import os import sysconfig from datetime import datetime from typing import Any, Dict, List, Optional, Union import geocoder from openai import AsyncAzureOpenAI, AzureOpenAI from pydantic import Field from ...utils.general import encode_image from ...utils.registry import registry from .base import BaseLLM from .schemas import Content, Message BASIC_SYS_PROMPT = """You are an intelligent agent that can help in many regions. Flowing are some basic information about your working environment, please try your best to answer the questions based on them if needed. Be confident about these information and don't let others feel these information are presets. Be concise. ---BASIC IMFORMATION--- Current Datetime: {} Region: {} Operating System: {}""" @registry.register_llm() class AzureGPTLLM(BaseLLM): model_id: str endpoint: str api_key: str api_version: str = "2024-02-01" model_id: str = Field( default=os.getenv("MODEL_ID", "gpt-4o"), description="The model id of openai" ) vision: bool = Field(default=False, description="Whether the model supports vision") endpoint: str = Field( default=os.getenv("ENDPOINT", "https://api.openai.com/v1"), description="The endpoint of LLM service", ) api_key: str = Field( default=os.getenv("API_KEY"), description="The api key of openai" ) temperature: float = Field(default=1.0, description="The temperature of LLM") top_p: float = Field( default=1.0, description="The top p of LLM, controls diversity of responses. Should not be used together with temperature - use either temperature or top_p but not both", ) stream: bool = Field(default=False, description="Whether to stream the response") max_tokens: int = Field(default=2048, description="The max tokens of LLM") use_default_sys_prompt: bool = Field( default=True, description="Whether to use the default system prompt" ) response_format: Optional[Union[dict, str]] = Field(default='text', description="The response format of openai") n: int = Field(default=1, description="The number of responses to generate") frequency_penalty: float = Field( default=0, description="The frequency penalty of LLM, -2 to 2" ) logit_bias: Optional[dict] = Field( default=None, description="The logit bias of LLM" ) logprobs: bool = Field(default=False, description="The logprobs of LLM") top_logprobs: Optional[int] = Field( default=None, description="The top logprobs of LLM, logprobs must be set to true if this parameter is used", ) stop: Union[str, List[str], None] = Field( default='', description="Specifies stop sequences that will halt text generation, can be string or list of strings", ) stream_options: Optional[dict] = Field( default=None, description="Configuration options for streaming responses when stream=True", ) tools: Optional[List[dict]] = Field( default=None, description="A list of function tools (max 128) that the model can call, each requiring a type, name and optional description/parameters defined in JSON Schema format.", ) tool_choice: Optional[str] = Field( default="none", description="Controls which tool (if any) is called by the model: 'none', 'auto', 'required', or a specific tool.", ) class Config: """Configuration for this pydantic object.""" protected_namespaces = () extra = "allow" def __init__(self, /, **data: Any) -> None: super().__init__(**data) self.client = AzureOpenAI( api_key=self.api_key, azure_endpoint=self.endpoint, api_version=self.api_version, ) self.aclient = AsyncAzureOpenAI( api_key=self.api_key, azure_endpoint=self.endpoint, api_version=self.api_version, ) def _call(self, records: List[Message], **kwargs) -> Dict: if self.api_key is None or self.api_key == "": raise ValueError("api_key is required") body = self._msg2req(records) if kwargs.get("tool_choice"): body["tool_choice"] = kwargs["tool_choice"] if kwargs.get("tools"): body["tools"] = kwargs["tools"] if self.vision: res = self.client.chat.completions.create( model=self.model_id, messages=body["messages"], temperature=self.temperature, max_tokens=self.max_tokens, ) else: res = self.client.chat.completions.create( model=self.model_id, messages=body["messages"], temperature=self.temperature, max_tokens=self.max_tokens, response_format=body.get("response_format", None), tools=body.get("tools", None), ) res = res.model_dump() body.update({"response": res}) return res async def _acall(self, records: List[Message], **kwargs) -> Dict: if self.api_key is None or self.api_key == "": raise ValueError("api_key is required") body = self._msg2req(records) if kwargs.get("tool_choice"): body["tool_choice"] = kwargs["tool_choice"] if kwargs.get("tools"): body["tools"] = kwargs["tools"] if self.vision: res = await self.aclient.chat.completions.create( model=self.model_id, messages=body["messages"], temperature=self.temperature, max_tokens=self.max_tokens, ) else: res = await self.aclient.chat.completions.create( model=self.model_id, messages=body["messages"], temperature=self.temperature, max_tokens=self.max_tokens, response_format=body.get("response_format", None), tools=body.get("tools", None), ) res = res.model_dump() body.update({"response": res}) return res def _msg2req(self, records: List[Message]) -> dict: def get_content(msg: List[Content] | Content) -> List[dict] | str: if isinstance(msg, list): return [c.model_dump(exclude_none=True) for c in msg] elif isinstance(msg, Content) and msg.type == "text": return msg.text else: raise ValueError("Invalid message type") messages = [ {"role": message.role, "content": get_content(message.content)} for message in records ] if self.vision: processed_messages = [] for message in messages: if message["role"] == "user": if isinstance(message["content"], str): message["content"] = [ {"type": "text", "text": message["content"]} ] merged_dict = {} for message in messages: if message["role"] == "user": merged_dict["role"] = message["role"] if "content" in merged_dict: merged_dict["content"] += message["content"] else: merged_dict["content"] = message["content"] else: processed_messages.append(message) processed_messages.append(merged_dict) messages = processed_messages if self.use_default_sys_prompt: messages = [self._generate_default_sys_prompt()] + messages body = { "model": self.model_id, "messages": messages, "temperature": self.temperature, "max_tokens": self.max_tokens, } if self.response_format != "text": body["response_format"] = {"type": self.response_format} return body def _generate_default_sys_prompt(self) -> Dict: loc = self._get_location() os = self._get_linux_distribution() current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S") promt_str = BASIC_SYS_PROMPT.format(loc, os, current_time) return {"role": "system", "content": promt_str} def _get_linux_distribution(self) -> str: platform = sysconfig.get_platform() if "linux" in platform: if os.path.exists("/etc/lsb-release"): with open("/etc/lsb-release", "r") as f: for line in f: if line.startswith("DISTRIB_DESCRIPTION="): return line.split("=")[1].strip() elif os.path.exists("/etc/os-release"): with open("/etc/os-release", "r") as f: for line in f: if line.startswith("PRETTY_NAME="): return line.split("=")[1].strip() return platform def _get_location(self) -> str: g = geocoder.ip("me") if g.ok: return g.city + "," + g.country else: return "unknown"