Update index name in quickstart notebook (#442)

## Problem

Want to make it easier to track indexes created from this notebook.

## Solution

Use a unique index name to make tracking easier. 

## Type of Change

- [ ] Bug fix (non-breaking change which fixes an issue)
- [ ] New feature (non-breaking change which adds functionality)
- [ ] Breaking change (fix or feature that would cause existing
functionality to not work as expected)
- [ ] This change requires a documentation update
- [ ] Infrastructure change (CI configs, etc)
- [ ] Non-code change (docs, etc)
- [ ] None of the above: (explain here)

## Test Plan

Describe specific steps for validating this change.

Author: jseldess

docs/pinecone-quickstart.ipynb

@@ -107,11 +107,11 @@
    "source": [
     "## Create an index\n",
     "\n",
-    "In Pinecone, there are two types of indexes for storing vector data: [Dense indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes#dense-indexes) store dense vectors for semantic search, and [sparse indexes](https://docs.pinecone.io/guides/indexes/understanding-indexes#sparse-indexes) store sparse vectors for lexical/keyword search.\n",
+    "In Pinecone, there are two types of indexes for storing vector data: [Dense indexes](https://docs.pinecone.io/guides/index-data/indexing-overview#dense-indexes) store dense vectors for semantic search, and [sparse indexes](https://docs.pinecone.io/guides/index-data/indexing-overview#sparse-indexes) store sparse vectors for lexical/keyword search.\n",
     "\n",
-    "For this quickstart, create a dense index that is integrated with an [embedding model hosted by Pinecone](https://docs.pinecone.io/guides/inference/understanding-inference#embedding-models). With integrated models, you upsert and search with text and have Pinecone generate vectors automatically.\n",
+    "For this quickstart, create a dense index that is integrated with an [embedding model hosted by Pinecone](https://docs.pinecone.io/guides/index-data/create-an-index#embedding-models). With integrated models, you upsert and search with text and have Pinecone generate vectors automatically.\n",
     "\n",
-    "**Note:** If you prefer to use external embedding models, see [Bring your own vectors](https://docs.pinecone.io/guides/indexes/understanding-indexes#bring-your-own-vectors)."
+    "**Note:** If you prefer to use external embedding models, see [Bring your own vectors](https://docs.pinecone.io/guides/index-data/indexing-overview#bring-your-own-vectors)."
    ]
   },
   {
@@ -124,7 +124,7 @@
    "source": [
     "# Create a dense index with integrated embedding\n",
     "\n",
-    "index_name = \"dense-index\"\n",
+    "index_name = \"quickstart-notebook\"\n",
     "\n",
     "if not pc.has_index(name=index_name):\n",
     "    pc.create_index_for_model(\n",
@@ -220,7 +220,7 @@
     "id": "f5vNb1pugnR5"
    },
    "source": [
-    "[Upsert](https://docs.pinecone.io/guides/data/upsert-data) the sample dataset into a new namespace in your index.\n",
+    "[Upsert](https://docs.pinecone.io/guides/index-data/upsert-data) the sample dataset into a new namespace in your index.\n",
     "\n",
     "Because your index is integrated with an embedding model, you provide the textual statements and Pinecone converts them to dense vectors automatically."
    ]
@@ -251,7 +251,7 @@
    "source": [
     "## Check index stats\n",
     "\n",
-    "Pinecone is eventually consistent, so there can be a slight delay before new or changed records are visible to queries. You can [view index stats](https://docs.pinecone.io/guides/data/check-data-freshness#verify-record-counts) to check if the current vector count matches the number of vectors you upserted (50):"
+    "Pinecone is eventually consistent, so there can be a slight delay before new or changed records are visible to queries. You can [view index stats](https://docs.pinecone.io/guides/index-data/check-data-freshness#verify-record-counts) to check if the current vector count matches the number of vectors you upserted (50):"
    ]
   },
   {
@@ -296,7 +296,7 @@
    "source": [
     "## Semantic search\n",
     "\n",
-    "[Search the dense index](https://docs.pinecone.io/guides/data/query-data#semantic-search) for ten records that are most semantically similar to the query, `Famous historical structures and monuments`.\n",
+    "[Search the dense index](https://docs.pinecone.io/guides/search/semantic-search) for ten records that are most semantically similar to the query, `Famous historical structures and monuments`.\n",
     "\n",
     "Again, because your index is integrated with an embedding model, you provide the query as text and Pinecone converts the text to a dense vector automatically."
    ]
@@ -362,7 +362,7 @@
    "source": [
     "Notice that most of the results are about historical structures and monuments. However, a few unrelated statements are included as well and are ranked high in the list, for example, statements about Shakespeare and renewable energy.\n",
     "\n",
-    "To get a more accurate ranking, search again but this time [rerank the initial results](https://docs.pinecone.io/guides/data/query-data#rerank-results) based on their relevance to the query."
+    "To get a more accurate ranking, search again but this time [rerank the initial results](https://docs.pinecone.io/guides/search/rerank-results) based on their relevance to the query."
    ]
   },
   {
@@ -426,9 +426,9 @@
     "\n",
     "Reranking results is one of the most effective ways to improve search accuracy and relevance, but there are many other techniques to consider. For example:\n",
     "\n",
-    "* [Filtering by metadata](https://docs.pinecone.io/guides/data/query-data#filter-by-metadata): When records contain additional metadata, you can limit the search to records matching a filter expression.\n",
+    "* [Filtering by metadata](https://docs.pinecone.io/guides/search/filter-by-metadata): When records contain additional metadata, you can limit the search to records matching a filter expression.\n",
     "\n",
-    "* [Hybrid search](https://docs.pinecone.io/guides/data/query-data#hybrid-search): You can add lexical search to capture precise keyword matches (e.g., product SKUs, email addresses, domain-specific terms) in addition to semantic matches.\n",
+    "* [Hybrid search](https://docs.pinecone.io/guides/search/hybrid-search): You can add lexical search to capture precise keyword matches (e.g., product SKUs, email addresses, domain-specific terms) in addition to semantic matches.\n",
     "\n",
     "* [Chunking strategies](https://www.pinecone.io/learn/chunking-strategies/): You can chunk your content in different ways to get better results. Consider factors like the length of the content, the complexity of queries, and how results will be used in your application."
    ]