AWSBedrockLLMService: fix function calling

examples/foundational/07m-interruptible-aws.py

@@ -17,7 +17,6 @@ from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
 from pipecat.services.aws.llm import AWSBedrockLLMService
 from pipecat.services.aws.stt import AWSTranscribeSTTService
 from pipecat.services.aws.tts import AWSPollyTTSService
-from pipecat.transcriptions.language import Language
 from pipecat.transports.base_transport import TransportParams
 from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
 from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
@@ -42,9 +41,7 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
     tts = AWSPollyTTSService(
         region="us-west-2",  # only specific regions support generative TTS
         voice_id="Joanna",
-        params=AWSPollyTTSService.InputParams(
+        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
-            engine="generative", language=Language.EN_US, rate="1.1"
-        ),
     )
 
     llm = AWSBedrockLLMService(

examples/foundational/14r-function-calling-aws.py

@@ -0,0 +1,139 @@
+#
+# Copyright (c) 2024–2025, Daily
+#
+# SPDX-License-Identifier: BSD 2-Clause License
+#
+
+import argparse
+import os
+
+from dotenv import load_dotenv
+from loguru import logger
+
+from pipecat.adapters.schemas.function_schema import FunctionSchema
+from pipecat.adapters.schemas.tools_schema import ToolsSchema
+from pipecat.audio.vad.silero import SileroVADAnalyzer
+from pipecat.pipeline.pipeline import Pipeline
+from pipecat.pipeline.runner import PipelineRunner
+from pipecat.pipeline.task import PipelineParams, PipelineTask
+from pipecat.processors.aggregators.openai_llm_context import OpenAILLMContext
+from pipecat.services.aws.llm import AWSBedrockLLMService
+from pipecat.services.aws.stt import AWSTranscribeSTTService
+from pipecat.services.aws.tts import AWSPollyTTSService
+from pipecat.services.llm_service import FunctionCallParams
+from pipecat.transports.base_transport import TransportParams
+from pipecat.transports.network.small_webrtc import SmallWebRTCTransport
+from pipecat.transports.network.webrtc_connection import SmallWebRTCConnection
+
+load_dotenv(override=True)
+
+
+async def fetch_weather_from_api(params: FunctionCallParams):
+    await params.result_callback({"conditions": "nice", "temperature": "75"})
+
+
+async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespace):
+    logger.info(f"Starting bot")
+
+    transport = SmallWebRTCTransport(
+        webrtc_connection=webrtc_connection,
+        params=TransportParams(
+            audio_in_enabled=True,
+            audio_out_enabled=True,
+            vad_analyzer=SileroVADAnalyzer(),
+        ),
+    )
+
+    stt = AWSTranscribeSTTService()
+
+    tts = AWSPollyTTSService(
+        region="us-west-2",  # only specific regions support generative TTS
+        voice_id="Joanna",
+        params=AWSPollyTTSService.InputParams(engine="generative", rate="1.1"),
+    )
+
+    llm = AWSBedrockLLMService(
+        aws_region="us-west-2",
+        model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
+        params=AWSBedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
+    )
+
+    # You can also register a function_name of None to get all functions
+    # sent to the same callback with an additional function_name parameter.
+    llm.register_function("get_current_weather", fetch_weather_from_api)
+
+    weather_function = FunctionSchema(
+        name="get_current_weather",
+        description="Get the current weather",
+        properties={
+            "location": {
+                "type": "string",
+                "description": "The city and state, e.g. San Francisco, CA",
+            },
+            "format": {
+                "type": "string",
+                "enum": ["celsius", "fahrenheit"],
+                "description": "The temperature unit to use. Infer this from the user's location.",
+            },
+        },
+        required=["location", "format"],
+    )
+    tools = ToolsSchema(standard_tools=[weather_function])
+
+    messages = [
+        {
+            "role": "system",
+            "content": "You are a helpful LLM in a WebRTC call. Your goal is to demonstrate your capabilities in a succinct way. Your output will be converted to audio so don't include special characters in your answers. Respond to what the user said in a creative and helpful way.",
+        },
+    ]
+
+    context = OpenAILLMContext(messages, tools)
+    context_aggregator = llm.create_context_aggregator(context)
+
+    pipeline = Pipeline(
+        [
+            transport.input(),
+            stt,
+            context_aggregator.user(),
+            llm,
+            tts,
+            transport.output(),
+            context_aggregator.assistant(),
+        ]
+    )
+
+    task = PipelineTask(
+        pipeline,
+        params=PipelineParams(
+            allow_interruptions=True,
+            enable_metrics=True,
+            enable_usage_metrics=True,
+            report_only_initial_ttfb=True,
+        ),
+    )
+
+    @transport.event_handler("on_client_connected")
+    async def on_client_connected(transport, client):
+        logger.info(f"Client connected")
+        # Kick off the conversation.
+        messages.append({"role": "user", "content": "Please introduce yourself to the user."})
+        await task.queue_frames([context_aggregator.user().get_context_frame()])
+
+    @transport.event_handler("on_client_disconnected")
+    async def on_client_disconnected(transport, client):
+        logger.info(f"Client disconnected")
+
+    @transport.event_handler("on_client_closed")
+    async def on_client_closed(transport, client):
+        logger.info(f"Client closed connection")
+        await task.cancel()
+
+    runner = PipelineRunner(handle_sigint=False)
+
+    await runner.run(task)
+
+
+if __name__ == "__main__":
+    from run import main
+
+    main()

src/pipecat/adapters/services/anthropic_adapter.py

@@ -4,7 +4,7 @@
 # SPDX-License-Identifier: BSD 2-Clause License
 #
 
-from typing import Any, Dict, List, Union
+from typing import Any, Dict, List
 
 from pipecat.adapters.base_llm_adapter import BaseLLMAdapter
 from pipecat.adapters.schemas.function_schema import FunctionSchema

src/pipecat/adapters/services/bedrock_adapter.py

@@ -11,7 +11,7 @@ from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import ToolsSchema
 
 
-class BedrockLLMAdapter(BaseLLMAdapter):
+class AWSBedrockLLMAdapter(BaseLLMAdapter):
     @staticmethod
     def _to_bedrock_function_format(function: FunctionSchema) -> Dict[str, Any]:
         return {

src/pipecat/services/aws/llm.py

@@ -17,6 +17,7 @@ from loguru import logger
 from PIL import Image
 from pydantic import BaseModel, Field
 
+from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
 from pipecat.frames.frames import (
     Frame,
     FunctionCallCancelFrame,
@@ -92,7 +93,6 @@ class AWSBedrockLLMContext(OpenAILLMContext):
 
     @classmethod
     def from_openai_context(cls, openai_context: OpenAILLMContext):
-        logger.debug("from_openai_context called")
         self = cls(
             messages=openai_context.messages,
             tools=openai_context.tools,
@@ -105,7 +105,7 @@ class AWSBedrockLLMContext(OpenAILLMContext):
     @classmethod
     def from_messages(cls, messages: List[dict]) -> "AWSBedrockLLMContext":
         self = cls(messages=messages)
-        # self._restructure_from_openai_messages()
+        self._restructure_from_openai_messages()
         return self
 
     @classmethod
@@ -118,7 +118,7 @@ class AWSBedrockLLMContext(OpenAILLMContext):
 
     def set_messages(self, messages: List):
         self._messages[:] = messages
-        # self._restructure_from_openai_messages()
+        self._restructure_from_openai_messages()
 
     # convert a message in AWS Bedrock format into one or more messages in OpenAI format
     def to_standard_messages(self, obj):
@@ -334,7 +334,6 @@ class AWSBedrockLLMContext(OpenAILLMContext):
 
         """
         # Handle system message if present at the beginning
-        logger.debug(f"_restructure_from_bedrock_messages: {self.messages}")
         if self.messages and self.messages[0]["role"] == "system":
             if len(self.messages) == 1:
                 self.messages[0]["role"] = "user"
@@ -375,7 +374,6 @@ class AWSBedrockLLMContext(OpenAILLMContext):
         self.messages.extend(merged_messages)
 
     def _restructure_from_openai_messages(self):
-        logger.debug(f"_restructure_from_openai_messages: {self.messages}")
         # first, map across self._messages calling self.from_standard_message(m) to modify messages in place
         try:
             self._messages[:] = [self.from_standard_message(m) for m in self._messages]
@@ -517,6 +515,9 @@ class AWSBedrockLLMService(LLMService):
 
     """
 
+    # Overriding the default adapter to use the Anthropic one.
+    adapter_class = AWSBedrockLLMAdapter
+
     class InputParams(BaseModel):
         max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
         temperature: Optional[float] = Field(default_factory=lambda: 0.7, ge=0.0, le=1.0)

tests/test_function_calling_adapters.py

@@ -11,6 +11,7 @@ from openai.types.chat import ChatCompletionToolParam
 from pipecat.adapters.schemas.function_schema import FunctionSchema
 from pipecat.adapters.schemas.tools_schema import AdapterType, ToolsSchema
 from pipecat.adapters.services.anthropic_adapter import AnthropicLLMAdapter
+from pipecat.adapters.services.bedrock_adapter import AWSBedrockLLMAdapter
 from pipecat.adapters.services.gemini_adapter import GeminiLLMAdapter
 from pipecat.adapters.services.open_ai_adapter import OpenAILLMAdapter
 from pipecat.adapters.services.open_ai_realtime_adapter import OpenAIRealtimeLLMAdapter
@@ -174,3 +175,32 @@ class TestFunctionAdapters(unittest.TestCase):
         tools_def = self.tools_def
         tools_def.custom_tools = {AdapterType.GEMINI: [search_tool]}
         assert GeminiLLMAdapter().to_provider_tools_format(tools_def) == expected
+
+    def test_bedrock_adapter(self):
+        """Test AWS Bedrock adapter format transformation."""
+        expected = [
+            {
+                "toolSpec": {
+                    "name": "get_weather",
+                    "description": "Get the weather in a given location",
+                    "inputSchema": {
+                        "json": {
+                            "type": "object",
+                            "properties": {
+                                "format": {
+                                    "type": "string",
+                                    "enum": ["celsius", "fahrenheit"],
+                                    "description": "The temperature unit to use.",
+                                },
+                                "location": {
+                                    "type": "string",
+                                    "description": "The city, e.g. San Francisco",
+                                },
+                            },
+                            "required": ["location", "format"],
+                        }
+                    },
+                }
+            }
+        ]
+        assert AWSBedrockLLMAdapter().to_provider_tools_format(self.tools_def) == expected