New Show Hacker News story: Show HN: Natural Language to SQL "Text-to-SQL" API

Show HN: Natural Language to SQL "Text-to-SQL" API
15 by saigal | 9 comments on Hacker News.
Hi HN- Today, we are releasing the hosted API for our natural language to SQL engine, which allows you to: (1) Explain Your Data: Feed in dictionaries, dbt, schemas, Confluence docs - we'll understand the business context to your data. (2) Train Your AI: Fine-tune an LLM (including GPT-4) specifically for your data, increasing accuracy and lowering latency (3) Trust the Answer: See confidence scores with each AI-generated query, stay in control. (4) Conduct complex SQL queries Problem background - Developers struggle to build NL-to-SQL into products because LLMs do not work out-of-the-box; they lack metadata and business definitions. Existing NL-to-SQL tools struggle with context, complexity, and adapting to your data. For example, given the question “what was the average rent in Los Angeles in May 2023?” a reasonable human would either assume the question is about Los Angeles, CA or would confirm the state with the question asker in a follow up. However, an LLM translates this to: select price from rent_prices where city=”Los Angeles” AND month=”05” AND year=”2023” Dataherald integrates with major data warehouses, including PostgreSQL, Databricks, Snowflake, BigQuery, and DuckDB. You can try it now free – no fees, no credit card, no sales pitches, just get the API key and get going. Let us know if it works for you, even your complex queries. ( https://ift.tt/TOcGfS7 ) While the open source version works just fine ( https://ift.tt/iwAzN97 ), the hosted API might be a better fit for those looking for: (1) someone else to take care of infrastructure setup, (2) access to an Admin UI console where you can configure and monitor performance, and (3) ability to invite team members to a project. We're looking for feedback, particularly from anyone who can compare this performance to other NL-to-SQL products. Share your thoughts and join the conversation For more background on the release: https://ift.tt/O2Qo9BL

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