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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... show more

Daniel San

32,692 subscribers

35,682 просмотров • 1 год назад •via X (Twitter)

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Комментарии: 10

Фото профиля Daniel San

Daniel San1 год назад

Hey @alexalbert__ , we’d love to teach the Anthropic developer community how to use the SDK with our repository knowledge graphs 🌐 Happy to collaborate on something together!

Фото профиля Cris

Cris1 год назад

@AnthropicAI I dont think people realize what can be achieved with this 🍰

Фото профиля Arjun

Arjun1 год назад

@AnthropicAI Looks amazing... I bet this would be more useful than the repo search that Cursor does. @cursor_ai should open up the ecosystem for us to integrate with such tools!

Фото профиля Daniel San

Daniel San1 год назад

@aiguy_arjun @AnthropicAI @cursor_ai We would love to connect our Knowledge Graphs with Cursor... happy to collaborate! 🙌 @cursor_ai @amanrsanger

Фото профиля Rethynk AI

Rethynk AI1 год назад

@AnthropicAI This is an incredible start to 2025! CodeGPT’s Knowledge Graphs are a game-changer for navigating complex repositories like Anthropic’s SDK. Simplifying codebase understanding for developers will boost productivity and collaboration.

Фото профиля David Olivencia

David Olivencia1 год назад

@AnthropicAI Let's GOOOOOOO @codegptAI 🚀 #AI #Copilot #AgenticAI

Фото профиля stijn sagaert

stijn sagaert1 год назад

@AnthropicAI @VictorTaelin is this something you are looking for to use with your codebase?

Фото профиля 🐧 lalo adrian morales 𝕏

🐧 lalo adrian morales 𝕏1 год назад

@AnthropicAI and it looks cool too!

Фото профиля Saïd Aitmbarek

Saïd Aitmbarek1 год назад

@AnthropicAI looks amazing, big fan of ontologies brings a lot of explainability to datasets!

Фото профиля Kekius Optimus

Kekius Optimus1 год назад

@AnthropicAI "In this example, we loaded @AnthropicAI Python SDK repository" Nothing like scratching his own back first, right?

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230,303 просмотров • 1 год назад

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126,434 просмотров • 11 месяцев назад

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155,647 просмотров • 1 год назад

Exploring Andrej Karpathy Nanochat through a Knowledge Graph Love how this project combines simplicity with power. Want to navigate and understand the entire repo? Check it out here: Plus, you can connect it via MCP to any AI coding assistant and work directly on the repository. Powered by Deep Graph MCP AI at Meta and GroqInc

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190,584 просмотров • 2 лет назад

$Did these Orthodox monks know something we don't? Religious symbology or fractal antennas? Domed resonators, the use of MERCURY and knowledge of the AETHER... that provided clean ENERGY for this realm. Who built these structures with this knowledge in ancient times? Were these MONKS the original CUSTODIANS? The buildings 'change of use' and the symbols coming to represent something else?$

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WhatdoIknow

109,394 просмотров • 2 лет назад

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ELON: WE’RE BUILDING A MODERN LIBRARY OF ALEXANDRIA ACROSS EARTH, THE MOON, AND MARS “The idea behind Galactica is to create an open source repository of all knowledge, a distillation of all knowledge, meaning anyone can access it, anyone can use it, and if other people want to train on it, they can do so. We want to create copies of this and distribute them throughout Earth, and even put them on the Moon, Mars, and out into deep space. In a way, it’s a modern day Library of Alexandria. It was a great tragedy that the Library of Alexandria burned down, and so to preserve this knowledge, we want to literally etch it in stone and distribute it widely." Source: Source: Baron Capital

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90,633 просмотров • 10 месяцев назад

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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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