Prefer Service.ThinkingConfig over raw ThinkingConfig class names in Anthropic and Google services and examples
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@@ -22,7 +22,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.anthropic.llm import AnthropicLLMService, AnthropicThinkingConfig
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from pipecat.services.anthropic.llm import AnthropicLLMService
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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@@ -64,7 +64,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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settings=AnthropicLLMService.Settings(
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thinking=AnthropicThinkingConfig(
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thinking=AnthropicLLMService.ThinkingConfig(
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type="enabled",
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budget_tokens=2048,
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),
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@@ -24,7 +24,7 @@ from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.google.llm import GoogleLLMService, GoogleThinkingConfig
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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from pipecat.transports.websocket.fastapi import FastAPIWebsocketParams
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@@ -65,7 +65,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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api_key=os.getenv("GOOGLE_API_KEY"),
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# model="gemini-3-pro-preview", # A more powerful reasoning model, but slower
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settings=GoogleLLMService.Settings(
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thinking=GoogleThinkingConfig(
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thinking=GoogleLLMService.ThinkingConfig(
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thinking_budget=-1, # Dynamic thinking
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include_thoughts=True,
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),
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@@ -23,7 +23,7 @@ from pipecat.processors.aggregators.llm_response_universal import (
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)
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from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.anthropic.llm import AnthropicLLMService, AnthropicThinkingConfig
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from pipecat.services.anthropic.llm import AnthropicLLMService
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.llm_service import FunctionCallParams
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@@ -85,7 +85,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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llm = AnthropicLLMService(
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api_key=os.getenv("ANTHROPIC_API_KEY"),
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settings=AnthropicLLMService.Settings(
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thinking=AnthropicThinkingConfig(
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thinking=AnthropicLLMService.ThinkingConfig(
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type="enabled",
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budget_tokens=2048,
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),
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@@ -25,7 +25,7 @@ from pipecat.runner.types import RunnerArguments
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from pipecat.runner.utils import create_transport
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from pipecat.services.cartesia.tts import CartesiaTTSService
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from pipecat.services.deepgram.stt import DeepgramSTTService
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from pipecat.services.google.llm import GoogleLLMService, GoogleThinkingConfig
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from pipecat.services.google.llm import GoogleLLMService
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from pipecat.services.llm_service import FunctionCallParams
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from pipecat.transports.base_transport import BaseTransport, TransportParams
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from pipecat.transports.daily.transport import DailyParams
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@@ -86,7 +86,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
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api_key=os.getenv("GOOGLE_API_KEY"),
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# model="gemini-3-pro-preview", # A more powerful reasoning model, but slower
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settings=GoogleLLMService.Settings(
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thinking=GoogleThinkingConfig(
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thinking=GoogleLLMService.ThinkingConfig(
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thinking_budget=-1, # Dynamic thinking
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include_thoughts=True,
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),
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