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from openai import AzureOpenAI, OpenAI,AsyncAzureOpenAI,AsyncOpenAI
from abc import abstractmethod
import os
import httpx
import base64
import logging
import asyncio
import numpy as np
from tenacity import (
retry,
stop_after_attempt,
wait_fixed,
)
def get_content_between_a_b(start_tag, end_tag, text):
extracted_text = ""
start_index = text.find(start_tag)
while start_index != -1:
end_index = text.find(end_tag, start_index + len(start_tag))
if end_index != -1:
extracted_text += text[start_index + len(start_tag) : end_index] + " "
start_index = text.find(start_tag, end_index + len(end_tag))
else:
break
return extracted_text.strip()
def before_retry_fn(retry_state):
if retry_state.attempt_number > 1:
logging.info(f"Retrying API call. Attempt #{retry_state.attempt_number}, f{retry_state}")
def encode_image(image_path):
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode('utf-8')
def get_openai_url(img_pth):
end = img_pth.split(".")[-1]
if end == "jpg":
end = "jpeg"
base64_image = encode_image(img_pth)
return f"data:image/{end};base64,{base64_image}"
class base_llm:
def __init__(self) -> None:
pass
@abstractmethod
def response(self,messages,**kwargs):
pass
def get_imgs(self,prompt, save_path="saves/dalle3.jpg"):
pass
class openai_llm(base_llm):
def __init__(self,model = "gpt4o-0513") -> None:
super().__init__()
self.model = model
if "AZURE_OPENAI_ENDPOINT" not in os.environ or os.environ["AZURE_OPENAI_ENDPOINT"] == "":
raise ValueError("AZURE_OPENAI_ENDPOINT is not set")
if "AZURE_OPENAI_KEY" not in os.environ or os.environ["AZURE_OPENAI_KEY"] == "":
raise ValueError("AZURE_OPENAI_KEY is not set")
api_version = os.environ.get("AZURE_OPENAI_API_VERSION",None)
if api_version == "":
api_version = None
self.client = AzureOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
api_key=os.environ["AZURE_OPENAI_KEY"],
api_version= api_version
)
self.async_client = AsyncAzureOpenAI(
azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
api_key=os.environ["AZURE_OPENAI_KEY"],
api_version= api_version
)
def cal_cosine_similarity(self, vec1, vec2):
if isinstance(vec1, list):
vec1 = np.array(vec1)
if isinstance(vec2, list):
vec2 = np.array(vec2)
return np.dot(vec1, vec2) / (np.linalg.norm(vec1) * np.linalg.norm(vec2))
@retry(wait=wait_fixed(10), stop=stop_after_attempt(10), before=before_retry_fn)
def response(self,messages,**kwargs):
try:
response = self.client.chat.completions.create(
model=kwargs.get("model", self.model),
messages=messages,
n = kwargs.get("n", 1),
temperature= kwargs.get("temperature", 0.7),
max_tokens=kwargs.get("max_tokens", 4000),
timeout=kwargs.get("timeout", 180)
)
except Exception as e:
model = kwargs.get("model", self.model)
print(f"get {model} response failed: {e}")
print(e)
logging.info(e)
return
return response.choices[0].message.content
@retry(wait=wait_fixed(10), stop=stop_after_attempt(10), before=before_retry_fn)
def get_embbeding(self,text):
if os.environ.get("EMBEDDING_API_ENDPOINT"):
client = AzureOpenAI(
azure_endpoint=os.environ.get("EMBEDDING_API_ENDPOINT",None),
api_key=os.environ.get("EMBEDDING_API_KEY",None),
api_version= os.environ.get("AZURE_OPENAI_API_VERSION",None),
azure_deployment="embedding-3-large"
)
else:
client = self.client
try:
embbeding = client.embeddings.create(
model=os.environ.get("EMBEDDING_MODEL","text-embedding-3-large"),
input=text,
timeout= 180
)
return embbeding.data[0].embedding
except Exception as e:
print(f"get embbeding failed: {e}")
print(e)
logging.info(e)
return
async def get_embbeding_async(self,text):
if os.environ.get("EMBEDDING_API_ENDPOINT",None):
async_client = AsyncAzureOpenAI(
azure_endpoint=os.environ.get("EMBEDDING_API_ENDPOINT",None),
api_key=os.environ.get("EMBEDDING_API_KEY",None),
api_version= os.environ.get("AZURE_OPENAI_API_VERSION",None),
azure_deployment="embedding-3-large"
)
else:
async_client = self.async_client
try:
embbeding = await async_client.embeddings.create(
model=os.environ.get("EMBEDDING_MODEL","text-embedding-3-large"),
input=text,
timeout= 180
)
return embbeding.data[0].embedding
except Exception as e:
await asyncio.sleep(0.1)
print(f"get embbeding failed: {e}")
print(e)
logging.info(e)
return
@retry(wait=wait_fixed(10), stop=stop_after_attempt(10), before=before_retry_fn)
async def response_async(self,messages,**kwargs):
try:
response = await self.async_client.chat.completions.create(
model=kwargs.get("model", self.model),
messages=messages,
n = kwargs.get("n", 1),
temperature= kwargs.get("temperature", 0.7),
max_tokens=kwargs.get("max_tokens", 4000),
timeout=kwargs.get("timeout", 180)
)
except Exception as e:
await asyncio.sleep(0.1)
model = kwargs.get("model", self.model)
print(f"get {model} response failed: {e}")
print(e)
logging.info(e)
return
return response.choices[0].message.content
if __name__ == "__main__":
llm = gemini_llm(api_key="")
prompt = """
"""
messages = [{"role":"user","content":prompt}]
response = asyncio.run(llm.response_async(messages))
print(response) |