Add extra input param to LLMs

examples/foundational/07l-interruptible-together.py

@@ -57,10 +57,12 @@ async def main():
             model=os.getenv("TOGETHER_MODEL"),
             params=TogetherLLMService.InputParams(
                 temperature=1.0,
-                frequency_penalty=2.0,
-                presence_penalty=0.0,
                 top_p=0.9,
-                top_k=40
+                top_k=40,
+                extra={
+                    "frequency_penalty": 2.0,
+                    "presence_penalty": 0.0,
+                }
             )
         )
 

src/pipecat/services/anthropic.py

@@ -8,7 +8,7 @@ import base64
 import json
 import io
 import copy
-from typing import List, Optional
+from typing import Any, Dict, List, Optional
 from dataclasses import dataclass
 from PIL import Image
 from asyncio import CancelledError
@@ -81,6 +81,7 @@ class AnthropicLLMService(LLMService):
         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)
+        extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
     def __init__(
             self,
@@ -97,6 +98,7 @@ class AnthropicLLMService(LLMService):
         self._temperature = params.temperature
         self._top_k = params.top_k
         self._top_p = params.top_p
+        self._extra = params.extra if isinstance(params.extra, dict) else {}
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -134,6 +136,10 @@ class AnthropicLLMService(LLMService):
         logger.debug(f"Switching LLM top_p to: [{top_p}]")
         self._top_p = top_p
 
+    async def set_extra(self, extra: Dict[str, Any]):
+        logger.debug(f"Switching LLM extra to: [{extra}]")
+        self._extra = extra
+
     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
@@ -163,16 +169,21 @@ class AnthropicLLMService(LLMService):
 
             await self.start_ttfb_metrics()
 
-            response = await api_call(
+            params = {
-                tools=context.tools or [],
+                "tools": context.tools or [],
-                system=context.system,
+                "system": context.system,
-                messages=messages,
+                "messages": messages,
-                model=self.model_name,
+                "model": self.model_name,
-                max_tokens=self._max_tokens,
+                "max_tokens": self._max_tokens,
-                stream=True,
+                "stream": True,
-                temperature=self._temperature,
+                "temperature": self._temperature,
-                top_k=self._top_k,
+                "top_k": self._top_k,
-                top_p=self._top_p)
+                "top_p": self._top_p
+            }
+
+            params.update(self._extra)
+
+            response = await api_call(**params)
 
             await self.stop_ttfb_metrics()
 

src/pipecat/services/openai.py

@@ -11,7 +11,7 @@ import json
 import httpx
 from dataclasses import dataclass
 
-from typing import AsyncGenerator, Dict, List, Literal, Optional
+from typing import Any, AsyncGenerator, Dict, List, Literal, Optional
 from pydantic import BaseModel, Field
 
 from loguru import logger
@@ -90,6 +90,7 @@ class BaseOpenAILLMService(LLMService):
         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)
+        extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
     def __init__(
             self,
@@ -107,6 +108,7 @@ class BaseOpenAILLMService(LLMService):
         self._seed = params.seed
         self._temperature = params.temperature
         self._top_p = params.top_p
+        self._extra = params.extra if isinstance(params.extra, dict) else {}
 
     def create_client(self, api_key=None, base_url=None, **kwargs):
         return AsyncOpenAI(
@@ -141,23 +143,32 @@ class BaseOpenAILLMService(LLMService):
         logger.debug(f"Switching LLM top_p to: [{top_p}]")
         self._top_p = top_p
 
+    async def set_extra(self, extra: Dict[str, Any]):
+        logger.debug(f"Switching LLM extra to: [{extra}]")
+        self._extra = extra
+
     async def get_chat_completions(
             self,
             context: OpenAILLMContext,
             messages: List[ChatCompletionMessageParam]) -> AsyncStream[ChatCompletionChunk]:
-        chunks = await self._client.chat.completions.create(
+
-            model=self.model_name,
+        params = {
-            stream=True,
+            "model": self.model_name,
-            messages=messages,
+            "stream": True,
-            tools=context.tools,
+            "messages": messages,
-            tool_choice=context.tool_choice,
+            "tools": context.tools,
-            stream_options={"include_usage": True},
+            "tool_choice": context.tool_choice,
-            frequency_penalty=self._frequency_penalty,
+            "stream_options": {"include_usage": True},
-            presence_penalty=self._presence_penalty,
+            "frequency_penalty": self._frequency_penalty,
-            seed=self._seed,
+            "presence_penalty": self._presence_penalty,
-            temperature=self._temperature,
+            "seed": self._seed,
-            top_p=self._top_p
+            "temperature": self._temperature,
-        )
+            "top_p": self._top_p,
+        }
+
+        params.update(self._extra)
+
+        chunks = await self._client.chat.completions.create(**params)
         return chunks
 
     async def _stream_chat_completions(

src/pipecat/services/together.py

@@ -9,7 +9,7 @@ import re
 import uuid
 from pydantic import BaseModel, Field
 
-from typing import List
+from typing import Any, Dict, List, Optional
 from dataclasses import dataclass
 from asyncio import CancelledError
 
@@ -64,6 +64,7 @@ class TogetherLLMService(LLMService):
         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)
+        extra: Optional[Dict[str, Any]] = Field(default_factory=dict)
 
     def __init__(
             self,
@@ -81,6 +82,7 @@ class TogetherLLMService(LLMService):
         self._temperature = params.temperature
         self._top_k = params.top_k
         self._top_p = params.top_p
+        self._extra = params.extra if isinstance(params.extra, dict) else {}
 
     def can_generate_metrics(self) -> bool:
         return True
@@ -118,6 +120,10 @@ class TogetherLLMService(LLMService):
         logger.debug(f"Switching LLM top_p to: [{top_p}]")
         self._top_p = top_p
 
+    async def set_extra(self, extra: Dict[str, Any]):
+        logger.debug(f"Switching LLM extra to: [{extra}]")
+        self._extra = extra
+
     async def _process_context(self, context: OpenAILLMContext):
         try:
             await self.push_frame(LLMFullResponseStartFrame())
@@ -127,17 +133,21 @@ class TogetherLLMService(LLMService):
 
             await self.start_ttfb_metrics()
 
-            stream = await self._client.chat.completions.create(
+            params = {
-                messages=context.messages,
+                "messages": context.messages,
-                model=self.model_name,
+                "model": self.model_name,
-                max_tokens=self._max_tokens,
+                "max_tokens": self._max_tokens,
-                stream=True,
+                "stream": True,
-                frequency_penalty=self._frequency_penalty,
+                "frequency_penalty": self._frequency_penalty,
-                presence_penalty=self._presence_penalty,
+                "presence_penalty": self._presence_penalty,
-                temperature=self._temperature,
+                "temperature": self._temperature,
-                top_k=self._top_k,
+                "top_k": self._top_k,
-                top_p=self._top_p
+                "top_p": self._top_p
-            )
+            }
+
+            params.update(self._extra)
+
+            stream = await self._client.chat.completions.create(**params)
 
             # Function calling
             got_first_chunk = False