Feature Description

The GoogleGenAI LLM integration should expose token usage metadata for structured prediction methods (structured_predict, astructured_predict, stream_structured_predict, astream_structured_predict).

Token tracking works correctly for chat(), achat(), complete(), and acomplete() via the chat_from_gemini_response() utility function which extracts usage_metadata and populates additional_kwargs with prompt_tokens, completion_tokens, and total_tokens.

However, structured prediction methods bypass this utility and return only the parsed Pydantic model, discarding all token usage information from the API response.

Expected behavior: Token usage should be accessible for all LLM methods, including structured predictions.

if response.usage_metadata:
    raw["usage_metadata"] = response.usage_metadata.model_dump()
    additional_kwargs["prompt_tokens"] = response.usage_metadata.prompt_token_count
    additional_kwargs["completion_tokens"] = response.usage_metadata.candidates_token_count
    additional_kwargs["total_tokens"] = response.usage_metadata.total_token_count

# response.usage_metadata exists but is discarded
if isinstance(response.parsed, BaseModel):
    return response.parsed  # No token metadata attached

Reason

What is stopping LlamaIndex from supporting this feature today?

The structured_predict() implementation calls self._client.models.generate_content() directly and returns response.parsed without extracting usage_metadata from the response object. The data exists in the response, but is discarded.

What existing approaches have not worked for you?

1. Phoenix/Arize observability - Transactions using structured_predict() appear in traces without token counts, making it impossible to benchmark across methods.

2. TokenCountingHandler - Cannot count tokens for structured predictions, breaking cost analysis.

3. Workaround using chat() - While technically possible to pass generation_config={"response_schema": MyModel} to chat(), this requires manual JSON parsing, loses the convenience of structured_predict() returning typed objects, is undocumented, and creates API inconsistency.

4. Thinking models - Gemini 3.1, 3, and 3 Flash Lite have reasoning/thinking capabilities with thoughts_token_count. Engineers are unaware that structured predictions have untracked thinking tokens.

Value of Feature

1. Observability parity - Phoenix OpenInference traces for structured predictions currently lack token counts, making it difficult for engineers to benchmark across different methods.

2. Cost tracking - Teams allocating costs by token usage cannot accurately track structured prediction usage.

3. Thinking/reasoning models - Modern Gemini models (3.1, 3, 3 Flash Lite) perform reasoning with thoughts_token_count. Without tracking, engineers cannot measure reasoning token efficiency or optimize prompt strategies.

4. API consistency - Users expect all LLM methods to return consistent metadata. The current gap creates confusion—why does chat() show token counts but structured_predict() doesn't?

5. Downstream tool compatibility - Tools like MLFlow, Phoenix, and custom callback handlers expect token counts in additional_kwargs. Structured predictions break this contract.

Impact if not fixed:

• Engineers may not realize structured predictions aren't being tracked
• Production systems have incomplete observability data
• Cost allocation for structured workflows is impossible
• Comparison benchmarks between methods are incomplete

Related Issues

• [Bug]: Missing token usage information in GoogleGenAI metadata for MLFlow Tracing #20218 - Missing token usage information in GoogleGenAI metadata for MLFlow Tracing (Closed - Fixed for chat()/achat() only, structured_predict() methods not addressed)
• [Feature Request] StructuredLLM - Add raw completion response alongside structured output #17736 - StructuredLLM - Add raw completion response alongside structured output (Open - Broader request for all LLMs and all raw response fields)
• [Bug]: No Input/Output Token count for Gemini 2.5 models #19293 - No Input/Output Token count for Gemini 2.5 models (Open - May be related; reports missing token counts for Gemini 2.5 in instrumentation)
• [Feature Request]: Get thoughts_token_count from gemini response #19662 - Get thoughts_token_count from gemini response (Closed - Fixed in chat_from_gemini_response() but thoughts_token_count still not extracted in structured_predict())

#20218 is the closest predecessor - it was closed after fixing token tracking for chat() methods, but the fix did not extend to structured_predict() methods. This issue effectively completes the work started in #20218.