docs(docs): update RAG tutorials link across multiple vector store docs (AstraDB, DatabricksVectorSearch, FAISS, Redis, etc.) (#32301)

## **Description:** 
This PR updates the internal documentation link for the RAG tutorials to
reflect the updated path. Previously, the link pointed to the root
`/docs/tutorials/`, which was generic. It now correctly routes to the
RAG-specific tutorial page for the following vector store docs.

1. AstraDBVectorStore
2. Clickhouse
3. CouchbaseSearchVectorStore
4. DatabricksVectorSearch
5. ElasticsearchStore
6. FAISS
7. Milvus
8. MongoDBAtlasVectorSearch
9. openGauss
10. PGVector
11. PGVectorStore
12. PineconeVectorStore
13. QdrantVectorStore
14. Redis
15. SQLServer

## **Issue:** N/A
## **Dependencies:** None
## **Twitter handle:** N/A

docs/docs/integrations/vectorstores/astradb.ipynb

@@ -606,7 +606,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/clickhouse.ipynb

@@ -358,7 +358,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/couchbase.ipynb

@@ -918,7 +918,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/databricks_vector_search.ipynb

@@ -494,7 +494,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/elasticsearch.ipynb

@@ -940,7 +940,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/faiss.ipynb

@@ -410,7 +410,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/milvus.ipynb

@@ -671,7 +671,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/mongodb_atlas.ipynb

@@ -488,7 +488,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/opengauss.ipynb

@@ -264,7 +264,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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/)"
    ]

docs/docs/integrations/vectorstores/pgvector.ipynb

@@ -435,7 +435,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/pgvectorstore.ipynb

@@ -741,6 +741,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/pinecone.ipynb

@@ -342,7 +342,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/qdrant.ipynb

@@ -749,7 +749,7 @@
     "\n",
     "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
     "\n",
-    "- [Tutorials](/docs/tutorials/)\n",
+    "- [Tutorials](/docs/tutorials/rag)\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)"
    ]

docs/docs/integrations/vectorstores/redis.ipynb

@@ -762,7 +762,7 @@
         "\n",
         "For guides on how to use this vector store for retrieval-augmented generation (RAG), see the following sections:\n",
         "\n",
-        "- [Tutorials](/docs/tutorials/)\n",
+        "- [Tutorials](/docs/tutorials/rag)\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)"
       ]

docs/docs/integrations/vectorstores/sqlserver.ipynb

@@ -756,7 +756,7 @@
     "\n",
     "The LangChain Vector store simplifies building sophisticated Q&A systems by enabling efficient similarity searches to find the top 10 relevant documents based on the user's query. The **retriever** is created from the **vector\\_store,** and the question-answer chain is built using the **create\\_stuff\\_documents\\_chain** function. A prompt template is crafted using the **ChatPromptTemplate** class, ensuring structured and context-rich responses. Often in Q&A applications it's important to show users the sources that were used to generate the answer. LangChain's built-in **create\\_retrieval\\_chain** will propagate retrieved source documents to the output under the \"context\" key:\n",
     "\n",
-    "Read more about Langchain RAG tutorials & the terminologies mentioned above [here](https:/python.langchain.com/docs/tutorials/rag)"
+    "Read more about Langchain RAG tutorials & the terminologies mentioned above [here](/docs/tutorials/rag)"
    ]
   },
   {