{"id":17058,"date":"2024-11-25T12:11:52","date_gmt":"2024-11-25T12:11:52","guid":{"rendered":"https:\/\/jetsoftpro.com\/?p=17058"},"modified":"2024-11-25T12:39:12","modified_gmt":"2024-11-25T12:39:12","slug":"text-to-sql-how-this-trending-technology-works","status":"publish","type":"post","link":"https:\/\/jetsoftpro.com\/blog\/text-to-sql-how-this-trending-technology-works\/","title":{"rendered":"Text-to-SQL: How This Trending Technology Works"},"content":{"rendered":"<p><span data-contrast=\"auto\">In today&#8217;s data-driven world, the ability to quickly retrieve and manipulate information from databases is a must. However, interacting with databases can be tricky, especially if you don\u2019t have technical expertise. That\u2019s where Text-to-SQL technology comes in.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Text-to-SQL (or Text2SQL) is an advanced natural language processing (<a href=\"https:\/\/www.ibm.com\/topics\/natural-language-processing#:~:text=Natural%20language%20processing%20(NLP)%20is,and%20communicate%20with%20human%20language.\">NLP<\/a>) technology that allows users to convert plain language queries into SQL (Structured Query Language) commands. This makes it easier for anyone to interact with databases and retrieve the information they need.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">For example, instead of having to <\/span><span data-contrast=\"auto\">write complex SQL queries to your sales database, you can simply ask a question like, \u201cHow many products were sold in September?\u201d and the system will automatically generate the correct SQL query for you.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\"> <img loading=\"lazy\" class=\"wp-image-17059 aligncenter\" src=\"https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-1-1-300x175.webp\" alt=\"Scheme 1 Text-to-SQL\" width=\"699\" height=\"408\" srcset=\"https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-1-1-300x175.webp 300w, https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-1-1-768x447.webp 768w, https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-1-1.webp 800w\" sizes=\"(max-width: 699px) 100vw, 699px\" \/><\/span><\/p>\n<h2><b><span data-contrast=\"auto\">How Text-to-SQL Technology Works?\u00a0<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">The process of converting natural language queries into SQL consists of 4 key steps:<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">1. User Input<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The user asks a question in natural language. It can be anything from \u201cWhat\u2019s the revenue for product X?\u201d to \u201cHow many customers signed up last month?\u201d This input can be in any language, making it both intuitive and accessible for a wide range of users.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">2. NLP Processing<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Once the query is received, the system uses Natural Language Processing (NLP) to understand the user&#8217;s intent and context. This involves several steps:<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Tokenization: The query is broken down into smaller parts, or tokens, making it easier to analyze and search for relevant data.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"10\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Named Entity Recognition: The system identifies key elements in the query, such as dates, product names, or other important terms that need to be included in the SQL query.<\/span><\/li>\n<\/ul>\n<p><b><span data-contrast=\"auto\">3. Query Generation<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Using the information from the NLP processing, a large language model (LLM) that has been trained on the organization\u2019s data generates the SQL query. The model ensures that the query is both accurate and syntactically correct, ready to be executed against the database.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><b><span data-contrast=\"auto\">4. Execution<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">The SQL query is executed on the database, and the results are returned to the user.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Seems simple, right? In reality, building effective Text-to-SQL systems is much more complex than it might appear at first glance.\u00a0<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h2><b><span data-contrast=\"auto\">2 Levels of Complexity in Text-to-SQL Applications<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">Text-to-SQL technology can vary significantly in complexity, depending on the type of dataset it operates on. Understanding these levels is key to evaluating its practical application in real-world scenarios.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ol>\n<li>\n<h3><b><span data-contrast=\"auto\"> Text-to-SQL on<\/span><\/b> <b><span data-contrast=\"auto\">Pre-Curated Datasets<\/span><\/b><\/h3>\n<\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">This is the simpler approach. The data is already organized, with clearly defined columns and relationships. The system just needs to match the user\u2019s question to existing columns and generate SQL statements, such as simple SELECT queries with basic clauses like GROUP BY and FILTER. These queries typically work with one or a few pre-curated tables, making the task straightforward.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ol start=\"2\">\n<li>\n<h3><b><span data-contrast=\"auto\"> Building Datasets from Raw Tables<\/span><\/b><\/h3>\n<\/li>\n<\/ol>\n<p><span data-contrast=\"auto\">This approach involves generating SQL queries from scratch, using raw, un-curated tables. The system must not only understand the data structure but also the relationships between tables. This requires a deeper level of intelligence and precision, making the task more challenging and nuanced.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">It gets even harder when the data comes from private or domain-specific sources like APIs, SQL databases, PDFs, or slide decks. In these cases, <\/span><b><span data-contrast=\"auto\">Retrieval Augmented Generation (RAG)<\/span><\/b><span data-contrast=\"auto\"> presents a promising alternative. By supplementing the input context with additional information or data, RAG enhances the model\u2019s understanding and performance, resulting in outputs that are more accurate and contextually relevant.<\/span><span data-ccp-props=\"{}\">\u00a0<\/span><\/p>\n<h3><span data-contrast=\"auto\">READ: <\/span><a href=\"https:\/\/jetsoftpro.com\/blog\/ai-call-center-opportunities-and-risks\/\"><span data-contrast=\"none\">AI Call Center: Opportunities and Risks<\/span><\/a><span data-contrast=\"auto\">\u00a0<\/span><\/h3>\n<h2><b><span data-contrast=\"auto\">How RAG Enhances Text-to-SQL Technology?<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">RAG takes Text-to-SQL to the next level by retrieving contextually relevant information from various sources (e.g., databases, documents, APIs) before generating the query.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">Here\u2019s how RAG works in Text-to-SQL:<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Contextual Data Retrieval<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">The system uses vector databases to perform similarity searches, identifying the most relevant information from various sources like databases, documents, and APIs. This ensures the system doesn\u2019t just extract keywords, but also understands the meaning behind the query, which makes the SQL query more accurate.<\/span><span data-ccp-props=\"{&quot;335559685&quot;:720,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"3\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Data Enrichment<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">Once relevant data is retrieved, it\u2019s enriched with additional context. This step ensures that the system has all the necessary information before generating the SQL query.<\/span><span data-ccp-props=\"{&quot;335559685&quot;:720,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"6\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"4\" data-aria-level=\"1\"><b><span data-contrast=\"auto\">Query Generation<\/span><\/b><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">The enriched data is then processed by a large language model (LLM), which generates a precise SQL query based on the complete context.<\/span><span data-ccp-props=\"{&quot;335559685&quot;:720,&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><img loading=\"lazy\" class=\"wp-image-17060 aligncenter\" src=\"https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-2-300x175.webp\" alt=\"Scheme 2 Text-to-SQL\" width=\"699\" height=\"408\" srcset=\"https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-2-300x175.webp 300w, https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-2-768x447.webp 768w, https:\/\/jetsoftpro.com\/wp-content\/uploads\/2024\/11\/Scheme-2.webp 800w\" sizes=\"(max-width: 699px) 100vw, 699px\" \/><\/p>\n<h2><b><span data-contrast=\"auto\">Chaining with LangChain in Text-to-SQL Workflows<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">LangChain enhances Text-to-SQL workflows by allowing you to chain different processes together. This technique is invaluable when creating sophisticated workflows that link data retrieval, query generation, and execution in one seamless chain.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">With LangChain Expression Language (LCEL), you can automate multiple steps like:<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Extracting relevant data<\/span><span data-ccp-props=\"{&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Generating accurate SQL queries<\/span><span data-ccp-props=\"{&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"2\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Enriching the context before query generation<\/span><span data-ccp-props=\"{&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">LangChain also supports <\/span><b><span data-contrast=\"auto\">off-the-shelf chains<\/span><\/b><span data-contrast=\"auto\">, which can be customized for various needs. For example, <\/span><b><span data-contrast=\"auto\">create_sql_query_chain<\/span><\/b><span data-contrast=\"auto\"> automates SQL query generation based on natural language inputs, while <\/span><b><span data-contrast=\"auto\">create_extraction_chain_pydantic<\/span><\/b><span data-contrast=\"auto\"> extracts data based on predefined schemas.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">By combining <\/span><b><span data-contrast=\"auto\">Chaining<\/span><\/b><span data-contrast=\"auto\"> and <\/span><b><span data-contrast=\"auto\">RAG<\/span><\/b><span data-contrast=\"auto\">, LangChain ensures that the generated SQL queries are contextually precise and optimized. It integrates with <\/span><b><span data-contrast=\"auto\">ChromaDB<\/span><\/b><span data-contrast=\"auto\"> for metadata storage and filtering, and <\/span><b><span data-contrast=\"auto\">OpenAI embeddings<\/span><\/b><span data-contrast=\"auto\"> for search indexing, making the entire process more efficient and accurate.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<p><span data-contrast=\"auto\">In practice, this means you can create chains that:<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"1\" data-aria-level=\"1\"><span data-contrast=\"auto\">Automatically identify the relevant tables and columns<\/span><span data-ccp-props=\"{&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"2\" data-aria-level=\"1\"><span data-contrast=\"auto\">Build complex queries (including joins and filtering)<\/span><span data-ccp-props=\"{&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<ul>\n<li data-leveltext=\"\uf0b7\" data-font=\"Symbol\" data-listid=\"1\" data-list-defn-props=\"{&quot;335552541&quot;:1,&quot;335559685&quot;:720,&quot;335559991&quot;:360,&quot;469769226&quot;:&quot;Symbol&quot;,&quot;469769242&quot;:[8226],&quot;469777803&quot;:&quot;left&quot;,&quot;469777804&quot;:&quot;\uf0b7&quot;,&quot;469777815&quot;:&quot;hybridMultilevel&quot;}\" aria-setsize=\"-1\" data-aria-posinset=\"3\" data-aria-level=\"1\"><span data-contrast=\"auto\">Execute them efficiently against the database<\/span><span data-ccp-props=\"{&quot;335559739&quot;:0}\">\u00a0<\/span><\/li>\n<\/ul>\n<p><span data-contrast=\"auto\">This approach simplifies Text-to-SQL tasks, making them more scalable, flexible, and accurate.<\/span><span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n<h2><b><span data-contrast=\"auto\">How JetSoftPro Can Help<\/span><\/b><span data-ccp-props=\"{}\">\u00a0<\/span><\/h2>\n<p><span data-contrast=\"auto\">At JetSoftPro, we specialize in implementing cutting-edge Text-to-SQL solutions. Our expertise includes integrating advanced AI tools, large language models, and RAG-based systems, ensuring that we can create tailored solutions for your data needs.<\/span><br \/>\n<span data-ccp-props=\"{&quot;335559738&quot;:240,&quot;335559739&quot;:240}\">\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>In today&#8217;s data-driven world, the ability to quickly retrieve and manipulate information from databases is a must. However, interacting with databases can be tricky, especially if you don\u2019t have technical&#8230;<\/p>\n","protected":false},"author":26,"featured_media":17065,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[183,184],"tags":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v18.5.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Text-to-SQL: How This Trending Technology Works - JetSoftPro Blog<\/title>\n<meta name=\"description\" content=\"Text-to-SQL significantly expands the range of possibilities when working with databases. 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