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Magic_yuan
2024-11-21 11:11:23 +00:00
parent 9c612833a8
commit 4763bdc272
1 changed files with 5 additions and 9 deletions
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14
examples/lightrag_azure_openai_demo.py
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@@ -4,7 +4,6 @@ from lightrag import LightRAG, QueryParam
from lightrag.utils import EmbeddingFunc from lightrag.utils import EmbeddingFunc
import numpy as np import numpy as np
from dotenv import load_dotenv from dotenv import load_dotenv
import aiohttp
import logging import logging
from openai import AzureOpenAI from openai import AzureOpenAI
@@ -31,12 +30,12 @@ os.mkdir(WORKING_DIR)
async def llm_model_func( async def llm_model_func(
prompt, system_prompt=None, history_messages=[], **kwargs prompt, system_prompt=None, history_messages=[], **kwargs
) -> str: ) -> str:
client = AzureOpenAI( client = AzureOpenAI(
api_key=AZURE_OPENAI_API_KEY, api_key=AZURE_OPENAI_API_KEY,
api_version=AZURE_OPENAI_API_VERSION, api_version=AZURE_OPENAI_API_VERSION,
azure_endpoint=AZURE_OPENAI_DEPLOYMENT azure_endpoint=AZURE_OPENAI_ENDPOINT,
) )
messages = [] messages = []
@@ -47,7 +46,7 @@ async def llm_model_func(
messages.append({"role": "user", "content": prompt}) messages.append({"role": "user", "content": prompt})
chat_completion = client.chat.completions.create( chat_completion = client.chat.completions.create(
model=LLM_AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name". model=AZURE_OPENAI_DEPLOYMENT, # model = "deployment_name".
messages=messages, messages=messages,
temperature=kwargs.get("temperature", 0), temperature=kwargs.get("temperature", 0),
top_p=kwargs.get("top_p", 1), top_p=kwargs.get("top_p", 1),
@@ -60,12 +59,9 @@ async def embedding_func(texts: list[str]) -> np.ndarray:
client = AzureOpenAI( client = AzureOpenAI(
api_key=AZURE_OPENAI_API_KEY, api_key=AZURE_OPENAI_API_KEY,
api_version=AZURE_EMBEDDING_API_VERSION, api_version=AZURE_EMBEDDING_API_VERSION,
azure_endpoint=AZURE_OPENAI_ENDPOINT azure_endpoint=AZURE_OPENAI_ENDPOINT,
)
embedding = client.embeddings.create(
model=AZURE_EMBEDDING_DEPLOYMENT,
input=texts
) )
embedding = client.embeddings.create(model=AZURE_EMBEDDING_DEPLOYMENT, input=texts)
embeddings = [item.embedding for item in embedding.data] embeddings = [item.embedding for item in embedding.data]
return np.array(embeddings) return np.array(embeddings)