support prompt caching token tracking in langchain #1250
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Extract cache_read and cache_creation from LangChain's nested input_token_details object and map them to Braintrust's standard metric names (prompt_cached_tokens, prompt_cache_creation_tokens). This enables accurate cache token tracking in the UI and correct cost calculations for cached prompts. Fixes Pylon #10400 Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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Added a focused unit test for cache-token extraction in langchain-py.
Commit: 08205f4 |
cpinn
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This change looks great to me with the added test, was what I was going to take a look into today. Usually for changes like this we also record and save a cassette to have the tests run the tests a little faster.
cc: @ibolmo should we add a cassette to the change or should we merge as is and add it later?
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| assert "prompt_cache_creation_tokens" in first_metrics | ||
| assert first_metrics["prompt_cache_creation_tokens"] > 0 | ||
| if "prompt_cached_tokens" in first_metrics: |
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nit. do we want this if statement in the test because then the assertion may or may not run?
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braintrust-langchain 0.2.1 and @braintrust/langchain-js 0.2.3 are both out. |
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cache_readandcache_creationfrom LangChain's nestedinput_token_detailsobjectprompt_cached_tokens,prompt_cache_creation_tokens)Problem
The LangChain callback handler only extracts top-level fields from
usage_metadata(input_tokens, output_tokens, total_tokens) but not the nestedinput_token_detailscontaining cache metrics. This causes: