Alternatives
Products that do what RegSQL does
Write SQL & Regex with AI. No syntax pain.
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Hi everyone — thanks for your interest in PRQL — let us know any questions or feedback! We're excited to be releasing 0.2[1], the first version of PRQL you can use in your own projects. It wouldn't exist without the feedback we got from HackerNews when we originally posted the proposal. [1]: https://github.com/prql/prql/releases/tag/0.2.0
2022 · github.com
- 4RA
Regex.ai is an AI-powered tool that generates regular expressions. It can accurately generate regular expressions that match specific patterns in text with precision. Whether you're a novice or an expert, Regex.ai's intuitive interface makes it easy to input sample text and generate complex regular expressions quickly and efficiently. Overall, Regex.ai is a game-changer that will save you time and streamline your workflow.
2023 · regex.ai
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Long story short: We (Dataherald) just open-sourced our entire codebase, including the core engine, the clients that interact with it and the backend application layer for authentication and RBAC. You can now use the full solution to build text-to-SQL into your product. The Problem: modern LLMs write syntactically correct SQL, but they struggle with real-world relational data. This is because real world data and schema is messy, natural language can often be ambiguous and LLMs are not trained on your specific dataset. Solution: The core NL-to-SQL engine in Dataherald is an LLM based agent…
2024 · github.com
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Would love thoughts! Here is the HF page: https://huggingface.co/chatdb/natural-sql-7b
2024 · github.com
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Hey everyone! I built a tool to help you visually build SQL queries - if you write a lot of SQL at work (or help people write it), check it out! You can try it out without needing to sign up, or integrate with any databases. Would love all your thoughts! Link: https://craftsql.com/
2022 · craftsql.com
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I played around with GPT-3 to build this demo. Select a public BigQuery dataset and describe your query in natural English, then edit the generated SQL as needed and execute it. https://app.tabbydata.com/sql-assistant-demo
2021
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This extension let's you write PRQL functions in PostgreSQL. When I first saw PRQL on Hacker News a few months ago, I was immediately captivated by the idea, yet equally disappointed that there was no integration for PostgreSQL. Having previous experience with writing PostgreSQL extensions in C, I thought this was a great opportunity to try out the pgrx framework and decided to integrate PRQL with PostgreSQL myself. The maintainers of both PRQL and pgrx were very nice to work with. Thanks guys.
2024 · github.com
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2019 · github.com
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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;…
2024 · dataherald.com
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2021 · github.com
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SQL-tString is a SQL builder that utilises the recently accepted PEP-750, https://peps.python.org/pep-0750/, t-strings to build SQL queries, for example, from sql_tstring import sql val = 2 query, values = sql(t"SELECT x FROM y WHERE x = {val}") assert query == "SELECT x FROM y WHERE x = ?" assert values == [2] db.execute(query, values) # Most DB engines support this The placeholder ? protects against SQL injection, but cannot be used everywhere. For example, a column name cannot be a placeholder. If you try this SQL-tString will raise an error, col = "x" sql(t"SELECT…
2025 · github.com
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I've always been curious about how SQL engines actually work. So I built a minimum viable SQL engine in Go. - Supports CSV files as tables - Supports SELECT, FROM, WHERE, LIMIT It's very simple: 1. Parses query string 2. Converts it into an AST representation 3. Executes the query against the CSV 4. Returns the results
2024 · github.com
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