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Managing Small Knowledge Graphs for Multi-agent Systems // Tom Smoker // #236
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/
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.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// 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
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/
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
[01:03:50] Wrap up
View all episodes
By Demetrios
4.9
2020 ratings
Join us at our first in-person conference on June 25 all about AI Quality: https://www.aiqualityconference.com/
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.
// MLOps Jobs board
https://mlops.pallet.xyz/jobs
// MLOps Swag/Merch
https://mlops-community.myshopify.com/
// 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
--------------- ✌️Connect With Us ✌️ -------------
Join our slack community: https://go.mlops.community/slack
Follow us on Twitter: @mlopscommunity
Sign up for the next meetup: https://go.mlops.community/register
Catch all episodes, blogs, newsletters, and more: https://mlops.community/
Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Tom on LinkedIn: https://www.linkedin.com/in/thomassmoker/
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
[01:03:50] Wrap up
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