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Meet the new Microsoft PostgreSQL extension for Visual Studio Code - packed with powerful tools to supercharge your PostgreSQL workflow, like: 🤖 Agent mode integration 💡 Schema visualization ⚡ Instant Docker PostgreSQL 🔎 IntelliSense-powered queries Learn more:

Visual Studio Code

800,207 subscribers

246,042 次观看 • 1 年前 •via X (Twitter)

艺术科学技术教育

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10 条评论

Nick Muller 的头像

Nick Muller1 年前

Curious if this can finally replace Azure Data Studio’s Postgres plugin for me 😁

Manticore Search 的头像

Manticore Search1 年前

🚀 Star Manticore Search on #GitHub today and join our quest for the ultimate search solution!

Ben Dower 的头像

Ben Dower1 年前

i usually used to tell AI to make a script that sets up the database.

NFLComedySkits 的头像

NFLComedySkits1 年前

VS Code has been cooking lately! My next project will be creating a database so I need to try this extension 💯

Kingsley Uyi Idehen 的头像

Kingsley Uyi Idehen1 年前

Why is it DBMS-specific in the age of MCP (Model Context Protocol) and the existence of MCP servers for ODBC, JDBC, and

Vinayak Soni 的头像

Vinayak Soni1 年前

hi can we use mssql database with this? or any other mcp you know @code

ἀꑭατολή ✙ ➔ 的头像

ἀꑭατολή ✙ ➔1 年前

Finally

Andy Targaryen 的头像

Andy Targaryen1 年前

wow

Iliya Kisliy 的头像

Iliya Kisliy1 年前

@theo wdyt

Vishal 的头像

Vishal1 年前

This Microsoft PostgreSQL extension looks really useful. It has smart tools to make working with PostgreSQL easier and faster.

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The PostgreSQL extension for Visual Studio Code is here! Gwyneth Peña-Siguenza shows you how to get started with creating a new Docker PostreSQL server, managing your connections, chatting with your database, and more.

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This new release of Visual Studio Code is INCREDIBLE. One of my favorite new features is the new integrated browser with agent mode integration. It is 🔥.

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This is, by far, one of the best uses of modern AI. If you don't use embeddings when querying your database, you are definitely leaving a lot on the table. In this video, I'll show you how to run semantic searches using OpenAI and PostgreSQL. It's all thanks to Pgai, an open-source PostgreSQL extension: Here's what will happen: 1. We'll create a simple table with news articles 2. We'll generate embeddings for those articles 3. We'll run queries on top of those embeddings For this video, I generated the embeddings using a simple query, but pgai Vectorizer would do the same automatically as new information makes it into the database. This is awesome! If you have a PostgreSQL database with data you are searching over, you should start experimenting with semantic searches immediately. For most use cases, a combination of full-text search + semantic search is the best approach. If you don't have a PostgreSQL database around, you can try free for 30 days using Timescale: Thanks to the Timescale (now TigerData) team for partnering with me on this post!

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