Input params to OpenAI LLM

src/pipecat/services/openai.py

@@ -11,7 +11,8 @@
 import httpx
 from dataclasses import dataclass
 
-from typing import AsyncGenerator, Dict, List, Literal
+from typing import AsyncGenerator, Dict, List, Literal, Optional
+from pydantic import BaseModel, Field
 
 from loguru import logger
 from PIL import Image
@@ -48,7 +49,7 @@
 )
 
 try:
-    from openai import AsyncOpenAI, AsyncStream, DefaultAsyncHttpxClient, BadRequestError
+    from openai import AsyncOpenAI, AsyncStream, DefaultAsyncHttpxClient, BadRequestError, NOT_GIVEN
     from openai.types.chat import ChatCompletionChunk, ChatCompletionMessageParam
 except ModuleNotFoundError as e:
     logger.error(f"Exception: {e}")
@@ -81,11 +82,31 @@ class BaseOpenAILLMService(LLMService):
     as well as tool choices and the tool, which is used if requesting function
     calls from the LLM.
     """
+    class InputParams(BaseModel):
+        frequency_penalty: Optional[float] = Field(
+            default_factory=lambda: NOT_GIVEN, ge=-2.0, le=2.0)
+        presence_penalty: Optional[float] = Field(
+            default_factory=lambda: NOT_GIVEN, ge=-2.0, le=2.0)
+        seed: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=0)
+        temperature: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=2.0)
+        top_p: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
 
-    def __init__(self, *, model: str, api_key=None, base_url=None, **kwargs):
+    def __init__(
+            self,
+            *,
+            model: str,
+            api_key=None,
+            base_url=None,
+            params: InputParams = InputParams(),
+            **kwargs):
         super().__init__(**kwargs)
         self.set_model_name(model)
         self._client = self.create_client(api_key=api_key, base_url=base_url, **kwargs)
+        self._frequency_penalty = params.frequency_penalty
+        self._presence_penalty = params.presence_penalty
+        self._seed = params.seed
+        self._temperature = params.temperature
+        self._top_p = params.top_p
 
     def create_client(self, api_key=None, base_url=None, **kwargs):
         return AsyncOpenAI(
@@ -100,6 +121,26 @@ def create_client(self, api_key=None, base_url=None, **kwargs):
     def can_generate_metrics(self) -> bool:
         return True
 
+    async def set_frequency_penalty(self, frequency_penalty: float):
+        logger.debug(f"Switching LLM frequency_penalty to: [{frequency_penalty}]")
+        self._frequency_penalty = frequency_penalty
+
+    async def set_presence_penalty(self, presence_penalty: float):
+        logger.debug(f"Switching LLM presence_penalty to: [{presence_penalty}]")
+        self._presence_penalty = presence_penalty
+
+    async def set_seed(self, seed: int):
+        logger.debug(f"Switching LLM seed to: [{seed}]")
+        self._seed = seed
+
+    async def set_temperature(self, temperature: float):
+        logger.debug(f"Switching LLM temperature to: [{temperature}]")
+        self._temperature = temperature
+
+    async def set_top_p(self, top_p: float):
+        logger.debug(f"Switching LLM top_p to: [{top_p}]")
+        self._top_p = top_p
+
     async def get_chat_completions(
             self,
             context: OpenAILLMContext,
@@ -110,7 +151,12 @@ async def get_chat_completions(
             messages=messages,
             tools=context.tools,
             tool_choice=context.tool_choice,
-            stream_options={"include_usage": True}
+            stream_options={"include_usage": True},
+            frequency_penalty=self._frequency_penalty,
+            presence_penalty=self._presence_penalty,
+            seed=self._seed,
+            temperature=self._temperature,
+            top_p=self._top_p
         )
         return chunks
 
@@ -248,8 +294,13 @@ def assistant(self) -> 'OpenAIAssistantContextAggregator':
 
 class OpenAILLMService(BaseOpenAILLMService):
 
-    def __init__(self, *, model: str = "gpt-4o", **kwargs):
-        super().__init__(model=model, **kwargs)
+    def __init__(
+            self,
+            *,
+            model: str = "gpt-4o",
+            params: BaseOpenAILLMService.InputParams = BaseOpenAILLMService.InputParams(),
+            **kwargs):
+        super().__init__(model=model, params=params, **kwargs)
 
     @staticmethod
     def create_context_aggregator(context: OpenAILLMContext) -> OpenAIContextAggregatorPair: