oracle-livelabs · lfoinding · Sep 25, 2025 · Sep 25, 2025
diff --git a/sample-quickstart-template/intro/intro.md b/sample-quickstart-template/intro/intro.md
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+# Introduction
+
+## About this LiveLabs QuickStart
+
+
+(placeholder for 30 second hook)
+[](youtube:REPLACE_WITH_VIDEO_ID)
+
+
+Messy data buries meaning. Keywords miss the mark. Customers, employees, and analysts waste time chasing the right document, product, or answer.
+
+AI Vector Search in Oracle Database 23ai fixes that. It stores embeddings—the numeric “fingerprints” of meaning—inside the database. With them, you find what things mean, not just what words they contain. That makes Vector Search a foundation for Retrieval-Augmented Generation (RAG) and agentic workflows, where LLMs need fast, accurate access to trusted enterprise data.
+
+What you gain with Vector Search in Oracle Database 23ai:
+
+- **Semantic search:** Discover documents, products, and images by meaning, not spelling.
+
+- **One platform:** Blend vectors with relational, JSON, and graph data in Oracle Database 23ai.
+
+- **Speed at scale:** Use HNSW indexes and efficient distance metrics (cosine, Euclidean, dot product).
+
+- **Enterprise strength:** Count on Oracle’s security, manageability, and recovery.
+
+- **AI-ready workflows:** Power RAG, recommendations, deduplication, and clustering.
+
+- **Multimodal power:** Apply the same search to text, images, or any embedding.
+
+- **Simpler stack:** Keep search and data together—cutting latency, risk, and cost.
+
+This QuickStart shows you how similarity search works, how distance functions shape results, and how you can bring these capabilities into real applications with simple code.
+
+
+
+## Acknowledgements
+* **Author** - Kevin Lazarz, Database Product Management
+* **Contributors** -  William Masdon, Linda Foinding, Pat Sheppard, Francis Regalado,
+* **Last Updated By/Date** - Kevin Lazarz, September 2025
diff --git a/sample-quickstart-template/next-steps/next-steps.md b/sample-quickstart-template/next-steps/next-steps.md
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+# Next Steps
+
+Ready to go deeper? These workshops let you move from demo to hands-on practice.
+
+
+## Try our Signature Workshop
+
+What you’ll do:
+
+- Add the new vector datatype.
+
+- Verify ONNX embedding models.
+
+- Create embeddings for text and images.
+
+- Run exact and approximate similarity searches with indexes.
+
+- Combine similarity search with relational queries.
+
+- Apply everything in a pre-built APEX app.
+
+👉 [Start the Advanced Workshop](https://livelabs.oracle.com/pls/apex/r/dbpm/livelabs/view-workshop?wid=4166)
+
+
+## Learn More
+
+* [AI Vector Search Docs](https://docs.oracle.com/en/database/oracle/oracle-database/23/vecse/)
+* [AI Vector Search Product Page](https://www.oracle.com/database/ai-vector-search/)
+* [LiveLabs Workshop](https://livelabs.oracle.com/pls/apex/f?p=133:180:114898719666832::::wid:4166)
+
+
+## Acknowledgements
+* **Author** - Kevin Lazarz, Database Product Management
+* **Contributors** -  William Masdon, Linda Foinding, Pat Sheppard, Francis Regalado,
+* **Last Updated By/Date** - Kevin Lazarz, September 2025
diff --git a/sample-quickstart-template/quickstart/quickstart.md b/sample-quickstart-template/quickstart/quickstart.md
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+# QuickStart Demo
+
+## Try AI Vector Search in Oracle Database 23ai
+
+This QuickStart provides hands-on code snippets to get you started with AI Vector Search. You can copy and run these SQL commands in your Oracle Database 23ai environment.
+
+### Step 1: Create a Table with a Vector Column
+
+First, create a table to store vectors. The VECTOR datatype is new in Oracle Database 23ai.
+
+```sql
+CREATE TABLE my_vectors (
+    id NUMBER,
+    text_content VARCHAR2(4000),
+    embedding VECTOR
+);
+```
+
+### Step 2: Generate Embeddings
+
+Use the built-in embedding function to convert text into vectors. You'll need an ONNX model loaded in the database.
+
+```sql
+<copy>
+-- Assuming a model named 'demo_model' is loaded
+INSERT INTO my_vectors (id, text_content, embedding)
+VALUES (
+    1,
+    'Oracle Database 23ai introduces AI Vector Search',
+    dbms_vector_chain.utl_to_embedding(
+        'Oracle Database 23ai introduces AI Vector Search',
+        JSON('{"provider":"database", "model":"demo_model"}')
+    )
+);
+</copy>
+```
+
+### Step 3: Perform Similarity Search
+
+Find similar content using vector distance functions.
+
+```sql
+-- Search for similar content
+SELECT id, text_content,
+       VECTOR_DISTANCE(
+           embedding,
+           dbms_vector_chain.utl_to_embedding(
+               'What is new in Oracle 23ai?',
+               JSON('{"provider":"database", "model":"demo_model"}')
+           ),
+           COSINE
+       ) AS distance
+FROM my_vectors
+ORDER BY distance
+FETCH FIRST 5 ROWS ONLY;
+```
+
+
+These snippets demonstrate the core concepts. For production use, consider security, error handling, and performance tuning.
+
+## Walkthrough
+
+(placeholder for walkthrough video)
+[](youtube:REPLACE_WITH_VIDEO_ID)
+
+## Acknowledgements
+* **Author** - Kevin Lazarz, Database Product Management
+* **Contributors** -  William Masdon, Linda Foinding, Pat Sheppard, Francis Regalado,
+* **Last Updated By/Date** - Kevin Lazarz, September 2025
diff --git a/sample-quickstart-template/workshops/quickstart/index.html b/sample-quickstart-template/workshops/quickstart/index.html
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diff --git a/sample-quickstart-template/workshops/quickstart/manifest.json b/sample-quickstart-template/workshops/quickstart/manifest.json
@@ -0,0 +1,17 @@
+{"help": "livelabs-help-db_us@oracle.com",
+  "workshoptitle": "LiveLabs QuickStart - AI Vector Search",
+    "tutorials": [
+        {
+            "title": "Introduction",
+            "filename": "../../intro/intro.md"
+        },
+        {
+            "title": "QuickStart",
+            "filename": "../../quickstart/quickstart.md"
+        },
+        {
+            "title": "Next Steps",
+            "filename": "../../next-steps/next-steps.md"
+        }
+    ]
+}