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fix formatting

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This commit is contained in:
Aleix Conchillo Flaqué
2025-05-06 11:37:23 -07:00
parent 664111a3c9
commit a4b9db9e07
5 changed files with 85 additions and 99 deletions
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38
examples/foundational/07m-interruptible-aws.py
View File

@@ -13,13 +13,13 @@ 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.services.aws.llm import BedrockLLMContext, BedrockLLMService
from pipecat.services.aws.stt import TranscribeSTTService
from pipecat.services.aws.tts import PollyTTSService
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
from pipecat.services.aws.llm import BedrockLLMService, BedrockLLMContext
from pipecat.services.aws.stt import TranscribeSTTService
from pipecat.services.aws.tts import PollyTTSService
load_dotenv(override=True)
@@ -42,28 +42,26 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
region="us-west-2", # only specific regions support generative TTS
voice_id="Joanna",
params=PollyTTSService.InputParams(
engine="generative",
language=Language.EN_US,
rate="1.1"
engine="generative", language=Language.EN_US, rate="1.1"
),
)
llm = BedrockLLMService(
aws_region="us-west-2",
model="us.anthropic.claude-3-5-haiku-20241022-v1:0",
params=BedrockLLMService.InputParams(
temperature=0.8,
latency="optimized"
)
params=BedrockLLMService.InputParams(temperature=0.8, latency="optimized"),
)
messages = [
{
"role": "system",
"content": [{"text": "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."}],
},
]
)
{
"role": "system",
"content": [
{
"text": "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 = BedrockLLMContext(messages)
context_aggregator = llm.create_context_aggregator(context)
@@ -77,8 +75,8 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
tts, # TTS
transport.output(), # Transport bot output
context_aggregator.assistant(), # Assistant spoken responses
]
)
]
)
task = PipelineTask(
pipeline,
@@ -94,7 +92,9 @@ async def run_bot(webrtc_connection: SmallWebRTCConnection, _: argparse.Namespac
async def on_client_connected(transport, client):
logger.info(f"Client connected")
# Kick off the conversation.
messages.append({"role": "user", "content": [{"text": "Please introduce yourself to the user."}]})
messages.append(
{"role": "user", "content": [{"text": "Please introduce yourself to the user."}]}
)
await task.queue_frames([context_aggregator.user().get_context_frame()])
@transport.event_handler("on_client_disconnected")

2
src/pipecat/adapters/services/bedrock_adapter.py
View File

@@ -24,7 +24,7 @@ class BedrockLLMAdapter(BaseLLMAdapter):
"properties": function.properties,
"required": function.required,
},
}
},
}
}

134
src/pipecat/services/aws/llm.py
View File

@@ -135,7 +135,7 @@ class BedrockLLMContext(OpenAILLMContext):
"""
role = obj.get("role")
content = obj.get("content")
if role == "assistant":
if isinstance(content, str):
return [{"role": role, "content": [{"type": "text", "text": content}]}]
@@ -184,7 +184,7 @@ class BedrockLLMContext(OpenAILLMContext):
result_content = json.dumps(content_item["json"])
else:
result_content = tool_result["content"]
tool_items.append(
{
"role": "tool",
@@ -226,26 +226,28 @@ class BedrockLLMContext(OpenAILLMContext):
if message["role"] == "tool":
# Try to parse the content as JSON if it looks like JSON
try:
if message["content"].strip().startswith('{') and message["content"].strip().endswith('}'):
if message["content"].strip().startswith("{") and message[
"content"
].strip().endswith("}"):
content_json = json.loads(message["content"])
tool_result_content = [{"json": content_json}]
else:
tool_result_content = [{"text": message["content"]}]
except:
tool_result_content = [{"text": message["content"]}]
return {
"role": "user",
"content": [
{
"toolResult": {
"toolUseId": message["tool_call_id"],
"content": tool_result_content
"content": tool_result_content,
},
},
],
}
if message.get("tool_calls"):
tc = message["tool_calls"]
ret = {"role": "assistant", "content": []}
@@ -261,7 +263,7 @@ class BedrockLLMContext(OpenAILLMContext):
}
ret["content"].append(new_tool_use)
return ret
# Handle text content
content = message.get("content")
if isinstance(content, str):
@@ -276,7 +278,7 @@ class BedrockLLMContext(OpenAILLMContext):
text_content = item["text"] if item["text"] != "" else "(empty)"
new_content.append({"text": text_content})
return {"role": message["role"], "content": new_content}
return message
def add_image_frame_message(
@@ -287,15 +289,7 @@ class BedrockLLMContext(OpenAILLMContext):
encoded_image = base64.b64encode(buffer.getvalue()).decode("utf-8")
# Image should be the first content block in the message
content = [
{
"type": "image",
"format": "jpeg",
"source": {
"bytes": encoded_image
}
}
]
content = [{"type": "image", "format": "jpeg", "source": {"bytes": encoded_image}}]
if text:
content.append({"text": text})
self.add_message({"role": "user", "content": content})
@@ -309,9 +303,7 @@ class BedrockLLMContext(OpenAILLMContext):
# if the last message has just a content string, convert it to a list
# in the proper format
if isinstance(self.messages[-1]["content"], str):
self.messages[-1]["content"] = [
{"text": self.messages[-1]["content"]}
]
self.messages[-1]["content"] = [{"text": self.messages[-1]["content"]}]
# if this message has just a content string, convert it to a list
# in the proper format
if isinstance(message["content"], str):
@@ -326,7 +318,7 @@ class BedrockLLMContext(OpenAILLMContext):
logger.error(f"Error adding message: {e}")
def _restructure_from_bedrock_messages(self):
"""Restructure messages in Bedrock format by handling system messages,
"""Restructure messages in Bedrock format by handling system messages,
merging consecutive messages with the same role, and ensuring proper content formatting.
"""
# Handle system message if present at the beginning
@@ -338,7 +330,7 @@ class BedrockLLMContext(OpenAILLMContext):
system_content = self.messages.pop(0)["content"]
if isinstance(system_content, str):
system_content = [{"text": system_content}]
if self.system:
if isinstance(self.system, str):
self.system = [{"text": self.system}]
@@ -366,7 +358,7 @@ class BedrockLLMContext(OpenAILLMContext):
merged_messages[-1]["content"].extend(msg["content"])
else:
merged_messages.append(msg)
self.messages.clear()
self.messages.extend(merged_messages)
@@ -452,7 +444,7 @@ class BedrockAssistantContextAggregator(LLMAssistantContextAggregator):
"toolUse": {
"toolUseId": frame.tool_call_id,
"name": frame.function_name,
"input": frame.arguments if frame.arguments else {}
"input": frame.arguments if frame.arguments else {},
}
}
],
@@ -465,11 +457,7 @@ class BedrockAssistantContextAggregator(LLMAssistantContextAggregator):
{
"toolResult": {
"toolUseId": frame.tool_call_id,
"content": [
{
"text": "IN_PROGRESS"
}
],
"content": [{"text": "IN_PROGRESS"}],
}
}
],
@@ -517,9 +505,10 @@ class BedrockAssistantContextAggregator(LLMAssistantContextAggregator):
class BedrockLLMService(LLMService):
"""This class implements inference with AWS Bedrock models including Amazon Nova and Anthropic Claude.
Requires AWS credentials to be configured in the environment or through boto3 configuration.
"""
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)
@@ -541,34 +530,33 @@ class BedrockLLMService(LLMService):
**kwargs,
):
super().__init__(**kwargs)
# Initialize the Bedrock client
if not client_config:
client_config = Config(
connect_timeout=300, # 5 minutes
read_timeout=300, # 5 minutes
retries={'max_attempts': 3}
read_timeout=300, # 5 minutes
retries={"max_attempts": 3},
)
session = boto3.Session(
aws_access_key_id=aws_access_key,
aws_secret_access_key=aws_secret_key,
aws_session_token=aws_session_token,
region_name=aws_region
region_name=aws_region,
)
self._client = session.client(
service_name='bedrock-runtime',
config=client_config
)
self._client = session.client(service_name="bedrock-runtime", config=client_config)
self.set_model_name(model)
self._settings = {
"max_tokens": params.max_tokens,
"temperature": params.temperature,
"top_p": params.top_p,
"latency": params.latency,
"additional_model_request_fields": params.additional_model_request_fields if isinstance(params.additional_model_request_fields, dict) else {},
"additional_model_request_fields": params.additional_model_request_fields
if isinstance(params.additional_model_request_fields, dict)
else {},
}
logger.info(f"Using AWS Bedrock model: {model}")
def can_generate_metrics(self) -> bool:
@@ -603,7 +591,7 @@ class BedrockLLMService(LLMService):
if isinstance(context, OpenAILLMContext) and not isinstance(context, BedrockLLMContext):
context = BedrockLLMContext.from_openai_context(context)
user = BedrockUserContextAggregator(context, **user_kwargs)
assistant = BedrockAssistantContextAggregator(context, **assistant_kwargs)
return BedrockContextAggregatorPair(_user=user, _assistant=assistant)
@@ -626,31 +614,29 @@ class BedrockLLMService(LLMService):
# )
await self.start_ttfb_metrics()
# Set up inference config
inference_config = {
"maxTokens": self._settings["max_tokens"],
"temperature": self._settings["temperature"],
"topP": self._settings["top_p"],
}
# Prepare request parameters
request_params = {
"modelId": self.model_name,
"messages": context.messages,
"inferenceConfig": inference_config,
"additionalModelRequestFields": self._settings["additional_model_request_fields"]
"additionalModelRequestFields": self._settings["additional_model_request_fields"],
}
# Add system message
request_params["system"] = context.system
# Add tools if present
if context.tools:
tool_config = {
"tools": context.tools
}
tool_config = {"tools": context.tools}
# Add tool_choice if specified
if context.tool_choice:
if context.tool_choice == "auto":
@@ -658,32 +644,30 @@ class BedrockLLMService(LLMService):
elif context.tool_choice == "none":
# Skip adding toolChoice for "none"
pass
elif isinstance(context.tool_choice, dict) and "function" in context.tool_choice:
elif (
isinstance(context.tool_choice, dict) and "function" in context.tool_choice
):
tool_config["toolChoice"] = {
"tool": {
"name": context.tool_choice["function"]["name"]
}
"tool": {"name": context.tool_choice["function"]["name"]}
}
request_params["toolConfig"] = tool_config
# Add performance config if latency is specified
if self._settings["latency"] in ["standard", "optimized"]:
request_params["performanceConfig"] = {
"latency": self._settings["latency"]
}
request_params["performanceConfig"] = {"latency": self._settings["latency"]}
logger.debug(f"Calling Bedrock model with: {request_params}")
# Call Bedrock with streaming
response = self._client.converse_stream(**request_params)
await self.stop_ttfb_metrics()
# Process the streaming response
tool_use_block = None
json_accumulator = ""
for event in response["stream"]:
# Handle text content
if "contentBlockDelta" in event:
@@ -694,18 +678,20 @@ class BedrockLLMService(LLMService):
elif "toolUse" in delta and "input" in delta["toolUse"]:
# Handle partial JSON for tool use
json_accumulator += delta["toolUse"]["input"]
completion_tokens_estimate += self._estimate_tokens(delta["toolUse"]["input"])
completion_tokens_estimate += self._estimate_tokens(
delta["toolUse"]["input"]
)
# Handle tool use start
elif "contentBlockStart" in event:
content_block_start = event["contentBlockStart"]['start']
content_block_start = event["contentBlockStart"]["start"]
if "toolUse" in content_block_start:
tool_use_block = {
"id": content_block_start["toolUse"].get("toolUseId", ""),
"name": content_block_start["toolUse"].get("name", "")
"name": content_block_start["toolUse"].get("name", ""),
}
json_accumulator = ""
# Handle message completion with tool use
elif "messageStop" in event and "stopReason" in event["messageStop"]:
if event["messageStop"]["stopReason"] == "tool_use" and tool_use_block:
@@ -719,7 +705,7 @@ class BedrockLLMService(LLMService):
)
except json.JSONDecodeError:
logger.error(f"Failed to parse tool arguments: {json_accumulator}")
# Handle usage metrics if available
if "metadata" in event and "usage" in event["metadata"]:
usage = event["metadata"]["usage"]
@@ -750,7 +736,7 @@ class BedrockLLMService(LLMService):
prompt_tokens=prompt_tokens,
completion_tokens=comp_tokens,
cache_read_input_tokens=cache_read_input_tokens,
cache_creation_input_tokens=cache_creation_input_tokens
cache_creation_input_tokens=cache_creation_input_tokens,
)
async def process_frame(self, frame: Frame, direction: FrameDirection):
@@ -783,7 +769,7 @@ class BedrockLLMService(LLMService):
prompt_tokens: int,
completion_tokens: int,
cache_read_input_tokens: int,
cache_creation_input_tokens: int
cache_creation_input_tokens: int,
):
if prompt_tokens or completion_tokens:
tokens = LLMTokenUsage(
@@ -791,6 +777,6 @@ class BedrockLLMService(LLMService):
completion_tokens=completion_tokens,
total_tokens=prompt_tokens + completion_tokens,
cache_read_input_tokens=cache_read_input_tokens,
cache_creation_input_tokens=cache_creation_input_tokens
cache_creation_input_tokens=cache_creation_input_tokens,
)
await self.start_llm_usage_metrics(tokens)

4
src/pipecat/services/aws/stt.py
View File

@@ -19,7 +19,7 @@ from pipecat.frames.frames import (
Frame,
TranscriptionFrame,
InterimTranscriptionFrame,
StartFrame
StartFrame,
)
from pipecat.services.ai_services import STTService
from pipecat.transcriptions.language import Language
@@ -597,4 +597,4 @@ class TranscribeSTTService(STTService):
except Exception as e:
logger.error(f"Unexpected error in receive loop: {e}")
finally:
logger.debug("Receive loop ended")
logger.debug("Receive loop ended")

6
src/pipecat/services/aws/tts.py
View File

@@ -17,7 +17,7 @@ from pipecat.frames.frames import (
Frame,
TTSAudioRawFrame,
TTSStartedFrame,
TTSStoppedFrame
TTSStoppedFrame,
)
from pipecat.services.ai_services import TTSService
from pipecat.transcriptions.language import Language
@@ -187,7 +187,7 @@ class PollyTTSService(TTSService):
if self._settings["engine"] == "standard":
if self._settings["pitch"]:
prosody_attrs.append(f"pitch='{self._settings['pitch']}'")
if self._settings["rate"]:
prosody_attrs.append(f"rate='{self._settings['rate']}'")
if self._settings["volume"]:
@@ -195,7 +195,7 @@ class PollyTTSService(TTSService):
# logger.warning("Prosody tags are not supported for generative engine. Ignoring.")
if prosody_attrs:
ssml += f"<prosody {' '.join(prosody_attrs)}>"
ssml += f"<prosody {' '.join(prosody_attrs)}>"
ssml += text