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You can now use Knowledge Graphs with Claude 3.5 Sonnet in Cursor! 🤩 CodeGPT’s Knowledge Graphs (KG) let you explore your entire codebase. In this case, we're navigating through Anthropic Python SDK. We asked how to send messages in batches and the tool identified the nodes and relationships in...

42,005 görüntüleme • 1 yıl önce •via X (Twitter)

12 Yorum

Daniel San profil fotoğrafı
Daniel San1 yıl önce

Si nos you can use this in Cursor 🙌

SecurityPal profil fotoğrafı
SecurityPal1 yıl önce

Questionnaire Concierge is now available as an API! With the new API, you can: 📝 Create new questionnaire request directly 🔍 Instantly search questionnaire details ⚒️ Build custom form and dashboard 🔗: #SecurityPal #SecurityQuestionnaires #API

Pratik Kadam profil fotoğrafı
Pratik Kadam1 yıl önce

@AnthropicAI I have used it through MCP by @AnthropicAI

Pratik Kadam profil fotoğrafı
Pratik Kadam1 yıl önce

@AnthropicAI Yes, I have also tried it!

Vaibhav Patil profil fotoğrafı
Vaibhav Patil1 yıl önce

@AnthropicAI game changer for navigating and understanding codebases

ren simmons profil fotoğrafı
ren simmons1 yıl önce

@AnthropicAI Incredible

CBir profil fotoğrafı
CBir1 yıl önce

@AnthropicAI Awesome

Michael Mazarian - AI Kingpin profil fotoğrafı
Michael Mazarian - AI Kingpin1 yıl önce

@BadTechBandit @AnthropicAI Curious—what’s the one key takeaway here? 👏

Gaurav profil fotoğrafı
Gaurav1 yıl önce

@AnthropicAI How do you compare this with @github CoPilot with the model options they provide now? Especially with the free or $10 Pro plans

Daniel San profil fotoğrafı
Daniel San1 yıl önce

@AnthropicAI @github Great question GitHub Copilot is accessible, but CodeGPT goes further by integrating knowledge graphs to provide more accurate, contextual suggestions. At the end of the day, it’s all about quality, and that’s our focus. 💪

Dr. profil fotoğrafı
Dr.1 yıl önce

@AnthropicAI Can it be used for analyze how to refactor a class/module?

Daniel San profil fotoğrafı
Daniel San1 yıl önce

@AnthropicAI Yes, absolutely! CodeGPT can already assist with refactoring classes/modules, and we’re working on adding specialized options for refactoring, integrations, and keeping up with language updates. Stay tuned 🙌

Benzer Videolar

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

167,710 görüntüleme • 9 ay önce