refactor!: Refactor PostgresVectorStore and PostgresEngine to depend on PGVectorstore and PGEngine respectively (#316)

* refactor: Refactor PostgresEngine to depend on PGEngine

* update deps

* update deps

* Linter fix

* fix tests

* linter fix

* refactor!: Refactor PostgresVectorStore to depend on PGVectorstore (#319)

* refactor!: Refactor PostgresVectorStore to depend on PGVectorstore

* Liter fix

* fix tests

* re-expose hybrid search config

* header fix

* fix tests

* fix tests

* fix tests

* fix tests

* fix tests

* expose hybrid search config through init

---------

Co-authored-by: Averi Kitsch <akitsch@google.com>

Author: dishaprakash

docs/vector_store.ipynb

@@ -585,10 +585,49 @@
     "all_texts = [\"Apples and oranges\", \"Cars and airplanes\", \"Pineapple\", \"Train\", \"Banana\"]\n",
     "metadatas = [{\"len\": len(t)} for t in all_texts]\n",
     "ids = [str(uuid.uuid4()) for _ in all_texts]\n",
-    "await custom_store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)\n",
+    "await custom_store.aadd_texts(all_texts, metadatas=metadatas, ids=ids)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### For v0.15.0+\n",
+    "\n",
+    "**Important Update:** Support for string filters has been deprecated. Please use dictionaries to add filters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use filter on search\n",
+    "docs = await custom_store.asimilarity_search_by_vector(\n",
+    "    query_vector, filter={\"len\": {\"$gte\": 6}}\n",
+    ")\n",
     "\n",
+    "print(docs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### For v0.14 and under\n",
+    "\n",
+    "You can make use of the string filters to filter on metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
     "# Use filter on search\n",
-    "docs = await custom_store.asimilarity_search_by_vector(query_vector, filter=\"len >= 6\")\n",
+    "docs = await custom_store.asimilarity_search(query, filter=\"len >= 6\")\n",
     "\n",
     "print(docs)"
    ]

pyproject.toml

@@ -11,12 +11,10 @@ authors = [
 
 dependencies = [
     "cloud-sql-python-connector[asyncpg] >= 1.10.0, <2.0.0",
-    "langchain-core>=0.2.36, <1.0.0 ",
     "numpy>=1.24.4, <3.0.0; python_version >= '3.11'",
     "numpy>=1.24.4, <=2.2.6; python_version == '3.10'",
     "numpy>=1.24.4, <=2.0.2; python_version <= '3.9'",
-    "pgvector>=0.2.5, <1.0.0",
-    "SQLAlchemy[asyncio]>=2.0.25, <3.0.0"
+    "langchain-postgres>=0.0.15",
 ]
 
 classifiers = [

requirements.txt

@@ -1,8 +1,6 @@
 cloud-sql-python-connector[asyncpg]==1.18.2
-langchain-core==0.3.68
 numpy==2.3.1; python_version >= "3.11"
 numpy==2.2.6; python_version == "3.10"
 numpy==2.0.2; python_version <= "3.9"
-pgvector==0.4.1
-SQLAlchemy[asyncio]==2.0.41
 langgraph==0.6.0
+langchain-postgres==0.0.15

src/langchain_google_cloud_sql_pg/__init__.py

@@ -12,10 +12,17 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+from langchain_postgres import Column
+from langchain_postgres.v2.hybrid_search_config import (
+    HybridSearchConfig,
+    reciprocal_rank_fusion,
+    weighted_sum_ranking,
+)
+
 from . import indexes
 from .chat_message_history import PostgresChatMessageHistory
 from .checkpoint import PostgresSaver
-from .engine import Column, PostgresEngine
+from .engine import PostgresEngine
 from .loader import PostgresDocumentSaver, PostgresLoader
 from .vectorstore import PostgresVectorStore
 from .version import __version__
@@ -29,5 +36,8 @@
     "PostgresLoader",
     "PostgresDocumentSaver",
     "PostgresSaver",
+    "HybridSearchConfig",
+    "reciprocal_rank_fusion",
+    "weighted_sum_ranking",
     "__version__",
 ]