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fix(gemini realtime): scope dotted-key event lookup and propagate session metadata to tool-call response.done
- map_openai_event: only check the current key/value pair when resolving dotted map entries (e.g. serverContent.turnComplete) so a sibling key in the same frame can't misclassify the event being processed (e.g. toolCall returning RESPONSE_DONE). - tool-call path: extract generationConfig once and include modalities, temperature, and max_output_tokens on response.done so its shape matches response.created and the non-tool-call response.done. Co-authored-by: Yassin Kortam <yassin@berri.ai>
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Cursor Agent
parent
2c41400bdf
commit
61928b9704
1 changed files with 76 additions and 50 deletions
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@ -1152,15 +1152,25 @@ class GeminiRealtimeConfig(BaseRealtimeConfig):
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else:
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# Check if this key or any nested key matches our mapping. Use a
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# distinct loop variable so we don't shadow ``openai_event`` and
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# leak the last dict value when no entry matches.
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# leak the last dict value when no entry matches. Scope dotted-key
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# lookups to the current ``key``/``value`` pair — checking the
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# whole ``json_message`` would let a sibling key (e.g.
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# ``serverContent.turnComplete``) misclassify the event currently
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# being processed (e.g. ``toolCall``).
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for map_key, candidate_event in MAP_GEMINI_FIELD_TO_OPENAI_EVENT.items():
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if map_key == key or (
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"." in map_key
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and GeminiRealtimeConfig.get_nested_value(json_message, map_key)
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is not None
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):
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if map_key == key:
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openai_event = candidate_event
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break
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if "." in map_key:
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prefix, _, nested_path = map_key.partition(".")
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if (
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prefix == key
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and isinstance(value, dict)
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and GeminiRealtimeConfig.get_nested_value(value, nested_path)
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is not None
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):
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openai_event = candidate_event
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break
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if openai_event is None:
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raise ValueError(f"Unknown openai event: {key}, value: {value}")
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return openai_event
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@ -1311,6 +1321,30 @@ class GeminiRealtimeConfig(BaseRealtimeConfig):
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if current_conversation_id is None:
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current_conversation_id = f"conv_{uuid.uuid4()}"
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# Extract session-level response metadata once so both
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# response.created and response.done can include matching
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# modalities/temperature/max_output_tokens fields.
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session_setup: BidiGenerateContentSetup = {}
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if session_configuration_request is not None:
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try:
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session_setup = json.loads(session_configuration_request).get(
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"setup", {}
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)
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except (json.JSONDecodeError, TypeError):
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session_setup = {}
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tool_call_generation_config = (
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session_setup.get("generationConfig", {}) or {}
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)
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tool_call_modalities = [
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modality.lower()
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for modality in cast(
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List[str],
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tool_call_generation_config.get(
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"responseModalities", ["AUDIO"]
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),
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)
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]
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# Emit response.created preamble if this is the first event in the response
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if current_response_id is None:
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current_response_id = f"resp_{uuid.uuid4()}"
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@ -1321,26 +1355,6 @@ class GeminiRealtimeConfig(BaseRealtimeConfig):
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# spec-compliant clients see consistent response metadata
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# regardless of whether the response starts with content or
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# a tool call.
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session_setup: BidiGenerateContentSetup = {}
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if session_configuration_request is not None:
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try:
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session_setup = json.loads(
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session_configuration_request
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).get("setup", {})
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except (json.JSONDecodeError, TypeError):
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session_setup = {}
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tool_call_generation_config = (
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session_setup.get("generationConfig", {}) or {}
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)
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tool_call_modalities = [
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modality.lower()
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for modality in cast(
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List[str],
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tool_call_generation_config.get(
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"responseModalities", ["AUDIO"]
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),
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)
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]
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returned_message.append(
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{
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"type": "response.created",
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@ -1420,31 +1434,43 @@ class GeminiRealtimeConfig(BaseRealtimeConfig):
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tool_call_responses_api_usage = LiteLLMCompletionResponsesConfig._transform_chat_completion_usage_to_responses_usage(
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get_empty_usage(),
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)
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returned_message.append(
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OpenAIRealtimeDoneEvent(
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type="response.done",
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event_id=f"event_{uuid.uuid4()}",
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response=OpenAIRealtimeResponseDoneObject(
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id=current_response_id,
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object="realtime.response",
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status="completed",
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output=[
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{
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"id": te["item_id"],
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"object": "realtime.item",
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"type": "function_call",
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"status": "completed",
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"call_id": te["call_id"],
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"name": te["name"],
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"arguments": te["arguments"],
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}
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for te in tool_call_events
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],
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conversation_id=current_conversation_id,
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usage=tool_call_responses_api_usage.model_dump(),
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),
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)
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tool_call_done_event = OpenAIRealtimeDoneEvent(
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type="response.done",
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event_id=f"event_{uuid.uuid4()}",
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response=OpenAIRealtimeResponseDoneObject(
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id=current_response_id,
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object="realtime.response",
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status="completed",
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output=[
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{
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"id": te["item_id"],
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"object": "realtime.item",
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"type": "function_call",
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"status": "completed",
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"call_id": te["call_id"],
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"name": te["name"],
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"arguments": te["arguments"],
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}
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for te in tool_call_events
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],
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conversation_id=current_conversation_id,
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modalities=tool_call_modalities,
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usage=tool_call_responses_api_usage.model_dump(),
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),
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)
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tool_call_temperature = tool_call_generation_config.get("temperature")
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if tool_call_temperature is not None:
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tool_call_done_event["response"][
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"temperature"
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] = tool_call_temperature
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tool_call_max_output_tokens = tool_call_generation_config.get(
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"maxOutputTokens"
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)
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if tool_call_max_output_tokens is not None:
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tool_call_done_event["response"][
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"max_output_tokens"
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] = tool_call_max_output_tokens
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returned_message.append(tool_call_done_event)
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# Reset IDs so the next model turn (after tool results) starts a
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# fresh response with its own response.created preamble.
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current_output_item_id = None
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