Use LLM APIs responses in token counting #5604
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…sponse "usage" data" This reverts commit d317a3e.
enyst
commented
Dec 14, 2024
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@openhands-agent Read the diff of this PR, PR 5604. And add unit tests for the functionality we change or introduce. Please make sure to explore the unit tests a bit to find if we already have test files appropriate for these tests, if not make a new one. |
* Replace prompt_tokens, completion_tokens, total_tokens fields with a single usage field * Add properties to maintain backward compatibility * Update tests to verify Usage object is stored and properties work * Update LLM.get_token_count() to use usage.total_tokens
* Keep Usage-based token counting for Message objects * Add support for dict messages from main * Add custom tokenizer support from main * Improve error logging from main * Add new tests from main
* Add back the initialization of self.tokenizer that was lost in merge * This is needed for custom tokenizer support in token counting
* Add back the import that was lost in merge * This import is needed for custom tokenizer initialization
* Add explicit type check for Message in token counting loop * This fixes mypy errors about accessing .usage on dict
* Add back the logger mock that was lost in merge * This is needed for token counting error handling tests
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enyst
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enyst
commented
Dec 17, 2024
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Status Summary: UNRESOLVED ISSUES:
NEXT STEPS NEEDED:
Overall Assessment: The original issue remains entirely unresolved as the agent did not perform any of the requested merge-related tasks and instead went off-topic with code analysis. |
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OVERVIEW:
STATUS: ✅ FULLY RESOLVED
NEXT STEPS:
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End-user friendly description of the problem this fixes or functionality that this introduces
Give a summary of what the PR does, explaining any non-trivial design decisions
Follow-up on #5550
Litellm has token data for certain LLMs. We can use it if it's available and more accurate/appropriate than its token counting with tiktoken.
Link of any specific issues this addresses
Fix #2947
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