opt

omagent_core/models/llms/azure_gpt.py

@@ -1,10 +1,11 @@
 import os
 import sysconfig
 from datetime import datetime
-from typing import Any, Dict, List
+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
@@ -24,15 +25,59 @@ Operating System: {}"""
 @registry.register_llm()
 class AzureGPTLLM(BaseLLM):
     model_id: str
-    vision: bool = False
     endpoint: str
     api_key: str
     api_version: str = "2024-02-15-preview"
-    temperature: float = 1.0
-    max_tokens: int = 2048
-    use_default_sys_prompt: bool = True
-    response_format: str = "text"
-
+    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."""