This forces the user to replace the whole FTS directory when re-creating
the index, prevent duplicate data from being created. Previously, the
whole dataset was re-added to the existing index, duplicating existing
rows in the index.
This (in combination with lancedb/lance#1707) caused #726, since the
duplicate data emitted duplicate indices for `take()` and an upstream
issue caused those queries to fail.
This solution isn't ideal, since it makes the FTS index temporarily
unavailable while the index is built. In the future, we should have
multiple FTS index directories, which would allow atomic commits of new
indexes (as well as multiple indexes for different columns).
Fixes#498.
Fixes#726.
---------
Co-authored-by: Chang She <759245+changhiskhan@users.noreply.github.com>
Closes https://github.com/lancedb/lance/issues/1738
We add a `flatten` parameter to the signature of `to_pandas`. By default
this is None and does nothing.
If set to True or -1, then LanceDB will flatten structs before
converting to a pandas dataframe. All nested structs are also flattened.
If set to any positive integer, then LanceDB will flatten structs up to
the specified level of nesting.
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
- Register open_table as event
- Because we're dropping 'seach' event currently, changed the name to
'search_table' and introduced throttling
- Throttled events will be counted once per time batch so that the user
is registered but event count doesn't go up by a lot
Closes#594
The embedding functions are pydantic models so multiple instances with
the same parameters are considered ==, which means that if you have
multiple embedding columns it's possible for the embeddings to get
overwritten. Instead we use `is` instead of == to avoid this problem.
testing: modified unit test to include this case
Sets things up for this -> https://github.com/lancedb/lancedb/issues/579
- Just separates out the registry/ingestion code from the function
implementation code
- adds a `get_registry` util
- package name "open-clip" -> "open-clip-torch"
This PR adds an overview of embeddings docs:
- 2 ways to vectorize your data using lancedb - explicit & implicit
- explicit - manually vectorize your data using `wit_embedding` function
- Implicit - automatically vectorize your data as it comes by ingesting
your embedding function details as table metadata
- Multi-modal example w/ disappearing embedding function
Add `to_list` to return query results as list of python dict (so we're
not too pandas-centric). Closes#555
Add `to_pandas` API and add deprecation warning on `to_df`. Closes#545
Co-authored-by: Chang She <chang@lancedb.com>
