cli[patch]: Update integration template for embedding models (#25248)

Update integration template for embedding models

libs/cli/langchain_cli/integration_template/docs/text_embedding.ipynb

@@ -24,13 +24,9 @@
     "## Overview\n",
     "### Integration details\n",
     "\n",
-    "- TODO: Fill in table features.\n",
-    "- TODO: Remove JS support link if not relevant, otherwise ensure link is correct.\n",
-    "- TODO: Make sure API reference links are correct.\n",
+    "import { ItemTable } from \"@theme/FeatureTables\";\n",
     "\n",
-    "| Class | Package | Local | Serializable | [JS support](https://js.langchain.com/v0.2/docs/integrations/text_embedding/__package_name_short_snake__) | Package downloads | Package latest |\n",
-    "| :--- | :--- | :---: | :---: |  :---: | :---: | :---: |\n",
-    "| [__ModuleName__Embeddings](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html) | [__package_name__](https://api.python.langchain.com/en/latest/__package_name_short_snake___api_reference.html) | ✅/❌ | beta/❌ | ✅/❌ | ![PyPI - Downloads](https://img.shields.io/pypi/dm/__package_name__?style=flat-square&label=%20) | ![PyPI - Version](https://img.shields.io/pypi/v/__package_name__?style=flat-square&label=%20) |\n",
+    "<ItemTable category=\"text_embedding\" item=\"__ModuleName__\" />\n",
     "\n",
     "## Setup\n",
     "\n",
@@ -157,10 +153,10 @@
     "retriever = vectorstore.as_retriever()\n",
     "\n",
     "# Retrieve the most similar text\n",
-    "retrieved_document = retriever.invoke(\"What is LangChain?\")\n",
+    "retrieved_documents = retriever.invoke(\"What is LangChain?\")\n",
     "\n",
     "# show the retrieved document's content\n",
-    "retrieved_document.page_content"
+    "retrieved_documents[0].page_content"
    ]
   },
   {
@@ -210,7 +206,7 @@
     "text2 = (\n",
     "    \"LangGraph is a library for building stateful, multi-actor applications with LLMs\"\n",
     ")\n",
-    "two_vectors = embeddings.embed_queries([text, text2])\n",
+    "two_vectors = embeddings.embed_documents([text, text2])\n",
     "for vector in two_vectors:\n",
     "    print(str(vector)[:100]) # Show the first 100 characters of the vector"
    ]
@@ -220,34 +216,10 @@
    "id": "98785c12",
    "metadata": {},
    "source": [
-    "### Async Usage\n",
+    "## API Reference\n",
     "\n",
-    "You can also use `aembed_query` and `aembed_documents` for producing embeddings asynchronously:\n"
+    "For detailed documentation on `__ModuleName__Embeddings` features and configuration options, please refer to the [API reference](https://api.python.langchain.com/en/latest/embeddings/__module_name__.embeddings.__ModuleName__Embeddings.html).\n"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "4c3bef91",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import asyncio\n",
-    "\n",
-    "async def async_example():\n",
-    "    single_vector = await embeddings.embed_query(text)\n",
-    "    print(str(single_vector)[:100]) # Show the first 100 characters of the vector\n",
-    "\n",
-    "asyncio.run(async_example())"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1bd4396",
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {