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Natural Language to SQL "Text-to-SQL" API

Hacker News

Natural Language to SQL "Text-to-SQL" API

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://console.dataherald.ai/playground ) While the open source version works just fine ( https://github.com/Dataherald/dataherald ), 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://www.dataherald.com/news/introducing-dhai

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Product HuntOn track for Day 1 leaderboard · Strong signals: new, context, open · Missing: mac, agents, macos
86%86% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Indie HackersFits the IH revenue-focused audience · Strong signals: including · Missing: supports, reddit linkedin, podcasting
85%85% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: exist, open source, existing · Missing: https docs, excited, just released
68%68% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Strong signals: month · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: host · Missing: plus, platform, intuitive
28%28% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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