Basic full text search capabilities #62
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eddyxu
reviewed
May 7, 2023
python/lancedb/fts.py
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| for b in dataset.to_batches(columns=fields): | ||
| for i in range(b.num_rows): | ||
| doc = tantivy.Document() | ||
| doc.add_integer("doc_id", i) |
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This id is the id in a RecordBatch, not in the dataset.
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eddyxu
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May 25, 2023
| df = table.search("puppy").limit(10).select(["text"]).to_df() | ||
| ``` | ||
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| LanceDB automatically looks for an FTS index if the input is str. |
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This is a python only feature, right?
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For now. Ideally we can integrate Tantivy from Rust but I think that takes more architectural clarity than we currently have (since we don't really know how the usage will look)
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Add some doc w.r.t. of this python only API?
| # get the index path | ||
| index_path = self._table._get_fts_index_path() | ||
| # open the index | ||
| index = tantivy.Index.open(index_path) |
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this does not support multi-column?
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So with fts indices you can have a single index that covers multiple columns.
| @@ -130,6 +133,24 @@ def create_index(self, metric="L2", num_partitions=256, num_sub_vectors=96): | |||
| ) | |||
| self._reset_dataset() | |||
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| def create_fts_index(self, field_names: Union[str, List[str]]): | |||
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can we share the create_index() API ?
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We can, it just makes that api harder to use because now you have to figure out the index type name and also which kwargs go with which index type etc.
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would that make a bunch of create_foo_index() ? i.e., a create_btree_index() or create_vector_index()
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I think we can refactor this into an Index/IndexBuilder abstraction. I just didn't want to commit to it now because this is an experimental feature.
eddyxu
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May 25, 2023
jaichopra
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Jun 2, 2023
This is v1 of integrating full text search index into LanceDB.
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.
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()`
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.
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>
jaichopra
pushed a commit
that referenced
this pull request
Jun 2, 2023
This is v1 of integrating full text search index into LanceDB.
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.
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()`
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.
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>
jaichopra
pushed a commit
that referenced
this pull request
Jun 2, 2023
This is v1 of integrating full text search index into LanceDB.
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.
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()`
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.
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>
raghavdixit99
pushed a commit
to raghavdixit99/lancedb
that referenced
this pull request
Apr 5, 2024
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>
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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
tableis 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