{"id":7566,"date":"2025-04-08T08:50:32","date_gmt":"2025-04-08T07:50:32","guid":{"rendered":"https:\/\/dasini.net\/blog\/?p=7566"},"modified":"2025-04-15T09:28:13","modified_gmt":"2025-04-15T08:28:13","slug":"build-an-ai-powered-search-engine-with-heatwave-genai-part-2","status":"publish","type":"post","link":"https:\/\/dasini.net\/blog\/2025\/04\/08\/build-an-ai-powered-search-engine-with-heatwave-genai-part-2\/","title":{"rendered":"Build an AI-Powered Search Engine with HeatWave GenAI (part 2)"},"content":{"rendered":"\n

In Build an AI-Powered Search Engine with HeatWave GenAI (part 1)<\/a>, we explored how to build an AI-powered search engine using HeatWave GenAI. We highlighted the advantages of AI-driven semantic search over traditional SQL-based methods and provided a detailed guide on generating embeddings and conducting similarity searches. These techniques enhance the retrieval of relevant articles, improving the user\u2019s ability to find information efficiently.<\/p>\n\n\n\n

In this second part, we will explore how to enhance the relevance of our answers using reranking techniques<\/strong>. Next, we will further refine our results by instructing the model to generate embeddings based on article summaries. All these steps will be performed within HeatWave, leveraging its capability to write Stored Programs in JavaScript<\/a><\/strong>.<\/p>\n\n\n\n

<\/div>\n\n\n\n

I’am using HeatWave 9.2.1:<\/p>\n\n\n\n

SELECT version();\n+-------------+\n| version()   |\n+-------------+\n| 9.2.1-cloud |\n+-------------+<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
In the previous episode<\/a><\/h2>\n\n\n\n
if you remember, in example 3.1<\/a>, we saw how to run a similarity search on title & excerpt (post_title_embedding & post_excerpt_embedding), using an elegant CTE:<\/p>\n\n\n\n
SQL >\n-- Ex 3.1 Similarity search on title & excerpt (post_title_embedding & post_excerpt_embedding)\n\nWITH distances AS (\n    SELECT\n        ID, \n        post_title,\n        post_excerpt,\n        (\n          DISTANCE(post_title_embedding, @searchItemEmbedding, 'COSINE') + \n          DISTANCE(post_excerpt_embedding, @searchItemEmbedding, 'COSINE')\n        ) \/ 2 AS avg_distance\n    FROM WP_embeddings.wp_posts_embeddings_minilm\n)\nSELECT *\nFROM distances\nORDER BY avg_distance\nLIMIT 5\\G\n*************************** 1. row ***************************\n          ID: 1234\n  post_title: HeatWave GenAI: Your AI-Powered Content Creation Partner\npost_excerpt: Generative artificial intelligence (GenAI) is reshaping the content creation landscape. By training on vast datasets, these \"intelligent\" systems can produce new, human-quality content across a multitude of domains.\n\nOracle's HeatWave GenAI (starting with version 9.0.1) is at the forefront of this revolution, offering an integrated platform that combines in-database large language models (LLMs), vector stores, and scale-out vector processing to streamline content generation.\nThis article explores how HeatWave GenAI is empowering businesses to produce high-quality content rapidly and effectively, making it an indispensable tool for industries demanding speed, accuracy, and security.\navg_distance: 0.5131600499153137\n...<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
This result can be further improved using reranking techniques. 
Reranking involves reordering or refining an initial set of retrieved documents to enhance their relevance to a user\u2019s query. This step is essential for optimizing search quality, often leading to a significant boost in the relevance of the retrieved information.<\/p>\n\n\n\n
<\/div>\n\n\n\n
Reranking<\/h2>\n\n\n\n
Here we setting up the same context (ie embedding model, natural language query encoding and vector similarity operations) than in part 1<\/a>:<\/p>\n\n\n\n
SQL > \n-- Set variables\nSET @embeddOptions = '{\"model_id\": \"minilm\"}';\nSET @searchItem = \"Generative artificial intelligence\";\n\n-- Encode the query using the embedding model\nSELECT sys.ML_EMBED_ROW(@searchItem, @embeddOptions) into @searchItemEmbedding;\n<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
The retrieved results is now sorted using weights on title and excerpt distances:<\/p>\n\n\n\n
-- Ex 3.2 Similarity search on title & excerpt (post_title_embedding & post_excerpt_embedding) with weights\n\nWITH initial_results AS (\n    SELECT\n        ID, \n        post_title,\n        post_excerpt,\n        DISTANCE(post_title_embedding, @searchItemEmbedding, 'COSINE') AS title_distance, \n        DISTANCE(post_excerpt_embedding, @searchItemEmbedding, 'COSINE') AS excerpt_distance, \n    guid \n    FROM WP_embeddings.wp_posts_embeddings_minilm \n    ORDER BY title_distance + excerpt_distance  -- Simple combination\n    LIMIT 15 -- Retrieve a larger initial set\n),\nreranked_results AS (\n    SELECT\n        ID,\n        post_title,\n        post_excerpt,\n        (0.3 * title_distance + 0.7 * excerpt_distance) AS combined_distance,   -- Weighted combination\n    guid \n    FROM initial_results\n)\nSELECT post_title, post_excerpt, combined_distance, guid\nFROM reranked_results\nORDER BY combined_distance ASC\nLIMIT 5\\G\n\n*************************** 1. row ***************************\n       post_title: HeatWave GenAI: Sentiment Analysis Made Easy-Peasy\n     post_excerpt: This new AI tech, called generative AI (or GenAI), can dive deep into what people are saying and tell us if they\u2019re feeling positive, negative, or neutral.\nLet\u2019s see how HeatWave GenAI, can help you to enhance your understanding of customer sentiment, improve decision-making, and drive business success.\ncombined_distance: 0.49683985114097595\n             guid: https:\/\/dasini.net\/blog\/?p=3456\n*************************** 2. row ***************************\n       post_title: HeatWave GenAI: Your AI-Powered Content Creation Partner\n     post_excerpt: Generative artificial intelligence (GenAI) is reshaping the content creation landscape. By training on vast datasets, these \"intelligent\" systems can produce new, human-quality content across a multitude of domains.\n\nOracle's HeatWave GenAI (starting with version 9.0.1) is at the forefront of this revolution, offering an integrated platform that combines in-database large language models (LLMs), vector stores, and scale-out vector processing to streamline content generation.\nThis article explores how HeatWave GenAI is empowering businesses to produce high-quality content rapidly and effectively, making it an indispensable tool for industries demanding speed, accuracy, and security.\ncombined_distance: 0.4994780898094177\n             guid: https:\/\/dasini.net\/blog\/?p=1234\n*************************** 3. row ***************************\n       post_title: Simplifying AI Development: A Practical Guide to HeatWave GenAI\u2019s RAG &amp; Vector Store Features\n     post_excerpt: This tutorial explores HeatWave GenAI, a cloud service that simplifies interacting with unstructured data using natural language. It combines large language models, vector stores, and SQL queries to enable tasks like content generation, chatbot, and retrieval-augmented generation (RAG). The focus is on RAG and how HeatWave GenAI\u2019s architecture helps users gain insights from their data.\ncombined_distance: 0.6582363367080688\n             guid: https:\/\/dasini.net\/blog\/?p=2345\n*************************** 4. row ***************************\n       post_title: Webinar - Apprentissage automatique avec MySQL HeatWave\n     post_excerpt: HeatWave Machine Learning (ML) inclut tout ce dont les utilisateurs ont besoin pour cr\u00e9er, former, d\u00e9ployer et expliquer des mod\u00e8les d\u2019apprentissage automatique dans MySQL HeatWave, sans co\u00fbt suppl\u00e9mentaire.\n\nDans ce webinaire vous apprendrez...\ncombined_distance: 0.694593733549118\n             guid: https:\/\/dasini.net\/blog\/?p=6789\n*************************** 5. row ***************************\n       post_title: Building an Interactive LLM Chatbot with  HeatWave Using Python\n     post_excerpt: AI-powered applications require robust and scalable database solutions to manage and process large amounts of data efficiently. HeatWave is an excellent choice for such applications, providing high-performance OLTP, analytics, machine learning and generative artificial intelligence capabilities.\n\nIn this article, we will explore a Python 3 script that connects to an HeatWave instance and enables users to interact with different large language models (LLMs) dynamically.\ncombined_distance: 0.7135995388031006\n             guid: https:\/\/dasini.net\/blog\/?p=5678<\/code><\/pre>\n\n\n\n
In the example above, the excerpt is given more than twice the weight of the title (0.7 vs 0.3), based on the assumption that the excerpt holds more relevant information in this context. 
Depending on your use case, you may want to fine-tune these weights, as adjusting them can significantly improve the quality of search results.<\/p>\n\n\n\n
Now, let\u2019s explore how we can further refine our results by leveraging article summaries along with JavaScript-based stored procedures and functions.<\/p>\n\n\n\n
<\/div>\n\n\n\n
A Javascript, stored procedure & AI story<\/h2>\n\n\n\n
When searching through articles, relying solely on the title and excerpt may not yield the most relevant results. 
What if we used a summary instead? 
This approach can strike an excellent balance between relevance and implementation simplicity. With HeatWave GenAI, the entire workflow can be handled directly within the database using SQL and JavaScript-based stored procedures.<\/p>\n\n\n\n
In WordPress, article content is stored as HTML in the post_content<\/em><\/code> column. To make it suitable for processing by a large language model (LLM), this content must first be sanitized \u2014 that is, all HTML tags need to be removed, as they are not meaningful to the LLM.<\/p>\n\n\n\n
Table preparation<\/h3>\n\n\n\n
As a preparation step, I added a new column (post_content_text longtext NOT NULL<\/code><\/em>) to the wp_posts_embeddings_minilm<\/code><\/em> table that we have used in part 1<\/a>.<\/p>\n\n\n\n
ALTER TABLE wp_posts_embeddings_minilm ADD COLUMN post_content_text longtext NOT NULL;<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
The structure of the table is now:<\/p>\n\n\n\n
CREATE TABLE `wp_posts_embeddings_minilm` (\n  `ID` bigint unsigned NOT NULL AUTO_INCREMENT,\n  `post_title` text NOT NULL,\n  `post_excerpt` text NOT NULL,\n  `guid` varchar(255) NOT NULL DEFAULT '',\n  `post_title_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  `post_excerpt_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  `post_content_text` longtext NOT NULL,\n  PRIMARY KEY (`ID`),\n  KEY `post_title` (`post_title`(255)),\n  KEY `post_excerpt` (`post_excerpt`(255)),\n  KEY `guid` (`guid`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci<\/code><\/pre>\n\n\n\n
This new column \u2014 post_content_text<\/code><\/em> \u2014 will be populated with the sanitized content of post_content<\/code><\/em>.<\/p>\n\n\n\n
For example, original HTML in post_content<\/em>:<\/p>\n\n\n\n
<!-- wp:paragraph -->
<p>HeatWave GenAI brings LLMs directly into your database, enabling powerful AI capabilities and natural language processing.<\/p>
<!-- \/wp:paragraph --><\/code><\/pre>\n\n\n\n
Should be stored in post_content_text<\/code><\/em> as:<\/p>\n\n\n\n
HeatWave GenAI brings LLMs directly into your database, enabling powerful AI capabilities and natural language processing<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
Strip HTML tags with a javascript stored routine <\/h2>\n\n\n\n
This could be done in Javascript, inside HeatWave, as a stored program<\/a>. Isn’t it magnificent?
HeatWave supports stored routines written in JavaScript, since version 9.0.<\/p>\n\n\n\n
A simple implementation could be the following:<\/p>\n\n\n\n
CREATE FUNCTION sp.stripHtmlTags(htmlString LONGTEXT) RETURNS LONGTEXT NO SQL LANGUAGE JAVASCRIPT AS\n$$\n    if (!htmlString) {\n        return \"\";\n    }\n\n    \/\/ Replace HTML tags with a space\n    return htmlString\n        .replace(\/<[^>]+>\/g, \" \")  \/\/ Replace all tags with a space\n        .replace(\/\\s+\/g, \" \")      \/\/ Replace multiple spaces with a single space\n        .trim(); \n$$\n;<\/code><\/pre>\n\n\n\n
Please bear in mind that I\u2019m not a developer<\/strong>, so this code is provided for illustrative purposes only<\/strong>. It may contain errors or limitations. Please use it at your own risk<\/strong> and adapt it to your specific needs (also feel free to share back).<\/p>\n\n\n\n
Let’s see if it works:<\/p>\n\n\n\n
SELECT sp.stripHtmlTags('<!-- wp:paragraph --><p>HeatWave GenAI brings LLMs directly into your database, enabling powerful AI capabilities and natural language processing.<\/p><!-- \/wp:paragraph -->') ;\n+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| sp.stripHtmlTags('<!-- wp:paragraph --><p>HeatWave GenAI brings LLMs directly into your database, enabling powerful AI capabilities and natural language processing.<\/p><!-- \/wp:paragraph -->') |\n+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+\n| HeatWave GenAI brings LLMs directly into your database, enabling powerful AI capabilities and natural language processing.                                                                       |\n+--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------+<\/code><\/pre>\n\n\n\n
Looks like this Javascript stored function is doing the job. <\/p>\n\n\n\n
We can now uses it to sanitized all the articles.<\/p>\n\n\n\n
UPDATE wp_posts_embeddings_minilm \n    INNER JOIN wp_posts USING (ID) \n    SET post_content_text = sp.stripHtmlTags(post_content) \n    WHERE post_status = 'publish' \n       AND post_type = 'post' ; <\/code><\/pre>\n\n\n\n
Now all the published posts have a sanitized text only version in the database. We can now uses it to generate article summaries.<\/p>\n\n\n\n
<\/div>\n\n\n\n
Generating summaries with the JavaScript HeatWave GenAI API <\/h2>\n\n\n\n
HeatWave offers a JavaScript API that enables seamless integration with HeatWave GenAI, allowing you to perform natural language searches powered by LLMs. You can find more details in the official documentation<\/a>.<\/p>\n\n\n\n
To generate summaries for all my articles, I\u2019ll create a stored procedure using the  ml.generate<\/em><\/code><\/strong><\/a> method. This method supports two modes: single invocation and batch processing. While single invocation is ideal for handling new articles individually, we\u2019ll focus on the batch mode here to process all existing articles efficiently:<\/p>\n\n\n\n
SQL > \nCREATE PROCEDURE sp.summarizePostBatch() LANGUAGE JAVASCRIPT AS\n$$\n    let schema = session.getSchema(\"wordpress\");\n    let table = schema.getTable(\"wp_posts_embeddings_minilm\");\n\n    ml.generate(table, \"post_content_text\", \"post_summary_json\", {model_id: \"mistral-7b-instruct-v1\", task: \"summarization\"});\n$$\n;<\/code><\/pre>\n\n\n\n
ml.generate<\/code><\/em><\/a> loads the model (mistral-7b-instruct-v1), generates a response (article summary) inside the post_summary_json<\/code><\/em> column (automatically created), based on the prompt (article) from the post_content_text column and returns the response (a summary).<\/p>\n\n\n\n
Then call the stored procedure:<\/p>\n\n\n\n
CALL sp.summarizePostBatch();\n\n Query OK, 0 rows affected (1 hour 2 min 16.3605 sec)<\/code><\/pre>\n\n\n\n
The process successfully created a new JSON column named post_summary_json<\/code><\/em>:<\/p>\n\n\n\n
SQL > SHOW CREATE TABLE wp_posts_embeddings_minilm;\n\nCREATE TABLE `wp_posts_embeddings_minilm` (\n  `ID` bigint unsigned NOT NULL AUTO_INCREMENT,\n  `post_title` text NOT NULL,\n  `post_excerpt` text NOT NULL,\n  `guid` varchar(255) NOT NULL DEFAULT '',\n  `post_title_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  `post_excerpt_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  `post_content_text` longtext NOT NULL,\n  `post_summary_json` json DEFAULT NULL,\n  PRIMARY KEY (`ID`),\n  KEY `post_title` (`post_title`(255)),\n  KEY `post_excerpt` (`post_excerpt`(255)),\n  KEY `guid` (`guid`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci<\/code><\/pre>\n\n\n\n
Using the HeatWave MySQL JSON functions<\/a>, we can check the content of this new column:<\/p>\n\n\n\n
SELECT LEFT(post_summary_json->>\"$.text\", 100) AS json FROM wp_posts_embeddings_minilm WHERE ID = 1234\\G\n*************************** 1. row ***************************\njson: Generative artificial intelligence (GenAI) is reshaping the content creation landscape. By training <\/code><\/pre>\n\n\n\n
Now let’s see how to create embeddings for all the summaries<\/p>\n\n\n\n
<\/div>\n\n\n\n
Create the embeddings<\/h2>\n\n\n\n
We have seen in Build an AI-Powered Search Engine with HeatWave GenAI (part 1)<\/a> how to create embeddings with the minilm<\/code><\/em> model, using sys.ML_EMBED_TABLE<\/strong><\/a> routine.
We\u2019ll follow the same process, but this time using JavaScript stored routines. Specifically, we\u2019ll use the ml.embed<\/a><\/em><\/strong> <\/code>method.<\/p>\n\n\n\n
Before implementing the routine, we need to do some modification on the table. The reason is that in HeatWave 9.2.1, the table columns must be in one of the following format: varchar, tinytext, text, mediumtext and longtext. If it is not the case you’ll trigger the ERROR: 1644 (45000): ML006093: Type of 'post_summary_json' must be in [\"varchar\", \"tinytext\", \"text\", \"mediumtext\", \"longtext\"]<\/code><\/strong><\/em>
Quite explicit though!<\/p>\n\n\n\n
We’ll transfer the content of post_summary_json<\/code><\/em> into a text column named post_summary<\/code><\/em>:<\/p>\n\n\n\n
SQL >\nALTER TABLE wp_posts_embeddings_minilm ADD COLUMN post_summary text;\n\nUPDATE wp_posts_embeddings_minilm SET post_summary =  post_summary_json->>\"$.text\"; \n<\/code><\/pre>\n\n\n\n
And finally to avoid error ERROR: 1644 (45000): ML006093<\/em>, we must drop post_summary_json<\/code><\/em>:<\/p>\n\n\n\n
ALTER TABLE wp_posts_embeddings_minilm DROP COLUMN post_summary_json;<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
ml.embed<\/code><\/a><\/em><\/strong>  also supports two variants, one for a single invocation, and one for batch processing.<\/p>\n\n\n\n
Single invocation of ml.embed  <\/h3>\n\n\n\n
The goal is to create a stored procedure that encodes a summarized article into a vector embedding using the minilm<\/code><\/em> embedding model:<\/p>\n\n\n\n
SQL >\nCREATE PROCEDURE sp.createEmbeddings(IN text2embed LONGTEXT, OUT vect VECTOR) LANGUAGE JAVASCRIPT AS\n$$\n    let embedding = ml.embed(text2embed, {model_id: \"minilm\"});\n    vect = embedding;\n$$\n;<\/code><\/pre>\n\n\n\n
The input parameter is the summarized article:<\/p>\n\n\n\n
SQL >\nCALL sp.createEmbeddings(\" This article explores the integration of HeatWave GenAI with Python for AI-driven applications. It provides a step-by-step guide to building a simple chatbot system that interacts with HeatWave using its in-database LLMs and external APIs from OCI Generative AI Service. The script demonstrates how to establish a connection to the HeatWave MySQL database, load and manage multiple LLMs within HeatWave, allow users to select their preferred model dynamically, facilitate chatbot interactions using HeatWave Chat, and retrieve and manage chat options. The article also discusses the benefits of using HeatWave GenAI for AI-driven applications, including its modular and scalable design, dynamic LLM selection, and powerful capabilities in AI.\", @myVect);<\/code><\/pre>\n\n\n\n
The output parameter \u2014 @myVect \u2014 contains the embedding:<\/p>\n\n\n\n
SQL >\nSELECT @myVect\\G\n*************************** 1. row ***************************\n@myVect: ?RK?p?????!<Q?c?\n?????h?[???;??qV@=?mW???F?_?M=??<??$=EN&??Xv?6?`=E????P?V^v????=Dw?<Q?f?.?x?\n                                                                            ?N?????<?1=\n                                                                                        P-???=??O=\n                                                                                                  ?A=21?=Ez????@??1j??*]?T??K????o?V?\u017d?l\n??+??=W??=3??????                                                                                                                       >?<iyx<?????<j????<???;z?_?H<??,=?'@?j?<N??=???M?C?`Wb?Qr?U?c???V<DmT<{N6=\n                ????T??$?<?u\n                            ?v$?=g?=?k???D?\"??6?\\??\n<\/code><\/pre>\n\n\n\n
A more human representation (or not) is possible using FROM_VECTOR<\/em><\/code><\/a>:<\/p>\n\n\n\n
SQL >\nSELECT FROM_VECTOR(@myVect)\\G\n*************************** 1. row ***************************\nFROM_VECTOR(@myVect): [-4.96396e-02,-8.32623e-02,9.86042e-03,-1.38911e-02,-1.98374e-02,-5.67944e-02,-3.55247e-02,2.43442e-03,-7.37070e-03,4.69574e-02,-5.25947e-02,-4.85626e-02,5.02704e-02,9.01929e-03,4.01000e-02,-2.53762e-03,-1.50358e-02,5.48794e-02,-3.82407e-02,-5.09363e-02,-6.01486e-02,6.62680e-02,1.97102e-02,-3.52313e-03,-6.07311e-02,-5.04761e-02,-2.28147e-02,2.70393e-02,3.71569e-02,-2.64454e-03,1.19642e-01,5.07343e-02,4.72823e-02,6.94298e-02,-1.87351e-02,-1.17510e-02,-5.71763e-02,-5.39956e-02,-1.96540e-02,-2.69486e-02,-3.46894e-02,-3.81926e-02,-9.65525e-02,3.40393e-02,2.23684e-02,1.51657e-02,-1.00843e-01,2.82697e-02,-2.81484e-02,2.06150e-02,-8.44855e-02,-9.88659e-02,1.22386e-02,4.21836e-02,-2.93204e-03,7.91035e-03,7.20659e-02,-8.70026e-03,-4.77326e-02,-1.38148e-02,-2.58113e-02,-4.66768e-03,-9.27910e-03,1.30998e-02,1.29655e-02,4.45084e-02,-2.69566e-02,7.25501e-02,7.15968e-02,-1.46195e-01,-6.30012e-02,-7.91235e-02,-5.19264e-02,2.40655e-02,-3.42919e-02,7.72180e-02,7.22950e-02,-7.19829e-02,-7.17984e-03,-2.89341e-02,-1.34624e-02,-4.61435e-02,1.58105e-02,7.25958e-02,-2.99175e-02,-7.80187e-02,-5.74389e-03,-2.57604e-02,7.18643e-02,4.47061e-02,2.45664e-02,-1.13831e-02,4.58998e-02,-1.19727e-02,5.77563e-02,9.52516e-02,3.84456e-02,-3.92485e-02,-3.90266e-02,2.87119e-02,-7.21781e-02,4.85492e-02,-8.24129e-02,-2.01809e-02,-1.77014e-02,6.55753e-02,-4.22585e-02,-6.67007e-02,6.90129e-02,-2.04837e-02,-5.49840e-02,-8.76547e-03,4.42857e-02,5.63733e-02,7.53812e-02,-4.63425e-02,1.82254e-02,1.18100e-02,-7.03449e-02,7.29235e-02,2.41146e-02,-3.35154e-02,-3.56129e-02,7.28611e-02,6.86842e-02,-5.54628e-02,-1.93234e-02,-1.89223e-03,9.01134e-02,6.44596e-02,2.64851e-02,9.06968e-02,4.06748e-02,5.16320e-02,3.33822e-02,5.54104e-02,-3.21358e-02,-6.66219e-02,2.00648e-02,1.09276e-01,-6.25206e-02,4.89027e-02,-4.17487e-02,-5.67389e-02,7.44691e-03,-4.42884e-02,3.18385e-02,-1.98049e-02,-7.42827e-02,-6.14671e-02,7.44849e-02,3.02982e-02,2.98403e-02,7.55829e-02,1.20768e-01,1.26906e-02,1.04550e-01,6.59261e-02,1.30538e-02,-1.39226e-02,-3.02682e-02,7.50245e-02,1.57195e-02,-2.63368e-03,-9.18113e-02,-6.67840e-02,-2.06254e-02,7.86020e-02,-2.10694e-02,1.30604e-03,5.31497e-02,5.42747e-02,-1.07493e-01,-4.50561e-02,-1.25854e-01,-2.63633e-02,2.99242e-02,-2.35737e-02,-1.81825e-02,2.76637e-02,-4.12063e-02,4.46310e-02,-1.92102e-02,-8.92773e-02,4.45359e-02,-1.11110e-02,-5.22624e-02,-2.45076e-02,-1.96262e-02,-1.98535e-02,8.15337e-02,7.87625e-02,5.73686e-02,1.47477e-02,6.91806e-03,1.53566e-02,7.33980e-03,1.07936e-02,1.51880e-03,2.81434e-02,-6.74229e-02,2.64910e-02,-1.99371e-02,5.59245e-02,2.73276e-02,-1.19287e-01,8.55642e-03,6.91925e-02,1.21589e-02,-4.05408e-02,-7.55252e-02,-3.61071e-02,2.72453e-02,-8.27803e-02,3.18795e-02,-5.05173e-03,-1.99760e-02,3.63341e-02,-6.50054e-02,-8.56357e-03,5.15133e-02,6.26782e-02,-4.57907e-02,-1.35400e-33,-3.12649e-02,1.42193e-02,-6.76464e-02,2.92021e-02,-5.87203e-02,-1.18268e-01,6.15763e-02,4.44978e-02,4.98419e-02,-1.72100e-02,-1.13092e-01,6.02123e-02,1.55891e-02,-5.68059e-02,-1.35132e-02,-4.19842e-02,-2.60558e-02,2.80308e-03,1.32598e-01,1.07273e-01,-3.47401e-02,4.75040e-02,-7.63112e-02,-2.59081e-02,2.11842e-02,2.25822e-02,-1.98663e-02,-1.04105e-02,-6.49599e-02,1.09372e-03,-5.17167e-02,1.38617e-02,-6.26832e-02,1.26345e-02,-1.71078e-02,5.77133e-02,-5.38819e-03,-6.03902e-02,1.65896e-02,-8.53037e-02,6.79781e-02,-2.80634e-02,-8.47060e-02,5.93425e-02,-1.46708e-02,4.85509e-02,-1.12832e-01,6.27912e-03,-6.16615e-03,-2.94036e-02,3.11167e-02,2.30855e-03,6.65979e-02,-6.83186e-02,4.35274e-02,-1.24276e-01,1.87970e-02,-3.84284e-02,-1.53174e-02,-3.97068e-02,1.07243e-02,-3.07626e-02,2.31247e-02,-3.29633e-02,-6.20930e-02,1.08394e-01,7.25451e-02,7.60207e-02,-4.54095e-02,-8.78456e-02,4.84518e-02,-4.07884e-02,7.50114e-02,1.14496e-02,2.41463e-02,5.81300e-02,-1.30148e-02,1.39014e-02,-1.40898e-02,-1.72861e-02,-3.93898e-02,-2.61493e-02,2.51394e-02,-2.53317e-02,-9.82916e-04,-2.21086e-02,5.22690e-02,3.53555e-02,6.22829e-03,3.66027e-02,2.17727e-03,6.67700e-02,-2.32061e-02,-1.69419e-03,-9.06145e-03,6.34304e-32,-1.78320e-02,-2.15657e-02,-6.16218e-03,5.44959e-02,1.88435e-02,-1.29032e-02,-4.80581e-02,4.21709e-02,1.50341e-02,4.37988e-02,8.35770e-02,-8.84255e-02,7.72715e-02,5.24237e-02,-3.56883e-02,-8.12593e-03,-3.41413e-02,-1.69388e-02,3.47573e-02,-1.21286e-01,3.22201e-02,3.63997e-02,-2.33719e-02,-2.07887e-02,-8.05931e-02,-7.69887e-02,-4.39181e-02,-9.76368e-03,-1.48867e-03,3.44279e-02,-3.62778e-02,-7.50277e-03,4.86021e-02,5.18756e-02,1.85040e-02,-2.77495e-03,-3.53111e-02,5.16714e-02,-2.28214e-02,-6.10510e-02,2.86529e-02,1.37693e-01,8.80472e-03,-5.79396e-02,3.57790e-02,1.58254e-03,3.59281e-02,-9.29721e-02,1.09798e-02,1.83488e-02,-1.42538e-03,-3.40717e-02,3.98733e-02,-1.05952e-02,3.72981e-02,-5.85627e-02,3.13814e-02,-5.80935e-02,-3.15143e-03,6.94674e-02,-6.51819e-03,6.39965e-02,3.89966e-02,-7.56700e-02]\n\n<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
Batch processing of ml.embed  <\/h3>\n\n\n\n
To generate embeddings for all current posts at once, batch processing is the most efficient approach. Let\u2019s create a new stored procedure in JavaScript:<\/p>\n\n\n\n
SQL >\nCREATE PROCEDURE sp.createEmbeddingsBatch() LANGUAGE JAVASCRIPT AS\n$$\n    let schema = session.getSchema(\"wordpress\");\n    let table = schema.getTable(\"wp_posts_embeddings_minilm\");\n\n    ml.embed(table, \"post_summary\", \"post_summary_embedding\", {model_id: \"minilm\"});\n$$\n;<\/code><\/pre>\n\n\n\n
ml.embed<\/code><\/em><\/a> loads the model (minilm<\/code><\/em>), generates an embedding inside the post_summary_embedding<\/em><\/code> column (automatically created), based on the post summaries from the post_summary<\/em><\/code> column.<\/p>\n\n\n\n
Then run the stored procedure:<\/p>\n\n\n\n
SQL > \nCALL sp.createEmbeddingsBatch();\nQuery OK, 0 rows affected (32.9131 sec)<\/code><\/pre>\n\n\n\n
The process successfully created a new VECTOR column named post_summary_embedding<\/code><\/em>:<\/p>\n\n\n\n
SQL > SHOW CREATE TABLE wp_posts_embeddings_minilm;\n\nCREATE TABLE `wp_posts_embeddings_minilm` (\n  `ID` bigint unsigned NOT NULL AUTO_INCREMENT,\n  `post_title` text NOT NULL,\n  `post_excerpt` text NOT NULL,\n  `guid` varchar(255) NOT NULL DEFAULT '',\n  `post_title_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  `post_excerpt_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  `post_content_text` longtext NOT NULL,\n  `post_summary` text,\n  `post_summary_embedding` vector(2048) NOT NULL COMMENT 'GENAI_OPTIONS=EMBED_MODEL_ID=minilm',\n  PRIMARY KEY (`ID`),\n  KEY `post_title` (`post_title`(255)),\n  KEY `post_excerpt` (`post_excerpt`(255)),\n  KEY `guid` (`guid`)\n) ENGINE=InnoDB DEFAULT CHARSET=utf8mb4 COLLATE=utf8mb4_0900_ai_ci<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
Query Encoding and Vector Similarity Operations<\/h2>\n\n\n\n
The penultimate step is to convert the user\u2019s query into a vector representation that captures its semantic meaning. This process, known as query encoding, transforms text into numerical embeddings. Once encoded, we will be able to perform a similarity search to find the most relevant results by comparing the query\u2019s embedding with precomputed vectors from HeatWave.<\/p>\n\n\n\n
Encode the query into a vector embedding<\/h3>\n\n\n\n
Like we have seen in part1<\/a>, to generate a vector embedding for the query, we use the ML_EMBED_ROW<\/a> routine. This function applies the specified embedding model to encode the given text into a vector representation. The routine returns a VECTOR<\/code><\/a><\/code> containing the numerical embedding of the text.<\/p>\n\n\n\n
Using it is straightforward. Let’s define two variables: @searchItem<\/em><\/code> (the text to encode) and @embeddOptions<\/code><\/em> (the embedding model used for encoding):<\/p>\n\n\n\n
SQL >\n-- Set variables\nSET @embeddOptions = '{\"model_id\": \"minilm\"}';\nSET @searchItem = \"Generative artificial intelligence\";\n\n-- Encode the query using the embedding model\nSELECT sys.ML_EMBED_ROW(@searchItem, @embeddOptions) into @searchItemEmbedding;<\/code><\/pre>\n\n\n\n
You can print the vector using the following query;<\/p>\n\n\n\n
SELECT FROM_VECTOR(vect) \nFROM (\n      SELECT sys.ML_EMBED_ROW(@searchItem, @embeddOptions) AS vect\n     ) AS dt;\n[-6.21515e-02,1.61460e-02,1.25987e-02,-1.98096e-02,... (truncated)<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
Similarity search<\/h3>\n\n\n\n
To retrieve relevant blog content, we perform vector similarity calculations using the DISTANCE<\/strong><\/a><\/code> function. This function computes the distance between two vectors using COSINE<\/strong><\/code>, DOT<\/strong><\/code>, or EUCLIDEAN<\/strong><\/code> distance metrics. 
Here, the two vectors being compared are the encoded query (@searchItemEmbedding<\/code>) and the precomputed embeddings stored in the wp_posts_embeddings_minilm<\/em><\/code> table (post_summary_embedding<\/code><\/em>)<\/p>\n\n\n\n
A cosine similarity search<\/strong> for article summaries can be conducted using:<\/p>\n\n\n\n
-- Ex 4.1. Similarity search only on the post summaries (post_summary_embedding)\n\nWITH distances AS (\n    SELECT\n        ID, \n        post_title,\n        post_excerpt,\n        DISTANCE(@searchItemEmbedding, post_summary_embedding, 'COSINE') AS min_distance\n    FROM WP_embeddings.wp_posts_embeddings_minilm\n)\nSELECT *\nFROM distances\nORDER BY min_distance\nLIMIT 5\\G<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
A cosine similarity search<\/strong> for article titles, excerpts and summaries can be conducted using:<\/p>\n\n\n\n
-- Ex 5.1 Similarity search on title, excerpt & post summary (post_title_embedding, post_excerpt_embedding & post_summary_embedding)\n\nWITH distances AS (\n    SELECT\n        post_title,\n        post_excerpt,\n        (\n          DISTANCE(post_title_embedding, @searchItemEmbedding, 'COSINE') + \n          DISTANCE(post_excerpt_embedding, @searchItemEmbedding, 'COSINE') + \n          DISTANCE(post_summary_embedding, @searchItemEmbedding, 'COSINE')\n        ) \/ 3 AS avg_distance,\n        guid\n    FROM WP_embeddings.wp_posts_embeddings_minilm\n)\nSELECT *\nFROM distances\nORDER BY avg_distance\nLIMIT 5\\G<\/code><\/pre>\n\n\n\n
<\/div>\n\n\n\n
Finally, you can (try to) improve the results using a reranking technique:<\/p>\n\n\n\n
-- Ex 5.2 Weighted Similarity search on title, excerpt & post summary (post_title_embedding, post_excerpt_embedding & post_summary_embedding)\n\nWITH initial_results AS (\n    SELECT\n        post_title,\n        post_excerpt,\n        DISTANCE(post_title_embedding, @searchItemEmbedding, 'COSINE') AS title_distance, \n        DISTANCE(post_excerpt_embedding, @searchItemEmbedding, 'COSINE') AS excerpt_distance,\n        DISTANCE(post_summary_embedding,  @searchItemEmbedding, 'COSINE') AS summary_distance, \n        guid\n    FROM WP_embeddings.wp_posts_embeddings_minilm \n    ORDER BY title_distance + excerpt_distance + summary_distance ASC -- Simple combination\n    LIMIT 15 -- Retrieve a larger initial set\n),\nreranked_results AS (\n    SELECT\n        ID,\n        post_title,\n        post_excerpt,\n        (0.2 * title_distance + 0.3 * excerpt_distance + 0.5 * summary_distance) AS combined_distance,  -- Weighted combination\n        guid\n    FROM initial_results\n)\nSELECT post_title, post_excerpt, combined_distance, guid\nFROM reranked_results\nORDER BY combined_distance ASC\nLIMIT 5\\G\n\n*************************** 1. row ***************************\n       post_title: HeatWave GenAI: Your AI-Powered Content Creation Partner\n     post_excerpt: Generative artificial intelligence (GenAI) is reshaping the content creation landscape. By training on vast datasets, these \"intelligent\" systems can produce new, human-quality content across a multitude of domains.\n\nOracle's HeatWave GenAI (starting with version 9.0.1) is at the forefront of this revolution, offering an integrated platform that combines in-database large language models (LLMs), vector stores, and scale-out vector processing to streamline content generation.\nThis article explores how HeatWave GenAI is empowering businesses to produce high-quality content rapidly and effectively, making it an indispensable tool for industries demanding speed, accuracy, and security.\ncombined_distance: 0.5093500733375549\n             guid: https:\/\/dasini.net\/blog\/?p=1234\n*************************** 2. row ***************************\n       post_title: Simplifying AI Development: A Practical Guide to HeatWave GenAI\u2019s RAG &amp; Vector Store Features\n     post_excerpt: This tutorial explores HeatWave GenAI, a cloud service that simplifies interacting with unstructured data using natural language. It combines large language models, vector stores, and SQL queries to enable tasks like content generation, chatbot, and retrieval-augmented generation (RAG). The focus is on RAG and how HeatWave GenAI\u2019s architecture helps users gain insights from their data.\ncombined_distance: 0.637738311290741\n             guid: https:\/\/dasini.net\/blog\/?p=2345\n*************************** 3. row ***************************\n       post_title: HeatWave GenAI: Sentiment Analysis Made Easy-Peasy\n     post_excerpt: This new AI tech, called generative AI (or GenAI), can dive deep into what people are saying and tell us if they\u2019re feeling positive, negative, or neutral.\nLet\u2019s see how HeatWave GenAI, can help you to enhance your understanding of customer sentiment, improve decision-making, and drive business success.\ncombined_distance: 0.6417026937007904\n             guid: https:\/\/dasini.net\/blog\/?p=3456\n*************************** 4. row ***************************\n       post_title: Building an Interactive LLM Chatbot with  HeatWave Using Python\n     post_excerpt: AI-powered applications require robust and scalable database solutions to manage and process large amounts of data efficiently. HeatWave is an excellent choice for such applications, providing high-performance OLTP, analytics, machine learning and generative artificial intelligence capabilities.\n\nIn this article, we will explore a Python 3 script that connects to an HeatWave instance and enables users to interact with different large language models (LLMs) dynamically.\ncombined_distance: 0.6545232772827148\n             guid: https:\/\/dasini.net\/blog\/?p=5678\n*************************** 5. row ***************************\n       post_title: Webinar - Apprentissage automatique avec MySQL HeatWave\n     post_excerpt: HeatWave Machine Learning (ML) inclut tout ce dont les utilisateurs ont besoin pour cr\u00e9er, former, d\u00e9ployer et expliquer des mod\u00e8les d\u2019apprentissage automatique dans MySQL HeatWave, sans co\u00fbt suppl\u00e9mentaire.\n\nDans ce webinaire vous apprendrez...\ncombined_distance: 0.7031511843204499\n             guid: https:\/\/dasini.net\/blog\/?p=6789<\/code><\/pre>\n\n\n\n
\"Similarity\n\t\t\t\n\t\t\t\t\n\t\t\t<\/svg>\n\t\t<\/button><\/figure>\n\n\n\n
<\/div>\n\n\n\n
Peroration<\/h2>\n\n\n\n
In this second part of our journey into building an AI-powered search engine with HeatWave GenAI<\/strong>, we explored advanced techniques to refine search relevance. By incorporating reranking strategies and leveraging article summaries for embedding generation, we significantly improved the quality of retrieved results.<\/p>\n\n\n\n
Furthermore, we demonstrated how to harness HeatWave\u2019s support for JavaScript-based stored programs<\/strong> to sanitize content, generate summaries, and compute embeddings \u2014 all within the database. This seamless integration of AI-powered search within HeatWave showcases its potential for efficient, scalable, and intelligent information retrieval.<\/p>\n\n\n\n
With these enhancements, our search engine is now more capable of understanding and delivering relevant results, although  we still can further optimize performance by experimenting with different embedding models, or even integrating additional AI-driven ranking techniques. <\/p>\n\n\n\n
Stay tuned for more insights!<\/p>\n\n\n\n
<\/div>\n\n\n\n
To be continued…<\/em><\/p>\n\n\n\n
<\/div>\n\n\n\n
Follow me on Linkedin<\/a><\/p>\n\n\n\n
Watch my videos on my YouTube channel<\/a> and subscribe<\/a>.<\/p>\n\n\n\n
My Slideshare account<\/a>.<\/p>\n\n\n\n
My Speaker Deck account<\/a>.<\/p>\n\n\n\n
<\/div>\n\n\n\n
Thanks for using HeatWave & MySQL!<\/strong><\/p>\n\n\n\n
<\/div>\n\n\n\n
<\/p>\n","protected":false},"excerpt":{"rendered":"
In this part 2 we focused on enhancing search relevance. We introduced reranking techniques using weighted distances of titles and excerpts to refine initial search results. Then we delved into leveraging article summaries for more effective semantic search, utilizing HeatWave’s capability to execute JavaScript stored procedures for sanitizing HTML content and generating these summaries. Finally, we demonstrated how to create embeddings from these summaries and perform similarity searches, showcasing HeatWave GenAI’s power for advanced information retrieval directly within the database.<\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"footnotes":""},"categories":[1702,1740,1694,339],"tags":[1704,306,1700,1697,1742,1738],"class_list":["post-7566","post","type-post","status-publish","format-standard","hentry","category-ai","category-artificial-intelligence","category-heatwave-en","category-tuto-en","tag-ai","tag-cloud","tag-genai","tag-heatwave-fr-en","tag-javascript","tag-llm"],"aioseo_notices":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"jetpack_shortlink":"https:\/\/wp.me\/p9LfWW-1Y2","jetpack-related-posts":[{"id":7711,"url":"https:\/\/dasini.net\/blog\/2025\/04\/15\/build-an-ai-powered-search-engine-with-heatwave-genai-part-3\/","url_meta":{"origin":7566,"position":0},"title":"Build an AI-Powered Search Engine with HeatWave GenAI (part 3)","author":"Olivier DASINI","date":"15 avril 2025","format":false,"excerpt":"In this latest post, the final part of my series on building an AI-powered search engine with HeatWave GenAI, I dive into enhancing AI-powered search by embedding full article content into HeatWave. By cleaning HTML, chunking content, generating embeddings, and running semantic similarity searches directly within HeatWave, we unlock highly\u2026","rel":"","context":"Dans "AI"","block_context":{"text":"AI","link":"https:\/\/dasini.net\/blog\/category\/ai\/"},"img":{"alt_text":"","src":"","width":0,"height":0},"classes":[]},{"id":7363,"url":"https:\/\/dasini.net\/blog\/2025\/03\/13\/build-an-ai-powered-search-engine-with-heatwave-genai-part-1\/","url_meta":{"origin":7566,"position":1},"title":"Build an AI-Powered Search Engine with HeatWave GenAI (part 1)","author":"Olivier DASINI","date":"13 mars 2025","format":false,"excerpt":"Discover how to build an AI-powered search engine for your applications using HeatWave GenAI. This approach leverages large language models (LLMs) for semantic search, offering a smarter alternative to traditional SQL and full-text search methods. 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