community[patch]: Kinetica Integrations handled error in querying; quotes in table names; updated gpudb API (#22724)

- [ ] **Miscellaneous updates and fixes**: 
- **Description:** Handled error in querying; quotes in table names;
updated gpudb API
- **Issue:** Threw an error with an error message difficult to
understand if a query failed or returned no records
    - **Dependencies:** Updated GPUDB API version to `7.2.0.9`


@baskaryan @hwchase17

docs/docs/integrations/document_loaders/kinetica.ipynb

@@ -15,7 +15,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%pip install gpudb==7.2.0.1"
+    "%pip install gpudb==7.2.0.9"
    ]
   },
   {
@@ -97,14 +97,14 @@
     "# data and the `SCHEMA.TABLE` combination must exist in Kinetica.\n",
     "\n",
     "QUERY = \"select text, survey_id as source from SCHEMA.TABLE limit 10\"\n",
-    "snowflake_loader = KineticaLoader(\n",
+    "kl = KineticaLoader(\n",
     "    query=QUERY,\n",
     "    host=HOST,\n",
     "    username=USERNAME,\n",
     "    password=PASSWORD,\n",
     "    metadata_columns=[\"source\"],\n",
     ")\n",
-    "kinetica_documents = snowflake_loader.load()\n",
+    "kinetica_documents = kl.load()\n",
     "print(kinetica_documents)"
    ]
   }

docs/docs/integrations/retrievers/kinetica.ipynb

@@ -22,7 +22,7 @@
    "outputs": [],
    "source": [
     "# Please ensure that this connector is installed in your working environment.\n",
-    "%pip install gpudb==7.2.0.1"
+    "%pip install gpudb==7.2.0.9"
    ]
   },
   {

docs/docs/integrations/vectorstores/kinetica.ipynb

@@ -61,7 +61,7 @@
    "source": [
     "# Pip install necessary package\n",
     "%pip install --upgrade --quiet  langchain-openai langchain-community\n",
-    "%pip install gpudb==7.2.0.1\n",
+    "%pip install gpudb==7.2.0.9\n",
     "%pip install --upgrade --quiet  tiktoken"
    ]
   },

libs/community/langchain_community/document_loaders/kinetica_loader.py

@@ -49,7 +49,7 @@ class KineticaLoader(BaseLoader):
         except ImportError:
             raise ImportError(
                 "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb==7.2.0.1`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
             )
 
         try:

libs/community/langchain_community/vectorstores/kinetica.py

@@ -168,7 +168,7 @@ class Kinetica(VectorStore):
         except ImportError:
             raise ImportError(
                 "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb==7.2.0.1`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
             )
 
         self.dimensions = dimensions
@@ -197,7 +197,7 @@ class Kinetica(VectorStore):
         except ImportError:
             raise ImportError(
                 "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb==7.2.0.1`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
             )
 
         options = GPUdb.Options()
@@ -224,6 +224,8 @@ class Kinetica(VectorStore):
         distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
         pre_delete_collection: bool = False,
         logger: Optional[logging.Logger] = None,
+        *,
+        schema_name: str = _LANGCHAIN_DEFAULT_SCHEMA_NAME,
         **kwargs: Any,
     ) -> Kinetica:
         """Class method to assist in constructing the `Kinetica` store instance
@@ -239,8 +241,10 @@ class Kinetica(VectorStore):
                         with each text. Defaults to None.
             ids (Optional[List[str]], optional): List of unique IDs (UUID by default)
                         associated with each text. Defaults to None.
-            collection_name (str, optional): Kinetica schema name.
+            collection_name (str, optional): Kinetica table name.
                         Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
+            schema_name (str, optional): Kinetica schema name.
+                        Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
             distance_strategy (DistanceStrategy, optional): Not used for now.
                         Defaults to DEFAULT_DISTANCE_STRATEGY.
             pre_delete_collection (bool, optional): Whether to delete the Kinetica
@@ -260,8 +264,8 @@ class Kinetica(VectorStore):
         store = cls(
             config=config,
             collection_name=collection_name,
+            schema_name=schema_name,
             embedding_function=embedding,
-            # dimensions=dimensions,
             distance_strategy=distance_strategy,
             pre_delete_collection=pre_delete_collection,
             logger=logger,
@@ -284,7 +288,7 @@ class Kinetica(VectorStore):
         except ImportError:
             raise ImportError(
                 "Could not import Kinetica python API. "
-                "Please install it with `pip install gpudb==7.2.0.1`."
+                "Please install it with `pip install gpudb==7.2.0.9`."
             )
         return GPUdbTable(
             _type=self.table_schema,
@@ -422,12 +426,17 @@ class Kinetica(VectorStore):
         k: int = 4,
         filter: Optional[dict] = None,
     ) -> List[Tuple[Document, float]]:
+        from gpudb import GPUdbException
+
         resp: Dict = self.__query_collection(embedding, k, filter)
+        if resp and resp["status_info"]["status"] == "OK" and "records" in resp:
+            records: OrderedDict = resp["records"]
+            results = list(zip(*list(records.values())))
 
-        records: OrderedDict = resp["records"]
+            return self._results_to_docs_and_scores(results)
-        results = list(zip(*list(records.values())))
+        else:
-
+            self.logger.error(resp["status_info"]["message"])
-        return self._results_to_docs_and_scores(results)
+            raise GPUdbException(resp["status_info"]["message"])
 
     def similarity_search_by_vector(
         self,
@@ -545,7 +554,7 @@ class Kinetica(VectorStore):
         query_string = f"""
                 SELECT text, metadata, {dist_strategy}(embedding, '{embedding_str}') 
                 as distance, embedding
-                FROM {self.table_name}
+                FROM "{self.schema_name}"."{self.collection_name}"
                 {where_clause}
                 ORDER BY distance asc NULLS LAST
                 LIMIT {k}
@@ -760,6 +769,8 @@ class Kinetica(VectorStore):
         distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
+        *,
+        schema_name: str = _LANGCHAIN_DEFAULT_SCHEMA_NAME,
         **kwargs: Any,
     ) -> Kinetica:
         """Adds the texts passed in to the vector store and returns it
@@ -773,6 +784,8 @@ class Kinetica(VectorStore):
             config (KineticaSettings): a `KineticaSettings` instance
             collection_name (str, optional): Kinetica schema name.
                         Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
+            schema_name (str, optional): Kinetica schema name.
+                        Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
             distance_strategy (DistanceStrategy, optional): Distance strategy
                         e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY.
             ids (Optional[List[str]], optional): A list of UUIDs for each
@@ -804,6 +817,7 @@ class Kinetica(VectorStore):
             metadatas=metadatas,
             ids=ids,
             collection_name=collection_name,
+            schema_name=schema_name,
             distance_strategy=distance_strategy,
             pre_delete_collection=pre_delete_collection,
             **kwargs,
@@ -823,6 +837,8 @@ class Kinetica(VectorStore):
         distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
+        *,
+        schema_name: str = _LANGCHAIN_DEFAULT_SCHEMA_NAME,
         **kwargs: Any,
     ) -> Kinetica:
         """Adds the embeddings passed in to the vector store and returns it
@@ -839,6 +855,8 @@ class Kinetica(VectorStore):
                         default will be used. Defaults to Dimension.OPENAI.
             collection_name (str, optional): Kinetica schema name.
                         Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
+            schema_name (str, optional): Kinetica schema name.
+                        Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
             distance_strategy (DistanceStrategy, optional): Distance strategy
                         e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY.
             ids (Optional[List[str]], optional): A list of UUIDs for each text/document.
@@ -863,6 +881,7 @@ class Kinetica(VectorStore):
             metadatas=metadatas,
             ids=ids,
             collection_name=collection_name,
+            schema_name=schema_name,
             distance_strategy=distance_strategy,
             pre_delete_collection=pre_delete_collection,
             **kwargs,
@@ -879,6 +898,8 @@ class Kinetica(VectorStore):
         distance_strategy: DistanceStrategy = DEFAULT_DISTANCE_STRATEGY,
         ids: Optional[List[str]] = None,
         pre_delete_collection: bool = False,
+        *,
+        schema_name: str = _LANGCHAIN_DEFAULT_SCHEMA_NAME,
         **kwargs: Any,
     ) -> Kinetica:
         """Adds the list of `Document` passed in to the vector store and returns it
@@ -892,6 +913,8 @@ class Kinetica(VectorStore):
                         the texts/documents. Defaults to None.
             collection_name (str, optional): Kinetica schema name.
                         Defaults to _LANGCHAIN_DEFAULT_COLLECTION_NAME.
+            schema_name (str, optional): Kinetica schema name.
+                        Defaults to _LANGCHAIN_DEFAULT_SCHEMA_NAME.
             distance_strategy (DistanceStrategy, optional): Distance strategy
                         e.g., l2, cosine etc.. Defaults to DEFAULT_DISTANCE_STRATEGY.
             ids (Optional[List[str]], optional): A list of UUIDs for each text/document.
@@ -912,6 +935,7 @@ class Kinetica(VectorStore):
             metadatas=metadatas,
             config=config,
             collection_name=collection_name,
+            schema_name=schema_name,
             distance_strategy=distance_strategy,
             ids=ids,
             pre_delete_collection=pre_delete_collection,

libs/community/tests/integration_tests/vectorstores/test_kinetica.py

@@ -46,7 +46,8 @@ def test_kinetica(create_config: KineticaSettings) -> None:
         texts=texts,
         metadatas=metadatas,
         embedding=FakeEmbeddingsWithAdaDimension(),
-        collection_name="test_kinetica",
+        collection_name="1test_kinetica",
+        schema_name="1test",
         pre_delete_collection=True,
     )
     output = docsearch.similarity_search("foo", k=1)