Tom Smoker, Cofounder of WhyHow.ai, discusses using knowledge graphs in multi-agent systems. Topics include mitigating hallucination issues, optimizing search with knowledge graphs, agile problem-solving, and integrating vector databases. The conversation explores agents in multi-agent systems, stepping back for growth, and using AI models for automated content creation and revenue generation.
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Legal RAG hallucinations
Tom Smoker and his co-founders built a legal RAG system that hallucinated in high-value cases.
They needed to prioritize accuracy and determinism, leading them to knowledge graphs.
insights INSIGHT
Knowledge Graphs and Determinism
Knowledge graphs offer structured grounding for RAG systems, enabling determinism and explainability.
They complement vector databases and enhance retrieval pipelines.
insights INSIGHT
Exactly Wrong vs. Exactly Right
Aim for "exactly wrong" as a first step towards "exactly right" in RAG systems.
Being precisely wrong allows for iterative improvement and deterministic behavior.
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Tom Smoker is the cofounder of an early stage tech company empowering developers to create knowledge graphs within their RAG pipelines. Tom is a technical founder, and owns the research and development of knowledge graphs tooling for the company.
Managing Small Knowledge Graphs for Multi-agent Systems // MLOps podcast #236 with Tom Smoker, Technical Founder of whyhow.ai.
A big thank you to @latticeflow for sponsoring this episode! LatticeFlow - https://latticeflow.ai/
// Abstract
RAG is one of the more popular use cases for generative models, but there can be issues with repeatability and accuracy. This is especially applicable when it comes to using many agents within a pipeline, as the uncertainty propagates. For some multi-agent use cases, knowledge graphs can be used to structurally ground the agents and selectively improve the system to make it reliable end to end.
// Bio
Technical Founder of WhyHow.ai. Did Masters and PhD in CS, specializing in knowledge graphs, embeddings, and NLP. Worked as a data scientist to senior machine learning engineer at large resource companies and startups.
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// Related Links
A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models: https://arxiv.org/abs/2401.01313Understanding the type of Knowledge Graph you need — Fixed vs Dynamic Schema/Data: https://medium.com/enterprise-rag/understanding-the-type-of-knowledge-graph-you-need-fixed-vs-dynamic-schema-data-13f319b27d9e
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Timestamps:
[00:00] Tom's preferred coffee
[00:33] Takeaways
[03:04] Please like, share, leave a review, and subscribe to our MLOps channels!
[03:23] Academic Curiosity and Knowledge Graphs
[05:07] Logician
[05:53] Knowledge graphs incorporated into RAGs
[07:53] Graphs & Vectors Integration
[10:49] "Exactly wrong"
[12:14] Data Integration for Robust Knowledge Graph
[14:53] Structured and Dynamic Data
[21:44] Scoped Knowledge Retrieval Strategies
[28:01 - 29:32] LatticeFlow Ad
[29:33] RAG Limitations and Solutions
[36:10] Working on multi agents, questioning agent definition
[40:01] Concerns about performance of agent information transfer
[43:45] Anticipating agent-based systems with modular processes
[52:04] Balancing risk tolerance in company operations and control
[54:11] Using AI to generate high-quality, efficient content
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