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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 次观看 • 1 年前 •via X (Twitter)

10 条评论

Defi.Central 的头像
Defi.Central1 年前

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

Rainmaker 的头像
Rainmaker2 年前

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.

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

awesome, now go 3D and AR

ANIRUDDHA ADAK 的头像
ANIRUDDHA ADAK1 年前

That is just a wow

Parag 的头像
Parag1 年前

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

Jacek (Jomsborg.eth) 的头像
Jacek (Jomsborg.eth)1 年前

what is addeded value vs RAG

Ben Woodward 的头像
Ben Woodward1 年前

@kevinvangundy really cool

UmbraAtrox 的头像
UmbraAtrox1 年前

I've been waiting for this

options enjoyer 的头像
options enjoyer1 年前

Is this similar to lightRAG ?

David Olivencia 的头像
David Olivencia1 年前

Great feature. Go @codegptAI

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

167,710 次观看 • 9 个月前