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Slight refactor of handling thought-signature-containing special context messages in the Gemini adapter

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This commit is contained in:
Paul Kompfner
2025-12-04 17:02:40 -05:00
parent 747bd4f737
commit 4ea51ff67c
2 changed files with 38 additions and 35 deletions
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2
src/pipecat/adapters/services/anthropic_adapter.py
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@@ -198,7 +198,7 @@ class AnthropicLLMAdapter(BaseLLMAdapter[AnthropicLLMInvocationParams]):
],
}
# Fallback to assumption that the message is already in Anthropic format
# Fall back to assuming that the message is already in Anthropic format
return copy.deepcopy(message.message)
def _from_standard_message(self, message: LLMStandardMessage) -> MessageParam:

71
src/pipecat/adapters/services/gemini_adapter.py
View File

@@ -167,7 +167,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
class MessageConversionResult:
"""Result of converting a single universal context message to Google format.
# TODO: content could be other things, like {"tool_call_extra": ...}, for example. All bets are off when it's LLMSpecificMessage.
Either content (a Google Content object) or a system instruction string
is guaranteed to be set.
@@ -212,9 +211,44 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
tool_call_id_to_name_mapping = {}
non_fn_thought_signatures = []
# Process each message, preserving Google-formatted messages and converting others
# Process each message, converting to Google format as needed
for message in universal_context_messages:
result = self._from_universal_context_message(
# We have a Google-specific message; this may either be a
# thought-signature-containing message that we need to handle in a
# special way, or a message already in Google format that we can
# use directly
if isinstance(message, LLMSpecificMessage):
# Special handling for function-call-related thought signature
# messages
if (
isinstance(message.message, dict)
and message.message.get("type") == "tool_call_extra"
and isinstance(data := message.message.get("data"), dict)
and (thought_signature := data.get("thought_signature"))
):
self._apply_function_call_thought_signature_to_messages(
thought_signature, message.message.get("tool_call_id"), messages
)
continue
# Special handling for non-function-call-related thought
# signature messages (Gemini 3 Pro)
if (
isinstance(message.message, dict)
and message.message.get("type") == "thought_signature"
and (thought_signature := message.message.get("signature"))
):
non_fn_thought_signatures.append(thought_signature)
continue
# Fall back to assuming that the message is already in Google
# format
messages.append(message.message)
continue
# We have a standard universal context message; convert it to
# Google format
result = self._from_standard_message(
message,
params=self.MessageConversionParams(
already_have_system_instruction=bool(system_instruction),
@@ -222,30 +256,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
),
)
# If we found a function-call-related thought_signature, modify the
# corresponding function call message to include it
if (
isinstance(result.content, dict)
and result.content.get("type") == "tool_call_extra"
and isinstance(data := result.content.get("data"), dict)
and (thought_signature := data.get("thought_signature"))
):
self._apply_function_call_thought_signature_to_messages(
thought_signature, result.content.get("tool_call_id"), messages
)
continue
# If we found a standalone non-function-call-related thought
# signature (Gemini 3 Pro), store it to apply later to the
# corresponding assistant message
if (
isinstance(result.content, dict)
and result.content.get("type") == "thought_signature"
and (thought_signature := result.content.get("signature"))
):
non_fn_thought_signatures.append(thought_signature)
continue
# Each result is either a Content or a system instruction
if result.content:
messages.append(result.content)
@@ -278,13 +288,6 @@ class GeminiLLMAdapter(BaseLLMAdapter[GeminiLLMInvocationParams]):
return self.ConvertedMessages(messages=messages, system_instruction=system_instruction)
def _from_universal_context_message(
self, message: LLMContextMessage, *, params: MessageConversionParams
) -> MessageConversionResult:
if isinstance(message, LLMSpecificMessage):
return self.MessageConversionResult(content=message.message)
return self._from_standard_message(message, params=params)
def _from_standard_message(
self, message: LLMStandardMessage, *, params: MessageConversionParams
) -> MessageConversionResult: