This PR upgrade lance to `0.7.5`, which include fixes for searching an
empty dataset.
This PR also adds two tests in node SDK to make sure searching empty
dataset do no throw
Co-authored-by: rmeng <rob@lancedb.com>
in #486 `connect` started converting path into uri. However, the PR
didn't handle relative path and appended `file://` to relative path.
This PR changes the parsing strat to be more rational. If a path is
provided instead of url, we do not try anythinng special.
engine and engine params may only be specified when a url with schema is
provided
Co-authored-by: rmeng <rob@lancedb.com>
aws integration tests are flaky because we didn't wait for the services
to become healthy. (we only waited for the localstack service, this PR
adds wait for sub services)
# WARNING: specifying engine is NOT a publicly supported feature in
lancedb yet. THE API WILL CHANGE.
This PR exposes dynamodb based commit to `vectordb` and JS SDK (will do
python in another PR since it's on a different release track)
This PR also added aws integration test using `localstack`
## What?
This PR adds uri parameters to DB connection string. User may specify
`engine` in the connection string to let LanceDB know that the user
wants to use an external store when reading and writing a table. User
may also pass any parameters required by the commitStore in the
connection string, these parameters will be propagated to lance.
e.g.
```
vectordb.connect("s3://my-db-bucket?engine=ddb&ddbTableName=my-commit-table")
```
will automatically convert table path to
```
s3+ddb://my-db-bucket/my_table.lance?&ddbTableName=my-commit-table
```
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>
This reverts commit 87e9a0250f.
I triggered the nodejs release commit GHA by mistake. Reverting it.
The tag will be removed manually.
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>
- Supports creating an empty table as long as an Arrow Schema is provided
- Supports creating a table from an Arrow Table (can be passed as data)
- Simplified some Arrow code in the TS/FFI side
- removed createTableArrow method, it was never documented / tested.
Previously when logging the `LocalConnection` and `LocalTable` classes,
we would expose the aws creds inside them. This PR changes the stored
creds to a anonymous function to hide the creds
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>
Lance Release
Rob Meng
Chang She
Will Jones
Lei Xu
Ayush Chaurasia
QianZhu
Tevin Wang
Jai
gsilvestrin