Input params for Anthropic LLM

src/pipecat/services/anthropic.py

@@ -13,6 +13,7 @@ from dataclasses import dataclass
 from PIL import Image
 from asyncio import CancelledError
 import re
+from pydantic import BaseModel, Field
 
 from pipecat.frames.frames import (
     Frame,
@@ -74,20 +75,28 @@ class AnthropicContextAggregatorPair:
 class AnthropicLLMService(LLMService):
     """This class implements inference with Anthropic's AI models
     """
+    class InputParams(BaseModel):
+        enable_prompt_caching_beta: Optional[bool] = False
+        max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
+        temperature: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
+        top_k: Optional[int] = Field(default_factory=lambda: NOT_GIVEN, ge=0)
+        top_p: Optional[float] = Field(default_factory=lambda: NOT_GIVEN, ge=0.0, le=1.0)
 
     def __init__(
             self,
             *,
             api_key: str,
             model: str = "claude-3-5-sonnet-20240620",
-            max_tokens: int = 4096,
+            params: InputParams = InputParams(),
-            enable_prompt_caching_beta: bool = False,
             **kwargs):
         super().__init__(**kwargs)
         self._client = AsyncAnthropic(api_key=api_key)
         self.set_model_name(model)
-        self._max_tokens = max_tokens
+        self._max_tokens = params.max_tokens
-        self._enable_prompt_caching_beta = enable_prompt_caching_beta
+        self._enable_prompt_caching_beta: bool = params.enable_prompt_caching_beta or False
+        self._temperature = params.temperature
+        self._top_k = params.top_k
+        self._top_p = params.top_p
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -105,6 +114,26 @@ class AnthropicLLMService(LLMService):
             _assistant=assistant
         )
 
+    async def set_enable_prompt_caching_beta(self, enable_prompt_caching_beta: bool):
+        logger.debug(f"Switching LLM enable_prompt_caching_beta to: [{enable_prompt_caching_beta}]")
+        self._enable_prompt_caching_beta = enable_prompt_caching_beta
+
+    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_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):
         # Usage tracking. We track the usage reported by Anthropic in prompt_tokens and
         # completion_tokens. We also estimate the completion tokens from output text
@@ -140,7 +169,10 @@ class AnthropicLLMService(LLMService):
                 messages=messages,
                 model=self.model_name,
                 max_tokens=self._max_tokens,
-                stream=True)
+                stream=True,
+                temperature=self._temperature,
+                top_k=self._top_k,
+                top_p=self._top_p)
 
             await self.stop_ttfb_metrics()