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Introducing Auto Documentation powered by Codebase Knowledge Graphs With CodeGPT, we traverse your entire repo to build a Knowledge Graph that understands how your software works. Capturing relationships between classes, modules, and dependencies. This allows us to generate robust, accurate documentation that misses no detail. We’re helping top companies...

53,329 просмотров • 1 год назад •via X (Twitter)

Комментарии: 10

Фото профиля Defi.Central
Defi.Central1 год назад

You must make it public. Because their is no bigger company then opensource code lib. That uses by developers around world.

Фото профиля Rainmaker
Rainmaker2 лет назад

Here I share an XGBoost model that delivers a 25% CAGR with minimal drawdown on Visa stock. In this free Substack post I share code and commentary for a powerful Machine Learning strategy that delivers powerful returns.

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

awesome, now go 3D and AR

Фото профиля ANIRUDDHA ADAK
ANIRUDDHA ADAK1 год назад

That is just a wow

Фото профиля Parag
Parag1 год назад

How does CodeGPT handle dynamically typed or metaprogrammed code when constructing the Knowledge Graph, especially in languages like Python or JavaScript?

Фото профиля Jacek (Jomsborg.eth)
Jacek (Jomsborg.eth)1 год назад

what is addeded value vs RAG

Фото профиля Ben Woodward
Ben Woodward1 год назад

@kevinvangundy really cool

Фото профиля UmbraAtrox
UmbraAtrox1 год назад

I've been waiting for this

Фото профиля options enjoyer
options enjoyer1 год назад

Is this similar to lightRAG ?

Фото профиля David Olivencia
David Olivencia1 год назад

Great feature. Go @codegptAI

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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:

Andrew Ng

167,710 просмотров • 9 месяцев назад