# text2sql

Published articles for text2sql.

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## Reducing Text2SQL latency with parameterized query templates

DevFeed: [Reducing Text2SQL latency with parameterized query templates](<https://devfeed.tech/articles/reducing-text2sql-latency-with-parameterized-query-templates-4649.md>)

Original publisher: [Read original article](<https://aws.amazon.com/blogs/architecture/reducing-text2sql-latency-with-parameterized-query-templates/>)

Author: Yury Brukau

Published: 2026-08-13T00:40:32Z

Content type: article

Language: en

Sources: [AWS Architecture Blog](<https://devfeed.tech/sources/aws-architecture-blog.md>)

Topics: [text2sql](<https://devfeed.tech/topics/text2sql.md>), [Language models](<https://devfeed.tech/topics/language-models.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [amazon-bedrock](<https://devfeed.tech/tags/amazon-bedrock.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [aws-lambda](<https://devfeed.tech/tags/aws-lambda.md>), [database](<https://devfeed.tech/tags/database.md>), [intermediate-200](<https://devfeed.tech/tags/intermediate-200.md>), [latency](<https://devfeed.tech/tags/latency.md>), [llm](<https://devfeed.tech/tags/llm.md>), [performance](<https://devfeed.tech/tags/performance.md>), [production](<https://devfeed.tech/tags/production.md>), [sql](<https://devfeed.tech/tags/sql.md>), [technical-how-to](<https://devfeed.tech/tags/technical-how-to.md>), [text2sql](<https://devfeed.tech/tags/text2sql.md>)

### AI overview

The article describes using parameterized SQL query templates as a semantic caching layer for a production Text2SQL system. It reports lower latency and token consumption by matching similar questions to templates and avoiding some LLM calls.

### Source excerpt

Learn how parameterized query templates reduced Text2SQL latency by 80% and cut token consumption by over 50%. This post covers the architecture behind an intelligent caching layer that uses semantic similarity to match user questions to SQL templates, bypassing expensive LLM calls.

## Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B

DevFeed: [Text2SQL using Hugging Face Dataset Viewer API and Motherduck DuckDB-NSQL-7B](<https://devfeed.tech/articles/text2sql-using-hugging-face-dataset-viewer-api-and-motherduck-duckdb-nsql-7b-7176.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/duckdb-nsql-7b>)

Author: Andrea Soria; Till Döhmen; Sen Wu; Laurel Orr; Vishal

Published: 2024-04-04T00:00:00Z

Content type: tutorial

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [text2sql](<https://devfeed.tech/topics/text2sql.md>), [DuckDB](<https://devfeed.tech/topics/duckdb.md>), [hugging face](<https://devfeed.tech/topics/hugging-face.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [SQL](<https://devfeed.tech/topics/sql.md>), [API](<https://devfeed.tech/topics/api.md>), [parquet](<https://devfeed.tech/topics/parquet.md>), [data](<https://devfeed.tech/topics/data.md>), [Data analysis](<https://devfeed.tech/topics/data-analysis.md>), [Databases](<https://devfeed.tech/topics/databases.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [api](<https://devfeed.tech/tags/api.md>), [artificial-intelligence](<https://devfeed.tech/tags/artificial-intelligence.md>), [datasets](<https://devfeed.tech/tags/datasets.md>), [duckdb](<https://devfeed.tech/tags/duckdb.md>), [guide](<https://devfeed.tech/tags/guide.md>), [hugging-face](<https://devfeed.tech/tags/hugging-face.md>), [llm](<https://devfeed.tech/tags/llm.md>), [parquet](<https://devfeed.tech/tags/parquet.md>), [sql](<https://devfeed.tech/tags/sql.md>), [text2sql](<https://devfeed.tech/tags/text2sql.md>)

### AI overview

This tutorial explains how to use the DuckDB-NSQL-7B large language model with the Hugging Face Dataset Viewer API, parquet files, and DuckDB to perform text2sql tasks. The model translates plain-language data requests into valid DuckDB SQL statements for data exploration and analysis.

### Source excerpt

We're on a journey to advance and democratize artificial intelligence through open source and open science.