Azure OpenAI Embeddings
Azure OpenAI Embeddings
Azure OpenAI의 임베딩 모델을 LiteLLM의 azure/ 라우트로 호출해요.
출처: 문서
본문
API 키
import os
os.environ['AZURE_API_KEY'] = ""
os.environ['AZURE_API_BASE'] = ""
os.environ['AZURE_API_VERSION'] = ""
환경 변수로 설정하거나 litellm.embedding()의 파라미터로 전달할 수 있어요.
사용법 (Usage)
from litellm import embedding
response = embedding(
model="azure/<your deployment name>",
input=["good morning from litellm"],
api_key=api_key,
api_base=api_base,
api_version=api_version,
)
print(response)
| 모델 이름 | 함수 호출 |
|---|---|
| text-embedding-ada-002 | embedding(model="azure/<your deployment name>", input=input) |
LiteLLM Proxy Server 사용법
1. 환경에 키 저장
export AZURE_API_KEY=""
2. Proxy 시작
model_list:
- model_name: text-embedding-ada-002
litellm_params:
model: azure/my-deployment-name
api_base: https://openai-gpt-4-test-v-1.openai.azure.com/
api_version: "2023-05-15"
api_key: os.environ/AZURE_API_KEY # The `os.environ/` prefix tells litellm to read this from the env.
3. 테스트
curl:
curl --location 'http://0.0.0.0:4000/embeddings' \
--header 'Content-Type: application/json' \
--data ' {
"model": "text-embedding-ada-002",
"input": ["write a litellm poem"]
}'
OpenAI Python SDK:
import openai
from openai import OpenAI
# set base_url to your proxy server
# set api_key to send to proxy server
client = OpenAI(api_key="<proxy-api-key>", base_url="http://0.0.0.0:4000")
response = client.embeddings.create(
input=["hello from litellm"],
model="text-embedding-ada-002",
)
print(response)