docs: update quickstart (#76)

googleapis · Feb 29, 2024 · 37b4380 · 37b4380
1 parent 7f3bc26
commit 37b4380
Showing 1 changed file with 10 additions and 61 deletions.
diff --git a/samples/langchain_quick_start.ipynb b/samples/langchain_quick_start.ipynb
@@ -109,7 +109,7 @@
             "outputs": [],
             "source": [
                 "# TODO: Update this to install from Pypi\n",
-                "%pip install --upgrade --quiet git+https://github.com/googleapis/langchain-google-cloud-sql-pg-python.git langchain-google-vertexai langchain"
+                "%pip install --upgrade --quiet langchain-google-cloud-sql-pg langchain-google-vertexai langchain"
             ]
         },
         {
@@ -169,8 +169,8 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {
-                "id": "qjFuhRhVxlWP",
-                "cellView": "form"
+                "cellView": "form",
+                "id": "qjFuhRhVxlWP"
             },
             "outputs": [],
             "source": [
@@ -263,8 +263,8 @@
             "cell_type": "code",
             "execution_count": null,
             "metadata": {
-                "id": "XXI1uUu3y8gc",
-                "cellView": "form"
+                "cellView": "form",
+                "id": "XXI1uUu3y8gc"
             },
             "outputs": [],
             "source": [
@@ -413,31 +413,6 @@
                 ")"
             ]
         },
-        {
-            "cell_type": "markdown",
-            "metadata": {
-                "id": "xvCEAp97fXRt"
-            },
-            "source": [
-                "Next let's define a function \"collect_async_items\" to asynchronously pull documents from our database."
-            ]
-        },
-        {
-            "cell_type": "code",
-            "execution_count": 16,
-            "metadata": {
-                "id": "-p9UiHFbdyYa"
-            },
-            "outputs": [],
-            "source": [
-                "async def collect_async_items(docs_generator):\n",
-                "    \"\"\"Collects items from an async generator.\"\"\"\n",
-                "    docs = []\n",
-                "    async for doc in docs_generator:\n",
-                "        docs.append(doc)\n",
-                "    return docs"
-            ]
-        },
         {
             "cell_type": "markdown",
             "metadata": {
@@ -472,7 +447,7 @@
                 }
             ],
             "source": [
-                "documents = await collect_async_items(loader.alazy_load())\n",
+                "documents = await loader.aload()\n",
                 "print(f\"Loaded {len(documents)} from the database. 5 Examples:\")\n",
                 "for doc in documents[:5]:\n",
                 "    print(doc)"
@@ -519,15 +494,6 @@
                 "\n",
                 "sample_vector_table_name = \"movie_vector_table_samples\"\n",
                 "\n",
-                "pg_engine = PostgresEngine.from_instance(\n",
-                "    project_id=project_id,\n",
-                "    instance=instance_name,\n",
-                "    region=region,\n",
-                "    database=database_name,\n",
-                "    user=\"postgres\",\n",
-                "    password=password,\n",
-                ")\n",
-                "\n",
                 "pg_engine.init_vectorstore_table(\n",
                 "    sample_vector_table_name,\n",
                 "    vector_size=768,\n",
@@ -565,22 +531,13 @@
             "outputs": [],
             "source": [
                 "from langchain_google_vertexai import VertexAIEmbeddings\n",
-                "from langchain_google_cloud_sql_pg import PostgresVectorStore, PostgresEngine\n",
+                "from langchain_google_cloud_sql_pg import PostgresVectorStore\n",
                 "\n",
                 "# Initialize the embedding service. In this case we are using version 003 of Vertex AI's textembedding-gecko model. In general, it is good practice to specify the model version used.\n",
                 "embeddings_service = VertexAIEmbeddings(\n",
                 "    model_name=\"textembedding-gecko@003\", project=project_id\n",
                 ")\n",
                 "\n",
-                "pg_engine = PostgresEngine.from_instance(\n",
-                "    project_id=project_id,\n",
-                "    instance=instance_name,\n",
-                "    region=region,\n",
-                "    database=database_name,\n",
-                "    user=\"postgres\",\n",
-                "    password=password,\n",
-                ")\n",
-                "\n",
                 "vector_store = PostgresVectorStore.create_sync(\n",
                 "    engine=pg_engine,\n",
                 "    embedding_service=embeddings_service,\n",
@@ -685,15 +642,6 @@
             "source": [
                 "from langchain_google_cloud_sql_pg import PostgresChatMessageHistory, PostgresEngine\n",
                 "\n",
-                "pg_engine = PostgresEngine.from_instance(\n",
-                "    project_id=project_id,\n",
-                "    instance=instance_name,\n",
-                "    region=region,\n",
-                "    database=database_name,\n",
-                "    user=\"postgres\",\n",
-                "    password=password,\n",
-                ")\n",
-                "\n",
                 "message_table_name = \"message_store\"\n",
                 "\n",
                 "pg_engine.init_chat_history_table(table_name=message_table_name)\n",
@@ -1041,9 +989,10 @@
             "name": "python3"
         },
         "language_info": {
-            "name": "python"
+            "name": "python",
+            "version": "3.11.5"
         }
     },
     "nbformat": 4,
     "nbformat_minor": 0
-}
+}