If you add timezone information in the Field annotation for a datetime
then that will now be passed to the pyarrow data type.
I'm not sure how pyarrow enforces timezones, right now, it silently
coerces to the timezone given in the column regardless of whether the
input had the matching timezone or not. This is probably not the right
behavior. Though we could just make it so the user has to make the
pydantic model do the validation instead of doing that at the pyarrow
conversion layer.
Closes#721
fts will return results as a pyarrow table. Pyarrow tables has a
`filter` method but it does not take sql filter strings (only pyarrow
compute expressions). Instead, we do one of two things to support
`tbl.search("keywords").where("foo=5").limit(10).to_arrow()`:
Default path: If duckdb is available then use duckdb to execute the sql
filter string on the pyarrow table.
Backup path: Otherwise, write the pyarrow table to a lance dataset and
then do `to_table(filter=<filter>)`
Neither is ideal.
Default path has two issues:
1. requires installing an extra library (duckdb)
2. duckdb mangles some fields (like fixed size list => list)
Backup path incurs a latency penalty (~20ms on ssd) to write the
resultset to disk.
In the short term, once #676 is addressed, we can write the dataset to
"memory://" instead of disk, this makes the post filter evaluate much
quicker (ETA next week).
In the longer term, we'd like to be able to evaluate the filter string
on the pyarrow Table directly, one possibility being that we use
Substrait to generate pyarrow compute expressions from sql string. Or if
there's enough progress on pyarrow, it could support Substrait
expressions directly (no ETA)
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
For object detection, each row may correspond to an image and each image
can have multiple bounding boxes of x-y coordinates. This means that a
`bbox` field is potentially "list of list of float". This adds support
in our pydantic-pyarrow conversion for nested lists.
Use pathlib for local paths so that pathlib
can handle the correct separator on windows.
Closes#703
---------
Co-authored-by: Will Jones <willjones127@gmail.com>
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>
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"
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>
We have experimental support for prefiltering (without ANN) in pylance.
This means that we can now apply a filter BEFORE vector search is
performed. This can be done via the `.where(filter_string,
prefilter=True)` kwargs of the query.
Limitations:
- When connecting to LanceDB cloud, `prefilter=True` will raise
NotImplemented
- When an ANN index is present, `prefilter=True` will raise
NotImplemented
- This option is not available for full text search query
- This option is not available for empty search query (just
filter/project)
Additional changes in this PR:
- Bump pylance version to v0.8.0 which supports the experimental
prefiltering.
---------
Co-authored-by: Chang She <chang@lancedb.com>
1. Support persistent embedding function so users can just search using
query string
2. Add fixed size list conversion for multiple vector columns
3. Add support for empty query (just apply select/where/limit).
4. Refactor and simplify some of the data prep code
---------
Co-authored-by: Chang She <chang@lancedb.com>
Co-authored-by: Weston Pace <weston.pace@gmail.com>
Combine delete and append to make a temporary update feature that is
only enabled for the local python lancedb.
The reason why this is temporary is because it first has to load the
data that matches the where clause into memory, which is technical
unbounded.
---------
Co-authored-by: Chang She <chang@lancedb.com>
Previously if you needed to add a column to a table you'd have to
rewrite the whole table. Instead,
we use the merge functionality from Lance format
to incrementally add columns from another table
or dataframe.
---------
Co-authored-by: Chang She <chang@lancedb.com>
Co-authored-by: Weston Pace <weston.pace@gmail.com>
Previously the temporary restore feature required copying data. The new
feature in pylance does not.
---------
Co-authored-by: Chang She <chang@lancedb.com>
Co-authored-by: Weston Pace <weston.pace@gmail.com>
It improves the UX as iterators can be of any type supported by the
table (plus recordbatch) & there is no separate requirement.
Also expands the test cases for pydantic & arrow schema.
If this is looks good I'll update the docs.
Example usage:
```
class Content(LanceModel):
vector: vector(2)
item: str
price: float
def make_batches():
for _ in range(5):
yield from [
# pandas
pd.DataFrame({
"vector": [[3.1, 4.1], [1, 1]],
"item": ["foo", "bar"],
"price": [10.0, 20.0],
}),
# pylist
[
{"vector": [3.1, 4.1], "item": "foo", "price": 10.0},
{"vector": [5.9, 26.5], "item": "bar", "price": 20.0},
],
# recordbatch
pa.RecordBatch.from_arrays(
[
pa.array([[3.1, 4.1], [5.9, 26.5]], pa.list_(pa.float32(), 2)),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"],
),
# pydantic list
[
Content(vector=[3.1, 4.1], item="foo", price=10.0),
Content(vector=[5.9, 26.5], item="bar", price=20.0),
]]
db = lancedb.connect("db")
tbl = db.create_table("tabley", make_batches(), schema=Content, mode="overwrite")
tbl.add(make_batches())
```
Same should with arrow schema.
---------
Co-authored-by: Weston Pace <weston.pace@gmail.com>
This adds LanceTable.restore as a temporary feature. It reads data from
a previous version and creates
a new snapshot version using that data. This makes the version writeable
unlike checkout. This should be replaced once the feature is implemented
in pylance.
Co-authored-by: Chang She <chang@lancedb.com>
BREAKING CHANGE: The `score` column has been renamed to `_distance` to
more accurately describe the semantics (smaller means closer / better).
---------
Co-authored-by: Lei Xu <lei@lancedb.com>
#416 Fixed.
added drop_database() method . This deletes all the tables from the
database with a single command.
---------
Signed-off-by: Ashis Kumar Naik <ashishami2002@gmail.com>
Saves users from having to explicitly call
`LanceModel.to_arrow_schema()` when creating an empty table.
See new docs for full details.
---------
Co-authored-by: Chang She <chang@lancedb.com>
Makes the following work so all the formats accepted by `create_table()`
are also accepted by `add()`
```
import lancedb
import pyarrow as pa
db = lancedb.connect("/tmp")
def make_batches():
for i in range(5):
yield pa.RecordBatch.from_arrays(
[
pa.array([[3.1, 4.1], [5.9, 26.5]]),
pa.array(["foo", "bar"]),
pa.array([10.0, 20.0]),
],
["vector", "item", "price"],
)
schema = pa.schema([
pa.field("vector", pa.list_(pa.float32())),
pa.field("item", pa.utf8()),
pa.field("price", pa.float32()),
])
tbl = db.create_table("table4", make_batches(), schema=schema)
tbl.add(make_batches())
```
