Liter fix

Author: dishaprakash

docs/vector_store.ipynb

@@ -604,7 +604,9 @@
    "outputs": [],
    "source": [
     "# Use filter on search\n",
-    "docs = await custom_store.asimilarity_search_by_vector(query_vector, filter={\"len\": {\"$gte\": 6}})\n",
+    "docs = await custom_store.asimilarity_search_by_vector(\n",
+    "    query_vector, filter={\"len\": {\"$gte\": 6}}\n",
+    ")\n",
     "\n",
     "print(docs)"
    ]

src/langchain_google_cloud_sql_pg/async_vectorstore.py

@@ -23,4 +23,3 @@ class AsyncPostgresVectorStore(AsyncPGVectorStore):
 
     def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
-

src/langchain_google_cloud_sql_pg/vectorstore.py

@@ -166,4 +166,4 @@ def create_sync(
             hybrid_search_config=hybrid_search_config,
         )
         vs = engine._run_as_sync(coro)
-        return cls(cls._PGVectorStore__create_key, engine, vs)  #type: ignore
+        return cls(cls._PGVectorStore__create_key, engine, vs)  # type: ignore

tests/test_async_vectorstore_index.py

@@ -21,11 +21,11 @@
 import pytest_asyncio
 from langchain_core.documents import Document
 from langchain_core.embeddings import DeterministicFakeEmbedding
+from langchain_postgres.v2.hybrid_search_config import HybridSearchConfig
 from sqlalchemy import text
 
 from langchain_google_cloud_sql_pg import PostgresEngine
 from langchain_google_cloud_sql_pg.async_vectorstore import AsyncPostgresVectorStore
-from langchain_postgres.v2.hybrid_search_config import HybridSearchConfig
 from langchain_google_cloud_sql_pg.indexes import (
     DEFAULT_INDEX_NAME_SUFFIX,
     DistanceStrategy,

tests/test_async_vectorstore_search.py

@@ -19,17 +19,17 @@
 import pytest_asyncio
 from langchain_core.documents import Document
 from langchain_core.embeddings import DeterministicFakeEmbedding
+from langchain_postgres.v2.hybrid_search_config import (
+    HybridSearchConfig,
+    reciprocal_rank_fusion,
+    weighted_sum_ranking,
+)
 from metadata_filtering_data import FILTERING_TEST_CASES, METADATAS
 from sqlalchemy import text
 
 from langchain_google_cloud_sql_pg import Column, PostgresEngine
 from langchain_google_cloud_sql_pg.async_vectorstore import AsyncPostgresVectorStore
 from langchain_google_cloud_sql_pg.indexes import DistanceStrategy, HNSWQueryOptions
-from langchain_postgres.v2.hybrid_search_config import (
-    HybridSearchConfig,
-    reciprocal_rank_fusion,
-    weighted_sum_ranking,
-)
 
 DEFAULT_TABLE = "test_table" + str(uuid.uuid4()).replace("-", "_")
 CUSTOM_TABLE = "test_table_custom" + str(uuid.uuid4()).replace("-", "_")

tests/test_engine.py

@@ -21,14 +21,14 @@
 import pytest_asyncio
 from google.cloud.sql.connector import Connector, IPTypes
 from langchain_core.embeddings import DeterministicFakeEmbedding
+from langchain_postgres.v2.hybrid_search_config import HybridSearchConfig
 from sqlalchemy import VARCHAR, text
 from sqlalchemy.engine import URL
 from sqlalchemy.engine.row import RowMapping
 from sqlalchemy.ext.asyncio import create_async_engine
 from sqlalchemy.pool import NullPool
 
 from langchain_google_cloud_sql_pg import Column, PostgresEngine
-from langchain_postgres.v2.hybrid_search_config import HybridSearchConfig
 
 DEFAULT_TABLE = "test_table" + str(uuid.uuid4()).replace("-", "_")
 CUSTOM_TABLE = "test_table_custom" + str(uuid.uuid4()).replace("-", "_")

tests/test_vectorstore_search.py

@@ -19,25 +19,22 @@
 import pytest_asyncio
 from langchain_core.documents import Document
 from langchain_core.embeddings import DeterministicFakeEmbedding
-from metadata_filtering_data import FILTERING_TEST_CASES, METADATAS, NEGATIVE_TEST_CASES
-from sqlalchemy import text
-
-from langchain_google_cloud_sql_pg import Column, PostgresEngine, PostgresVectorStore
-from langchain_google_cloud_sql_pg.indexes import DistanceStrategy, HNSWQueryOptions
-
 from langchain_postgres.v2.hybrid_search_config import (
     HybridSearchConfig,
     reciprocal_rank_fusion,
     weighted_sum_ranking,
 )
+from metadata_filtering_data import FILTERING_TEST_CASES, METADATAS, NEGATIVE_TEST_CASES
+from sqlalchemy import text
+
+from langchain_google_cloud_sql_pg import Column, PostgresEngine, PostgresVectorStore
+from langchain_google_cloud_sql_pg.indexes import DistanceStrategy, HNSWQueryOptions
 
 DEFAULT_TABLE = "default" + str(uuid.uuid4()).replace("-", "_")
 CUSTOM_TABLE = "custom" + str(uuid.uuid4()).replace("-", "_")
 CUSTOM_TABLE_SYNC = "custom_sync" + str(uuid.uuid4()).replace("-", "_")
 CUSTOM_FILTER_TABLE = "custom_filter" + str(uuid.uuid4()).replace("-", "_")
-CUSTOM_FILTER_TABLE_SYNC = "custom_filter_sync" + str(uuid.uuid4()).replace(
-    "-", "_"
-)
+CUSTOM_FILTER_TABLE_SYNC = "custom_filter_sync" + str(uuid.uuid4()).replace("-", "_")
 VECTOR_SIZE = 768
 
 embeddings_service = DeterministicFakeEmbedding(size=VECTOR_SIZE)