Files
t6_mem0/mem0/llms/azure_openai_structured.py
2025-03-20 00:09:00 +05:30

69 lines
2.4 KiB
Python

import json
import os
from typing import Dict, List, Optional
from openai import AzureOpenAI
from mem0.configs.llms.base import BaseLlmConfig
from mem0.llms.base import LLMBase
class AzureOpenAIStructuredLLM(LLMBase):
def __init__(self, config: Optional[BaseLlmConfig] = None):
super().__init__(config)
# Model name should match the custom deployment name chosen for it.
if not self.config.model:
self.config.model = "gpt-4o-2024-08-06"
api_key = os.getenv("LLM_AZURE_OPENAI_API_KEY") or self.config.azure_kwargs.api_key
azure_deployment = os.getenv("LLM_AZURE_DEPLOYMENT") or self.config.azure_kwargs.azure_deployment
azure_endpoint = os.getenv("LLM_AZURE_ENDPOINT") or self.config.azure_kwargs.azure_endpoint
api_version = os.getenv("LLM_AZURE_API_VERSION") or self.config.azure_kwargs.api_version
default_headers = self.config.azure_kwargs.default_headers
# Can display a warning if API version is of model and api-version
self.client = AzureOpenAI(
azure_deployment=azure_deployment,
azure_endpoint=azure_endpoint,
api_version=api_version,
api_key=api_key,
http_client=self.config.http_client,
default_headers=default_headers,
)
def generate_response(
self,
messages: List[Dict[str, str]],
response_format: Optional[str] = None,
) -> str:
"""
Generate a response based on the given messages using Azure OpenAI.
Args:
messages (List[Dict[str, str]]): A list of dictionaries, each containing a 'role' and 'content' key.
response_format (Optional[str]): The desired format of the response. Defaults to None.
Returns:
str: The generated response.
"""
params = {
"model": self.config.model,
"messages": messages,
"temperature": self.config.temperature,
"max_tokens": self.config.max_tokens,
"top_p": self.config.top_p,
}
if response_format:
params["response_format"] = response_format
if tools:
params["tools"] = tools
params["tool_choice"] = tool_choice
if tools:
params["tools"] = tools
params["tool_choice"] = tool_choice
response = self.client.chat.completions.create(**params)
return self._parse_response(response, tools)