wire up system_instruction in OpenAI, Anthropic and AWS Bedrock

src/pipecat/services/anthropic/llm.py

@@ -17,7 +17,7 @@ import io
 import json
 import re
 from dataclasses import dataclass, field
-from typing import Any, ClassVar, Dict, List, Literal, Optional, Union
+from typing import Any, Dict, List, Literal, Optional, Union
 
 import httpx
 from loguru import logger
@@ -38,7 +38,6 @@ from pipecat.frames.frames import (
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
-    LLMTextFrame,
     LLMThoughtEndFrame,
     LLMThoughtStartFrame,
     LLMThoughtTextFrame,
@@ -219,6 +218,7 @@ class AnthropicLLMService(LLMService):
         client=None,
         retry_timeout_secs: Optional[float] = 5.0,
         retry_on_timeout: Optional[bool] = False,
+        system_instruction: Optional[str] = None,
         **kwargs,
     ):
         """Initialize the Anthropic LLM service.
@@ -230,6 +230,7 @@ class AnthropicLLMService(LLMService):
             client: Optional custom Anthropic client instance.
             retry_timeout_secs: Request timeout in seconds for retry logic.
             retry_on_timeout: Whether to retry the request once if it times out.
+            system_instruction: Optional system instruction to use as the system prompt.
             **kwargs: Additional arguments passed to parent LLMService.
         """
         params = params or AnthropicLLMService.InputParams()
@@ -265,6 +266,9 @@ class AnthropicLLMService(LLMService):
         )  # if the client is provided, use it and remove it, otherwise create a new one
         self._retry_timeout_secs = retry_timeout_secs
         self._retry_on_timeout = retry_on_timeout
+        self._system_instruction = system_instruction
+        if self._system_instruction:
+            logger.debug(f"{self}: Using system instruction: {self._system_instruction}")
 
     def can_generate_metrics(self) -> bool:
         """Check if this service can generate usage metrics.
@@ -395,9 +399,11 @@ class AnthropicLLMService(LLMService):
         # Universal LLMContext
         if isinstance(context, LLMContext):
             adapter: AnthropicLLMAdapter = self.get_llm_adapter()
-            params = adapter.get_llm_invocation_params(
+            params: AnthropicLLMInvocationParams = adapter.get_llm_invocation_params(
                 context, enable_prompt_caching=self._settings.enable_prompt_caching
             )
+            if self._system_instruction:
+                params["system"] = self._system_instruction
             return params
 
         # Anthropic-specific context

src/pipecat/services/aws/llm.py

@@ -19,7 +19,7 @@ import json
 import os
 import re
 from dataclasses import dataclass, field
-from typing import Any, ClassVar, Dict, List, Optional
+from typing import Any, Dict, List, Optional
 
 from loguru import logger
 from PIL import Image
@@ -39,7 +39,6 @@ from pipecat.frames.frames import (
     LLMFullResponseEndFrame,
     LLMFullResponseStartFrame,
     LLMMessagesFrame,
-    LLMTextFrame,
     UserImageRawFrame,
 )
 from pipecat.metrics.metrics import LLMTokenUsage
@@ -781,6 +780,7 @@ class AWSBedrockLLMService(LLMService):
         client_config: Optional[Config] = None,
         retry_timeout_secs: Optional[float] = 5.0,
         retry_on_timeout: Optional[bool] = False,
+        system_instruction: Optional[str] = None,
         **kwargs,
     ):
         """Initialize the AWS Bedrock LLM service.
@@ -795,6 +795,7 @@ class AWSBedrockLLMService(LLMService):
             client_config: Custom boto3 client configuration.
             retry_timeout_secs: Request timeout in seconds for retry logic.
             retry_on_timeout: Whether to retry the request once if it times out.
+            system_instruction: Optional system instruction to use as the system prompt.
             **kwargs: Additional arguments passed to parent LLMService.
         """
         params = params or AWSBedrockLLMService.InputParams()
@@ -840,8 +841,11 @@ class AWSBedrockLLMService(LLMService):
 
         self._retry_timeout_secs = retry_timeout_secs
         self._retry_on_timeout = retry_on_timeout
+        self._system_instruction = system_instruction
 
         logger.info(f"Using AWS Bedrock model: {model}")
+        if self._system_instruction:
+            logger.debug(f"{self}: Using system instruction: {self._system_instruction}")
 
     def can_generate_metrics(self) -> bool:
         """Check if the service can generate usage metrics.
@@ -1019,7 +1023,9 @@ class AWSBedrockLLMService(LLMService):
         # Universal LLMContext
         if isinstance(context, LLMContext):
             adapter: AWSBedrockLLMAdapter = self.get_llm_adapter()
-            params = adapter.get_llm_invocation_params(context)
+            params: AWSBedrockLLMInvocationParams = adapter.get_llm_invocation_params(context)
+            if self._system_instruction:
+                params["system"] = [{"text": self._system_instruction}]
             return params
 
         # AWS Bedrock-specific context

src/pipecat/services/openai/base_llm.py

@@ -11,7 +11,7 @@ import base64
 import json
 from contextlib import asynccontextmanager
 from dataclasses import dataclass, field
-from typing import Any, ClassVar, Dict, List, Mapping, Optional
+from typing import Any, Dict, List, Mapping, Optional
 
 import httpx
 from loguru import logger
@@ -117,6 +117,7 @@ class BaseOpenAILLMService(LLMService):
         params: Optional[InputParams] = None,
         retry_timeout_secs: Optional[float] = 5.0,
         retry_on_timeout: Optional[bool] = False,
+        system_instruction: Optional[str] = None,
         **kwargs,
     ):
         """Initialize the BaseOpenAILLMService.
@@ -131,6 +132,7 @@ class BaseOpenAILLMService(LLMService):
             params: Input parameters for model configuration and behavior.
             retry_timeout_secs: Request timeout in seconds. Defaults to 5.0 seconds.
             retry_on_timeout: Whether to retry the request once if it times out.
+            system_instruction: Optional system instruction to prepend to messages.
             **kwargs: Additional arguments passed to the parent LLMService.
         """
         params = params or BaseOpenAILLMService.InputParams()
@@ -155,6 +157,7 @@ class BaseOpenAILLMService(LLMService):
         )
         self._retry_timeout_secs = retry_timeout_secs
         self._retry_on_timeout = retry_on_timeout
+        self._system_instruction = system_instruction
         self._full_model_name: str = ""
         self._client = self.create_client(
             api_key=api_key,
@@ -165,6 +168,9 @@ class BaseOpenAILLMService(LLMService):
             **kwargs,
         )
 
+        if self._system_instruction:
+            logger.debug(f"{self}: Using system instruction: {self._system_instruction}")
+
     def create_client(
         self,
         api_key=None,
@@ -285,6 +291,14 @@ class BaseOpenAILLMService(LLMService):
         params.update(params_from_context)
 
         params.update(self._settings.extra)
+
+        # Prepend system instruction if set
+        if self._system_instruction:
+            messages = params.get("messages", [])
+            params["messages"] = [
+                {"role": "system", "content": self._system_instruction}
+            ] + messages
+
         return params
 
     async def run_inference(