docs: Update langchain_quick_start.ipynb (#32)

* docs: Update langchain_quick_start.ipynb

Add links + update introduction to match other code labs.

* docs: Update langchain_quick_start.ipynb

update gcs uri for spanner embeddings

* docs: Update langchain_quick_start.ipynb

fix error in gcs path

Author: tabbyl21

samples/langchain_quick_start.ipynb

@@ -34,7 +34,7 @@
     "---\n",
     "# **Introduction**\n",
     "\n",
-    "In this codelab you will learn how to create an interactive generative AI application with Retrieval Augmented Generation using Spanner and LangChain. We will be creating an application grounded in a [Netflix Movie dataset](https://www.kaggle.com/datasets/shivamb/netflix-shows)."
+    "In this codelab, you'll learn how to create a powerful interactive generative AI application using Retrieval Augmented Generation powered by [Spanner](https://cloud.google.com/spanner) and [LangChain](https://www.langchain.com/). We will be creating an application grounded in a [Netflix Movie dataset](https://www.kaggle.com/datasets/shivamb/netflix-shows), allowing you to interact with movie data in exciting new ways."
    ]
   },
   {
@@ -654,7 +654,7 @@
    "outputs": [],
    "source": [
     "vector_store_rows = read_csv_from_gcs(\n",
-    "    \"cloud-samples-data\", \"langchain/netflix_titles_computed_embeddings.csv\"\n",
+    "    \"cloud-samples-data\", \"langchain/spanner/netflix_titles_embeddings.csv\"\n",
     ")\n",
     "\n",
     "for vector_store_row in vector_store_rows:\n",