docs: update docs/vector_store.ipynb (#472)

* fix(docs): update docs/vector_store.ipynb

* chore: format notebook doc

---------

Co-authored-by: Averi Kitsch <[email protected]>

Author: gRedHeadphone

docs/vector_store.ipynb

@@ -53,7 +53,7 @@
    },
    "outputs": [],
    "source": [
-    "%pip install --upgrade --quiet  langchain-google-alloydb-pg langchain-google-vertexai google-cloud-alloydb-connector[pg8000]"
+    "%pip install --upgrade --quiet  langchain-google-alloydb-pg langchain-google-vertexai"
    ]
   },
   {
@@ -336,9 +336,7 @@
    "source": [
     "from langchain_google_vertexai import VertexAIEmbeddings\n",
     "\n",
-    "embedding = VertexAIEmbeddings(\n",
-    "    model_name=\"textembedding-gecko@latest\", project=PROJECT_ID\n",
-    ")"
+    "embedding = VertexAIEmbeddings(model_name=\"text-embedding-005\", project=PROJECT_ID)"
    ]
   },
   {
@@ -408,7 +406,7 @@
     "    engine=engine,\n",
     "    table_name=TABLE_NAME,\n",
     "    # schema_name=SCHEMA_NAME,\n",
-    "    embedding_service=embedding,\n",
+    "    embedding=embedding,\n",
     ")"
    ]
   },
@@ -553,7 +551,7 @@
     "\n",
     "VECTOR_SIZE = 768  # Replace with the vector size of your embedding model\n",
     "index = ScaNNIndex()\n",
-    "await store.set_maintenance_work_mem(index.num_leaves, VECTOR_SIZE)\n",
+    "await store.aset_maintenance_work_mem(index.num_leaves, VECTOR_SIZE)\n",
     "await store.aapply_vector_index(index)"
    ]
   },
@@ -818,8 +816,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uuid\n",
-    "\n",
     "docs = await custom_store.asimilarity_search(query, filter={\"price_usd\": {\"$gte\": 100}})\n",
     "\n",
     "print(docs)"
@@ -840,8 +836,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uuid\n",
-    "\n",
     "docs = await custom_store.asimilarity_search(query, filter=\"price_usd > 100\")\n",
     "\n",
     "print(docs)"
@@ -860,23 +854,15 @@
    "source": [
     "#### For v0.13.0+\n",
     "\n",
-    "**Important Update:** Support for string filters has been deprecated. To filter data on the JSON metadata, you must first create a new column for the specific key you wish to filter on. Use the following SQL command to set this up."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "vscode": {
-     "languageId": "sql"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "ALTER TABLE vectorstore_table ADD COLUMN category VARCHAR;\n",
-    "UPDATE vectorstore_table\n",
+    "**Important Update:** Support for string filters has been deprecated. To filter data on the JSON metadata, you must first create a new column for the specific key you wish to filter on. Use the following SQL command to set this up.\n",
+    "\n",
+    "```\n",
+    "ALTER TABLE products ADD COLUMN category_from_json VARCHAR;\n",
+    "\n",
+    "UPDATE products\n",
     "SET\n",
-    "    category = metadata ->> 'category';"
+    "    category_from_json = metadata ->> 'category';\n",
+    "```"
    ]
   },
   {
@@ -892,9 +878,34 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uuid\n",
+    "TABLE_NAME = \"products\"\n",
+    "# SCHEMA_NAME = \"my_schema\"\n",
+    "\n",
+    "# Reinitialize AlloyDBVectorStore with category_from_json\n",
+    "custom_store = await AlloyDBVectorStore.create(\n",
+    "    engine=engine,\n",
+    "    table_name=TABLE_NAME,\n",
+    "    # schema_name=SCHEMA_NAME,\n",
+    "    embedding_service=embedding,\n",
+    "    # Connect to existing VectorStore by customizing below column names\n",
+    "    id_column=\"product_id\",\n",
+    "    content_column=\"description\",\n",
+    "    embedding_column=\"embed\",\n",
+    "    metadata_columns=[\n",
+    "        \"name\",\n",
+    "        \"category\",\n",
+    "        \"category_from_json\",\n",
+    "        \"price_usd\",\n",
+    "        \"quantity\",\n",
+    "        \"sku\",\n",
+    "        \"image_url\",\n",
+    "    ],\n",
+    "    metadata_json_column=\"metadata\",\n",
+    ")\n",
     "\n",
-    "docs = await custom_store.asimilarity_search(query, filter={\"category\": \"Electronics\"})\n",
+    "docs = await custom_store.asimilarity_search(\n",
+    "    query, filter={\"category_from_json\": \"Electronics\"}\n",
+    ")\n",
     "\n",
     "print(docs)"
    ]
@@ -915,8 +926,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import uuid\n",
-    "\n",
     "docs = await custom_store.asimilarity_search(\n",
     "    query, filter=\"metadata->>'category' = 'Electronics'\"\n",
     ")\n",
@@ -1069,6 +1078,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "from langchain_google_alloydb_pg import Column\n",
+    "\n",
     "TABLE_NAME = \"hybrid_search_products\"\n",
     "\n",
     "await engine.ainit_vectorstore_table(\n",
@@ -1078,7 +1089,14 @@
     "    id_column=\"product_id\",\n",
     "    content_column=\"description\",\n",
     "    embedding_column=\"embed\",\n",
-    "    metadata_columns=[\"name\", \"category\", \"price_usd\", \"quantity\", \"sku\", \"image_url\"],\n",
+    "    metadata_columns=[\n",
+    "        Column(\"name\", \"VARCHAR(255)\"),\n",
+    "        Column(\"category\", \"VARCHAR(255)\"),\n",
+    "        Column(\"price_usd\", \"DECIMAL(10, 2)\"),\n",
+    "        Column(\"quantity\", \"INTEGER\"),\n",
+    "        Column(\"sku\", \"VARCHAR(255)\"),\n",
+    "        Column(\"image_url\", \"VARCHAR(255)\"),\n",
+    "    ],\n",
     "    metadata_json_column=\"metadata\",\n",
     "    hybrid_search_config=hybrid_search_config,\n",
     "    store_metadata=True,\n",
@@ -1212,7 +1230,8 @@
    "toc_visible": true
   },
   "kernelspec": {
-   "display_name": "Python 3",
+   "display_name": "venv (3.13.7)",
+   "language": "python",
    "name": "python3"
   },
   "language_info": {
@@ -1225,7 +1244,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.12.3"
+   "version": "3.13.7"
   }
  },
  "nbformat": 4,