Input params for Together AI LLM

src/pipecat/services/together.py

@@ -13,6 +13,7 @@
 from asyncio import CancelledError
 import re
 import uuid
+from pydantic import BaseModel, Field
 
 from pipecat.frames.frames import (
     Frame,
@@ -58,18 +59,30 @@ def assistant(self) -> 'TogetherAssistantContextAggregator':
 class TogetherLLMService(LLMService):
     """This class implements inference with Together's Llama 3.1 models
     """
+    class InputParams(BaseModel):
+        frequency_penalty: Optional[float] = Field(default=None, ge=-2.0, le=2.0)
+        max_tokens: Optional[int] = Field(default=4096, ge=1)
+        presence_penalty: Optional[float] = Field(default=None, ge=-2.0, le=2.0)
+        temperature: Optional[float] = Field(default=None, ge=0.0, le=1.0)
+        top_k: Optional[int] = Field(default=None, ge=0)
+        top_p: Optional[float] = Field(default=None, ge=0.0, le=1.0)
 
     def __init__(
             self,
             *,
             api_key: str,
             model: str = "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
-            max_tokens: int = 4096,
+            params: InputParams = InputParams(),
             **kwargs):
         super().__init__(**kwargs)
         self._client = AsyncTogether(api_key=api_key)
         self.set_model_name(model)
-        self._max_tokens = max_tokens
+        self._max_tokens = params.max_tokens
+        self._frequency_penalty = params.frequency_penalty
+        self._presence_penalty = params.presence_penalty
+        self._temperature = params.temperature
+        self._top_k = params.top_k
+        self._top_p = params.top_p
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -83,6 +96,30 @@ def create_context_aggregator(context: OpenAILLMContext) -> TogetherContextAggre
             _assistant=assistant
         )
 
+    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_max_tokens(self, max_tokens: int):
+        logger.debug(f"Switching LLM max_tokens to: [{max_tokens}]")
+        self._max_tokens = max_tokens
+
+    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_temperature(self, temperature: float):
+        logger.debug(f"Switching LLM temperature to: [{temperature}]")
+        self._temperature = temperature
+
+    async def set_top_k(self, top_k: float):
+        logger.debug(f"Switching LLM top_k to: [{top_k}]")
+        self._top_k = top_k
+
+    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 _process_context(self, context: OpenAILLMContext):
         try:
             await self.push_frame(LLMFullResponseStartFrame())
@@ -97,6 +134,11 @@ async def _process_context(self, context: OpenAILLMContext):
                 model=self.model_name,
                 max_tokens=self._max_tokens,
                 stream=True,
+                frequency_penalty=self._frequency_penalty,
+                presence_penalty=self._presence_penalty,
+                temperature=self._temperature,
+                top_k=self._top_k,
+                top_p=self._top_p
             )
 
             # Function calling