Fix SummarizeData so that downstream .materialize operations will work.#1030
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eric-anderson
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Nov 20, 2024
| # so that the materialized data is complete, even if they are not all included | ||
| # in the input prompt to the LLM. | ||
| for di, doc in enumerate(result.take_all()): | ||
| if isinstance(doc, MetadataDocument): |
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You shouldn't need MetadataDocument check. take_all removes those.
| # For query result caching in the executor, we need to consume the documents | ||
| # so that the materialized data is complete, even if they are not all included | ||
| # in the input prompt to the LLM. | ||
| for di, doc in enumerate(result.take_all()): |
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This works at small scale, but will blow up memory at large scale.
Approving as this is NTSB only, but I suggest a TODO or something.
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| # First run should populate cache. | ||
| executor = SycamoreExecutor(context, cache_dir=temp_dir) |
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Is this test fast? If it is, then fine to leave in unit tests, but if it's more than 5-10s, I'd like to get it moved into integration tests. I initially thought this would use ray (which basically guarantees it's slow), but I'm no longer sure.
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This should fix the issue where SummarizeData leaves us with an incomplete materialize dir when the total doc size exceeds the LLM token limit.