正在加载视频...

视频加载失败

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 次观看 • 1 年前 •via X (Twitter)

11 条评论

Daniel San 的头像
Daniel San1 年前

Fork the repo:

Daniel San 的头像
Daniel San1 年前

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

Daniel San 的头像
Daniel San1 年前

Select the repository and branch

Daniel San 的头像
Daniel San1 年前

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

Daniel San 的头像
Daniel San1 年前

Open VSCode and install the CodeGPT extension

Daniel San 的头像
Daniel San1 年前

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

Daniel San 的头像
Daniel San1 年前

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

Lab4crypto 的头像
Lab4crypto1 年前

🚀 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.

BigSeggsy 的头像
BigSeggsy1 年前

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.

benferrum - e/jounce 的头像
benferrum - e/jounce1 年前

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

Daniel San 的头像
Daniel San1 年前

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

相关视频

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

168,153 次观看 • 11 个月前