CrateDB: Vector Store -- rename to CrateDBVectorStore

Author: amotl

libs/community/langchain_community/vectorstores/__init__.py

@@ -93,7 +93,7 @@
         CouchbaseVectorStore,
     )
     from langchain_community.vectorstores.cratedb import (
-        CrateDBVectorSearch,
+        CrateDBVectorStore,
     )
     from langchain_community.vectorstores.dashvector import (
         DashVector,
@@ -337,7 +337,7 @@
     "Clickhouse",
     "ClickhouseSettings",
     "CouchbaseVectorStore",
-    "CrateDBVectorSearch",
+    "CrateDBVectorStore",
     "DashVector",
     "DatabricksVectorSearch",
     "DeepLake",
@@ -442,7 +442,7 @@
     "Clickhouse": "langchain_community.vectorstores.clickhouse",
     "ClickhouseSettings": "langchain_community.vectorstores.clickhouse",
     "CouchbaseVectorStore": "langchain_community.vectorstores.couchbase",
-    "CrateDBVectorSearch": "langchain_community.vectorstores.cratedb",
+    "CrateDBVectorStore": "langchain_community.vectorstores.cratedb",
     "DashVector": "langchain_community.vectorstores.dashvector",
     "DatabricksVectorSearch": "langchain_community.vectorstores.databricks_vector_search",  # noqa: E501
     "DeepLake": "langchain_community.vectorstores.deeplake",

libs/community/langchain_community/vectorstores/cratedb/__init__.py

@@ -1,7 +1,7 @@
-from .base import CrateDBVectorSearch
-from .extended import CrateDBVectorSearchMultiCollection
+from .base import CrateDBVectorStore
+from .extended import CrateDBVectorStoreMultiCollection
 
 __all__ = [
-    "CrateDBVectorSearch",
-    "CrateDBVectorSearchMultiCollection",
+    "CrateDBVectorStore",
+    "CrateDBVectorStoreMultiCollection",
 ]

libs/community/langchain_community/vectorstores/cratedb/base.py

@@ -42,7 +42,7 @@ def _results_to_docs(docs_and_scores: Any) -> List[Document]:
     return [doc for doc, _ in docs_and_scores]
 
 
-class CrateDBVectorSearch(PGVector):
+class CrateDBVectorStore(PGVector):
     """`CrateDB` vector store.
 
     To use it, you should have the ``crate[sqlalchemy]`` python package installed.
@@ -62,13 +62,13 @@ class CrateDBVectorSearch(PGVector):
     Example:
         .. code-block:: python
 
-            from langchain.vectorstores import CrateDBVectorSearch
+            from langchain.vectorstores import CrateDBVectorStore
             from langchain.embeddings.openai import OpenAIEmbeddings
 
             CONNECTION_STRING = "crate://crate@localhost:4200/test3"
             COLLECTION_NAME = "state_of_the_union_test"
             embeddings = OpenAIEmbeddings()
-            vectorestore = CrateDBVectorSearch.from_documents(
+            vectorestore = CrateDBVectorStore.from_documents(
                 embedding=embeddings,
                 documents=docs,
                 collection_name=COLLECTION_NAME,
@@ -337,7 +337,7 @@ def _query_collection_multi(
 
     @classmethod
     def from_texts(  # type: ignore[override]
-        cls: Type[CrateDBVectorSearch],
+        cls: Type[CrateDBVectorStore],
         texts: List[str],
         embedding: Embeddings,
         metadatas: Optional[List[dict]] = None,
@@ -346,7 +346,7 @@ def from_texts(  # type: ignore[override]
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
         **kwargs: Any,
-    ) -> CrateDBVectorSearch:
+    ) -> CrateDBVectorStore:
         """
         Return VectorStore initialized from texts and embeddings.
         Database connection string is required.
@@ -431,7 +431,7 @@ def _select_relevance_score_fn(self) -> Callable[[float], float]:
             raise ValueError(
                 "No supported normalization function for distance_strategy of "
                 f"{self._distance_strategy}. Consider providing relevance_score_fn to "
-                "CrateDBVectorSearch constructor."
+                "CrateDBVectorStore constructor."
             )
 
     @staticmethod

libs/community/langchain_community/vectorstores/cratedb/extended.py

@@ -12,13 +12,13 @@
 
 from langchain_community.vectorstores.cratedb.base import (
     DEFAULT_DISTANCE_STRATEGY,
-    CrateDBVectorSearch,
+    CrateDBVectorStore,
     DistanceStrategy,
 )
 from langchain_community.vectorstores.pgvector import _LANGCHAIN_DEFAULT_COLLECTION_NAME
 
 
-class CrateDBVectorSearchMultiCollection(CrateDBVectorSearch):
+class CrateDBVectorStoreMultiCollection(CrateDBVectorStore):
     """
     Provide functionality for searching multiple collections.
     It can not be used for indexing documents.
@@ -27,15 +27,15 @@ class CrateDBVectorSearchMultiCollection(CrateDBVectorSearch):
 
     Synopsis::
 
-        from langchain.vectorstores.cratedb import CrateDBVectorSearchMultiCollection
+        from langchain_community.vectorstores.cratedb import CrateDBVectorStoreMultiCollection
 
-        multisearch = CrateDBVectorSearchMultiCollection(
+        multisearch = CrateDBVectorStoreMultiCollection(
             collection_names=["collection_foo", "collection_bar"],
             embedding_function=embeddings,
             connection_string=CONNECTION_STRING,
         )
         docs_with_score = multisearch.similarity_search_with_score(query)
-    """
+    """  # noqa: E501
 
     def __init__(
         self,