Загрузка видео...

Не удалось загрузить видео

На главную

The OpenAI Codex repo turned into a Knowledge Graph! 🤩 If you want to start working on massive codebases and serious projects, you need to use CodeGPT’s Knowledge Graphs. Here’s a step-by-step 🧵 guide to create your Codex graph and connect it to VSCode 👇

179,466 просмотров • 1 год назад •via X (Twitter)

Комментарии: 11

Фото профиля Daniel San
Daniel San1 год назад

Fork the repo:

Фото профиля Daniel San
Daniel San1 год назад

Go to create your account, and open the Code Graph section

Фото профиля Daniel San
Daniel San1 год назад

Select the repository and branch

Фото профиля Daniel San
Daniel San1 год назад

Wait a few seconds while CodeGPT scans every corner of the repo

Фото профиля Daniel San
Daniel San1 год назад

Open VSCode and install the CodeGPT extension

Фото профиля Daniel San
Daniel San1 год назад

Set the repo as your codebase context — and you're all set!

Фото профиля Daniel San
Daniel San1 год назад

Now you can work with HUGE codebases effortlessly No special config, rules, or prompt engineering required. Enjoy! 🎉

Фото профиля Lab4crypto
Lab4crypto1 год назад

🚀 Don't gamble with your portfolio! Use our advanced hybrid quant risk tool using on/off-chain data and make informed decisions. 📈 Acess to 1000+ charts for your crypto journey. 📚Join our Premium Telegram for daily alerts. 📊+21 projects supported. 🏗️ Beginners and experts.

Фото профиля BigSeggsy
BigSeggsy1 год назад

your giving me an idea. I can just ask Copilot to go through my @obsdmd vault and finish all the backlinks ive been too lazy to go through and add in the last 4 years.

Фото профиля benferrum - e/jounce
benferrum - e/jounce1 год назад

sir, you are asking for a bit too much: how can I add it to only 1 repo to test it out?

Фото профиля Daniel San
Daniel San1 год назад

You can add a public or private repo, both work. We only get access at the moment the graph is created, so you can safely connect your account and test it with just one repo

Похожие видео

Build better RAG by letting a team of agents extract and connect your reference materials into a knowledge graph. Our new short course, “Agentic Knowledge Graph Construction,” taught by Neo4j Innovation Lead Andreas Kollegger, shows you how. Knowledge graphs are an important way to store information accurately but they are a lot of work to build manually. In this course you’ll learn how to build a team of agents that turn data– in this case product reviews and invoices from suppliers–into structured graphs of entities and relationships for RAG. Learn how agents can automatically handle the time-consuming work of building graphs — extracting entities and relationships (e.g., Product "contains" Assembly, Part "supplied_by" Supplier, Customer review "mentions" Product), deduplicating them, fact-checking them, and committing them to a graph database — so your retrieval system can find right information to generate accurate output. For example, you can use agents to help trace customer complaints directly to specific suppliers, manufacturing processes, and product hierarchies, thus turning fragmented information into queryable business intelligence. Skills you’ll gain: - Build, store, and access knowledge graphs using the Neo4j graph database - Build multi-agent systems using Google’s Agent Development Kit (ADK) - Set up a loop of agentic workflows to propose and refine a graph schema through fact-checking - Connect agent-generated graphs of unstructured and structured data into a unified knowledge graph This course gets into the practicum of why knowledge graphs give more accurate information retrieval than vector search alone, especially for high-stakes applications where precision matters more than fuzzy similarity matching. Sign up here:

Andrew Ng

167,963 просмотров • 10 месяцев назад