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Implement embeddings for use with LLM agents #680
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Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #680 +/- ##
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- Coverage 56.02% 55.82% -0.21%
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Files 98 98
Lines 11828 11871 +43
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Hits 6627 6627
- Misses 5201 5244 +43 ☔ View full report in Codecov by Sentry. |
text = "\n".join([message["content"] for message in messages]) | ||
# TODO: refactor this so it's not subsetting by index | ||
# [(0, 'The creator of this dataset is named Andrew HH', 0.7491879463195801, 'windturbines.parquet')] | ||
result = self.embeddings.query(text, table_name=table_name)[0][1] |
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Another TODO: handle ephemeral tables
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Uses duckdb instead of chromadb. Currently subsets embeddings by table name. Not sure how to handle ephemeral tables yet.