Update turbocharge-rag-with-langchain-and-vespa-streaming-mode-cloud.…

…ipynb (#654)

Fix broken link

docs/sphinx/source/examples/turbocharge-rag-with-langchain-and-vespa-streaming-mode-cloud.ipynb

@@ -531,7 +531,7 @@
     "\n",
     "[LangChain](https://python.langchain.com/) has a rich set of [document loaders](https://python.langchain.com/docs/modules/data_connection/document_loaders/) that can be used to load and process various file formats. In this notebook, we use the [PyPDFLoader](https://python.langchain.com/docs/modules/data_connection/document_loaders/pdf#using-pypdf). \n",
     "\n",
-    "We also want to split the extracted text into *chunks* using a [text splitter](https://python.langchain.com/docs/modules/data_connection/document_transformers/#get-started-with-text-splitters). Most text embedding models have limited input lengths (typically less than 512 language model tokens, so splitting the text\n",
+    "We also want to split the extracted text into *chunks* using a [text splitter](https://python.langchain.com/docs/modules/data_connection/document_transformers/). Most text embedding models have limited input lengths (typically less than 512 language model tokens, so splitting the text\n",
     "into multiple chunks that fits into the context limit of the embedding model is a common strategy. \n",
     "\n",
     "For embedding text data, models based on the Transformer architecture have become the de facto standard. A challenge with Transformer-based models is their input length limitation due to the quadratic self-attention computational complexity. For example, a popular open-source text embedding model like \n",