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>
- Fixed `add` unit test to create the correct expected result
- Added a unit test for LanceTable.add
- Need to discuss if len(LanceTable) is handled correctly
