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In the config below requests with:
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In the config below:
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- `model_name`: the name to pass TO litellm from the external client
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- `litellm_params.model`: the model string passed to the litellm.completion() function
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E.g.:
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- `model=vllm-models` will route to `openai/facebook/opt-125m`.
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- `model=gpt-3.5-turbo` will load balance between `azure/gpt-turbo-small-eu` and `azure/gpt-turbo-small-ca`
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```yaml
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model_list:
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- model_name: gpt-3.5-turbo # user-facing model alias
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- model_name: gpt-3.5-turbo ### RECEIVED MODEL NAME ###
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litellm_params: # all params accepted by litellm.completion() - https://docs.litellm.ai/docs/completion/input
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model: azure/gpt-turbo-small-eu
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model: azure/gpt-turbo-small-eu ### MODEL NAME sent to `litellm.completion()` ###
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api_base: https://my-endpoint-europe-berri-992.openai.azure.com/
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api_key: "os.environ/AZURE_API_KEY_EU" # does os.getenv("AZURE_API_KEY_EU")
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rpm: 6 # Rate limit for this deployment: in requests per minute (rpm)
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rpm: 6 # [OPTIONAL] Rate limit for this deployment: in requests per minute (rpm)
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- model_name: bedrock-claude-v1
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litellm_params:
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model: bedrock/anthropic.claude-instant-v1
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@ -43,6 +47,11 @@ model_list:
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api_base: https://my-endpoint-canada-berri992.openai.azure.com/
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api_key: "os.environ/AZURE_API_KEY_CA"
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rpm: 6
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- model_name: anthropic-claude
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litellm_params:
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model="bedrock/anthropic.claude-instant-v1"
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### [OPTIONAL] SET AWS REGION ###
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aws_region_name="us-east-1"
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- model_name: vllm-models
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litellm_params:
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model: openai/facebook/opt-125m # the `openai/` prefix tells litellm it's openai compatible
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@ -58,6 +67,11 @@ litellm_settings: # module level litellm settings - https://github.com/BerriAI/l
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general_settings:
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master_key: sk-1234 # [OPTIONAL] Only use this if you to require all calls to contain this key (Authorization: Bearer sk-1234)
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```
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:::info
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For more provider-specific info, [go here](../providers/)
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:::
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#### Step 2: Start Proxy with config
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@ -40,115 +40,6 @@ litellm --test
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This will now automatically route any requests for gpt-3.5-turbo to bigcode starcoder, hosted on huggingface inference endpoints.
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### Using LiteLLM Proxy - Curl Request, OpenAI Package, Langchain
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<Tabs>
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<TabItem value="Curl" label="Curl Request">
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```shell
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curl --location 'http://0.0.0.0:8000/chat/completions' \
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--header 'Content-Type: application/json' \
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--data ' {
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": "what llm are you"
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}
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]
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}
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'
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```
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</TabItem>
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<TabItem value="openai" label="OpenAI v1.0.0+">
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```python
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import openai
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client = openai.OpenAI(
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api_key="anything",
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base_url="http://0.0.0.0:8000"
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)
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# request sent to model set on litellm proxy, `litellm --model`
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response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
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{
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"role": "user",
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"content": "this is a test request, write a short poem"
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}
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])
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print(response)
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```
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</TabItem>
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<TabItem value="langchain" label="Langchain">
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```python
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain.schema import HumanMessage, SystemMessage
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chat = ChatOpenAI(
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openai_api_base="http://0.0.0.0:8000", # set openai_api_base to the LiteLLM Proxy
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model = "gpt-3.5-turbo",
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temperature=0.1
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)
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messages = [
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SystemMessage(
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content="You are a helpful assistant that im using to make a test request to."
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),
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HumanMessage(
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content="test from litellm. tell me why it's amazing in 1 sentence"
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),
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]
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response = chat(messages)
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print(response)
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```
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</TabItem>
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<TabItem value="langchain-embedding" label="Langchain Embeddings">
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```python
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings(model="sagemaker-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key")
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(f"SAGEMAKER EMBEDDINGS")
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print(query_result[:5])
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embeddings = OpenAIEmbeddings(model="bedrock-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key")
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(f"BEDROCK EMBEDDINGS")
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print(query_result[:5])
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embeddings = OpenAIEmbeddings(model="bedrock-titan-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key")
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(f"TITAN EMBEDDINGS")
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print(query_result[:5])
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```
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</TabItem>
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</Tabs>
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### Supported LLMs
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All LiteLLM supported LLMs are supported on the Proxy. Seel all [supported llms](https://docs.litellm.ai/docs/providers)
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<Tabs>
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@ -331,6 +222,113 @@ $ litellm --model command-nightly
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</Tabs>
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### Using LiteLLM Proxy - Curl Request, OpenAI Package, Langchain
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<Tabs>
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<TabItem value="Curl" label="Curl Request">
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```shell
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curl --location 'http://0.0.0.0:8000/chat/completions' \
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--header 'Content-Type: application/json' \
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--data ' {
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": "what llm are you"
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}
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]
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}
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'
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```
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</TabItem>
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<TabItem value="openai" label="OpenAI v1.0.0+">
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```python
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import openai
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client = openai.OpenAI(
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api_key="anything",
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base_url="http://0.0.0.0:8000"
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)
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# request sent to model set on litellm proxy, `litellm --model`
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response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
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{
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"role": "user",
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"content": "this is a test request, write a short poem"
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}
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])
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print(response)
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```
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</TabItem>
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<TabItem value="langchain" label="Langchain">
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```python
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from langchain.chat_models import ChatOpenAI
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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SystemMessagePromptTemplate,
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)
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from langchain.schema import HumanMessage, SystemMessage
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chat = ChatOpenAI(
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openai_api_base="http://0.0.0.0:8000", # set openai_api_base to the LiteLLM Proxy
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model = "gpt-3.5-turbo",
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temperature=0.1
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)
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messages = [
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SystemMessage(
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content="You are a helpful assistant that im using to make a test request to."
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),
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HumanMessage(
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content="test from litellm. tell me why it's amazing in 1 sentence"
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),
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]
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response = chat(messages)
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print(response)
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```
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</TabItem>
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<TabItem value="langchain-embedding" label="Langchain Embeddings">
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```python
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings(model="sagemaker-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key")
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(f"SAGEMAKER EMBEDDINGS")
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print(query_result[:5])
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embeddings = OpenAIEmbeddings(model="bedrock-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key")
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(f"BEDROCK EMBEDDINGS")
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print(query_result[:5])
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embeddings = OpenAIEmbeddings(model="bedrock-titan-embeddings", openai_api_base="http://0.0.0.0:8000", openai_api_key="temp-key")
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(f"TITAN EMBEDDINGS")
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print(query_result[:5])
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```
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</TabItem>
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</Tabs>
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## Quick Start - LiteLLM Proxy + Config.yaml
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