feat: support FTS for LanceDB

docs/docs/core/flow_def.mdx

@@ -327,6 +327,44 @@ Following metrics are supported:
 | L2Distance | [L2 distance (a.k.a. Euclidean distance)](https://en.wikipedia.org/wiki/Euclidean_distance) | Smaller is more similar |
 | InnerProduct | [Inner product](https://en.wikipedia.org/wiki/Inner_product_space) | Larger is more similar |
 
+### Full-Text Search (FTS) Index
+
+*Full-text search index* is specified by `fts_indexes` (`Sequence[FtsIndexDef]`). `FtsIndexDef` has the following fields:
+
+* `field_name`: the field to create FTS index.
+* `parameters` (optional): a dictionary of parameters to pass to the target's FTS index creation. The supported parameters vary by target.
+
+For example, with LanceDB:
+
+<Tabs>
+<TabItem value="python" label="Python" default>
+
+```python
+@cocoindex.flow_def(name="DemoFlow")
+def demo_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
+    ...
+    demo_collector = data_scope.add_collector()
+    ...
+    demo_collector.export(
+        "demo_target", DemoTargetSpec(...),
+        primary_key_fields=["id"],
+        fts_indexes=[
+            # Basic FTS index with default tokenizer
+            cocoindex.FtsIndexDef("content"),
+            # FTS index with custom tokenizer
+            cocoindex.FtsIndexDef("description", parameters={"language": "English"})
+        ])
+```
+
+</TabItem>
+</Tabs>
+
+:::note
+
+FTS indexes are currently only supported for LanceDB target on its enterprise edition. Other targets will raise an error if FTS indexes are specified.
+
+:::
+
 ## Miscellaneous
 
 ### Getting App Namespace

docs/docs/targets/lancedb.md

@@ -20,7 +20,6 @@ Here's how CocoIndex data elements map to LanceDB elements during export:
 | a collected row   | a row           |
 | a field           | a column        |
 
-
 ::::info Installation and import
 
 This target is provided via an optional dependency `[lancedb]`:
@@ -41,14 +40,15 @@ import cocoindex.targets.lancedb as coco_lancedb
 
 The spec `coco_lancedb.LanceDB` takes the following fields:
 
-*   `db_uri` (`str`, required): The LanceDB database location (e.g. `./lancedb_data`).
-*   `table_name` (`str`, required): The name of the table to export the data to.
-*   `db_options` (`coco_lancedb.DatabaseOptions`, optional): Advanced database options.
-    *   `storage_options` (`dict[str, Any]`, optional): Passed through to LanceDB when connecting.
+* `db_uri` (`str`, required): The LanceDB database location (e.g. `./lancedb_data`).
+* `table_name` (`str`, required): The name of the table to export the data to.
+* `db_options` (`coco_lancedb.DatabaseOptions`, optional): Advanced database options.
+  * `storage_options` (`dict[str, Any]`, optional): Passed through to LanceDB when connecting.
 
 Additional notes:
 
-*   Exactly one primary key field is required for LanceDB targets. We create B-Tree index on this key column.
+* Exactly one primary key field is required for LanceDB targets. We create B-Tree index on this key column.
+* **Full-Text Search (FTS) indexes** are supported via the `fts_indexes` parameter. Note that FTS functionality requires [LanceDB Enterprise](https://lancedb.com/docs/indexing/fts-index/). You can pass any parameters supported by the target's FTS index creation API (e.g., `tokenizer_name` for LanceDB). See [LanceDB FTS documentation](https://lancedb.com/docs/indexing/fts-index/) for full parameter details.
 
 :::info
 
@@ -59,6 +59,38 @@ If you want to use vector indexes, you can run the flow once to populate the tar
 
 You can find an end-to-end example here: [examples/text_embedding_lancedb](https://github.com/cocoindex-io/cocoindex/tree/main/examples/text_embedding_lancedb).
 
+### FTS Index Example
+
+```python
+import cocoindex
+import cocoindex.targets.lancedb as coco_lancedb
+
+@cocoindex.flow_def(name="DocumentSearchFlow")
+def document_search_flow(flow_builder: cocoindex.FlowBuilder, data_scope: cocoindex.DataScope):
+    # ... source and transformations ...
+
+    doc_collector = data_scope.add_collector()
+    # ... collect document data ...
+
+    doc_collector.export(
+        "documents",
+        coco_lancedb.LanceDB(
+            db_uri="./lancedb_data",
+            table_name="documents"
+        ),
+        primary_key_fields=["id"],
+        # Add FTS indexes for full-text search
+        fts_indexes=[
+            # Basic FTS index with default tokenizer
+            cocoindex.FtsIndexDef("content"),
+            # FTS index with stemming for better search recall
+            cocoindex.FtsIndexDef("description", parameters={"tokenizer_name": "en_stem"}),
+            # FTS index with position tracking for phrase searches
+            cocoindex.FtsIndexDef("title", parameters={"tokenizer_name": "default", "with_position": True})
+        ]
+    )
+```
+
 ## `connect_async()` helper
 
 We provide a helper to obtain a shared `AsyncConnection` that is reused across your process and shared with CocoIndex's writer for strong read-after-write consistency:
@@ -85,6 +117,7 @@ Once `db_uri` matches, it automatically reuses the same connection instance with
 This achieves strong consistency between your indexing and querying logic, if they run in the same process.
 
 ## Example
+
 <ExampleButton
   href="https://github.com/cocoindex-io/cocoindex/tree/main/examples/text_embedding_lancedb"
   text="Text Embedding LanceDB Example"

examples/text_embedding_lancedb/main.py

@@ -76,6 +76,11 @@ def text_embedding_flow(
         coco_lancedb.LanceDB(db_uri=LANCEDB_URI, table_name=LANCEDB_TABLE),
         primary_key_fields=["id"],
         vector_indexes=vector_indexes,
+        fts_indexes=[
+            cocoindex.FtsIndexDef(
+                field_name="text", parameters={"tokenizer_name": "simple"}
+            )
+        ],
     )
 
 

python/cocoindex/__init__.py

@@ -27,6 +27,7 @@
 from .lib import settings, init, start_server, stop
 from .llm import LlmSpec, LlmApiType
 from .index import (
+    FtsIndexDef,
     VectorSimilarityMetric,
     VectorIndexDef,
     IndexOptions,
@@ -95,6 +96,7 @@
     # Index
     "VectorSimilarityMetric",
     "VectorIndexDef",
+    "FtsIndexDef",
     "IndexOptions",
     "HnswVectorIndexMethod",
     "IvfFlatVectorIndexMethod",

python/cocoindex/flow.py

@@ -407,6 +407,7 @@ def export(
         primary_key_fields: Sequence[str],
         attachments: Sequence[op.TargetAttachmentSpec] = (),
         vector_indexes: Sequence[index.VectorIndexDef] = (),
+        fts_indexes: Sequence[index.FtsIndexDef] = (),
         vector_index: Sequence[tuple[str, index.VectorSimilarityMetric]] = (),
         setup_by_user: bool = False,
     ) -> None:
@@ -432,6 +433,7 @@ def export(
         index_options = index.IndexOptions(
             primary_key_fields=primary_key_fields,
             vector_indexes=vector_indexes,
+            fts_indexes=fts_indexes,
         )
         self._flow_builder_state.engine_flow_builder.export(
             target_name,

python/cocoindex/index.py

@@ -1,6 +1,6 @@
 from enum import Enum
 from dataclasses import dataclass
-from typing import Sequence, Union
+from typing import Sequence, Union, Any
 
 
 class VectorSimilarityMetric(Enum):
@@ -40,6 +40,19 @@ class VectorIndexDef:
     method: VectorIndexMethod | None = None
 
 
+@dataclass
+class FtsIndexDef:
+    """
+    Define a full-text search index on a field.
+
+    The parameters field can contain any keyword arguments supported by the target's
+    FTS index creation API (e.g., tokenizer_name for LanceDB).
+    """
+
+    field_name: str
+    parameters: dict[str, Any] | None = None
+
+
 @dataclass
 class IndexOptions:
     """
@@ -48,3 +61,4 @@ class IndexOptions:
 
     primary_key_fields: Sequence[str]
     vector_indexes: Sequence[VectorIndexDef] = ()
+    fts_indexes: Sequence[FtsIndexDef] = ()

python/cocoindex/targets/lancedb.py

@@ -20,7 +20,7 @@
     VectorTypeSchema,
     TableType,
 )
-from ..index import VectorIndexDef, IndexOptions, VectorSimilarityMetric
+from ..index import VectorIndexDef, FtsIndexDef, IndexOptions, VectorSimilarityMetric
 
 _logger = logging.getLogger(__name__)
 
@@ -48,11 +48,19 @@ class _VectorIndex:
     metric: VectorSimilarityMetric
 
 
+@dataclasses.dataclass
+class _FtsIndex:
+    name: str
+    field_name: str
+    parameters: dict[str, Any] | None = None
+
+
 @dataclasses.dataclass
 class _State:
     key_field_schema: FieldSchema
     value_fields_schema: list[FieldSchema]
     vector_indexes: list[_VectorIndex] | None = None
+    fts_indexes: list[_FtsIndex] | None = None
     db_options: DatabaseOptions | None = None
 
 
@@ -318,6 +326,18 @@ def get_setup_state(
                 if index_options.vector_indexes is not None
                 else None
             ),
+            fts_indexes=(
+                [
+                    _FtsIndex(
+                        name=f"__{index.field_name}__fts__idx",
+                        field_name=index.field_name,
+                        parameters=index.parameters,
+                    )
+                    for index in index_options.fts_indexes
+                ]
+                if index_options.fts_indexes is not None
+                else None
+            ),
         )
 
     @staticmethod
@@ -412,6 +432,30 @@ async def apply_setup_change(
             if vector_index_name in existing_vector_indexes:
                 await table.drop_index(vector_index_name)
 
+        # Handle FTS indexes
+        unseen_prev_fts_indexes = {
+            index.name for index in (previous and previous.fts_indexes) or []
+        }
+        existing_fts_indexes = {index.name for index in await table.list_indices()}
+
+        for fts_index in current.fts_indexes or []:
+            if fts_index.name in unseen_prev_fts_indexes:
+                unseen_prev_fts_indexes.remove(fts_index.name)
+            else:
+                try:
+                    # Create FTS index using create_fts_index() API
+                    # Pass parameters as kwargs to support any future FTS index options
+                    kwargs = fts_index.parameters if fts_index.parameters else {}
+                    await table.create_fts_index(fts_index.field_name, **kwargs)
+                except Exception as e:  # pylint: disable=broad-exception-caught
+                    raise RuntimeError(
+                        f"Exception in creating FTS index on field {fts_index.field_name}: {e}"
+                    ) from e
+
+        for fts_index_name in unseen_prev_fts_indexes:
+            if fts_index_name in existing_fts_indexes:
+                await table.drop_index(fts_index_name)
+
     @staticmethod
     async def prepare(
         spec: LanceDB,

rust/cocoindex/src/base/spec.rs

@@ -459,12 +459,33 @@ impl fmt::Display for VectorIndexDef {
     }
 }
 
+#[derive(Debug, Clone, Serialize, Deserialize, PartialEq, Eq)]
+pub struct FtsIndexDef {
+    pub field_name: FieldName,
+    #[serde(default, skip_serializing_if = "Option::is_none")]
+    pub parameters: Option<serde_json::Map<String, serde_json::Value>>,
+}
+
+impl fmt::Display for FtsIndexDef {
+    fn fmt(&self, f: &mut fmt::Formatter<'_>) -> fmt::Result {
+        match &self.parameters {
+            None => write!(f, "{}", self.field_name),
+            Some(params) => {
+                let params_str = serde_json::to_string(params).unwrap_or_else(|_| "{}".to_string());
+                write!(f, "{}:{}", self.field_name, params_str)
+            }
+        }
+    }
+}
+
 #[derive(Debug, Clone, Serialize, Deserialize, PartialEq)]
 pub struct IndexOptions {
     #[serde(default, skip_serializing_if = "Option::is_none")]
     pub primary_key_fields: Option<Vec<FieldName>>,
     #[serde(default, skip_serializing_if = "Vec::is_empty")]
     pub vector_indexes: Vec<VectorIndexDef>,
+    #[serde(default, skip_serializing_if = "Vec::is_empty")]
+    pub fts_indexes: Vec<FtsIndexDef>,
 }
 
 impl IndexOptions {
@@ -490,7 +511,16 @@ impl fmt::Display for IndexOptions {
             .map(|v| v.to_string())
             .collect::<Vec<_>>()
             .join(",");
-        write!(f, "keys={primary_keys}, indexes={vector_indexes}")
+        let fts_indexes = self
+            .fts_indexes
+            .iter()
+            .map(|f| f.to_string())
+            .collect::<Vec<_>>()
+            .join(",");
+        write!(
+            f,
+            "keys={primary_keys}, vector_indexes={vector_indexes}, fts_indexes={fts_indexes}"
+        )
     }
 }
 

rust/cocoindex/src/ops/targets/kuzu.rs

@@ -772,6 +772,9 @@ impl TargetFactoryBase for Factory {
                     if !data_coll.index_options.vector_indexes.is_empty() {
                         api_bail!("Vector indexes are not supported for Kuzu yet");
                     }
+                    if !data_coll.index_options.fts_indexes.is_empty() {
+                        api_bail!("FTS indexes are not supported for Kuzu target");
+                    }
                     fn to_dep_table(
                         field_mapping: &AnalyzedGraphElementFieldMapping,
                     ) -> Result<ReferencedNodeTable> {

rust/cocoindex/src/ops/targets/neo4j.rs

@@ -557,6 +557,9 @@ impl SetupState {
             .iter()
             .map(|f| (f.name.as_str(), &f.value_type.typ))
             .collect::<HashMap<_, _>>();
+        if !index_options.fts_indexes.is_empty() {
+            api_bail!("FTS indexes are not supported for Neo4j target");
+        }
         for index_def in index_options.vector_indexes.iter() {
             sub_components.push(ComponentState {
                 object_label: schema.elem_type.clone(),

rust/cocoindex/src/ops/targets/postgres.rs

@@ -384,6 +384,9 @@ impl SetupState {
         index_options: &IndexOptions,
         column_options: &HashMap<String, ColumnOptions>,
     ) -> Result<Self> {
+        if !index_options.fts_indexes.is_empty() {
+            api_bail!("FTS indexes are not supported for Postgres target");
+        }
         Ok(Self {
             columns: TableColumnsSchema {
                 key_columns: key_fields_schema

rust/cocoindex/src/ops/targets/qdrant.rs

@@ -409,6 +409,9 @@ impl TargetFactoryBase for Factory {
                     });
                 }
 
+                if !d.index_options.fts_indexes.is_empty() {
+                    api_bail!("FTS indexes are not supported for Qdrant target");
+                }
                 let mut specified_vector_fields = HashSet::new();
                 for vector_index in d.index_options.vector_indexes {
                     match vector_def.get_mut(&vector_index.field_name) {