This article discusses how to overcome limitations of retrieval-augmented generation (RAG) models by creating an AI assistant using advanced SQL vector queries. The author uses tools such as MyScaleDB, OpenAI, LangChain, Hugging Face and the HackerNews API to develop an application that enhances the accuracy and efficiency of data retrieval process.
An in-process analytics database, DuckDB can work with surprisingly large data sets without having to maintain a distributed multiserver system. Best of all? You can analyze data directly from your Python app.
Quadratic is a modern spreadsheet that combines the familiarity of a spreadsheet with the power of code, allowing you to work with data and code collaboratively in real-time. It supports popular programming languages like Python, SQL, and JavaScript, and offers features such as dynamic charts, APIs, multi-line formulas, and AI integration.
pg_timeseries is an open-source PostgreSQL extension focused on creating a cohesive user experience around the creation, maintenance, and use of time-series tables. It allows users to create time-series tables, configure the compression and retention of older data, monitor time-series partitions, and run complex time-series analytics functions with a user-friendly syntax.
Launched in 2007, Chess.com is a premium platform for online chess and one of the largest of its kind. A Cloud SQL for MySQL shop, it transitioned to Cloud SQL Enterprise Plus edition, improving the user experience, cutting costs, and significantly reducing response times, decreasing p99 latency response from 14ms to 4ms. Read on to learn more.
A prototype tool powered by Large Language Models to make querying your databases as easy as saying the word.
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
- Introduction to QueryGPT, a tool using Large Language Models (LLMs) for natural language database queries
- Focus on implementing a basic iteration of the system, with potential for significant enhancements
- Aim is to provide the LLM with the database schema and have it answer questions based on that context
- Discussion on prompt engineering, which is designing inputs for generative AI tools to produce optimal results
Tune a base LLama2 LLM to output SQL code. with Parameter Efficient Fine-Tuning techniques to optimise the process.
training GPT to query with few-shot prompting.
