Update example notebook URLs to point to GitHub (#735)

Co-authored-by: Maria Khalusova <[email protected]>

Author: Paul-Cornell

examplecode/notebooks.mdx

@@ -20,7 +20,7 @@ description: "Notebooks contain complete working sample code for end-to-end solu
        ``Unstructured API`` ``Partition Endpoint`` ``Local file``
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     </Card>
-    <Card title="Preserving Table Structure for Better Retrieval" href="https://colab.research.google.com/drive/1__axq0MRDR9i1M_uEW-pR8aKYH_Qk1hj?usp=sharing">
+    <Card title="Preserving Table Structure for Better Retrieval" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Preserving_Table_Structure_for_Better_Retrieval.ipynb">
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        This notebook explores using Unstructured API to process financial documents while preserving tabular structure in a way that's usable by downstream applications.
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@@ -33,14 +33,14 @@ description: "Notebooks contain complete working sample code for end-to-end solu
        ``Unstructured API`` ``Workflows`` ``S3`` ``VLM`` ``NER`` ``Elasticsearch`` ``MLK`` ``National Archives``
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     </Card>
-    <Card title="Rag without Embeddings" href="https://colab.research.google.com/drive/1s2sD4FXj8Kw0gzx_00D_09tuYMepKK3_?usp=sharing">
+    <Card title="RAG without Embeddings" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Rag_without_Embeddings.ipynb">
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        Learn how to build a RAG pipeline without any embedding models. Use Unstructured to preprocess documents, index them into Elasticsearch, and retrieve using classic BM25 scoring. 
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        ``Unstructured API`` ``Workflows`` ``Elasticsearch`` ``BM25`` 
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     </Card>
-    <Card title="Getting Started with Unstructured API and Redis" href="https://colab.research.google.com/drive/1MOi3OSpR14BFF6aaMwrld1d0u4mn8LGE?usp=sharing">
+    <Card title="Getting Started with Unstructured API and Redis" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Getting_Started_with_Unstructured_API_and_Redis.ipynb">
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        Learn how to build data processing workflows using the Unstructured API and Python SDK to preprocess unstructured files from S3 and store the structured outputs in Redis Cloud for retrieval.
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@@ -54,7 +54,7 @@ description: "Notebooks contain complete working sample code for end-to-end solu
        ``Unstructured API`` ``Workflows`` ``S3`` ``Qdrant`` ``VLM`` ``Embeddings``
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     </Card>
-    <Card title="Two-stage retrieval: similarity search + rerankers" href="https://colab.research.google.com/drive/1paDKrn_3WepcZ0D4eN-dhIv1s3oGU9Tl?usp=sharing">
+    <Card title="Two-stage retrieval: similarity search + rerankers" href="https://colab.research.google.com/github/Unstructured-IO/notebooks/blob/main/notebooks/Rag_with_Reranking.ipynb">
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         Improve RAG precision with a two-stage retrieval pipeline: fast vector search followed by reranking using Cohere’s re-ranker models. 
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