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Building an AI Vision Search Engine with
MySQL HeatWave GenAI
Lower Cost, Simpler Architecture, Faster AI Innovation
May 20, 2026
Olivier Dasini
MySQL AI & Analytics Solutions Architect @ Oracle
[email protected]
Blogs : www.dasini.net/blog/en
: www.dasini.net/blog/fr
Linkedin : www.linkedin.com/in/olivier-dasini
Slides : https://speakerdeck.com/freshdaz
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Safe harbor statement
The following is intended to outline our general product direction. It is intended for information
purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any
material, code, or functionality, and should not be relied upon in making purchasing decisions. The
development, release, timing, and pricing of any features or functionality described for Oracle’s
products may change and remains at the sole discretion of Oracle Corporation.
2 Copyright © 2026, Oracle and/or its affiliates. All rights reserved.
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Me, Myself & I
Olivier DASINI
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Data Geek
Addicted to MySQL for 20+ years
Playing with databases for 25+ years
Writer, Blogger and Speaker
Also former : DBA, Consultant, Architect, Trainer, ...
MySQL HeatWave AI & Analytics Solution Architect, at Oracle
Stay up to date!
Blog: www.dasini.net/blog/en
Linkedin: www.linkedin.com/in/olivier-dasini/
Slides: https://speakerdeck.com/freshdaz
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Resources & Slides
● Read the Deep Dive Article:
This presentation is based on the deeper implementation details found here
(Highly Recommended):
Building an AI Vision Search Engine with MySQL HeatWave GenAI
● Download the Deck:
Grab a copy of these slides anytime on my Speaker Deck:
https://speakerdeck.com/freshdaz/building-an-ai-vision-search-engine-with-mysql-heatwave-genai
4
https://dasini.net/blog/2026/05/14/building-an-ai-vision-search-engine-with-mysql-heatwave-genai/
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Agenda
1. Landscape of Vision-to-Embedding Approaches
2. Overview of MySQL HeatWave GenAI
3. Generating Image Understanding with sys.ML_GENERATE
4. Text-to-Image Search (Semantic Retrieval)
5. Reverse Image Search (Image-to-Image via Semantics)
6. Architectural Trade-offs & Design Considerations
7. Conclusion
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Modern AI applications are multimodal
• Modern AI systems increasingly rely on multimodal data:
●
text, images, documents, audio, and video
• Some of the requirements:
●
Search images using natural language
●
Compare visually similar assets
●
Validate AI-generated descriptions
●
Build semantic retrieval pipelines
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Why Semantic Image Understanding?
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Landscape of Vision-to-Embedding Approaches
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Operationally Heavy and Fragmented
• Specialized computer vision infrastructure
• External vector databases
• Custom ML pipelines
• Multiple orchestration frameworks
• Operational complexity
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Traditional AI Image Pipelines
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Three main paradigms
1. Direct Image Embeddings
2. Semantic Metadata Embeddings
3. Multimodal Fusion Models
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Vision-to-Embedding Approaches
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Powerful, Yet Hard to Explain
• Image → Neural Network → Vector
• Popular architectures include:
●
CLIP, ResNet, Vision Transformers (ViT)
●
Convert raw image pixels directly into vectors
✔ Fast similarity search
✔ Excellent for image-to-image matching
✗ Black-box behavior ie, No human-readable explanation
✗ Harder to debug and refine
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1. Direct Image Embeddings
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Human-Readable and SQL-Native
• Image → Text → Embedding
• Generate text (descriptions and/or keywords)
✔ Human-readable and explainable
✔
Useful for debug
✔ SQL-native workflow
✔ Ideal for MySQL HeatWave GenAI
✗ Less precise for purely visual similarity
✗ Depends on generated metadata quality
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2. Semantic Metadata Embeddings
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Create unified multimodal embeddings
• Multimodal architectures capable of jointly processing:
●
Images, Text, Audio, Video, Structured data, …
• These systems use:
●
{Early | Intermediate | Late | Hybrid} fusion architectures
✔ Very powerful semantic understanding
✔ Strong cross-modal reasoning
✗ More complex, Harder to operationalize
✗ Less transparent
✗ Difficult to integrate into SQL-centric architectures
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3. Multimodal Fusion Models
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The Semantic Metadata Embeddings pipeline
• ML_GENERATE handles image-to-text generation
• ML_EMBED_ROW handles text-to-vector conversion
• Human-readable semantic audit trail
• Explainability is critical for evaluation systems
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Why Choose Image → Text → Embedding?
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Overview of MySQL HeatWave GenAI
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MySQL HeatWave
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Generative AI Integrated and Automated, at no additional cost
• HeatWave GenAI lets you communicate with unstructured data in HeatWave using natural-
language queries
• Using HeatWave GenAI, you can perform natural-language searches in a single step using either in-
database or external large language models (LLMs)
• It uses a familiar SQL interface which makes it is easy to use for content generation, summarization,
sentiment analysis, retrieval-augmented generation (RAG), …
• All the elements that are necessary to use HeatWave GenAI with proprietary data are integrated and
optimized to work with each other
• HeatWave GenAI lets you integrate generative AI into the applications, providing an integrated end-
to-end pipeline including vector store generation, vector search using RAG, and an inbuilt chatbot
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MySQL HeatWave GenAI
https://dev.mysql.com/doc/heatwave/en/mys-hw-genai.html
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MySQL HeatWave GenAI
Provides integrated and automated generative AI with in-database large language models (LLMs)
An automated, in-database vector store & scale-out vector processing
The ability to have contextual conversations in natural language
Letting you take advantage of generative AI without AI expertise, data movement, or additional cost
https://www.oracle.com/heatwave/genai/
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A cost effective unified platform
• Unified OLTP + OLAP + ML + GenAI platform
• AI workflows directly inside SQL
• Built-in vector store capabilities
• Key elements: ML_GENERATE, ML_EMBED_ROW, DISTANCE, VECTOR
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MySQL HeatWave GenAI
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Demo
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https://www.youtube.com/channel/UC12TulyJsJZHoCmby3Nm3WQ
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Generating Image Understanding with ML_GENERATE
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From Images to Semantic Metadata with sys.ML_GENERATE
• The sys.ML_GENERATE routine supports multimodal prompts combining:
●
Text
●
Images
• This enables:
●
Caption generation
●
Object detection prompts
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Context extraction
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Semantic interpretation
• https://dev.mysql.com/doc/heatwave/en/mys-hwgenai-ml-generate.html
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What sys.ML_GENERATE Enables
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From Images to Semantic Metadata with sys.ML_GENERATE
• The supported vision models can be queried directly from MySQL HeatWave (9.6.1):
23 Copyright © 2026, Oracle and/or its affiliates
Supported Vision Models
SELECT *
FROM sys.ML_SUPPORTED_LLMS
WHERE model_id LIKE 'google.gemini%';
+---------------------------+--------------------------------+-------------------+---------------------+---------------+
| provider | model_id | availability_date | capabilities | default_model |
+---------------------------+--------------------------------+-------------------+---------------------+---------------+
| OCI Generative AI Service | google.gemini-2.5-flash | 2026-01-22 | ["GENERATION"] | 0 |
| OCI Generative AI Service | google.gemini-2.5-pro | 2026-01-22 | ["GENERATION"] | 0 |
| OCI Generative AI Service | google.gemini-2.5-flash-lite | 2026-01-22 | ["GENERATION"] | 0 |
+---------------------------+--------------------------------+-------------------+---------------------+---------------+
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• This table stores:
●
The original image (in base 64)
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Semantic metadata (Image description and keywords)
●
Vector embeddings (of the description and the keywords)
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Table Definition
CREATE TABLE image_details (
id_image int unsigned NOT NULL AUTO_INCREMENT,
image_name varchar(255) NOT NULL,
image_base64 longtext NOT NULL,
image_description json NOT NULL,
image_description_embedding vector(2048) NOT NULL COMMENT
'GENAI_OPTIONS=EMBED_MODEL_ID=cohere.embed-english-v3.0',
image_keywords json NOT NULL,
image_keywords_embedding vector(2048) NOT NULL COMMENT
'GENAI_OPTIONS=EMBED_MODEL_ID=cohere.embed-english-v3.0',
created_at timestamp NULL DEFAULT CURRENT_TIMESTAMP,
PRIMARY KEY (id_image)
) ENGINE=InnoDB;
Stored inside MySQL HeatWave
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SQL Example
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Object Detection Prompt
SELECT image_base64 FROM image_details WHERE id_image = 1 INTO @image_base64;
SET @prompt_desc_key_json = 'What objects are present?';
SELECT JSON_UNQUOTE(
JSON_EXTRACT(
sys.ML_GENERATE(@prompt_desc_key_json,
JSON_OBJECT("model_id", "google.gemini-2.5-pro",
"image", @image_base64)),
'$.text'
)
) AS description;
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SQL Example
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Scene Context Prompt
SET @prompt_desc_key_json = 'What is the context of this scene?';
SELECT JSON_UNQUOTE(
JSON_EXTRACT(
sys.ML_GENERATE(@prompt_desc_key_json,
JSON_OBJECT("model_id", "google.gemini-2.5-pro",
"image", @image_base64)),
'$.text'
)
) AS description;
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Text-to-Image Search (Semantic Retrieval)
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Query in Words, Retrieve by Meaning
• Input: Text
• Output: Relevant Images
• Convert text query into embedding
• Similarity search against image embeddings
• Search by meaning, not pixels
●
This is semantic retrieval
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Text-to-Image Search
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Query in Words, Retrieve by Meaning
• An embedding model is a machine learning model that transforms complex, high-dimensional data
(words, sentences, images, …) into compact, fixed-size lists of numbers called vectors (or
embeddings)
• The supported embedding models can be queried directly from MySQL HeatWave (9.6.1):
29 Copyright © 2026, Oracle and/or its affiliates
Supported Embedding Models
SELECT *
FROM sys.ML_SUPPORTED_LLMS
WHERE capabilities LIKE '["TEXT_EMBEDDINGS"]';
+---------------------------+--------------------------------+-------------------+---------------------+---------------+
| provider | model_id | availability_date | capabilities | default_model |
+---------------------------+--------------------------------+-------------------+---------------------+---------------+
| HeatWave | all_minilm_l12_v2 | 2024-07-01 | ["TEXT_EMBEDDINGS"] | 0 |
| HeatWave | multilingual-e5-small | 2024-07-24 | ["TEXT_EMBEDDINGS"] | 1 |
| OCI Generative AI Service | cohere.embed-english-v3.0 | 2024-02-17 | ["TEXT_EMBEDDINGS"] | 0 |
| OCI Generative AI Service | cohere.embed-multilingual-v3.0 | 2024-02-17 | ["TEXT_EMBEDDINGS"] | 0 |
+---------------------------+--------------------------------+-------------------+---------------------+---------------+
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2 steps
• The workflow contains two phases:
1) Preprocessing
●
Each image must first be semantically enriched.
2)Query execution
●
The user enters a text description
●
The text becomes an embedding
●
Similarity search retrieves matching images
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Workflow Overview
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SQL Example
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Preprocessing - Generating Semantic Metadata
SET @prompt_desc_key_json = '
Analyze the image and return a minified JSON object.
The JSON must contain two fields:
"description" (a concise, single-paragraph description of visible content with
no speculation) and "keywords" (a JSON array of relevant lowercase visual
keywords).
Output ONLY the raw JSON on a single line without any markdown formatting,
backticks, or preamble
';
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SQL Example
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Preprocessing - Generate Description and Keywords
SELECT JSON_UNQUOTE(
JSON_EXTRACT(
sys.ML_GENERATE(@prompt_desc_key_json,
JSON_OBJECT("model_id", "google.gemini-2.5-pro",
"image", @image_base64)),
'$.text'
)
) AS description;
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SQL Example
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Preprocessing - Generate Embeddings and Store Data
INSERT INTO image_details (
image_name,
image_base64,
image_description,
image_description_embedding,
image_keywords,
image_keywords_embedding
)
VALUES (
image_name,
image_base64,
image_description,
sys.ML_EMBED_ROW(
@source_image_descriptions,
'{"model_id": "cohere.embed-english-v3.0"}'
),
image_keywords,
sys.ML_EMBED_ROW(
@source_image_keywords,
'{"model_id": "cohere.embed-english-v3.0"}'
));
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Query in Words, Retrieve by Meaning
• At query time:
●
The user enters a text description
●
The text becomes an embedding
●
Similarity search retrieves matching images
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Text-to-Image Search
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SQL Example
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Query time - Generate Query Embedding
SET @embeddOptions = '{"model_id": "cohere.embed-english-v3.0"}';
SET @searchImage = 'street motorcycle';
SELECT sys.ML_EMBED_ROW(
@searchImage,
@embeddOptions
) INTO @searchImageEmbedding;
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SQL Example
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Query time - Similarity Search Using Descriptions
SELECT
image_name,
DISTANCE(
image_description_embedding,
@searchImageEmbedding,
'COSINE'
) AS min_distance
FROM image_details
ORDER BY min_distance
LIMIT 3;
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SQL Example
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Query time - Similarity Search Using Description + Keywords
WITH distances AS (
SELECT
image_name,
(
DISTANCE(
image_keywords_embedding,
@searchImageEmbedding,
'COSINE'
)
+
DISTANCE(
image_description_embedding,
@searchImageEmbedding,
'COSINE'
)
) / 2 AS avg_distance
FROM image_details
)
SELECT * FROM distances ORDER BY avg_distance ASC LIMIT 3;
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SQL Example
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Query time - Similarity Search with a simple reranking strategy
WITH initial_results AS (
SELECT image_name,
DISTANCE(image_keywords_embedding, @searchImageEmbedding, 'COSINE'
) AS keywords_distance,
DISTANCE(image_description_embedding, @searchImageEmbedding, 'COSINE'
) AS description_distance
FROM image_details
ORDER BY keywords_distance + description_distance LIMIT 15
),
reranked_results AS (
SELECT image_name,
(
0.3 * keywords_distance +
0.7 * description_distance
) AS combined_distance
FROM initial_results
)
SELECT * FROM reranked_results ORDER BY combined_distance ASC LIMIT 3;
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Reverse Image Search (Image-to-Image via Semantics)
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Similarity of meaning
• Input: Image
• Output: Relevant Images
• Semantic image-to-image search
• Similarity of meaning, not appearance
• No complex computer vision frameworks required
• This is:
●
Similarity of meaning
●
Not similarity of pixels
40 Copyright © 2026, Oracle and/or its affiliates
Reverse Image Search
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Why This Is Interesting
• 2 images may be visually different while still being semantically related
• For example:
●
Different motorcycles
●
Different lighting conditions
●
Different camera angles
• Yet both images may describe: « A motorcycle parked on an urban street »
• Semantic retrieval captures this meaning
41 Copyright © 2026, Oracle and/or its affiliates
Reverse Image Search
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Key benefits
• Simpler Architecture
●
Eliminates the need for complex computer vision frameworks, GPU-heavy pipelines, and specialized external
vector databases
• Complete Explainability
●
Audit retrieval results directly through generated metadata; refine descriptions and improve LLM prompts
based on clear text logic
• SQL-Native Workflow
●
Keep metadata, embeddings, similarity search, and ranking logic unified within the MySQL HeatWave engine
42 Copyright © 2026, Oracle and/or its affiliates
Advantages of the Semantic Approach
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Architectural Trade-offs & Design Considerations
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• Building AI-powered semantic image systems involves several important architectural decisions
1. Descriptions vs Keywords
2. Database vs Object Storage
3. Prompt engineering quality
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Architectural Trade-offs
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Description vs Keywords
Descriptions
• Descriptions provide:
●
Rich contextual understanding
●
Better semantic reasoning
●
Natural language flexibility
●
However:
●
They may introduce noise
●
Longer text may dilute embeddings
Keywords
• Keywords provide:
●
Focused semantic signals
●
Better precision
●
Faster matching
• However:
●
They lose contextual richness
Both representations serve different purposes
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Hybrid approach
Strategy implemented here
• A hybrid approach:
●
Used descriptions for semantic context
●
Used keywords for precision
●
Combined both during re-ranking
• However
●
I’m storing two embeddings doubles vector storage (2 × 2048 dimensions per image)
●
For very large libraries, consider using only one – either concatenated or choose the one that performs better on your
validation set
46 Copyright © 2026, Oracle and/or its affiliates
Description vs Keywords
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Storing Images in MySQL
• Advantages:
●
Simpler architecture
●
Centralized governance
●
Easier transactional consistency
• Disadvantages:
●
Larger database size increases buffer pool pressure
●
Backup overhead
●
Potentially performance impact (queries, replication, …)
47 Copyright © 2026, Oracle and/or its affiliates
Storing Images in the Database vs Object Storage
Storing Images in Object Storage
• Advantages:
●
Better scalability
●
Lower storage costs
●
Better CDN integration
• Disadvantages:
●
More complex architecture
●
Additional security management
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Recommended Hybrid Strategy
• A practical enterprise approach is often:
●
Store thumbnails (small, e.g., 512×512) as base64 in MySQL (should be sufficient for vision model analysis)
●
Store full-resolution images in object storage for later retrieval or user download
• This enables:
●
Fast previews
●
Efficient semantic retrieval
●
Scalable storage architecture
48 Copyright © 2026, Oracle and/or its affiliates
Storing Images in the Database vs Object Storage
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Semantic quality heavily depends on prompts
• Poor prompts generate:
●
Vague descriptions
●
Weak keywords
●
Low-quality embeddings
• Well-designed prompts improve:
●
Retrieval quality
●
Explainability
●
Evaluation consistency
Prompt engineering becomes a critical part of semantic architecture design!
49 Copyright © 2026, Oracle and/or its affiliates
Prompt Engineering Matters
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Conclusion
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• MySQL HeatWave GenAI significantly simplifies the implementation of semantic image
understanding systems
• By combining:
●
Vision-language models
●
Text embeddings
●
Vector search
●
SQL-native AI routines
• By consolidating multimodal AI directly within MySQL HeatWave, organizations can deploy
sophisticated vision systems while substantially lowering their Total Cost of Ownership.
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Wrap-up
MySQL HeatWave enables architects & developers to build AI systems that remain understandable, debuggable, & governable
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52
Merci!
Q&R
Olivier Dasini
MySQL AI & Analytics Solutions Architect @ Oracle
[email protected]
Blogs : www.dasini.net/blog/en
: www.dasini.net/blog/fr
Linkedin : www.linkedin.com/in/olivier-dasini
Slides : https://speakerdeck.com/freshdaz
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Follow us on Social Media
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55
Discovering MySQL Database Service
●
Discovering MySQL Database Service – Episode 1 – Introduction
– http://dasini.net/blog/2021/08/03/discovering-mysql-database-service-episode-1-introduction/
●
Discovering MySQL Database Service – Episode 2 – Create a compartment
– http://dasini.net/blog/2021/08/10/discovering-mysql-database-service-episode-2-create-a-compartment/
●
Discovering MySQL Database Service – Episode 3 – Create a Virtual Cloud Network
– http://dasini.net/blog/2021/08/17/discovering-mysql-database-service-episode-3-create-a-virtual-cloud-network/
●
Discovering MySQL Database Service – Episode 4 – Dump your MySQL data into an Object Storage bucket
– http://dasini.net/blog/2021/08/24/discovering-mysql-database-service-episode-4-dump-your-mysql-data-into-an-object-storage-bucket/
●
Discovering MySQL Database Service – Episode 5 – Create a MySQL DB system from a MySQL Shell dump
– http://dasini.net/blog/2021/08/31/discovering-mysql-database-service-episode-5-create-a-mysql-db-system-from-a-mysql-shell-dump/
●
Discovering MySQL Database Service – Episode 6 – Update the Private Subnet Security List
– http://dasini.net/blog/2021/09/07/discovering-mysql-database-service-episode-6-update-the-private-subnet-security-list/
●
Discovering MySQL Database Service – Episode 7 – Use a Bastion SSH port forwarding session
– http://dasini.net/blog/2021/09/14/discovering-mysql-database-service-episode-7-use-a-bastion-ssh-port-forwarding-session/
●
Discovering MySQL Database Service – Episode 8 – Connect to MySQL Database Service Using MySQL Shell
– http://dasini.net/blog/2021/09/21/discovering-mysql-database-service-episode-8-connect-to-mysql-database-service-using-mysql-shell/
●
Discovering MySQL Database Service – Episode 9 – Connect to MySQL Database Service Using MySQL Workbench
– http://dasini.net/blog/2021/09/28/discovering-mysql-database-service-episode-9-connect-to-mysql-database-service-using-mysql-workbench/
●
Discovering MySQL Database Service – Episode 10 – Connect to MySQL Database Service Using OCI Cloud Shell
– http://dasini.net/blog/2021/10/05/discovering-mysql-database-service-episode-10-connect-to-mysql-database-service-using-oci-cloud-shell/
●
...
Tutorials where I show you, step by step, how to use MDS and some other OCI services
Tutorials where I show you, step by step, how to use MDS and some other OCI services
http://dasini.net/blog/en
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Oracle Live Labs
Self-paced learning
MySQL HeatWave Live Labs
56