127 lines
4.5 KiB
Python
127 lines
4.5 KiB
Python
import json
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import os
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from typing import Dict, List, Optional, Union
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from openai import AzureOpenAI
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from mem0.configs.llms.azure import AzureOpenAIConfig
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from mem0.configs.llms.base import BaseLlmConfig
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from mem0.llms.base import LLMBase
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from mem0.memory.utils import extract_json
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class AzureOpenAILLM(LLMBase):
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def __init__(self, config: Optional[Union[BaseLlmConfig, AzureOpenAIConfig, Dict]] = None):
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# Convert to AzureOpenAIConfig if needed
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if config is None:
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config = AzureOpenAIConfig()
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elif isinstance(config, dict):
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config = AzureOpenAIConfig(**config)
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elif isinstance(config, BaseLlmConfig) and not isinstance(config, AzureOpenAIConfig):
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# Convert BaseLlmConfig to AzureOpenAIConfig
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config = AzureOpenAIConfig(
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model=config.model,
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temperature=config.temperature,
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api_key=config.api_key,
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max_tokens=config.max_tokens,
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top_p=config.top_p,
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top_k=config.top_k,
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enable_vision=config.enable_vision,
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vision_details=config.vision_details,
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http_client_proxies=config.http_client,
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)
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super().__init__(config)
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# Model name should match the custom deployment name chosen for it.
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if not self.config.model:
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self.config.model = "gpt-4o"
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api_key = self.config.azure_kwargs.api_key or os.getenv("LLM_AZURE_OPENAI_API_KEY")
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azure_deployment = self.config.azure_kwargs.azure_deployment or os.getenv("LLM_AZURE_DEPLOYMENT")
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azure_endpoint = self.config.azure_kwargs.azure_endpoint or os.getenv("LLM_AZURE_ENDPOINT")
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api_version = self.config.azure_kwargs.api_version or os.getenv("LLM_AZURE_API_VERSION")
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default_headers = self.config.azure_kwargs.default_headers
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self.client = AzureOpenAI(
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azure_deployment=azure_deployment,
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azure_endpoint=azure_endpoint,
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api_version=api_version,
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api_key=api_key,
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http_client=self.config.http_client,
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default_headers=default_headers,
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)
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def _parse_response(self, response, tools):
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"""
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Process the response based on whether tools are used or not.
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Args:
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response: The raw response from API.
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tools: The list of tools provided in the request.
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Returns:
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str or dict: The processed response.
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"""
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if tools:
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processed_response = {
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"content": response.choices[0].message.content,
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"tool_calls": [],
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}
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if response.choices[0].message.tool_calls:
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for tool_call in response.choices[0].message.tool_calls:
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processed_response["tool_calls"].append(
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{
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"name": tool_call.function.name,
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"arguments": json.loads(extract_json(tool_call.function.arguments)),
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}
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)
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return processed_response
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else:
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return response.choices[0].message.content
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def generate_response(
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self,
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messages: List[Dict[str, str]],
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response_format=None,
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tools: Optional[List[Dict]] = None,
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tool_choice: str = "auto",
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**kwargs,
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):
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"""
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Generate a response based on the given messages using Azure OpenAI.
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Args:
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messages (list): List of message dicts containing 'role' and 'content'.
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response_format (str or object, optional): Format of the response. Defaults to "text".
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tools (list, optional): List of tools that the model can call. Defaults to None.
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tool_choice (str, optional): Tool choice method. Defaults to "auto".
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**kwargs: Additional Azure OpenAI-specific parameters.
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Returns:
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str: The generated response.
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"""
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user_prompt = messages[-1]["content"]
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user_prompt = user_prompt.replace("assistant", "ai")
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messages[-1]["content"] = user_prompt
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params = self._get_supported_params(messages=messages, **kwargs)
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# Add model and messages
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params.update({
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"model": self.config.model,
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"messages": messages,
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})
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if tools:
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params["tools"] = tools
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params["tool_choice"] = tool_choice
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response = self.client.chat.completions.create(**params)
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return self._parse_response(response, tools)
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