fix(docs): fix broken links (#32083)

Author: mdrxy

docs/docs/integrations/text_embedding/ai21.ipynb

@@ -17,9 +17,9 @@
       "source": [
         "# AI21Embeddings\n",
         "\n",
-        ":::caution This service is deprecated. :::\n",
+        ":::caution This service is deprecated.\n",
         "\n",
-        "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/embeddings/langchain_ai21.embeddings.AI21Embeddings.html).\n",
+        "This will help you get started with AI21 embedding models using LangChain. For detailed documentation on `AI21Embeddings` features and configuration options, please refer to the [API reference](https://python.langchain.com/api_reference/ai21/index.html).\n",
         "\n",
         "## Overview\n",
         "### Integration details\n",
@@ -55,7 +55,9 @@
       "cell_type": "markdown",
       "id": "c84fb993",
       "metadata": {},
-      "source": "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      "source": [
+        "To enable automated tracing of your model calls, set your [LangSmith](https://docs.smith.langchain.com/) API key:"
+      ]
     },
     {
       "cell_type": "code",
@@ -123,7 +125,7 @@
       "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](/docs/tutorials/).\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/rag/).\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`."
       ]