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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...

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Daniel San

27,116 subscribers

42,005 Aufrufe • vor 1 Jahr •via X (Twitter)

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12 Kommentare

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Daniel Sanvor 1 Jahr

Si nos you can use this in Cursor 🙌

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SecurityPalvor 1 Jahr

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

Profilbild von Pratik Kadam
Pratik Kadamvor 1 Jahr

@AnthropicAI I have used it through MCP by @AnthropicAI

Profilbild von Pratik Kadam
Pratik Kadamvor 1 Jahr

@AnthropicAI Yes, I have also tried it!

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Vaibhav Patilvor 1 Jahr

@AnthropicAI game changer for navigating and understanding codebases

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ren simmonsvor 1 Jahr

@AnthropicAI Incredible

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CBirvor 1 Jahr

@AnthropicAI Awesome

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Michael Mazarian - AI Kingpinvor 1 Jahr

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

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Gauravvor 1 Jahr

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

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Daniel Sanvor 1 Jahr

@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. 💪

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Dr.vor 1 Jahr

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

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Daniel Sanvor 1 Jahr

@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 🙌

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Introducing Codebase Knowledge Graphs in Cursor 🤩 In this video, I’ll walk you through how we went from using a knowledge graph of a repository on the CodeGPT platform to leveraging it directly within the Cursor editor This tool is specifically designed to navigate massive codebases, identify nodes and relationships, and deliver richer context to models specialized in code development, such as Anthropic Claude 3.5 Sonnet Stay tuned for an upcoming tutorial where I'll show you step-by-step how to create knowledge graphs and integrate them seamlessly into your entire software development workflow, from Commits and Pull Requests to even Trello tickets!

Daniel San

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We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these knowledge graphs using the CodeGPT extension in VSCode by simply calling the Codebase command 👏 Stay tuned for more tutorials on how to use this powerful tool! Happy 2025! 🎉
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We’re kicking off 2025 with everything we've got! 🤩 Introducing CodeGPT's Knowledge Graphs, navigating through the entire Anthropic SDK repository. In this example, we loaded Anthropic Python SDK repository and successfully provided the LLM with all the knowledge it needs to fully understand the codebase. You can leverage these knowledge graphs using the CodeGPT extension in VSCode by simply calling the Codebase command 👏 Stay tuned for more tutorials on how to use this powerful tool! Happy 2025! 🎉

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I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.
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I added a Knowledge Graph to Cursor using MCP. You gotta see this working! Knowledge graphs are a game-changer for AI Agents, and this is one example of how you can take advantage of them. How this works: 1. Cursor connects to Graphiti's MCP Server. Graphiti is a very popular open-source Knowledge Graph library for AI agents. 2. Graphiti connects to Neo4j running locally. Now, every time I interact with Cursor, the information is synthesized and stored in the knowledge graph. In short, Cursor now "remembers" everything about our project. Huge! Here is the video I recorded.

Santiago

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So many people wanted to see my knowledge management system in Claude code, so here it is. All you need is Obsidian, Claude Code, GitHub, and the will to live and you too can set up your second brain in under 2 hours. If you want to learn how to build this, here’s the demo link:
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So many people wanted to see my knowledge management system in Claude code, so here it is. All you need is Obsidian, Claude Code, GitHub, and the will to live and you too can set up your second brain in under 2 hours. If you want to learn how to build this, here’s the demo link:

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Alex Finn

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Knowledge graphs for representing information are unbeatable. After this, you will never build a RAG system without knowledge graphs. It will take you five lines of code to build a knowledge graph with your data. I recorded a video to show you how you can do this. I used Cognee, an open-source library that outperforms any basic vector search approach in terms of retrieval relevance. They are collaborating with me on this post. Cognee is: • Easy to use • Reduces hallucinations • Open-source Here is a link to the repository: They also offer a comprehensive platform and UI with Python notebooks you can utilize to manage your data. Here is the link:
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I found an excellent use case for Remotion & claude code - get it to create a video explaining how the codebase works. In this case I got it to explain how codebase works
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Our new short course, Knowledge Graphs for RAG, is now available! Knowledge graphs are a data structure that is great at capturing complex relationships between data of multiple types. By enabling more sophisticated retrieval of text than similarity search alone, knowledge graphs can improve the context you pass to the LLM and the performance of your RAG applications. In this course, taught by Andreas Kollegger of Neo4j, you’ll - Explore how knowledge graphs work by building a graph of public financial documents from scratch - Learn to write queries that retrieve text and data from the graph and use it to enhance the context you pass to an LLM chatbot - Combine a knowledge graph with a question-answer chain to build better RAG-powered chat systems Sign up here!

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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:

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