docs: update tutorials (#28219)

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

@@ -127,7 +127,7 @@
    "source": [
     "## Indexing and Retrieval\n",
     "\n",
-    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [working with external knowledge tutorials](/docs/tutorials/#working-with-external-knowledge).\n",
+    "Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/docs/tutorials/).\n",
     "\n",
     "Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`."
    ]

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

@@ -292,7 +292,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials: working with external knowledge](https://python.langchain.com/docs/tutorials/#working-with-external-knowledge)\n",
+    "- [Tutorials](/docs/tutorials/)\n",
     "- [How-to: Question and answer with RAG](https://python.langchain.com/docs/how_to/#qa-with-rag)\n",
     "- [Retrieval conceptual docs](https://python.langchain.com/docs/concepts/#retrieval)"
    ]