ensure token_count is added to the final message - #963
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WalkthroughAdded token-usage extraction and token counting: tool_calling_llm now calls Changes
Sequence Diagram(s)sequenceDiagram
autonumber
actor Caller
participant ToolCallingLLM as ToolCallingLLM.call()
participant LLM as LLM
participant HolmesLLMModule as holmes.core.llm
Caller->>ToolCallingLLM: call(messages)
ToolCallingLLM->>LLM: generate(messages)
alt tools_to_call is empty
ToolCallingLLM->>ToolCallingLLM: post-process -> full_response
ToolCallingLLM->>LLM: count_tokens_for_message(messages)
ToolCallingLLM->>HolmesLLMModule: get_llm_usage(full_response)
ToolCallingLLM-->>Caller: LLMResult (metadata["usage"])
else tools_to_call not empty
ToolCallingLLM-->>Caller: trigger tool calls
end
sequenceDiagram
autonumber
actor Subscriber
participant ToolCallingLLM as ToolCallingLLM.call_stream()
participant LLM as LLM
participant HolmesLLMModule as holmes.core.llm
Subscriber->>ToolCallingLLM: subscribe(messages)
ToolCallingLLM->>LLM: stream(messages)
alt tools_to_call is empty
ToolCallingLLM-->>Subscriber: streamed chunks
Note right of ToolCallingLLM: on finalization -> assemble full_response
ToolCallingLLM->>LLM: count_tokens_for_message(messages)
ToolCallingLLM->>HolmesLLMModule: get_llm_usage(full_response)
ToolCallingLLM-->>Subscriber: ANSWER_END (metadata["usage"])
else tools_to_call not empty
ToolCallingLLM-->>Subscriber: tool-call events
end
Estimated code review effort🎯 3 (Moderate) | ⏱️ ~20 minutes Pre-merge checks (1 passed, 1 warning, 1 inconclusive)❌ Failed checks (1 warning, 1 inconclusive)
✅ Passed checks (1 passed)
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🚧 Files skipped from review as they are similar to previous changes (1)
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✨ Finishing touches
🧪 Generate unit tests
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Actionable comments posted: 1
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Review profile: CHILL
Plan: Pro
📒 Files selected for processing (2)
holmes/core/llm.py(2 hunks)holmes/core/tool_calling_llm.py(3 hunks)
🚧 Files skipped from review as they are similar to previous changes (1)
- holmes/core/tool_calling_llm.py
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📓 Path-based instructions (1)
**/*.py
📄 CodeRabbit inference engine (CLAUDE.md)
**/*.py: Use Ruff for formatting and linting
Type hints are required (checked by mypy)
Always place Python imports at the top of the file, not inside functions or methods
Files:
holmes/core/llm.py
⏰ Context from checks skipped due to timeout of 90000ms. You can increase the timeout in your CodeRabbit configuration to a maximum of 15 minutes (900000ms). (2)
- GitHub Check: Pre-commit checks
- GitHub Check: llm_evals
🔇 Additional comments (2)
holmes/core/llm.py (2)
533-534: No-op changeBlank lines only.
535-553: Tighten types, include TextCompletionResponse in signature, and verify stream consumption
- Apply the signature/typing changes and use typing.cast/getattr as shown in the diff below; add
from typing import cast.- Critically verify that
litellm.stream_chunk_builder(chunks=llm_response)does NOT consume theCustomStreamWrapper. If it does, move usage extraction to stream finalization or duplicate the stream upstream.- Run mypy/ruff and fix any warnings.
Apply within this hunk:
-def get_llm_usage(llm_response: Union[ModelResponse, CustomStreamWrapper]) -> dict: - usage: dict = {} - if ( - ( - isinstance(llm_response, ModelResponse) - or isinstance(llm_response, TextCompletionResponse) - ) - and hasattr(llm_response, "usage") - and llm_response.usage - ): # type: ignore - usage["prompt_tokens"] = llm_response.usage.prompt_tokens # type: ignore - usage["completion_tokens"] = llm_response.usage.completion_tokens # type: ignore - usage["total_tokens"] = llm_response.usage.total_tokens # type: ignore - elif isinstance(llm_response, CustomStreamWrapper): - complete_response = litellm.stream_chunk_builder(chunks=llm_response) # type: ignore - if complete_response: - return get_llm_usage(complete_response) # type: ignore - return usage +def get_llm_usage( + llm_response: Union[ModelResponse, TextCompletionResponse, CustomStreamWrapper] +) -> dict[str, int | None]: + usage: dict[str, int | None] = {} + if isinstance(llm_response, (ModelResponse, TextCompletionResponse)) and getattr( + llm_response, "usage", None + ): + u = llm_response.usage # type: ignore[assignment] + usage["prompt_tokens"] = getattr(u, "prompt_tokens", None) # type: ignore[attr-defined] + usage["completion_tokens"] = getattr(u, "completion_tokens", None) # type: ignore[attr-defined] + usage["total_tokens"] = getattr(u, "total_tokens", None) # type: ignore[attr-defined] + elif isinstance(llm_response, CustomStreamWrapper): + # Note: ensure this does not consume the stream before the caller iterates it. + complete_response = cast( + Optional[ModelResponse | TextCompletionResponse], + litellm.stream_chunk_builder(chunks=llm_response), + ) + if complete_response is not None: + return get_llm_usage(complete_response) + return usageAdd import (outside this hunk):
from typing import cast
aantn
previously approved these changes
Sep 12, 2025
nherment
enabled auto-merge (squash)
September 12, 2025 13:36
aantn
approved these changes
Sep 12, 2025
Contributor
This was referenced Sep 14, 2025
This was referenced Mar 6, 2026
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