docs(lancedb): add documentation for lancedb

docs/docs/ops/targets.md

@@ -98,6 +98,84 @@ The spec takes the following fields:
 
 You can find an end-to-end example [here](https://github.com/cocoindex-io/cocoindex/tree/main/examples/text_embedding_qdrant).
 
+### LanceDB
+
+Exports data to a [LanceDB](https://lancedb.github.io/lancedb/) table.
+
+#### Data Mapping
+
+Here's how CocoIndex data elements map to LanceDB elements during export:
+
+| CocoIndex Element | LanceDB Element |
+|-------------------|-----------------|
+| an export target  | a unique table  |
+| a collected row   | a row           |
+| a field           | a column        |
+
+
+::::info Installation and import
+
+This target is provided via an optional dependency `[lancedb]`:
+
+```sh
+pip install "cocoindex[lancedb]"
+```
+
+To use it, you need to import the submodule `cocoindex.targets.lancedb`:
+
+```python
+import cocoindex.targets.lancedb as coco_lancedb
+```
+
+::::
+
+#### Spec
+
+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.
+
+Additional notes:
+
+*   Exactly one primary key field is required for LanceDB targets. We create B-Tree index on this key column.
+
+:::info
+
+LanceDB has a limitation that it cannot build a vector index on an empty table (see [LanceDB issue #4034](https://github.com/lancedb/lance/issues/4034)).
+If you want to use vector indexes, you can run the flow once to populate the target table with data, and then create the vector indexes.
+
+:::
+
+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).
+
+#### `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:
+
+```python
+from cocoindex.targets import lancedb as coco_lancedb
+
+db = await coco_lancedb.connect_async("./lancedb_data")
+table = await db.open_table("TextEmbedding")
+```
+
+Signature:
+
+```python
+def connect_async(
+  db_uri: str,
+  *,
+  db_options: coco_lancedb.DatabaseOptions | None = None,
+  read_consistency_interval: datetime.timedelta | None = None
+) -> lancedb.AsyncConnection
+```
+
+Once `db_uri` matches, it automatically reuses the same connection instance without re-establishing a new connection.
+This achieves strong consistency between your indexing and querying logic, if they run in the same process.
+
 ## Property Graph Targets
 
 Property graph is a widely-adopted model for knowledge graphs, where both nodes and relationships can have properties.