wx44wx/pipecat
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Rename Anthropic enable_prompt_caching_beta parameter to just enable_prompt_caching

Browse Source
This commit is contained in:
Paul Kompfner
2025-09-04 09:49:15 -04:00
parent c11b207c97
commit b2e9fd9341
4 changed files with 28 additions and 21 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
examples/foundational/14b-function-calling-anthropic-video.py
View File

@@ -97,7 +97,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
llm = AnthropicLLMService(
api_key=os.getenv("ANTHROPIC_API_KEY"),
model="claude-3-7-sonnet-latest",
params=AnthropicLLMService.InputParams(enable_prompt_caching_beta=True),
params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
)
llm.register_function("get_weather", get_weather)
llm.register_function("get_image", get_image)

2
examples/foundational/14z-function-calling-anthropic-universal-context.py
View File

@@ -98,7 +98,7 @@ async def run_bot(transport: BaseTransport, runner_args: RunnerArguments):
llm = AnthropicLLMService(
api_key=os.getenv("ANTHROPIC_API_KEY"),
model="claude-3-7-sonnet-latest",
params=AnthropicLLMService.InputParams(enable_prompt_caching_beta=True),
params=AnthropicLLMService.InputParams(enable_prompt_caching=True),
)
llm.register_function("get_weather", get_weather)
llm.register_function("get_image", get_image)

8
src/pipecat/adapters/services/anthropic_adapter.py
View File

@@ -62,9 +62,11 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
messages = self._from_universal_context_messages(self._get_messages(context))
return {
"system": messages.system,
"messages": self._with_cache_control_markers(messages.messages)
if enable_prompt_caching
else messages.messages,
"messages": (
self._with_cache_control_markers(messages.messages)
if enable_prompt_caching
else messages.messages
),
# NOTE: LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
"tools": self.from_standard_tools(context.tools),
}

37
src/pipecat/services/anthropic/llm.py
View File

@@ -115,7 +115,12 @@ class AnthropicLLMService(LLMService):
"""Input parameters for Anthropic model inference.
Parameters:
enable_prompt_caching_beta: Whether to enable beta prompt caching feature.
enable_prompt_caching: Whether to enable the prompt caching feature.
enable_prompt_caching_beta (deprecated): Whether to enable the beta prompt caching feature.
.. deprecated:: 0.0.83
Use the `enable_prompt_caching` parameter instead.
max_tokens: Maximum tokens to generate. Must be at least 1.
temperature: Sampling temperature between 0.0 and 1.0.
top_k: Top-k sampling parameter.
@@ -123,7 +128,8 @@ class AnthropicLLMService(LLMService):
extra: Additional parameters to pass to the API.
"""
enable_prompt_caching_beta: Optional[bool] = False
enable_prompt_caching: Optional[bool] = None
enable_prompt_caching_beta: Optional[bool] = None
max_tokens: Optional[int] = Field(default_factory=lambda: 4096, ge=1)
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)
@@ -162,7 +168,15 @@ class AnthropicLLMService(LLMService):
self._retry_on_timeout = retry_on_timeout
self._settings = {
"max_tokens": params.max_tokens,
"enable_prompt_caching_beta": params.enable_prompt_caching_beta or False,
"enable_prompt_caching": (
params.enable_prompt_caching
if params.enable_prompt_caching is not None
else (
params.enable_prompt_caching_beta
if params.enable_prompt_caching_beta is not None
else False
)
),
"temperature": params.temperature,
"top_k": params.top_k,
"top_p": params.top_p,
@@ -222,7 +236,7 @@ class AnthropicLLMService(LLMService):
if isinstance(context, LLMContext):
adapter: AnthropicLLMAdapter = self.get_llm_adapter()
params = adapter.get_llm_invocation_params(
context, enable_prompt_caching=self._settings["enable_prompt_caching_beta"]
context, enable_prompt_caching=self._settings["enable_prompt_caching"]
)
messages = params["messages"]
system = params["system"]
@@ -242,15 +256,6 @@ class AnthropicLLMService(LLMService):
return response.content[0].text
@property
def enable_prompt_caching_beta(self) -> bool:
"""Check if prompt caching beta feature is enabled.
Returns:
True if prompt caching is enabled.
"""
return self._enable_prompt_caching_beta
def create_context_aggregator(
self,
context: OpenAILLMContext,
@@ -287,14 +292,14 @@ class AnthropicLLMService(LLMService):
if isinstance(context, LLMContext):
adapter: AnthropicLLMAdapter = self.get_llm_adapter()
params = adapter.get_llm_invocation_params(
context, enable_prompt_caching=self._settings["enable_prompt_caching_beta"]
context, enable_prompt_caching=self._settings["enable_prompt_caching"]
)
return params
# Anthropic-specific context
messages = (
context.get_messages_with_cache_control_markers()
if self._settings["enable_prompt_caching_beta"]
if self._settings["enable_prompt_caching"]
else context.messages
)
return AnthropicLLMInvocationParams(
@@ -494,7 +499,7 @@ class AnthropicLLMService(LLMService):
await self._update_settings(frame.settings)
elif isinstance(frame, LLMEnablePromptCachingFrame):
logger.debug(f"Setting enable prompt caching to: [{frame.enable}]")
self._settings["enable_prompt_caching_beta"] = frame.enable
self._settings["enable_prompt_caching"] = frame.enable
else:
await self.push_frame(frame, direction)