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Merge pull request #2653 from pipecat-ai/pk/llm-context-adapting-tests

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`LLMContext`-adapting unit tests
This commit is contained in:
kompfner
2025-09-15 16:38:46 -04:00
committed by GitHub
parent 7603203230 fe42187dc1
commit 81cede2c60
9 changed files with 1096 additions and 24 deletions
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39
src/pipecat/adapters/base_llm_adapter.py
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@@ -16,7 +16,12 @@ from typing import Any, Dict, Generic, List, TypeVar
from loguru import logger
from pipecat.adapters.schemas.tools_schema import ToolsSchema
from pipecat.processors.aggregators.llm_context import LLMContext, NotGiven
from pipecat.processors.aggregators.llm_context import (
LLMContext,
LLMContextMessage,
LLMSpecificMessage,
NotGiven,
)
# Should be a TypedDict
TLLMInvocationParams = TypeVar("TLLMInvocationParams", bound=dict[str, Any])
@@ -38,6 +43,16 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
Subclasses must implement provider-specific conversion logic.
"""
@property
@abstractmethod
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for this LLM provider.
Returns:
The identifier string.
"""
pass
@abstractmethod
def get_llm_invocation_params(self, context: LLMContext, **kwargs) -> TLLMInvocationParams:
"""Get provider-specific LLM invocation parameters from a universal LLM context.
@@ -76,6 +91,28 @@ class BaseLLMAdapter(ABC, Generic[TLLMInvocationParams]):
"""
pass
def create_llm_specific_message(self, message: Any) -> LLMSpecificMessage:
"""Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.
Args:
message: The message content.
Returns:
A LLMSpecificMessage instance.
"""
return LLMSpecificMessage(llm=self.id_for_llm_specific_messages, message=message)
def get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
"""Get messages from the LLM context, including standard and LLM-specific messages.
Args:
context: The LLM context containing messages.
Returns:
List of messages including standard and LLM-specific messages.
"""
return context.get_messages(self.id_for_llm_specific_messages)
def from_standard_tools(self, tools: Any) -> List[Any] | NotGiven:
"""Convert tools from standard format to provider format.

12
src/pipecat/adapters/services/anthropic_adapter.py
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@@ -42,6 +42,11 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
to the specific format required by Anthropic's Claude models for function calling.
"""
@property
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for Anthropic."""
return "anthropic"
def get_llm_invocation_params(
self, context: LLMContext, enable_prompt_caching: bool
) -> AnthropicLLMInvocationParams:
@@ -54,7 +59,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
Returns:
Dictionary of parameters for invoking Anthropic's LLM API.
"""
messages = self._from_universal_context_messages(self._get_messages(context))
messages = self._from_universal_context_messages(self.get_messages(context))
return {
"system": messages.system,
"messages": (
@@ -78,7 +83,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
List of messages in a format ready for logging about Anthropic.
"""
# Get messages in Anthropic's format
messages = self._from_universal_context_messages(self._get_messages(context)).messages
messages = self._from_universal_context_messages(self.get_messages(context)).messages
# Sanitize messages for logging
messages_for_logging = []
@@ -92,9 +97,6 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
messages_for_logging.append(msg)
return messages_for_logging
def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
return context.get_messages("anthropic")
@dataclass
class ConvertedMessages:
"""Container for Anthropic-formatted messages converted from universal context."""

5
src/pipecat/adapters/services/aws_nova_sonic_adapter.py
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@@ -31,6 +31,11 @@ class AWSNovaSonicLLMAdapter(BaseLLMAdapter[AWSNovaSonicLLMInvocationParams]):
specific function-calling format, enabling tool use with Nova Sonic models.
"""
@property
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for AWS Nova Sonic."""
raise NotImplementedError("Universal LLMContext is not yet supported for AWS Nova Sonic.")
def get_llm_invocation_params(self, context: LLMContext) -> AWSNovaSonicLLMInvocationParams:
"""Get AWS Nova Sonic-specific LLM invocation parameters from a universal LLM context.

12
src/pipecat/adapters/services/bedrock_adapter.py
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@@ -42,6 +42,11 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
into AWS Bedrock's expected tool format for function calling capabilities.
"""
@property
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for AWS Bedrock."""
return "aws"
def get_llm_invocation_params(self, context: LLMContext) -> AWSBedrockLLMInvocationParams:
"""Get AWS Bedrock-specific LLM invocation parameters from a universal LLM context.
@@ -51,7 +56,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
Returns:
Dictionary of parameters for invoking AWS Bedrock's LLM API.
"""
messages = self._from_universal_context_messages(self._get_messages(context))
messages = self._from_universal_context_messages(self.get_messages(context))
return {
"system": messages.system,
"messages": messages.messages,
@@ -75,7 +80,7 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
List of messages in a format ready for logging about AWS Bedrock.
"""
# Get messages in Anthropic's format
messages = self._from_universal_context_messages(self._get_messages(context)).messages
messages = self._from_universal_context_messages(self.get_messages(context)).messages
# Sanitize messages for logging
messages_for_logging = []
@@ -89,9 +94,6 @@ class AWSBedrockLLMAdapter(BaseLLMAdapter[AWSBedrockLLMInvocationParams]):
messages_for_logging.append(msg)
return messages_for_logging
def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
return context.get_messages("anthropic")
@dataclass
class ConvertedMessages:
"""Container for Anthropic-formatted messages converted from universal context."""

12
src/pipecat/adapters/services/gemini_adapter.py
View File

@@ -54,6 +54,11 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
- Extracting and sanitizing messages from the LLM context for logging with Gemini.
"""
@property
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for Google."""
return "google"
def get_llm_invocation_params(self, context: LLMContext) -> GeminiLLMInvocationParams:
"""Get Gemini-specific LLM invocation parameters from a universal LLM context.
@@ -63,7 +68,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
Returns:
Dictionary of parameters for Gemini's API.
"""
messages = self._from_universal_context_messages(self._get_messages(context))
messages = self._from_universal_context_messages(self.get_messages(context))
return {
"system_instruction": messages.system_instruction,
"messages": messages.messages,
@@ -103,7 +108,7 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
List of messages in a format ready for logging about Gemini.
"""
# Get messages in Gemini's format
messages = self._from_universal_context_messages(self._get_messages(context)).messages
messages = self._from_universal_context_messages(self.get_messages(context)).messages
# Sanitize messages for logging
messages_for_logging = []
@@ -119,9 +124,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
messages_for_logging.append(obj)
return messages_for_logging
def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
return context.get_messages("google")
@dataclass
class ConvertedMessages:
"""Container for Google-formatted messages converted from universal context."""

24
src/pipecat/adapters/services/open_ai_adapter.py
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@@ -24,6 +24,7 @@ from pipecat.processors.aggregators.llm_context import (
LLMContext,
LLMContextMessage,
LLMContextToolChoice,
LLMSpecificMessage,
NotGiven,
)
@@ -47,6 +48,11 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
- Extracting and sanitizing messages from the LLM context for logging about OpenAI.
"""
@property
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for OpenAI."""
return "openai"
def get_llm_invocation_params(self, context: LLMContext) -> OpenAILLMInvocationParams:
"""Get OpenAI-specific LLM invocation parameters from a universal LLM context.
@@ -57,7 +63,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
Dictionary of parameters for OpenAI's ChatCompletion API.
"""
return {
"messages": self._from_universal_context_messages(self._get_messages(context)),
"messages": self._from_universal_context_messages(self.get_messages(context)),
# NOTE; LLMContext's tools are guaranteed to be a ToolsSchema (or NOT_GIVEN)
"tools": self.from_standard_tools(context.tools),
"tool_choice": context.tool_choice,
@@ -91,7 +97,7 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
List of messages in a format ready for logging about OpenAI.
"""
msgs = []
for message in self._get_messages(context):
for message in self.get_messages(context):
msg = copy.deepcopy(message)
if "content" in msg:
if isinstance(msg["content"], list):
@@ -104,14 +110,18 @@ class OpenAILLMAdapter(BaseLLMAdapter[OpenAILLMInvocationParams]):
msgs.append(msg)
return msgs
def _get_messages(self, context: LLMContext) -> List[LLMContextMessage]:
return context.get_messages("openai")
def _from_universal_context_messages(
self, messages: List[LLMContextMessage]
) -> List[ChatCompletionMessageParam]:
# Just a pass-through: messages are already the right type
return messages
result = []
for message in messages:
if isinstance(message, LLMSpecificMessage):
# Extract the actual message content from LLMSpecificMessage
result.append(message.message)
else:
# Standard message, pass through unchanged
result.append(message)
return result
def _from_standard_tool_choice(
self, tool_choice: LLMContextToolChoice | NotGiven

5
src/pipecat/adapters/services/open_ai_realtime_adapter.py
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@@ -30,6 +30,11 @@ class OpenAIRealtimeLLMAdapter(BaseLLMAdapter):
OpenAI's Realtime API for function calling capabilities.
"""
@property
def id_for_llm_specific_messages(self) -> str:
"""Get the identifier used in LLMSpecificMessage instances for OpenAI Realtime."""
raise NotImplementedError("Universal LLMContext is not yet supported for OpenAI Realtime.")
def get_llm_invocation_params(self, context: LLMContext) -> OpenAIRealtimeLLMInvocationParams:
"""Get OpenAI Realtime-specific LLM invocation parameters from a universal LLM context.

13
src/pipecat/services/llm_service.py
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@@ -44,7 +44,7 @@ from pipecat.frames.frames import (
StartFrame,
UserImageRequestFrame,
)
from pipecat.processors.aggregators.llm_context import LLMContext
from pipecat.processors.aggregators.llm_context import LLMContext, LLMSpecificMessage
from pipecat.processors.aggregators.llm_response import (
LLMAssistantAggregatorParams,
LLMUserAggregatorParams,
@@ -195,6 +195,17 @@ class LLMService(AIService):
"""
return self._adapter
def create_llm_specific_message(self, message: Any) -> LLMSpecificMessage:
"""Create an LLM-specific message (as opposed to a standard message) for use in an LLMContext.
Args:
message: The message content.
Returns:
A LLMSpecificMessage instance.
"""
return self.get_llm_adapter().create_llm_specific_message(message)
async def run_inference(self, context: LLMContext | OpenAILLMContext) -> Optional[str]:
"""Run a one-shot, out-of-band (i.e. out-of-pipeline) inference with the given LLM context.