docs: add a section to describe scalar index (#1495)

docs/mkdocs.yml

@@ -58,7 +58,7 @@ plugins:
             - https://pandas.pydata.org/docs/objects.inv
   - mkdocs-jupyter
   - render_swagger:
-      allow_arbitrary_locations : true
+      allow_arbitrary_locations: true
 
 markdown_extensions:
   - admonition
@@ -89,9 +89,10 @@ nav:
           - Data management: concepts/data_management.md
       - 🔨 Guides:
           - Working with tables: guides/tables.md
-          - Building an ANN index: ann_indexes.md
+          - Building a vector index: ann_indexes.md
           - Vector Search: search.md
           - Full-text search: fts.md
+          - Building a scalar index: guides/scalar_index.md
           - Hybrid search:
               - Overview: hybrid_search/hybrid_search.md
               - Comparing Rerankers: hybrid_search/eval.md
@@ -128,12 +129,12 @@ nav:
           - Polars: python/polars_arrow.md
           - DuckDB: python/duckdb.md
           - LangChain:
-            - LangChain 🔗: integrations/langchain.md
-            - LangChain demo: notebooks/langchain_demo.ipynb
-            - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
+              - LangChain 🔗: integrations/langchain.md
+              - LangChain demo: notebooks/langchain_demo.ipynb
+              - LangChain JS/TS 🔗: https://js.langchain.com/docs/integrations/vectorstores/lancedb
           - LlamaIndex 🦙:
-            - LlamaIndex docs: integrations/llamaIndex.md
-            - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
+              - LlamaIndex docs: integrations/llamaIndex.md
+              - LlamaIndex demo: notebooks/llamaIndex_demo.ipynb
           - Pydantic: python/pydantic.md
           - Voxel51: integrations/voxel51.md
           - PromptTools: integrations/prompttools.md
@@ -145,7 +146,7 @@ nav:
               - Multimodal: examples/python_examples/multimodal.md
               - Rag: examples/python_examples/rag.md
               - Vector Search: examples/python_examples/vector_search.md
-              - Miscellaneous: 
+              - Miscellaneous:
                   - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
                   - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
                   - Multimodal search using CLIP: notebooks/multimodal_search.ipynb
@@ -182,6 +183,7 @@ nav:
       - Building an ANN index: ann_indexes.md
       - Vector Search: search.md
       - Full-text search: fts.md
+      - Building a scalar index: guides/scalar_index.md
       - Hybrid search:
           - Overview: hybrid_search/hybrid_search.md
           - Comparing Rerankers: hybrid_search/eval.md
@@ -231,7 +233,7 @@ nav:
           - Multimodal: examples/python_examples/multimodal.md
           - Rag: examples/python_examples/rag.md
           - Vector Search: examples/python_examples/vector_search.md
-          - Miscellaneous: 
+          - Miscellaneous:
               - YouTube Transcript Search: notebooks/youtube_transcript_search.ipynb
               - Documentation QA Bot using LangChain: notebooks/code_qa_bot.ipynb
               - Multimodal search using CLIP: notebooks/multimodal_search.ipynb

docs/src/guides/scalar_index.md

@@ -0,0 +1,108 @@
+# Building Scalar Index
+
+Similar to many SQL databases, LanceDB supports several types of Scalar indices to accelerate search
+over scalar columns.
+
+- `BTREE`: The most common type is BTREE. This index is inspired by the btree data structure
+  although only the first few layers of the btree are cached in memory.
+  It will perform well on columns with a large number of unique values and few rows per value.
+- `BITMAP`: this index stores a bitmap for each unique value in the column.
+  This index is useful for columns with a finite number of unique values and many rows per value.
+  For example, columns that represent "categories", "labels", or "tags"
+- `LABEL_LIST`: a special index that is used to index list columns whose values have a finite set of possibilities.
+  For example, a column that contains lists of tags (e.g. `["tag1", "tag2", "tag3"]`) can be indexed with a `LABEL_LIST` index.
+
+| Data Type                                                       | Filter                                    | Index Type   |
+| --------------------------------------------------------------- | ----------------------------------------- | ------------ |
+| Numeric, String, Temporal                                       | `<`, `=`, `>`, `in`, `between`, `is null` | `BTREE`      |
+| Boolean, numbers or strings with fewer than 1,000 unique values | `<`, `=`, `>`, `in`, `between`, `is null` | `BITMAP`     |
+| List of low cardinality of numbers or strings                   | `array_has_any`, `array_has_all`          | `LABEL_LIST` |
+
+=== "Python"
+
+    ```python
+    import lancedb
+    books = [
+      {"book_id": 1, "publisher": "plenty of books", "tags": ["fantasy", "adventure"]},
+      {"book_id": 2, "publisher": "book town", "tags": ["non-fiction"]},
+      {"book_id": 3, "publisher": "oreilly", "tags": ["textbook"]}
+    ]
+
+    db = lancedb.connect("./db")
+    table = db.create_table("books", books)
+    table.create_scalar_index("book_id")  # BTree by default
+    table.create_scalar_index("publisher", index_type="BITMAP")
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data");
+        const tbl = await db.openTable("my_vectors");
+
+        await tbl.create_index("book_id");
+        await tlb.create_index("publisher", { config: lancedb.Index.bitmap() })
+        ```
+
+For example, the following scan will be faster if the column `my_col` has a scalar index:
+
+=== "Python"
+
+    ```python
+    import lancedb
+
+    table = db.open_table("books")
+    my_df = table.search().where("book_id = 2").to_pandas()
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data");
+        const tbl = await db.openTable("books");
+
+        await tbl
+          .query()
+          .where("book_id = 2")
+          .limit(10)
+          .toArray();
+        ```
+
+Scalar indices can also speed up scans containing a vector search or full text search, and a prefilter:
+
+=== "Python"
+
+    ```python
+    import lancedb
+
+    data = [
+      {"book_id": 1, "vector": [1, 2]},
+      {"book_id": 2, "vector": [3, 4]},
+      {"book_id": 3, "vector": [5, 6]}
+    ]
+    table = db.create_table("book_with_embeddings", data)
+
+    (
+        table.search([1, 2])
+        .where("book_id != 3", prefilter=True)
+        .to_pandas()
+    )
+    ```
+
+=== "Typescript"
+
+    === "@lancedb/lancedb"
+
+        ```js
+        const db = await lancedb.connect("data/lance");
+        const tbl = await db.openTable("book_with_embeddings");
+
+        await tbl.search(Array(1536).fill(1.2))
+          .where("book_id != 3")  // prefilter is default behavior.
+          .limit(10)
+          .toArray();
+        ```

docs/test/md_testing.py

@@ -1,3 +1,5 @@
+#!/usr/bin/env python3
+
 import glob
 from typing import Iterator, List
 from pathlib import Path

python/python/lancedb/table.py

@@ -339,9 +339,9 @@ class Table(ABC):
     def create_scalar_index(
         self,
         column: str,
-        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST"] = "BTREE",
         *,
         replace: bool = True,
+        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST"] = "BTREE",
     ):
         """Create a scalar index on a column.
 
@@ -391,6 +391,8 @@ class Table(ABC):
             or string column.
         replace : bool, default True
             Replace the existing index if it exists.
+        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST"], default "BTREE"
+            The type of index to create.
 
         Examples
         --------
@@ -1232,9 +1234,9 @@ class LanceTable(Table):
     def create_scalar_index(
         self,
         column: str,
-        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST"] = "BTREE",
         *,
         replace: bool = True,
+        index_type: Literal["BTREE", "BITMAP", "LABEL_LIST"] = "BTREE",
     ):
         self._dataset_mut.create_scalar_index(
             column, index_type=index_type, replace=replace