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YC F24's Smooth is an open-source RAG that uses knowledge graphs and PageRank for more accurate retrieval. Their RAG is up to 3x more accurate than vector databases.

Y Combinator

1,622,263 subscribers

23,018 views • 1 year ago •via X (Twitter)

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5 Comments

parsa1 year ago

@circlemind_ai

race-of-sloths1 year ago

@circlemind_ai Y Combinator continues to drive innovation! At Race of Sloth, we’re excited to see open-source initiatives shaping the future of tech.

Fahad1 year ago

@circlemind_ai Can this be deployed on-prem?

Sankalp1 year ago

@circlemind_ai is it based on nearest neighbor retrieval

Alexander Ershov1 year ago

@circlemind_ai Is it already used in some production use cases?

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244,281 views • 2 years ago

R2R from is an open-source agentic retrieval system that transforms RAG with multi-step reasoning across your data and the web.

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168,600 views • 1 year ago

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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125,928 views • 9 months ago

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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167,892 views • 10 months ago

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91,045 views • 9 months ago

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This is more accurate than that AI generated finale. CC: seth

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Is this accurate? “Most plumbers and electricians who go on to own their own business will earn more than most doctors and lawyers.” “This is true for most trades” 🔊

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