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())
```
It's inconvenient to always require data at table creation time.
Here we enable you to create an empty table and add data and set schema
later.
---------
Co-authored-by: Chang She <chang@lancedb.com>
Sometimes LangChain would insert a single `[np.nan]` as a placeholder if
the embedding function failed. This causes a problem for Lance format
because then the array can't be stored as a FixedSizedListArray.
Instead:
1. By default we remove rows with embedding lengths less than the
maximum length in the batch
2. If `strict=True` kwargs is set to True, then a `ValueError` is raised
if the embeddings aren't all the same length
---------
Co-authored-by: Chang She <chang@lancedb.com>
* to_df() is now async, added `to_df_blocking` to convenience
* add remote lancedb client to public lancedb
* make lancedb connection class understand url scheme
`lancedb+<connection_type>://<host>:<port>`.
Changes include:
- Contexts of sizes less than window param to be included as well
- Added optional threshold parameter to to_df in Contextualizer
This should close#165
- If maintainers are satisfied with the implementation will add more
examples and test cases and update the documentations as well.
---------
Co-authored-by: Nithin PS <47279496+Nithinps021@users.noreply.github.com>
Co-authored-by: Will Jones <willjones127@gmail.com>
Adds:
* Make `mkdocstrings` aware we are using numpy-style docstrings
* Fixes broken link on `index.md` to Python API docs (and added link to
node ones)
* Added examples to various classes.
* Added doctest to verify examples work.
pypi does not allow packages to be uploaded that has a direct reference
for now we'll just ask the user to install tantivy separately
---------
Co-authored-by: Chang She <chang@lancedb.com>
This is v1 of integrating full text search index into LanceDB.
# API
The query API is roughly the same as before, except if the input is text
instead of a vector we assume that its fts search.
## Example
If `table` is a LanceDB LanceTable, then:
Build index: `table.create_fts_index("text")`
Query: `df = table.search("puppy").limit(10).select(["text"]).to_df()`
# Implementation
Here we use the tantivy-py package to build the index. We then use the
row id's as the full-text-search index's doc id then we just do a Take
operation to fetch the rows.
# Limitations
1. don't support incremental row appends yet. New data won't show up in
search
2. local filesystem only
3. requires building tantivy explicitly
---------
Co-authored-by: Chang She <chang@lancedb.com>
