Comments (4)
If you look at the Python examples, we have users providing the vector column in the Pydantic schema: https://lancedb.github.io/lancedb/embeddings/#openai-embedding-function
So it feels inconsistent that in the JS library we would tell them not to do that, right?
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This appears to be due to the fact that the user is specifying the "vector"
column which is also the output of the embed
function. The application logic is unable to handle this scenario.
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Does that mean we should be validating this at the time of create_table()
?
from lancedb.
i guess it depends on how we want to handle this. Do we want to treat this as a user error, or do we want to add logic to check for columns matching the embedding functions?
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Related Issues (20)
- bug(python): Cohere embedding functions use incorrect input type for query embeddings HOT 4
- CI(java): java 11 and java 17 stages should run in parallel HOT 1
- Bug: unable to run docs locally. HOT 1
- Feature: allow cache to be shared between multiple table instances HOT 1
- `Table.add()` should return number of rows written HOT 2
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- Feature: Unique Column Values (Aggregation) HOT 1
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