Make 'endpoint' parameter optional for DatabricksVectorSearch

libs/databricks/langchain_databricks/vectorstores.py

@@ -60,8 +60,23 @@ class DatabricksVectorSearch(VectorStore):
 
     Key init args — indexing params:
 
-        endpoint: The name of the Databricks Vector Search endpoint.
         index_name: The name of the index to use. Format: "catalog.schema.index".
+        endpoint: The name of the Databricks Vector Search endpoint. If not specified,
+            the endpoint name is automatically inferred based on the index name.
+
+            .. note::
+
+                If you are using `databricks-vectorsearch` version < 0.35, the `endpoint` parameter
+                is required when initializing the vector store.
+
+                .. code-block:: python
+
+                    vector_store = DatabricksVectorSearch(
+                        endpoint="<your-endpoint-name>",
+                        index_name="<your-index-name>",
+                        ...
+                    )
+
         embedding: The embedding model.
                   Required for direct-access index or delta-sync index
                   with self-managed embeddings.
@@ -89,7 +104,6 @@ class DatabricksVectorSearch(VectorStore):
             from langchain_databricks.vectorstores import DatabricksVectorSearch
 
             vector_store = DatabricksVectorSearch(
-                endpoint="<your-endpoint-name>",
                 index_name="<your-index-name>"
             )
 
@@ -102,7 +116,6 @@ class DatabricksVectorSearch(VectorStore):
             from langchain_openai import OpenAIEmbeddings
 
             vector_store = DatabricksVectorSearch(
-                endpoint="<your-endpoint-name>",
                 index_name="<your-index-name>",
                 embedding=OpenAIEmbeddings(),
                 text_column="document_content"
@@ -196,8 +209,8 @@ class DatabricksVectorSearch(VectorStore):
 
     def __init__(
         self,
-        endpoint: str,
         index_name: str,
+        endpoint: Optional[str] = None,
         embedding: Optional[Embeddings] = None,
         text_column: Optional[str] = None,
         columns: Optional[List[str]] = None,
@@ -212,7 +225,21 @@ def __init__(
                 "Please install it with `pip install databricks-vectorsearch`."
             ) from e
 
-        self.index = VectorSearchClient().get_index(endpoint, index_name)
+        try:
+            self.index = VectorSearchClient().get_index(
+                endpoint_name=endpoint, index_name=index_name
+            )
+        except Exception as e:
+            if endpoint is None and "Wrong vector search endpoint" in str(e):
+                raise ValueError(
+                    "The `endpoint` parameter is required for instantiating "
+                    "DatabricksVectorSearch with the `databricks-vectorsearch` "
+                    "version earlier than 0.35. Please provide the endpoint "
+                    "name or upgrade to version 0.35 or later."
+                ) from e
+            else:
+                raise
+
         self._index_details = IndexDetails(self.index)
 
         _validate_embedding(embedding, self._index_details)

libs/databricks/tests/unit_tests/test_vectorstore.py

@@ -133,12 +133,12 @@ def embed_query(self, text: str) -> List[float]:
 
 @pytest.fixture(autouse=True)
 def mock_vs_client() -> Generator:
-    def _get_index(endpoint: str, index_name: str) -> MagicMock:
+    def _get_index(
+        endpoint_name: Optional[str] = None,
+        index_name: str = None,  # type: ignore
+    ) -> MagicMock:
         from databricks.vector_search.client import VectorSearchIndex  # type: ignore
 
-        if endpoint != ENDPOINT_NAME:
-            raise ValueError(f"Unknown endpoint: {endpoint}")
-
         index = MagicMock(spec=VectorSearchIndex)
         index.describe.return_value = INDEX_DETAILS[index_name]
         index.similarity_search.return_value = EXAMPLE_SEARCH_RESPONSE
@@ -157,7 +157,6 @@ def init_vector_search(
     index_name: str, columns: Optional[List[str]] = None
 ) -> DatabricksVectorSearch:
     kwargs: Dict[str, Any] = {
-        "endpoint": ENDPOINT_NAME,
         "index_name": index_name,
         "columns": columns,
     }
@@ -177,10 +176,17 @@ def test_init(index_name: str) -> None:
     assert vectorsearch.index.describe() == INDEX_DETAILS[index_name]
 
 
+def test_init_with_endpoint_name() -> None:
+    vectorsearch = DatabricksVectorSearch(
+        endpoint=ENDPOINT_NAME,
+        index_name=DELTA_SYNC_INDEX,
+    )
+    assert vectorsearch.index.describe() == INDEX_DETAILS[DELTA_SYNC_INDEX]
+
+
 def test_init_fail_text_column_mismatch() -> None:
     with pytest.raises(ValueError, match=f"The index '{DELTA_SYNC_INDEX}' has"):
         DatabricksVectorSearch(
-            endpoint=ENDPOINT_NAME,
             index_name=DELTA_SYNC_INDEX,
             text_column="some_other_column",
         )
@@ -190,7 +196,6 @@ def test_init_fail_text_column_mismatch() -> None:
 def test_init_fail_no_text_column(index_name: str) -> None:
     with pytest.raises(ValueError, match="The `text_column` parameter is required"):
         DatabricksVectorSearch(
-            endpoint=ENDPOINT_NAME,
             index_name=index_name,
             embedding=EMBEDDING_MODEL,
         )
@@ -206,7 +211,6 @@ def test_init_fail_columns_not_in_schema() -> None:
 def test_init_fail_no_embedding(index_name: str) -> None:
     with pytest.raises(ValueError, match="The `embedding` parameter is required"):
         DatabricksVectorSearch(
-            endpoint=ENDPOINT_NAME,
             index_name=index_name,
             text_column="text",
         )
@@ -215,7 +219,6 @@ def test_init_fail_no_embedding(index_name: str) -> None:
 def test_init_fail_embedding_already_specified_in_source() -> None:
     with pytest.raises(ValueError, match=f"The index '{DELTA_SYNC_INDEX}' uses"):
         DatabricksVectorSearch(
-            endpoint=ENDPOINT_NAME,
             index_name=DELTA_SYNC_INDEX,
             embedding=EMBEDDING_MODEL,
         )
@@ -227,7 +230,6 @@ def test_init_fail_embedding_dim_mismatch(index_name: str) -> None:
         ValueError, match="embedding model's dimension '1000' does not match"
     ):
         DatabricksVectorSearch(
-            endpoint=ENDPOINT_NAME,
             index_name=index_name,
             text_column="text",
             embedding=FakeEmbeddings(1000),