Podcasts about Knowledge graph

Wenn du als Online-Marketer die digitale Sichtbarkeit deines Unternehmens auf ein neues Level heben willst, dann solltest du dir diese Podcast-Folge nicht entgehen lassen! Mario Jung (OMT GmbH) und Benny Windolph (HECHT INS GEFECHT) nehmen dich mit in die Welt des Google Knowledge Graph und zeigen dir, warum er für deine Marke unverzichtbar ist. Sie erklären, wie sich der Knowledge Graph von klassischen Suchergebnissen unterscheidet und welche Vorteile du als Unternehmer daraus ziehen kannst. Außerdem bekommst du praxisnahe Tipps, wie du aktiv daran arbeiten kannst, im Knowledge Graph sichtbar zu werden – inklusive der Rolle von strukturierten Daten (Schema Markup), Wikipedia und anderen relevanten Quellen. Doch Achtung: Wer hier Fehler macht, kann sich selbst schaden! Die Experten decken die häufigsten Fallstricke auf und zeigen dir, wie du veraltete oder falsche Informationen korrigieren kannst. Und natürlich werfen sie einen Blick in die Zukunft: Welche Rolle wird Künstliche Intelligenz (KI) spielen? Welche neuen Google-Produkte und Features sind für dich relevant? Abgerundet wird das Ganze mit echten Erfolgsbeispielen und wertvollen Tools, die dir helfen, deinen Platz im Knowledge Graph zu sichern. Diese Episode ist ein Must-Listen für alle, die ihr SEO-Game auf das nächste Level bringen und bei Google ganz oben mitspielen wollen!

Over the last year or so, Klarna has been on a rampage to automate away. They've sliced tools, costs and staff. Begging the question...Is this just a VC narrative or is there more than meets the eye?(00:00) - Introduction (02:44) - Klarna's Journey and Financials (05:49) - AI's Role in Cost Reduction (07:51) - Customer Service Innovations (10:28) - Internal Knowledge Management (13:47) - Introduction to Data Consolidation (14:29) - Enterprise Search and Internal Tools (15:20) - Klarna's Knowledge Graph and AI Integration (16:38) - Deprecating Salesforce and Workflow Changes (20:05) - HR and Employee Feedback Innovations (25:51) - Build vs. Buy Debate Never miss a new episode, join our newsletter on revenueformula.substack.com

In this new episode Niklas Siemer, Product Specialist for SAP Business Technology Platform, is talking to Shabana Samsudheen, Senior Product Manager for SAP HANA Cloud. We're making a deep dive into the new Knowledge Graph engine of SAP HANA Cloud. Talking about what graphs are and what they're used for. Typical uses cases of graphs and how to use them in SAP HANA Cloud.

ServiceNow, the AI platform for business transformation, has announced the Yokohama platform release, unleashing new AI agents across CRM, HR, IT, and more, for faster, smarter workflows and maximum, end-to-end business impact. These latest innovations include teams of preconfigured AI agents that deliver productivity and predictable outcomes from day one, on a single platform, as well as capabilities to build, onboard, and manage the entire AI agent lifecycle. Because data fuels AI, the company also announced expansion of its Knowledge Graph with advancements to its Common Service Data Model (CSDM) to break down barriers among data sources for more connected AI agents. According to Gartner, "By 2028, 40% of CIOs will demand "Guardian Agents" be available to autonomously track, oversee, or contain the results of AI agent actions," underscoring the growing need for a coordinated, enterprise-wide approach to AI deployment and management. As businesses race to unlock the full potential of agentic AI, ServiceNow serves as the AI agent control tower for enterprises, with solutions that remove common roadblocks like data fragmentation, governance gaps, and real-time performance challenges. Unlike other AI providers that operate in silos or require complex integrations, ServiceNow AI Agents are built on a single, enterprise-wide platform, helping ensure seamless data connectivity with Workflow Data Fabric. By providing a single view of all workflows, AI, and automation needs, ServiceNow enables companies to seamlessly coordinate thousands of AI agents across CRM, IT, HR, finance, and more, enabling total enterprise-wide visibility and control. "Agentic AI is the new frontier. Enterprise leaders are no longer just experimenting with AI agents; they're demanding AI solutions that can help them achieve productivity at scale," said Amit Zavery, president, chief product officer, and chief operating officer at ServiceNow. "ServiceNow's industry-leading agentic AI framework meets this need by delivering predictability and efficiency from the start. With the combination of agentic AI, data fabric, and workflow automation all on one platform, we're making it easier for organisations to embed connected AI where work happens and both measure and drive business outcomes faster, smarter, and at scale." ServiceNow AI Agents are now available to radically accelerate productivity at scale Enterprise leaders are moving beyond experimentation, demanding AI solutions that drive real outcomes. ServiceNow's AI capabilities generate insights that power AI agent reasoning, planning, learning, and orchestration, equipping businesses to more rapidly achieve impactful goals. New ServiceNow AI Agents are available today and ready to help businesses accelerate productivity, streamline operations, and drive real outcomes for enterprise-wide use cases. For example: Security Operations (SecOps) expert AI agents transform security operations by streamlining the entire incident lifecycle, eliminating repetitive tasks and empowering SecOps teams to focus on quickly stopping real threats. Autonomous change management AI agents act like a seasoned change manager, instantly generating custom implementation, test, and backout plans by analyzing impact, historical data, and similar changes - ensuring seamless execution with minimal risk. Proactive network test & repair AI agents operate as AI-powered troubleshooters that automatically detect, diagnose, and resolve network issues before they impact performance. Simplify AI agent management for a more streamlined lifecycle ServiceNow AI Agent Orchestrator and AI Agent Studio are also now generally available with expanded capabilities to govern the complete AI agent lifecycle - from building AI agents, to onboarding and monitoring their performance, to ensuring enterprises realize the value they need. This includes: Enhanced onboarding capabilities through AI Agent Studio to streamline the setup process with guided instru...

Organizations are facing a critical challenge to AI adoption: how to leverage their domain-specific knowledge to use AI in a way that delivers trustworthy results. Knowledge graphs provide the missing "truth layer" that transforms probabilistic AI outputs into real world business acceleration. Knowledge graphs are powering products for the likes of Amazon and Samsung. The Knowledge graph market is expected to grow to $6.93 Billion by 2030, at a CAGR of 36.6%. Gartner has been advocating for the role of knowledge graphs in AI and the downstream effects in organizations going forward for the last few years. Neither the technology nor the vision are new. Knowledge graph technology has been around for decades, and people like Tony Seale were early to identify its potential for AI. Seale, also known as "The Knowledge Graph Guy", is the founder of the eponymous consulting firm. In this extensive conversation, we covered everything from knowledge graph first principles to application patterns for safe, verifiable AI, real-world experience, trends, predictions, and the way forward. Read the article published on Orchestrate all the Things here: https://linkeddataorchestration.com/2025/03/11/knowledge-graphs-as-the-essential-truth-layer-for-pragmatic-ai/

Marcel Henriquez – Red Data Solutions. We specialize in combining (unstructured) data from multiple sources to provide insights. With the recent developments in AI, this capability is increasingly the base on which these new KM developments are built. We are mainly focused on getting as much data as possible and structuring it to make it work for the required application. On several occasions we team up with other companies specializing in Knowledge Graph systems and conversational chatbots. I am our companies first contact for clients, new and existing, and I am the first to work out requirements with the client team. This is very deliberate, because I do not want technology to limit the question when the client does not yet know what they want exactly. When we have a solution on paper, that is the moment I shift to my technical experience and start working out how the solution on paper can be transformed into working software. #knowledgemanagement #KMsystems #dataprocessing #searchengines #onedatastoremultipleapplications #conversational_intelligence #natural_language_querying https://www.researchgate.net/publication/373707915_CLARK_Building_Conversational_Intelligence_for_Knowledge_Management_in_the_Space_Domain https://reddata.nl/cases/eglossary/ (dutch) https://reddata.nl/cases/esa-taxonomy-tool/ (dutch) Andrew Herd has 25 years of experience in the space domain, with the past decade as Senior Engineer for Corporate Knowledge Management at the European Space Agency (ESA). He has led over 50 lesson capture and learning initiatives, managed knowledge for ESA's largest Directorate, and developed innovative Lessons Learned web and mobile applications. Andrew is a recognized thought leader with over 30 publications and has chaired international panels on lessons learned. He founded BraveLLAMA, dedicated to advancing knowledge management through enabling others to learn from experience, and launched #ExperienceXChange blog for this same purpose. His energies are currently dedicated towards leading a KMGN hosted project: KM Landscape@2025 – and for this he is grateful to all the co-creators, together with whom he is walking, talking and working. Screen Shot of the CLARK System. Like Superman and CLARK Kent: CLARK allows you to transform your data into a super-power.

Unlocking the Power of Business Research with GraphIQIn a recent episode of "The Thoughtful Entrepreneur," host Josh engages in an insightful discussion with Malcolm DeLeo, co-founder of GraphIQ. The conversation explores the complexities of business research, the hurdles organizations encounter in sourcing reliable information, and how GraphIQ is transforming data gathering and utilization for businesses. This episode distills key insights and practical advice, offering a guide for listeners aiming to enhance their business research strategies.Josh begins by emphasizing the significance of business connections, noting that strong relationships with partners, investors, influencers, and clients are crucial for success. Drawing from his extensive experience, he points out that the best business opportunities often stem from these connections. He encourages listeners to access a free video that outlines strategies for achieving 100% inbound business growth without relying on spam, ads, or sales tactics.Malcolm DeLeo shares insights from interviews with over 40 executives, revealing a common challenge: finding reliable companies to do business with. Many organizations rely on Google, personal networks, or consultants for research, leading to the creation of GraphIQ, a search engine tailored for business research. GraphIQ leverages natural language processing (NLP) to compile and organize data from billions of websites, allowing users to search for companies based on specific capabilities and attributes. Malcolm highlights the importance of accurate information in today's AI-driven age and encourages companies to invest in innovative solutions like GraphIQ, which has already transformed workflows and improved efficiency for its users.About Malcolm De Leo:Malcolm De Leo is an innovation expert who has successfully built and developed new markets for both Fortune 500 companies and Silicon Valley Startups. Over his 25 year career, Malcolm's leadership as an evangelist for new ideas, new technologies and for developing innovative cultures is what drives his work. Most recently, as Orbital Insight's Chief of Solution Strategy he was the customer facing person from the product team tasked with understanding the marketplace to provide strategic guidance on how the company delivered scalable and repeatable customer value. Prior to his time at Orbital insight; he was Chief Evangelist at both Quantifind and Netbase. At both companies, he helped pioneer the usage of social media data to drive business decisions across the Fortune 500 Landscape. Before entering Silicon Valley, Malcolm was Global Vice President of Innovation at Daymon Worldwide, the world's largest private brand product broker and also worked developing innovation partnerships and new products for the Clorox Company where he started his career. He holds a Ph.D. in Inorganic Chemistry from University of California at Santa Barbara and an MBA in Technology Management from the University of Phoenix.About GraphIQ:GraphIQ is a massive Knowledge Graph of business information containing trillions of organizational facts and relationships, sourced and continuously updated from billions of websites.Apply to be a Guest on The Thoughtful Entrepreneur: https://go.upmyinfluence.com/podcast-guestLinks Mentioned in this Episode:Want to learn more? Check out GraphIQ website athttps://graphiq.ai/Check out GraphIQ on LinkedIn athttps://www.linkedin.com/company/graphiq-ai/Check out Malcolm De Leo on LinkedIn

Tomaž Levak is the Co-founder and CEO of Trace Labs – OriginTrail core developers. OriginTrail is a web3 infrastructure project combining a decentralized knowledge graph (DKG) and blockchain technologies to create a neutral, inclusive ecosystem. Collective Memory for AI on Decentralized Knowledge Graph // MLOps Podcast #285 with Tomaz Levak, Founder of Trace Labs, Core Developers of OriginTrail. // Abstract The talk focuses on how OriginTrail Decentralized Knowledge Graph serves as a collective memory for AI and enables neuro-symbolic AI. We cover the basics of OriginTrail's symbolic AI fundamentals (i.e. knowledge graphs) and go over details how decentralization improves data integrity, provenance, and user control. We'll cover the DKG role in AI agentic frameworks and how it helps with verifying and accessing diverse data sources, while maintaining compatibility with existing standards. We'll explore practical use cases from the enterprise sector as well as latest integrations into frameworks like ElizaOS. We conclude by outlining the future potential of decentralized AI, AI becoming the interface to “eat” SaaS and the general convergence of AI, Internet and Crypto. // Bio Tomaz Levak, founder of OriginTrail, is active at the intersection of Cryptocurrency, the Internet, and Artificial Intelligence (AI). At the core of OriginTrail is a pursuit of Verifiable Internet for AI, an inclusive framework addressing critical challenges of the world in an AI era. To achieve the goal of Verifiable Internet for AI, OriginTrail's trusted knowledge foundation ensures the provenance and verifiability of information while incentivizing the creation of high-quality knowledge. These advancements are pivotal to unlock the full potential of AI as they minimize the technology's shortfalls such as hallucinations, bias, issues of data ownership, and model collapse. Tomaz's contributions to OriginTrail span over a decade and across multiple fields. He is involved in strategic technical innovations for OriginTrail Decentralized Knowledge Graph (DKG) and NeuroWeb blockchain and was among the authors of all three foundational White Paper documents that defined how OriginTrail technology addresses global challenges. Tomaz contributed to the design of OriginTrail token economies and is driving adoption with global brands such as British Standards Institution, Swiss Federal Railways and World Federation of Haemophilia, among others. Committed to the ongoing expansion of the OriginTrail ecosystem, Tomaz is a regular speaker at key industry events. In his appearances, he highlights the significant value that the OriginTrail DKG brings to diverse sectors, including supply chains, life sciences, healthcare, and scientific research. In a rapidly evolving digital landscape, Tomaz and the OriginTrail ecosystem as a whole are playing an important role in ensuring a more inclusive, transparent and decentralized AI. // MLOps Swag/Merch https://shop.mlops.community/ // Related Links Website: https://origintrail.io Song recommendation: https://open.spotify.com/track/5GGHmGNZYnVSdRERLUSB4w?si=ae744c3ad528424b --------------- ✌️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 Tomaz on LinkedIn: https://www.linkedin.com/in/tomazlevak/

Wikipedia is one of the top the most-visited websites in the world. Additionally, it has influence. The information on Wikipedia is also disseminated across the web on Google's Knowledge Graph and associated search page components, quick summaries of topics on Siri and Alexa, and even into news articles, podcast discussions, and government websites. Wikipedia is also of interest for SEO practitioners, heavily influencing Google search results and now impacting outputs from generative AI software like Chat-GPT.Everyone from journalists, investors, and your customers are using Wikipedia to understand your company. Do you understand how it works and what you can do about it?Guest: Rhiannon RuffEmail | LinkedIn | Lumino Digital's website | Rhiannon's book Managing a Crisis on WikipediaRate this podcast https://ratethispodcast.com/storiesstrategiesConnect with usLinkedIn | X | Instagram | You Tube | Facebook Request a transcript of this episodeSupport the show

In dieser Folge des Podcasts "Barriere los!" sprechen wir mit vier Projektpartnerinnen und -partnern, die gemeinsam am Modellprojekt „Smartes Fichtelgebirge“ arbeiten. Im Zentrum steht ein KI-gestützter Social Knowledge Graph, der verschiedene innovative Ansätze miteinander vereint. Ziel des Projekts ist es, mithilfe Künstlicher Intelligenz und einer strukturierten Wissensrepräsentation alltagsrelevantes Wissen schnell, leicht zugänglich und barrierefrei verfügbar zu machen – digital und für alle. Erfahren Sie im Interview, was dieses zukunftsweisende Projekt so besonders macht und wie daraus ein Produkt entsteht, das verständlich, leicht auffindbar und intuitiv zu bedienen ist.

In dieser Folge des Podcasts "Barriere los!" sprechen wir mit vier Projektpartnerinnen und -partnern, die gemeinsam am Modellprojekt „Smartes Fichtelgebirge“ arbeiten. Im Zentrum steht ein KI-gestützter Social Knowledge Graph, der verschiedene innovative Ansätze miteinander vereint. Ziel des Projekts ist es, mithilfe Künstlicher Intelligenz und einer strukturierten Wissensrepräsentation alltagsrelevantes Wissen schnell, leicht zugänglich und barrierefrei verfügbar zu machen – digital und für alle. Erfahren Sie im Interview, was dieses zukunftsweisende Projekt so besonders macht und wie daraus ein Produkt entsteht, das verständlich, leicht auffindbar und intuitiv zu bedienen ist.

In this episode, we dive into the world of generative AI with May Habib, co-founder of Writer, a platform transforming enterprise AI use. May shares her journey from Qordoba to Writer, emphasizing the impact of transformers in AI. We explore Writer's graph-based RAG approach, and their AI Studio for building custom applications. We also discuss Writer's Autonomous Action functionality, set to revolutionize AI workflows by enabling systems to act autonomously, highlighting AI's potential to accelerate product development and market entry with significant increases in capacity and capability. Writer Website - https://writer.com X/Twitter - https://x.com/get_writer  May Habib LinkedIn - https://www.linkedin.com/in/may-habib X/Twitter - https://x.com/may_habib FIRSTMARK Website - https://firstmark.com X/Twitter - https://twitter.com/FirstMarkCap Matt Turck (Managing Director) LinkedIn - https://www.linkedin.com/in/turck/ X/Twitter - https://twitter.com/mattturck This session was recorded live at a recent Data Driven NYC, our in-person, monthly event series, hosted at Ramp's beautiful HQ. If you are ever in New York, you can join the upcoming events here: https://www.eventbrite.com/o/firstmark-capital-2215570183 (00:00) Intro (01:47) What is Writer? (02:52) Writer's founding story (06:54) Writer is a full-stack company. Why? (07:57) Writer's enterprise use cases (10:51) Knowledge Graph (17:59) Guardrails (20:17) AI Studio (23:16) Palmyra X 004 (27:18) Current state of the AI adoption in enterprises (28:57) Writer's sales approach (31:25) What May Habib is excited about in AI (33:14) Autonomous Action use cases

Speaker Resources:Neo4j+Senzing Tutorial: https://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/#neo4jWhen GraphRAG Goes Bad: A Study in Why you Cannot Afford to Ignore Entity Resolution (Dr. Clair Sullivan): https://www.linkedin.com/pulse/when-graphrag-goesbad-study-why-you-cannot-afford-ignore-sullivan-7ymnc/Paco's NODES 2024 session: https://neo4j.com/nodes2024/agenda/entity-resolved-knowledge-graphs/Graph Power Hour: https://www.youtube.com/playlist?list=PL9-tchmsp1WMnZKYti-tMnt_wyk4nwcbHTomaz Bratanic on GraphReader: https://towardsdatascience.com/implementing-graphreader-with-neo4j-and-langgraph-e4c73826a8b7Tools of the Month:Neo4j GraphRAG Python package:  https://pypi.org/project/neo4j-graphrag/Spring Data Neo4j: https://spring.io/projects/spring-data-neo4jEntity Linking based on Entity Resolution tutorial: https://github.com/louisguitton/spacy-lancedb-linkerhttps://github.com/DerwenAI/strwythuraAskNews (build news datasets) https://asknews.app/The Sentry https://atlas.thesentry.org/azerbaijan-aliyev-empire/Announcements / News:Articles:GraphRAG – The Card Game https://neo4j.com/developer-blog/graphrag-card-game/Turn Your CSVs Into Graphs Using LLMs https://neo4j.com/developer-blog/csv-into-graph-using-llm/Detecting Bank Fraud With Neo4j: The Power of Graph Databases https://neo4j.com/developer-blog/detect-bank-fraud-neo4j-graph-database/Cypher Performance Improvements in Neo4j 5 https://neo4j.com/developer-blog/cypher-performance-neo4j-5/New GraphAcademy Course: Building Knowledge Graphs With LLMs https://neo4j.com/developer-blog/new-building-knowledge-graphs-llms/Efficiently Monitor Neo4j and Identify Problematic Queries https://neo4j.com/developer-blog/monitor-and-id-problem-queries/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEventsAll Neo4j events: https://neo4j.com/events/(Nov 5) Conference (virtual): XtremeJ https://xtremej.dev/2024/schedule/(Nov 7) Conference (virtual): NODES 2024 https://dev.neo4j.com/nodes24(Nov 8) Conference (Austin, TX, USA): MLOps World https://mlopsworld.com/(Nov 12) Conference (Baltimore, MD, USA): ISWC https://iswc2024.semanticweb.org/event/3715c6fc-e2d7-47eb-8c01-5fe4ac589a52/summary(Nov 13) Meetup (Seattle, WA, USA): Puget Sound Programming Python (PuPPY) - Talk night Rover https://www.meetup.com/psppython/events/303896335/?eventOrigin=group_events_list(Nov 14) Meetup (Seattle, WA, USA): AI Workflow Essentials (with Pinecone, Neo4J, Boundary, Union) https://lu.ma/75nv6dd3(Nov 14) Conference (Reston, VA, USA): Senzing User Conference https://senzing.com/senzing-event-calendar/(Nov 18) Meetup (Cleveland, OH, USA): Cleveland Big Data mega-meetup https://www.meetup.com/Cleveland-Hadoop/(Nov 19) Chicago Java User Group (Chicago, IL, USA): https://cjug.org/cjug-meeting-intro/#/(Dec 3) Conference (London, UK): Linkurious Days https://resources.linkurious.com/linkurious-days-london(Dec 10) Meetup (London, UK): ESR meetup in London by Neural Alpha(Dec 11-13) Conference (London, UK): Connected Data London https://2024.connected-data.london/

Many events have passed since our post-Sapphire clean core review. Time to hash out AI, clean core and more - in the context of ASUG's fall events, and fresh research data. Analyst Josh Greenbaum, ASUG CEO Geoff Scott and your host Jon Reed rejoin for another unscripted review of event lessons learned - from SAP shows and beyond. Along with Geoff's new ASUG data, Jon shares his top event gripes and learnings; Josh shares his clean core/BTP findings. Then we look ahead to what we want to learn from ASUG Tech Connect, and why Enterprise Architects are central to the changes afoot. Show notes: 1 - 31:00 - fall event reviews, ASUG research findings, clean core, and AI hype vs customer reality 31:00 - SAP TechEd news review 51:00 - ASUG Tech Connect preview, why Enterprise Architects matter, and community as a learning framework Jon recommends listeners look at SAP's Knowledge Graph pursuits, a notable TechEd topic not covered in this podcast.

Many events have passed since our post-Sapphire clean core review. Time to hash out AI, clean core and more - in the context of ASUG's fall events, and fresh research data. Analyst Josh Greenbaum, ASUG CEO Geoff Scott and your host Jon Reed rejoin for another unscripted review of event lessons learned - from SAP shows and beyond. Along with Geoff's new ASUG data, Jon shares his top event gripes and learnings; Josh shares his clean core/BTP findings. Then we look ahead to what we want to learn from ASUG Tech Connect, and why Enterprise Architects are central to the changes afoot. Show notes: 1 - 31:00 - fall event reviews, ASUG research findings, clean core, and AI hype vs customer reality 31:00 - SAP TechEd news review 51:00 - ASUG Tech Connect preview, why Enterprise Architects matter, and community as a learning framework Jon recommends listeners look at SAP's Knowledge Graph pursuits, a notable TechEd topic not covered in this podcast. Here is a link to the smartShift research mentioned in the podcast.

⚠️ SMART, advanced episode!

Speaker Resources:Eastridge Analytics: https://www.eastridge-analytics.com/Graph Data Science with Python and Neo4j book: https://a.co/d/hkfkxPrLinkedIn profile: https://www.linkedin.com/in/timeastridge/NODES 2024 (look for more info on Tim's talk soon!): https://dev.neo4j.com/nodes24Neo4j GraphAcademy: https://graphacademy.neo4j.com/Graph Algorithms for Data Science (Tomaž Bratanic): https://a.co/d/7WhibUkTools of the Month:Jennifer: VS Code https://code.visualstudio.com/Jason: Cursor AI https://www.cursor.com/Tim: Neo4j LLM Graph Builder https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/Announcements / News:Articles:Graph Databases Offer a Deeper Understanding of Organizational Risk https://neo4j.com/developer-blog/graph-database-organizational-risk/Using Embeddings to Represent String Edit Distance in Neo4j https://neo4j.com/developer-blog/embeddings-string-edit-distance/Build a Knowledge Graph-based Agent with Llama 3.1, NVIDIA NIM, and LangChain https://neo4j.com/developer-blog/knowledge-graph-llama-nvidia-langchain/Entity Linking and Relationship Extraction With Relik in LlamaIndex https://neo4j.com/developer-blog/entity-linking-relationship-extraction-relik-llamaindex/Integrating Microsoft GraphRAG into Neo4j https://neo4j.com/developer-blog/microsoft-graphrag-neo4j/Ingesting Documents Simultaneously to Neo4j & Milvus https://neo4j.com/developer-blog/ingest-documents-neo4j-milvus/Enriching Vector Search With Graph Traversal Using the Neo4j GenAI Package https://neo4j.com/developer-blog/graph-traversal-neo4j-genai-package/Create a Neo4j GraphRAG Workflow Using LangChain and LangGraph https://neo4j.com/developer-blog/neo4j-graphrag-workflow-langchain-langgraph/Introducing Concurrent Writes to Cypher Subqueries https://neo4j.com/developer-blog/concurrent-writes-cypher-subqueries/Running Neo4j on a Commodore 64 https://neo4j.com/developer-blog/neo4j-commodore-64/Change Data Capture and Neo4j Connector for Confluent and Apache Kafka Go GA https://neo4j.com/developer-blog/change-data-capture-cdc-ga/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEventsAll Neo4j events: https://neo4j.com/events/(Sep 9) Conference (San Francisco, CA, USA): Pre-AI Conference Hack Day:  https://lu.ma/bsype6t6?tk=1dgMCa(Sep 9-11) Conference (San Francisco, CA, USA): AI Conference: https://aiconference.com/(Sep 10) Meetup (San Francisco, CA, USA): AI Tools HackNight: https://lu.ma/ozt7jtq5(Sep 12) Meetup (San Jose, CA, USA): AI & Tech Talks:  https://lu.ma/jjgnoqik?tk=sMOLyE(Sep 24-26) Conference (Dallas, TX, USA): JConf https://2024.jconf.dev/(Sep 30-Oct 3) Conference (Denver, CO, USA): dev2next https://www.dev2next.com/(Oct - TBD) Meetup (Charlotte, NC, USA): Data Science Meetup https://www.meetup.com/Data-Science-Charlotte/

I'm talking with veteran SEO expert Erin Sparks, host of the "Edge of the Web" podcast. Erin shares insider knowledge on building trust with Google and mastering SEO foundations.Key Topics:The Evolution of SEO: Discover how SEO has transformed since 2004, emphasizing accountability and digital strategy. Erin explains the shift from traditional marketing to a data-driven approach, highlighting the importance of staying current in the fast-paced digital landscape.Google's Knowledge Graph: Learn how to leverage Google's Knowledge Graph to enhance your online visibility. Erin discusses how businesses can curate detailed information about their services and products to establish authority and trust with Google.Effective Content Strategy: Erin delves into the importance of content clustering and internal linking. He provides practical tips on organizing your content to improve user experience and increase your website's relevance in Google's eyes.Maximizing Multimedia in SEO: Understand the role of podcasts, videos, and structured data in boosting your SEO. Erin highlights the benefits of hosting content on your website and using structured data to make your content more discoverable.Practical SEO Tools and Techniques: Erin introduces tools like AlsoAsked.com for discovering user queries and content gaps. He also explains the use of structured data and schema to communicate effectively with search engines, enhancing your site's visibility.What you can do:Targeting Non-Branded Keywords: Learn why focusing on non-branded keywords can attract a broader audience and enhance your site's discoverability.Optimizing Internal Links: Find out how to use internal linking to guide users and improve your site's navigability, helping Google understand your content structure.Implementing Structured Data: Erin breaks down the process of using structured data to provide search engines with a clear understanding of your site's content, leading to better rankings.Featured Highlights:ErinSend me a text!The Growth GearExplore business growth and success strategies with Tim Jordan on 'The Growth Gear.Listen on: Apple Podcasts Spotify This Is PropagandaChallenging marketers' delusions about the cultural impact of our work. A WEBBY winner!Listen on: Apple Podcasts SpotifySupport the Show.Search the Simple and Smart SEO Show podcast for something you heard! It's free!Apply to be my podcast guest!

Speaker Resources:Testcontainers https://testcontainers.com/NODES 2024 https://dev.neo4j.com/nodes24Tools of the Month:Neo4j Kubernetes documentation https://neo4j.com/docs/operations-manual/current/kubernetes/ragas framework https://ragas.io/Haiper.ai https://haiper.ai/home (Neo4j GenAI Package + DreamStudio.ai)Announcements / News:Neo4j GenAI Ecosystem Tools https://neo4j.com/labs/genai-ecosystem/Neo4j Knowledge Graph Builder https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/Neoconverse (text-to-cypher) https://neo4j.com/labs/genai-ecosystem/neoconverse/LLM framework integrations: LlamaIndex, LangChain, Spring AI, Haystack, Langchain4j, Semantik Kernel, DSPy Project RunwayRepository https://github.com/a-s-g93/neo4j-runwayArticles:GenAI Starter Kit: Everything You Need to Build an Application with Spring AI in Java https://neo4j.com/developer-blog/genai-starter-kit-spring-java/Knowledge Graph vs. Vector RAG: Benchmarking, Optimization Levers, and a Financial Analysis Example https://neo4j.com/developer-blog/knowledge-graph-vs-vector-rag/From Ancient Epic to Modern Marvel: Demystifying the Mahabharata Chatbot with GraphRAG (Part 3) https://neo4j.com/developer-blog/mahabharata-epic-graph-database-3/Unleashing the Power of NLP with LlamaIndex and Neo4j https://neo4j.com/developer-blog/nlp-llamaindex-neo4j/Rags to Reqs: Making ASVS Accessible Through the Power of Graphs and Chatbots https://neo4j.com/developer-blog/asvs-security-graph-chatbot/Data Exploration With the Neo4j Runway Python Library https://neo4j.com/developer-blog/neo4j-runway-python-exploration/Easy Data Ingestion With Neo4j Runway and arrows.app https://neo4j.com/developer-blog/neo4j-runway-python-ingestion/A Tale of LLMs and Graphs: The GenAI Graph Gathering https://neo4j.com/developer-blog/genai-graph-gathering/Get Started With GraphRAG: Neo4j's Ecosystem Tools https://neo4j.com/developer-blog/graphrag-ecosystem-tools/LLM Knowledge Graph Builder: From Zero to GraphRAG in Five Minutes https://neo4j.com/developer-blog/graphrag-llm-knowledge-graph-builder/A Brief History of SQL and the Rise of Graph Queries https://neo4j.com/developer-blog/gql-sql-history/Customizing Property Graph Index in LlamaIndex https://neo4j.com/developer-blog/property-graph-index-llamaindex/Graph Exploration By All MEANS With mongo2neo4j and SemSpect https://neo4j.com/developer-blog/mean-stack-mongo2neo4j-semspect/Mix and Batch: A Technique for Fast, Parallel Relationship Loading in Neo4j https://neo4j.com/developer-blog/mix-and-batch-relationship-load/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Jul 4) Livestream (virtual): GraphAcademy Live: Cypher Fundamentals https://www.youtube.com/@neo4j/live(Jul 8) Workshop (Bengaluru, India): Neo4j and GCP Generative AI Workshop(Jul 9) GenAI + Graph Meetup (Osaka, Japan) https://jp-neo4j-usersgroup.connpass.com/event/322658/(Jul 17-19) Conference (Berlin, Germany): WeAreDevelopers World Congress 2024 https://www.wearedevelopers.com/world-congress(Jul 18) Meetup (Berlin, Germany): Ollama & Friends Coming to AI Tinkerers Berlin https://berlin.aitinkerers.org/p/ollama-friends-coming-to-ai-tinkerers-berlin(Jul 19) Meetup (Bengaluru, India): Graphing the Future: How Generative AI, RAGs and Knowledge Graphs are Shaping AI https://www.meetup.com/graph-database-bengaluru/events/301273119/?isFirstPublish=true(Jul 28-30) Conference (Sydney, Australia): Gartner Data & Analytics Summit Sydney https://neo4j.com/event/gartner-data-analytics-summit-sydney-2/(Jul 28 - Aug 2) Conference (Wisconsin Dells, Wisconsin, USA): THAT Conference https://thatconference.com/activities/4AlNeqK2OogWQFdhkfuc(Jul 31) Meetup (Richmond, Virginia, USA): Connecting the future: Integrating Neo4j with GenAI, LLMs and RAGs https://www.meetup.com/graphdb-melbourne/events/301618964/?isFirstPublish=true(Jul 31) Meetup (Sydney, Australia): Decoding the Generative AI Landscape: A Deep Dive into RAGs and Graphs https://www.meetup.com/graphdb-sydney/events/301635885/?isFirstPublish=true

In today's episode of the SEOLeverage Podcast, Gert and his guest, Tim Warren discussed the future of AI technology and its impact on SEO. They emphasize the need to understand and embrace these changes to remain competitive while acknowledging the limitations of machine learning and AI in capturing human emotions.  They also discuss the potential impact of AI on the legal industry, proposing specialist AI engines and a shared platform for the entire industry. Finally, Tim Warren tells Gert about his hopes that Knowledge Graph will make it possible for Google to have personalized conversations that understand who people are. Podcast Highlights: 00:00 Prologue 01:06 Introduction to the podcast episode topic and the guest 02:15 Tim Warren's background and his role as a Chief Provocation Officer 03:46 The importance of asking questions in the face of change 04:39 AI impact on white-collar jobs 08:06 The Gartner Hype Cycle and AI's evolution 13:26 Rise and fall of AI companies 16:06 The Importance of Human Expertise in SEO 23:12 Why more and more people are utilizing AI for their online search 32:13 AI in Personal Finance 36:35 The importance of trust and personalization in AI 43:35 The role of digital companions 50:41 Where to connect with Tim Warren? 51:09 End   Resources:  ChatGPT - https://chatgpt.com/ Anthropic - https://www.anthropic.com/ OpenAI - https://openai.com/ Claude - https://claude.ai CopilotAI - https://www.copilotai.com/ Perplexity - https://www.perplexity.ai/ Connect with Tim Warren: LinkedIn - https://www.linkedin.com/in/tswarren/   Connect with Gert Mellak: Website: https://seoleverage.com/ Email: info@seoleverage.com  

Speaker Resources:Diffbot https://www.diffbot.com/Tomaz Bratanic's Medium blog: https://bratanic-tomaz.medium.com/What is DSP/DSPy? https://github.com/stanfordnlp/dspyTools of the Month:cypher-shell command line tool https://neo4j.com/docs/operations-manual/current/tools/cypher-shell/Langchain/Diffbot graph transformer https://python.langchain.com/v0.1/docs/integrations/graphs/diffbot/st-cytoscape https://github.com/vivien000/st-cytoscapeAnnouncements / News:NODES 2024 CfP resources:GraphStuff episode https://graphstuff.fm/episodes/navigating-a-technical-conference-talk-from-submission-to-deliveryNODES submission tips: https://neo4j.com/blog/nodes-talk-submission-tips/How to Submit a Technical Presentation https://jmhreif.com/blog/nodes-2024-cfp/Articles:Topic Extraction with Neo4j GDS for Better Semantic Search in RAG applications https://neo4j.com/developer-blog/topic-extraction-semantic-search-rag/Using LlamaParse to Create Knowledge Graphs from Documents https://neo4j.com/developer-blog/llamaparse-knowledge-graph-documents/Going Meta: Wrapping Up GraphRAG, Vectors, and Knowledge Graphs https://neo4j.com/developer-blog/going-meta-knowledge-graph-rag-vector/Unveiling the Mahabharata's Web: Analyzing Epic Relationships with Neo4j Graph Database (Part 1) https://neo4j.com/developer-blog/mahabharata-epic-graph-database-1/Bringing the Mahabharata Epic to Life: A Neo4j-Powered Chatbot with Google Gemini (Part 2) https://neo4j.com/developer-blog/mahabharata-epic-graph-database-2/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Jun 4) Meetup (virtual): Tuesday Tech Talks: Graph Based RAG w/ Demo https://lu.ma/tys2a4zt?tk=ax2gtz(Jun 4) Workshop (virtual): Discover Neo4j Aura: The Future of Graph Database-as-a-Service https://go.neo4j.com/DE-240604-Discover-Aura-Workshop_Registration.html(Jun 5) Conference (Paris, France): GraphSummit Paris https://neo4j.com/graphsummit/paris24/(Jun 5) Workshop (Sydney, Australia): Neo4j and GCP Generative AI Workshop https://go.neo4j.com/LE240606Neo4jandGCPGenerativeAIWorkshop-Sydney_Registration.html(Jun 7) Conference (Athens, Greece): Generative AI for Front-end Developers https://athens.cityjsconf.org/talk/3b9XHj1HBahP8KJ13uWVui(Jun 10) Conference (San Francisco, California, USA): Data & AI Summit https://neo4j.com/event/data-ai-summit-2/(Jun 11) Meetup (San Francisco, California, USA): HackNight at GitHub with Graphs and Vectors https://www.meetup.com/graphdb-sf/events/301026060/?isFirstPublish=true(Jun 10) Workshop (Jakarta, Indonesia): Neo4j and GCP Generative AI Workshop https://go.neo4j.com/LE240423Neo4jandGCPGenerativeAIWorkshopJakarta_Registration.html(Jun 11) Conference (Oslo, Norway): NDC Oslo - Beyond Vectors: Evolving GenAI through Transformative Tools and Methods https://ndcoslo.com/agenda/beyond-vectors-evolving-genai-through-transformative-tools-and-methods-0x1u/011ha54g6jp(Jun 12) Conference (Munich, Germany): GraphTalk: Pharma https://go.neo4j.com/LE240612GraphTalkPharmaMunich_Registration.html(Jun 12) Conference (Frankfurt, Germany): Google Summit https://cloudonair.withgoogle.com/events/summit-mitte-2024(Jun 12) Livestream (virtual+München, Germany): LifeScience Hybrid Event 2024 https://go.neo4j.com/LE240612LifeScienceWorkshop2024_01Registration.html(Jun 12) Meetup (Brisbane, Australia): Graph Database Brisbane https://www.meetup.com/graph-database-brisbane/events/300367474/?isFirstPublish=true(Jun 12) Meetup (San Francisco, California, USA): Introduction to RAG https://lu.ma/u4uhtfqz(Jun 18) Meetup (London, UK): ISO GQL - The ISO Standard for Graph Has Arrived https://www.meetup.com/graphdb-uk/events/300712991/(Jun 20) Meetup (Stuttgart, Germany): Uniting Large Language Models and Knowledge Graphs https://neo4j.com/event/genai-breakfast-session-stuttgart-uniting-large-language-models-and-knowledge-graphs/(Jun 20) Meetup (Reston, Virginia, USA): LLMs, Vectors, Graph Databases and RAG in the Cloud https://lu.ma/mctijpjm(Jun 25) Conference (San Francisco, California, USA): AI Engineer World's Fair https://www.ai.engineer/worldsfair(Jun 26) Conference (virtual): Neo4j Connections GenAI https://neo4j.com/connections/go-from-genai-pilot-to-production-faster-with-a-knowledge-graph-june-26/(Jun 27) Conference (Kansas City, Missouri, USA): KCDC 2024 https://www.kcdc.info/(Jun 26) Conference (virtual): Neo4j Connections GenAI (Asia Pacific) https://neo4j.com/connections/go-from-genai-pilot-to-production-faster-with-a-knowledge-graph-asia-pacific-june-27/

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

Summary Artificial intelligence has dominated the headlines for several months due to the successes of large language models. This has prompted numerous debates about the possibility of, and timeline for, artificial general intelligence (AGI). Peter Voss has dedicated decades of his life to the pursuit of truly intelligent software through the approach of cognitive AI. In this episode he explains his approach to building AI in a more human-like fashion and the emphasis on learning rather than statistical prediction. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management Dagster offers a new approach to building and running data platforms and data pipelines. It is an open-source, cloud-native orchestrator for the whole development lifecycle, with integrated lineage and observability, a declarative programming model, and best-in-class testability. Your team can get up and running in minutes thanks to Dagster Cloud, an enterprise-class hosted solution that offers serverless and hybrid deployments, enhanced security, and on-demand ephemeral test deployments. Go to dataengineeringpodcast.com/dagster (https://www.dataengineeringpodcast.com/dagster) today to get started. Your first 30 days are free! Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics. Trusted by teams of all sizes, including Comcast and Doordash, Starburst is a data lake analytics platform that delivers the adaptability and flexibility a lakehouse ecosystem promises. And Starburst does all of this on an open architecture with first-class support for Apache Iceberg, Delta Lake and Hudi, so you always maintain ownership of your data. Want to see Starburst in action? Go to dataengineeringpodcast.com/starburst (https://www.dataengineeringpodcast.com/starburst) and get $500 in credits to try Starburst Galaxy today, the easiest and fastest way to get started using Trino. Your host is Tobias Macey and today I'm interviewing Peter Voss about what is involved in making your AI applications more "human" Interview Introduction How did you get involved in machine learning? Can you start by unpacking the idea of "human-like" AI? How does that contrast with the conception of "AGI"? The applications and limitations of GPT/LLM models have been dominating the popular conversation around AI. How do you see that impacting the overrall ecosystem of ML/AI applications and investment? The fundamental/foundational challenge of every AI use case is sourcing appropriate data. What are the strategies that you have found useful to acquire, evaluate, and prepare data at an appropriate scale to build high quality models? What are the opportunities and limitations of causal modeling techniques for generalized AI models? As AI systems gain more sophistication there is a challenge with establishing and maintaining trust. What are the risks involved in deploying more human-level AI systems and monitoring their reliability? What are the practical/architectural methods necessary to build more cognitive AI systems? How would you characterize the ecosystem of tools/frameworks available for creating, evolving, and maintaining these applications? What are the most interesting, innovative, or unexpected ways that you have seen cognitive AI applied? What are the most interesting, unexpected, or challenging lessons that you have learned while working on desiging/developing cognitive AI systems? When is cognitive AI the wrong choice? What do you have planned for the future of cognitive AI applications at Aigo? Contact Info LinkedIn (https://www.linkedin.com/in/vosspeter/) Website (http://optimal.org/voss.html) Parting Question From your perspective, what is the biggest barrier to adoption of machine learning today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. Links Aigo.ai (https://aigo.ai/) Artificial General Intelligence (https://aigo.ai/what-is-real-agi/) Cognitive AI (https://aigo.ai/cognitive-ai/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Causal Modeling (https://en.wikipedia.org/wiki/Causal_model) Bayesian Statistics (https://en.wikipedia.org/wiki/Bayesian_statistics) Thinking Fast & Slow (https://amzn.to/3UJKsmK) by Daniel Kahneman (affiliate link) Agent-Based Modeling (https://en.wikipedia.org/wiki/Agent-based_model) Reinforcement Learning (https://en.wikipedia.org/wiki/Reinforcement_learning) DARPA 3 Waves of AI (https://www.darpa.mil/about-us/darpa-perspective-on-ai) presentation Why Don't We Have AGI Yet? (https://arxiv.org/abs/2308.03598) whitepaper Concepts Is All You Need (https://arxiv.org/abs/2309.01622) Whitepaper Hellen Keller (https://en.wikipedia.org/wiki/Helen_Keller) Stephen Hawking (https://en.wikipedia.org/wiki/Stephen_Hawking) The intro and outro music is from Hitman's Lovesong feat. Paola Graziano (https://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Tales_Of_A_Dead_Fish/Hitmans_Lovesong/) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/)/CC BY-SA 3.0 (https://creativecommons.org/licenses/by-sa/3.0/)

Tools of the Month:Descript https://www.descript.com/LLM Graph Builder by Neo4j https://neo4j.com/labs/genai-ecosystem/llm-graph-builder/LangSmith https://www.langchain.com/langsmithObsidian https://obsidian.md/ Announcements / News:NODES 2024: Submit Your Talk https://neo4j.com/blog/nodes-by-neo4j/ISO GQL Standard:Press release: https://neo4j.com/press-releases/gql-standard/Blog post: https://neo4j.com/blog/gql-international-standard/Blog post: https://neo4j.com/blog/cypher-path-gql/Articles:Enhancing RAG with Neo4j Cypher and Vector Templates Using LangChain Agents https://neo4j.com/developer-blog/rag-cypher-vector-templates-langchain-agent/Graph Data Models for RAG Applications https://neo4j.com/developer-blog/graph-data-models-rag-applications/Maximizing Your Neo4j Project's Potential: An In-depth Guide to Solution Assessment https://neo4j.com/developer-blog/neo4j-project-solution-assessment-guide/LangChain Library Adds Full Support for Neo4j Vector Index https://neo4j.com/developer-blog/langchain-library-full-support-neo4j-vector-index/Constructing Knowledge Graphs From Unstructured Text Using LLMs https://neo4j.com/developer-blog/construct-knowledge-graphs-unstructured-text/Entity Resolved Knowledge Graphs: A Tutorial https://neo4j.com/developer-blog/entity-resolved-knowledge-graphs/The Future of Knowledge Graph: Will Structured and Semantic Search Become One? https://neo4j.com/developer-blog/knowledge-graph-structured-semantic-search/Building RAG Applications With the Neo4j GenAI Stack: A Comprehensive Guide https://neo4j.com/developer-blog/rag-genai-stack-guide/Adding Retrieval-Augmented Generation (RAG) to Your GraphQL API https://neo4j.com/developer-blog/rag-graphql-api/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(May 1) Fireside chat (virtual): Advanced RAG Techniques with Graph Databases for LLMs https://www.eventbrite.com/e/advanced-rag-techniques-with-graph-databases-for-llms-jason-koo-neo4j-tickets-878275013207(May 2) Hands-On Lab (Herndon, VA, USA): AWS and Neo4j Generative AI https://go.neo4j.com/LE-240502-AWS-GenAI-Workshop-Herndon_Registration.html(May 2) YT series (virtual): Knowledge Graph Builder App https://youtube.com/live/NbyxWAC2TLc(May 5) Conference (New York City, NY, USA): The Knowledge Graph Conference https://go.neo4j.com/TS-240506-The-Knowledge-Graph-Conference_Registration-Page.html(May 6) Conference (Singapore): AWS Summit 2024 https://neo4j.com/event/aws-summit-2024-singapore/(May 7) Meetup (virtual): Knowledge Graph-Based Chatbot https://www.meetup.com/nycneo4j/events/299160585/(May 7) Conference (virtual): WeAreDevelopers - Security Day https://www.wearedevelopers.com/event/security-day-may-2024(May 8) Webinar (virtual): Unveiling the Power of Knowledge Graphs and AWS Bedrock in Advancing Generative AI https://go.neo4j.com/WBR240508AWSBedrock_Registration.html(May 8) Meetup (Sydney, Australia): Graph Database Sydney Meetup https://www.meetup.com/graphdb-sydney/events/300446306/?isFirstPublish=true(May 9) Webinar (virtual): Building Powerful Fraud Detection Applications with Neo4j https://go.neo4j.com/WBR-240509-Fraud-Detection_Registration.html(May 11) Meetup (Bangalore, India): GenAI Meetup: Deep diving into RAG & Multi agent framework https://www.meetup.com/graph-database-bengaluru/events/300324064/?isFirstPublish=true(May 12) Conference (London, UK): Gartner Data & Analytics Summit https://www.gartner.com/en/conferences/emea/data-analytics-uk(May 13) Conference (Milan, Italy): GraphSummit Milan https://neo4j.com/graphsummit/milan24/(May 14) Hands-On Lab (Santa Monica, CA, USA): GenAI Hands-On Lab with AWS & Deloitte https://go.neo4j.com/LE-240514-AWS-Bedrock-Workshop-Santa-Monica_Registration.html(May 14) Conference (Kraków, Poland): Geecon 2024 https://2024.geecon.org/speakers/info.html?id=900(May 14) Conference (Bengaluru, India): AWS Summit https://neo4j.com/event/aws-summit-2024-bengaluru/(May 15) Conference (Berlin, Germany): AWS Summit Berlin https://pages.awscloud.com/aws-summit-berlin-2024-registration.html?Languages=German(May 15) Conference (Sydney, Australia): Google Cloud Summit https://neo4j.com/event/google-cloud-summit-sydney/(May 15) Hands-On Lab (Palo Alto, CA, USA): GenAI Hands-On Lab with AWS & Deloitte https://go.neo4j.com/LE-240507-AWS-Bedrock-Workshop-Palo-Alto_Registration.html(May 15) Meetup (Dresden, Germany): Dresdner Datenbankforum Graph Data Science with Neo4j https://www.dresdner-datenbankforum.de/anstehende-vortr%C3%A4ge#h.y2x2aesouh7p(May 15) Meetup (Mainz, Germany): Neo4j for Java Developers https://www.meetup.com/jug-mainz/events/299232685(May 16) Hands-on Lab (San Francisco, CA, USA): GenAI Hands-on Lab with AWS and Deloitte https://go.neo4j.com/LE-----240509-Neo4j-Deloitte--AWS-Generative-AI-Workshop_Registration.html(May 20) Conference (Stockholm, Sweden): GraphSummit Stockholm https://neo4j.com/graphsummit/stockholm24/(May 21) Hands-on Lab (Chicago, IL, USA): GenAI Hands-On Lab with AWS & Deloitte https://go.neo4j.com/LE-240521-AWS-GenAI-Workshop-Chicago_Registration.html(May 22) Conference (Milan, Italy): AWS Summit Milan https://aws.amazon.com/it/events/summits/emea/milano/(May 27) Conference (Jakarta, Indonesia): Google Cloud Summit https://neo4j.com/event/google-cloud-summit-jakarta/(May 27) Conference (Sofia, Bulgaria): jPrime 2024 https://jprime.io/

Want to learn how to use the Knowledge Graph for SEO benefits? I got you covered. I just had an incredible conversation with the one and only Jason Barnard from Kalicube about leveraging the power of Google's Knowledge Graph for SEO benefits. Jason shared his invaluable insights and strategies on how you can harness the Knowledge Graph to: ✅ Boost your online visibility ✅ Establish your brand authority ✅ Dominate search engine results pages If you missed the live show, don't worry! You can watch the full interview on my YouTube channel right now: https://youtube.com/live/eOOq8qOc4OY Follow SEO Consultant Olga Zarr or hire Olga to help you with SEO Follow Olga Zarr X/Twitter Follow Olga Zarr on LinkedIn The best SEO newsletter The best SEO podcast SEO consultant Olga Zarr

Tools of the Month:apoc.create.vRelationship https://neo4j.com/docs/apoc/current/overview/apoc.create/apoc.create.vRelationship/GenAI Starter Kits for Langchain, LlamaIndex, Spring.AI and Semantic Kernel, covering the most popular orchestration frameworks in Python, Java, and dotnet. https://neo4j.com/labs/genai-ecosystem/Vish: Vector support in Neo4j https://neo4j.com/docs/cypher-manual/current/indexes/semantic-indexes/vector-indexes/Articles:Implementing RAG: How to Write a Graph Retrieval Query in LangChain https://neo4j.com/developer-blog/rag-graph-retrieval-query-langchain/Implementing Advanced Retrieval RAG Strategies with Neo4j https://neo4j.com/developer-blog/advanced-rag-strategies-neo4j/Using a Knowledge Graph to Implement a RAG Application https://neo4j.com/developer-blog/knowledge-graph-rag-application/Generative Transformation from ER Diagram to Graph Model Using Google's Gemini Pro https://neo4j.com/developer-blog/genai-graph-model-google-gemini-pro/Cypher Workbench as a Neo4j Labs Project https://neo4j.com/developer-blog/cypher-workbench-neo4j-labs-project/Accelerate Neo4j App Development with Low-Code Keymaker Framework https://neo4j.com/developer-blog/keymaker-low-code-neo4j-framework/Needle StarterKit 2.0: Templates, Chatbot, and More! https://neo4j.com/developer-blog/needle-starterkit-2-0-templates-chatbot/Announcing Neo4j JDBC Driver Version 6 https://neo4j.com/developer-blog/neo4j-jdbc-driver-v6/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Apr 2) YouTube series: Going Meta - A Series on Graph, Semantics, and Knowledge Episode 27 https://www.youtube.com/@neo4j/live(Apr 2) Conference (Paris, France): AWS Summit Paris https://aws.amazon.com/fr/events/summits/emea/paris/(Apr 8) Conference (London, UK): QCon London https://qconlondon.com/(Apr 8) Conference (Madrid, Spain): GraphSummit Madrid https://neo4j.com/graphsummit/madrid24/(Apr 8) Conference (Nürburgring, Germany): Javaland 2024 https://www.javaland.eu/en/home/(Apr 9) Conference (Las Vegas, NV, USA): Google Cloud Next https://cloud.withgoogle.com/next(Apr 9) Conference (Atlanta, GA, USA): DevNexus 2024 https://devnexus.com/(Apr 9) Workshop (Munich, Germany): Amazon Bedrock & Neo4j https://go.neo4j.com/LE240409AWSBedrockWorkshopMunich_Registration.html(Apr 9) Conference (Sydney, Australia): AWS Summit Sydney https://neo4j.com/event/aws-summit-sydney/(Apr 13) Workshop (San Francisco, CA, USA): GenAI Beyond Chat with RAG, Knowledge Graphs and Python https://www.meetup.com/graphdb-sf/events/299339190/(Apr 16) Conference (Paris, France): Devoxx France https://www.devoxx.fr/(Apr 17) Workshop (Toronto, ON, Canada): Neo4j & AWS Generative AI https://go.neo4j.com/LE240417AWSandNeo4jGenerativeAIHands-onLabToronto_Registration.html(Apr 18) Meetup (San Francisco, CA, USA): Cloud-Native Geospatial Analytics Combining Spatial SQL & Graph Data Science https://www.meetup.com/graphdb-sf/events/297525658/(Apr 23) Conference (Bengaluru, India): GIDS India 2024 https://www.meetup.com/graphdb-sf/events/297525658/(Apr 23) Workshop (Chicago, IL, USA): Neo4j and Google Cloud GenAI Hands-On https://go.neo4j.com/LE240423-Neo4j-GCP-GenAI-Workshop---Chicago_Registration.html(Apr 23) Conference (Stockholm, Sweden): Penningtvattsdagarna https://penningtvattsdagarna.se/anmalan/(Apr 24) Conference (Stockholm, Sweden): Data Innovation Summit https://datainnovationsummit.com/(Apr 24) Conference (London, UK): AWS Summit London https://aws.amazon.com/events/summits/emea/london/(Apr 24) Conference (Munich, Germany): GraphSummit Munich https://neo4j.com/graphsummit/munich-apr-24/(Apr 25) Hands-On Lab (New York City, NY, USA): AWS and Neo4j Generative AI https://go.neo4j.com/LE-240425-LE-240425-AWS-GenAI-Workshop-NYC_Registration.html(Apr 25) Meetup (London, UK): Modern Java Ecosystems: Advancing Connectivity and Cloud Deployment https://www.meetup.com/graphdb-uk/events/299949029/

What is “entity SEO” and how can entities play a practical role when doing SEO?  Wix's Mordy Oberstein and Crystal Carter break down the role that entities have in foundational SEO. The duo shares actual cases where understanding how Google deals with entities plays a major role in doing good SEO.  Indeed's SEO Product Manager, Gus Pelogia, joins the conversation to show you how to create targeted entity associations on your website.  Plus, we explore YouTube's contextual approach to entities. Don't forget your name tag, as this week; we present the significance behind entities and SEO on this week's episode of the SERP's Up SEO Podcast! Key Segments [00:02:16] What's On This Episode of SERP's Up? [00:03:09] Focus Topic of the Week: Entities in SEO  [00:21:29] Focus Topic Guest: Gus Pelogia [00:25:28] Is This New?  [00:35:05] Snappy News [00:41:03] Follow of the Week Hosts, Guests, & Featured People: Mordy Oberstein Crystal Carter Gus Pelogia  Dixon Jones Resources: SERP's Up Podcast Wix SEO Learning Hub Searchlight SEO Newsletter Wix Studio Wix Studio YouTube News: Google Search Console Adds INP Metric In Core Web Vitals Report Google Clarifies Page Experience & Core Web Vitals Related To Search Rankings

Speaker Resources:Johannes Jolkkonen: https://www.linkedin.com/in/johannesjolkkonen/Johannes's YouTube channel: https://www.youtube.com/@johannesjolkkonenMar 12 Neo4j Live session: https://www.meetup.com/neo4j-online-meetup/events/299526466/Tools of the Month:PDF-bot chunker (GenAI stack): https://github.com/docker/genai-stack/blob/main/pdf_bot.pySpring AI: https://spring.io/projects/spring-aiInstructor (library): https://jxnl.github.io/instructor/Community Projects:Knowledge Graph for Social Science https://youtube.com/live/wBHgTheV08QArticles:Langchain v0.1 - Updating GraphAcademy Neo4j & LLM Courses https://neo4j.com/developer-blog/langchain-graphacademy-llm-courses/A GenAI-Powered Song Finder in Four Lines of Code https://neo4j.com/developer-blog/genai-powered-song-finder/Object Mapping in the Neo4j Driver for .NET https://neo4j.com/developer-blog/object-mapping-neo4j-driver-net/Slow Cypher Statements and How to Fix Them https://neo4j.com/developer-blog/slow-cypher-statements-fix/Using LangChain in Combination with Neo4j to Process YouTube Playlists and Perform Q&A Flow https://medium.com/neo4j/using-langchain-in-combination-with-neo4j-to-process-youtube-playlists-and-perform-q-a-flow-5d245d51a735PyNeoInstance: A User-Friendly Python Library for Neo4j https://neo4j.com/developer-blog/pyneoinstance-python-library-neo4j/Videos:NODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Mar 5) YouTube Series (virtual): Going Meta Episode 26 https://neo4j.com/event/going-meta-a-series-on-graphs-semantics-and-knowledge-episode-26/(Mar 6) Meetup (virtual): Exploring Graphs and Generative AI: Unlocking New Possibilities https://neo4j.com/event/exploring-graphs-and-generative-ai-unlocking-new-possibilities/(Mar 6) Meetup (virtual): Pass or Play: What Does GenAI Mean for the Java Developer? https://neo4j.com/event/pass-or-play-what-does-genai-mean-for-the-java-developer/(Mar 7) Meetup (Bangkok, Thailand): GraphDB Bangkok meetup w/ GraphQL BKK https://neo4j.com/event/graphdb-bangkok-meetup-w-graphql-bkk/(Mar 8) Conference (virtual): WeAreDevelopers Women In Tech Day https://neo4j.com/event/wearedevelopers-women-in-tech-day/(Mar 10) Conference (Orlando, Florida, USA): Gartner Data & Analytics Summit Orlando https://neo4j.com/event/gartner-data-analytics-summit-orlando/(Mar 11) Training (virtual): Knowledge Graphs & Large Language Models Bootcamp https://neo4j.com/event/knowledge-graphs-large-language-models-bootcamp/2024-03-11/(Mar 11) Workshop (Bengaluru, India): Neo4j and GCP Generative AI Workshop https://neo4j.com/event/neo4j-and-gcp-generative-ai-workshop-bengaluru/(Mar 12) Conference (Brussels, Belgium): AWS Public Sector Symposium https://neo4j.com/event/aws-public-sector-symposium-brussels/(Mar 13) Workshop (San Francisco, CA, USA): Google Gen AI Workshop https://neo4j.com/event/google-gen-ai-workshop-san-francisco/(Mar 13) Conference (Singapore): Singapore Data Innovation Summit 2024 https://neo4j.com/event/data-innovation-summit/(Mar 14) Conference (virtual): Data Next Engineering Summit https://neo4j.com/event/data-next-engineering-summit/(Mar 14) Training (virtual): Intro to Neo4j https://neo4j.com/event/training-series-intro-to-neo4j-2/(Mar 14) Workshop (Mountain View, CA, USA): Google Gen AI Workshop https://neo4j.com/event/google-gen-ai-workshop-mountain-view/(Mar 15) Meetup (Delhi, India): Pythonistas and Graphistas: Navigating the World of Graph Databases with Python https://neo4j.com/event/pythonistas-and-graphistas-navigating-the-world-of-graph-databases-with-python/(Mar 15) Meetup (Bengaluru, India): Graph Genesis: Building Tomorrow's Insights Today https://neo4j.com/event/graph-genesis-building-tomorrows-insights-today/(Mar 18) Training (virtual): Knowledge Graphs & Large Language Models Bootcamp https://neo4j.com/event/knowledge-graphs-large-language-models-bootcamp/2024-03-18/(Mar 18) Conference (Paris, France): KubeCon 2024 https://neo4j.com/event/kubecon2024/(Mar 18) Workshop (Singapore): Neo4j and GCP Generative AI https://neo4j.com/event/neo4j-and-gcp-generative-ai-workshop-singapore/(Mar 19) Conference (virtual): AI42 Conference https://neo4j.com/event/ai42-conference/(Mar 19) Workshop (virtual): Tame Your Graph with Liquibase for Neo4j https://neo4j.com/event/training-series-tame-your-graph-with-liquibase-for-neo4j/(Mar 20) Meetup (Melbourne, Australia): GraphDB Melbourne March Madness https://neo4j.com/event/graphdb-melbourne-march-madness/(Mar 20) Meetup (London, UK): The Perfect Couple: Uniting Large Language Models and Knowledge Graphs for Enhanced Knowledge Representation https://neo4j.com/event/the-perfect-couple-uniting-large-language-models-and-knowledge-graphs-for-enhanced-knowledge-representation/(Mar 21) Training (virtual): Mastering Neo4j Deployment for High-Performance RAG Applications https://neo4j.com/event/training-series-mastering-neo4j-deployment-for-high-performance-rag-applications/(Mar 21) Meetup (virtual): Neo4j & Haystack: Graph Databases for LLM Applications https://neo4j.com/event/neo4j-haystack-graph-databases-for-llm-applications/(Mar 21) Workshop (Los Angeles, CA, USA): Google Gen AI https://neo4j.com/event/google-gen-ai-workshop-los-angeles/(Mar 26) Meetup (Sydney, Australia): GraphSyd March Meetup: Unraveling Connections https://neo4j.com/event/graphsyd-march-meetup-unraveling-connections/(Mar 26) Conference (Las Vegas, NV, USA): Microsoft Fabric Community Conference https://neo4j.com/event/microsoft-fabric-community-conference/(Mar 26) Workshop (virtual): Large-Scale Geospatial Analytics with Graphs and the PyData Ecosystem https://neo4j.com/event/training-series-large-scale-geospatial-analytics-with-graphs-and-the-pydata-ecosystem/(Mar 27) Meetup: Graphs & Vectors: Navigating the Future with Neo4j and Vector Search https://neo4j.com/event/graphs-vectors-navigating-the-future-with-neo4j-and-vector-search/

Years ago, I got to be an advisor for this company called data.world, and at the time, they were just getting started on helping figuring out how do you converge all the data sets that are in the world and help people work with them and combine them and share them. They built this thing that was kind of like GitHub for data. I was interested in it because I could see at the time where the world was going and we're going to need these much more advanced tools for being able to manage data. I tried to contribute my small way, but my favorite thing about it is that I got to know Bryon Jacob, who's the CTO of data.world. Brian is delightful guy. This is one of the guys who's been thinking about the the nature of data, the structure of data, how we work with that in computers for his entire career. And he got onto a track that you could consider a little bit fringe, of using graph databases decades ago, the semantic models that we use to understand data from the thinking around RDF and the early semantic web. And now what he's built is the system that when ingests any kind of data, it parses that out, takes it in a graph database and makes it accessible through a query language called SPARQL, which you'll hear us refer to. This is a kind of "advanced mode episode" and I know we're going to lose some people We refer to a lot of technical stuff that probably only data nerds are really going to be interested in. I won't be offended if you check out. But, if you have any interest in data or the future of analyzing data and using data in AIS, you need to listen and understand this conversation. Brian is an expert. He's built one of the most important king pin tools for using all the data in large-scale organizations or projects within the new generative AI context. If you are trying to use something like ChatGPT or another LLM as an interface to structured data, you're doing it wrong, and I think you'll be convinced about that as you start to understand what we're discussing today. So, hang in there. I promise this is a really REALLY valuable conversation for anybody who is trying to work at the forefront of using AIS for data analytics. I'm thrilled that we get to share this conversation with Bryon with you today. Important Links SPARQL data.world HomeAway About Bryon Jacob Bryon Jacob is the CTO and co-founder of data.world - on a mission to build the world's most meaningful, collaborative, and abundant data resource. Bryon is a recognized leader in building large-scale consumer internet systems and an expert in data integration solutions. Bryon's twenty years of academic and professional experience spans AI research at Case Western Reserve University, enterprise configuration software at Trilogy, and consumer web experience at Amazon and most recently in ten years building HomeAway.com. At HomeAway, Bryon oversaw platform development and the integration of thirty acquisitions while building the world's largest online marketplace for vacation rentals.

Founder of data.world on using LLMs to explore your structured databases.

AI has the potential to revolutionize healthcare in areas that range from drug discover to the patient experience. In this podcast, Heather Lane from athenahealth shares the challenges and opportunities of using AI to improve the patient and clinician experience.Heather's Bio:Heather has a  PhD  from Purdue, where she focused on developing machine learning methods for the computer security problem of anomaly detection. She's worked at the   MIT AI Lab (now CSAIL) working with Leslie Kaelbling on reinforcement learning and decision-theoretic planning, Markov decision processes, and the tradeoff between stochastic and deterministic planning.In 2002, she moved to the University of New Mexico as an assistant professor in the Department of Computer Science. There she worked on a number of application areas of ML, including the bioinformatics of RNA interference, genomics, and computational neuroscience (inference of brain activity networks from neuroimaging data). Much of that work involved Bayesian networks and dynamic belief networks.In 2008, she was promoted to associate professor at UNM and was granted tenure. In 2012, she moved from academia to industry, joining Google in Cambridge, MA. working  on Knowledge Graph, Google Books, Project Sunroof, and Ads Latency.In 2017, she joined athenahealth to  lead a Data Science team working to use athena's immense store of healthcare data to improve healthcare experiences for clinicians and patients.Social LinksYou can follow Heather at: https://www.linkedin.com/in/terranlane/You can follow Maribel  at: X/Twitter: https://twitter.com/maribellopezLinkedIn: https://www.linkedin.com/in/maribellopezYouTube: https://www.youtube.com/c/MaribelLopezResearchHashtags: #AI, #Healthcare #PatientExperience

Tools of the Month:Remix for data-driven websites https://remix.run/HTTPie: https://httpie.io/cliPypeteer https://github.com/pyppeteer/pyppeteerRectangle https://rectangleapp.com/Fireflies.ai https://fireflies.ai/Video Speed Controller https://chromewebstore.google.com/detail/video-speed-controller/gioehmkjkeamcinbdelehlpnpdcdjpdp?pli=1Product updates:Neo4j release (5.15) https://neo4j.com/release-notes/database/neo4j-5/Neo4j Driver updatesAPOC Core https://github.com/neo4j/apoc/releases/tag/5.15.0GraphQL release (4.4.4) https://github.com/neo4j/graphql/releasesHelm chart update (5.14.0) https://github.com/neo4j/helm-charts/releases/tag/5.14.0Several Neo4j Connectors updatedArticles:Try Neo4j's Next-Gen Graph-Native Store Format https://neo4j.com/developer-blog/neo4j-graph-native-store-format/Implementing Advanced Retrieval RAG Strategies with Neo4j https://neo4j.com/developer-blog/advanced-rag-strategies-neo4j/Introducing Deno Runtime to the Neo4j Driver for Javascript https://neo4j.com/developer-blog/deno-runtime-neo4j-driver-javascript/Using a Knowledge Graph to Implement a DevOps RAG Application https://neo4j.com/developer-blog/knowledge-graph-devops-rag-application/Convenient Neo4j Integration Tests in Github Actions Using the Aura CLI https://neo4j.com/developer-blog/neo4j-integration-tests-github-actions-aura-cli/Neo4j x LangChain: Deep Dive Into the New Vector Index Implementation https://neo4j.com/developer-blog/neo4j-langchain-vector-index-implementation/Videos:RAG with a Neo4j Knowledge Graph: How it Works and How to Set It Up https://www.youtube.com/watch?v=ftlZ0oeXYRENODES 2023 playlist https://youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrb&si=8_0sYVRYz8CqqdIcEvents:(Jan 4) YouTube series (virtual): Going Meta Ep 24 https://neo4j.com/event/going-meta-a-series-on-graphs-semantics-and-knowledge-episode-24/(Jan 10) Meetup (Austin, TX and virtual): Airplane Route Optimization Using Neo4j's Graph Database https://neo4j.com/event/airplane-route-optimization-using-neo4js-graph-database/(Jan 10) Webinar (virtual): Neo4j: 2024 Trends: What Data and Analytics Leaders Need to Know - Asia https://neo4j.com/event/neo4j-2024-trends-what-data-and-analytics-leaders-need-to-know-asia-pacific-jan-11/(Jan 11) Webinar (virtual): Neo4j: 2024 Trends: What Data and Analytics Leaders Need to Know - Europe https://neo4j.com/event/neo4j-2024-trends-what-data-and-analytics-leaders-need-to-know-europe-jan-11/(Jan 11) Webinar (virtual): Neo4j: 2024 Trends: What Data and Analytics Leaders Need to Know - Americas https://neo4j.com/event/neo4j-2024-trends-what-data-and-analytics-leaders-need-to-know-jan-11/(Jan 17) Webinar (virtual): O'Reilly Media: Generative AI for Healthcare https://neo4j.com/event/oreilly-media-generative-ai-for-healthcare-jan-17/(Jan 22) Webinar (virtual): Neo4j: Building More Accurate GenAI Chatbots: A Technical Guide - Asia https://neo4j.com/event/neo4j-building-more-accurate-genai-chatbots-a-technical-guide-asia-pacific-jan-23/(Jan 23) Webinar (virtual): Neo4j: Building More Accurate GenAI Chatbots: A Technical Guide - Europe https://neo4j.com/event/neo4j-building-more-accurate-genai-chatbots-a-technical-guide-europe-jan-23/(Jan 23) Webinar (virtual): Neo4j: Building More Accurate GenAI Chatbots: A Technical Guide - Americas https://neo4j.com/event/neo4j-building-more-accurate-genai-chatbots-a-technical-guide-jan-23/(Jan 25) YouTube series (virtual): Neo4j Live: Building a Semantics-Based Recommender System for ESG Documents https://neo4j.com/event/neo4j-live-building-a-semantics-based-recommender-system-for-esg-documents/(Jan 25) Conference (Bristol, UK): GraphTalk Government https://neo4j.com/event/graphtalk-government/(Jan 31) Meetup (London, UK): LLM + Knowledge Graph FTW https://neo4j.com/event/llm-knowledge-graph-ftw/(Jan 31) Meetup: Cloud-Native Geospatial Analytics Combining Spatial SQL & Graph Data Science https://neo4j.com/event/cloud-native-geospatial-analytics-combining-spatial-sql-graph-data-science/

Like many digital practices, search engine optimization is becoming more conversational.  Not long ago, SEOs had to make their best educated guesses about what was working to get their websites to rank better. Now, by focusing on both feeding information to and gleaning feedback from Google's knowledge graph, Jason Barnard helps companies craft content strategies and messaging architectures that keep their brand prominent in Google's search results. https://ellessmedia.com/csi/jason-barnard/

Tools of the month:APOC OpenAI procedures: https://neo4j.com/labs/apoc/5/ml/openai/Warp terminal: https://www.warp.dev/Ollama for running LLMs: https://ollama.ai/Graphacademy courses:LLM Fundamentals: https://graphacademy.neo4j.com/courses/llm-fundamentals/Build a Neo4j-backed Chatbot using Python: https://graphacademy.neo4j.com/courses/llm-chatbot-python/Importing CSV data into Neo4j (updated!): https://graphacademy.neo4j.com/courses/importing-cypher/Product updates:Neo4j/AWS Bedrock integration: https://neo4j.com/press-releases/neo4j-aws-bedrock-integration/Articles:Rdflib-neo4j: A New Era in RDF Integration for Neo4j https://neo4j.com/developer-blog/rdflib-neo4j-rdf-integration-neo4j/Py2neo is End-of-Life - A Basic Migration Guide https://neo4j.com/developer-blog/py2neo-end-migration-guide/Enforcing Data Quality in Neo4j 5: New Property Type Constraints and Functions https://neo4j.com/developer-blog/data-quality-type-constraints-functions/Analyzing Annual Reports Using LLMs and Graph Technology https://neo4j.com/developer-blog/analyzing-annual-reports-llm-graph/Needle StarterKit: The Ultimate Tool for Accelerating Your Graph App Projects https://neo4j.com/developer-blog/needle-starterkit-tool-accelerate-graph-app/Clustering Graph Data with K-Medoids https://neo4j.com/developer-blog/clustering-graph-data-k-medoids/Videos:NODES 2023 playlist (videos added!) https://www.youtube.com/playlist?list=PL9Hl4pk2FsvUu4hzyhWed8Avu5nSUXYrbHumanitarian AI Meetup with IATI data https://youtu.be/ysBwXTR8390Events:(Dec 5) YouTube series: Going Meta Ep 23 https://neo4j.com/event/going-meta-a-series-on-graphs-semantics-and-knowledge-ep-23/(Dec 5) YouTube series: Discover Neo4j Aura: The Future of Graph Database-as-a-Service Workshop https://neo4j.com/event/discover-neo4j-auradb-workshop-copy/(Dec 5) Training (Atlanta, GA): Neo4j and Google Cloud Generative AI Hands-On Lab https://neo4j.com/event/neo4j-google-cloud-generative-ai-hands-on-lab-2/(Dec 6) Conference (London) https://neo4j.com/event/the-perfect-couple-uniting-large-language-models-and-knowledge-graphs-for-enhanced-knowledge-representation-2/(Dec 6) Conference (Paris): API Days Paris https://neo4j.com/event/api-days-paris/(Dec 6) YouTube series: Neo4j Live: Powering Advanced Streamlit Chatbots with GenAI https://neo4j.com/event/neo4j-live-powering-advanced-streamlit-chatbots-with-genai/(Dec 7) Conference (London): https://neo4j.com/event/the-perfect-couple-uniting-large-language-models-and-knowledge-graphs-for-enhanced-knowledge-representation-3/(Dec 7) Lunch-and-Learn (virtual): Tackling GenAI Challenges with Knowledge Graphs, Graph Data Science, and LLMs https://neo4j.com/event/lunch-and-learn-tackling-genai-challenges-with-knowledge-graphs-graph-data-science-and-llms/(Dec 12) Meetup (Australia): Graph Database Melbourne https://neo4j.com/event/graph-database-melbourne-meetup-dec-edition/(Dec 13) Conference (Chicago, IL): Evanta CDAO Executive Summit https://neo4j.com/event/evanta-cdao-execsummit-chi/(Dec 13) Conference (virtual) (2 offerings for timezones): Connections: Generative AI and Knowledge Graphs https://neo4j.com/event/neo4j-connections-generative-ai-and-knowledge-graphs-unveiling-the-future-of-knowledge-retrieval/(Dec 14) Meetup (Innsbruck, Austria?): Engineering Kiosk Alps Meetup Innsbruck https://neo4j.com/event/engineering-kiosk-alps-meetup-innsbruck/(Dec 14) Meetup (Tampa, FL): AWS re:Invent 2023 Recap Meetup https://neo4j.com/event/aws-reinvent-2023-recap-meetup/

Callum Scott talks with Jason Barnard about understanding and recovering from Google traffic drops. Callum Scott specializes in conducting data-driven and qualitative SEO analysis, focusing primarily on traffic drop analysis, technical SEO and content quality. Callum is expert on Google's Knowledge Graph and Google's use of Entity Understanding for information retrieval and the entire search ecosystem. With over 5 years of experience in complex SEO environments, Callum has conducted nearly 100 technical and content-focused SEO audits, helped many websites achieve consistent growth and integrated well with an organisation's existing team and framework. Imagine you're navigating through a busy city and suddenly your GPS goes off. That's the same kind of confusion and vexation you feel when Google traffic drops affect your website. It's unsettling, irritating, and can definitely impact your profits. But once you understand why it happens and how to recover from it, you'll be back on track in no time. This essential knowledge not only protects your website's performance, but also gives you the ability to navigate the ever-changing world of search engine optimization. In this incredibly awesome episode, Callum (Callie) Scott reveals great nuggets and some real-life examples about traffic drops, their causes and how a website can recover from them. There are also three categories of traffic drops due to core updates - Broad Site Quality Reassessments, Searcher Intent Shift and Relevance, which Callum insightfully explains and suggests alternative strategies to help websites with ranking issues. Callum also highlights how to deal with Google's changing understanding of intent and the shift in SERPs. As always, the show ends with passing the baton… Callum passes the virtual baton to next week's super groovy guest, Alex Sanfilippo. What you'll learn from Callum Scott 00:00 Callum Scott and Jason Barnard 01:08 Callum Scott's Generative AI Result on Google 01:30 Kalicube Support Group 01:38 Blue Orchid Digital Ltd Brand SERP 03:22 When Did Google Start Relying More on Quality Signals Than on Pagerank or Word Count? 04:51 How Has AI Affected Google's Categorization of Website Quality? 05:53 What Significant Algorithm Changes Did Google Make in 2017? 06:26 How Did the Shift Towards Machine Learning Unfold Within the Google Search Team Between 2014 and 2017? 07:34 Understanding the Role of Features in Machine Learning for E-E-A-T 08:25 How Does Google's Confidence in Displaying a Knowledge Panel Affect Users' Trust in Their Search Results? 09:51 What are Some Examples of Traffic Drops, Causes and Recoveries? 10:55 Three Categories of Traffic Drops Due to Core Updates 11:04 First Category: Broad Site Quality Reassessments 12:16 How Does the Persistence of Low-quality Content Affect Google's Focus and Resource Allocation for a Website? 12:48 Second Category: Searcher Intent Shift 14:29 How Feasible is it for a Single Website to be Ranked for Both Informational and Transactional Intents? 16:01 How to Deal with the Change in Google's Understanding of Intent and the Shift in SERPs 18:12 Third Category: Relevance 19:30 What is the Best Alternative Strategy for Websites with Ranking Issues? 21:25 What are the Challenges of Convincing Clients to Address Traffic Drops by Individually Prioritizing Pages and Queries? 23:18 Traffic Drop Following a Core Update: Wait or Act Immediately 24:28 How to Convince Clients Not to Panic When Traffic Drops? 27:36 How Can Branded Search Help to Mitigate a Traffic Drop 29:35 Padding the Baton: Callum (Callie) Scott to Alex Sanfilippo This episode was recorded live on video August 29th 2023

Callum Scott talks with Jason Barnard about understanding and recovering from Google traffic drops. Callum Scott specializes in conducting data-driven and qualitative SEO analysis, focusing primarily on traffic drop analysis, technical SEO and content quality. Callum is expert on Google's Knowledge Graph and Google's use of Entity Understanding for information retrieval and the entire search ecosystem. With over 5 years of experience in complex SEO environments, Callum has conducted nearly 100 technical and content-focused SEO audits, helped many websites achieve consistent growth and integrated well with an organisation's existing team and framework. Imagine you're navigating through a busy city and suddenly your GPS goes off. That's the same kind of confusion and vexation you feel when Google traffic drops affect your website. It's unsettling, irritating, and can definitely impact your profits. But once you understand why it happens and how to recover from it, you'll be back on track in no time. This essential knowledge not only protects your website's performance, but also gives you the ability to navigate the ever-changing world of search engine optimization. In this incredibly awesome episode, Callum (Callie) Scott reveals great nuggets and some real-life examples about traffic drops, their causes and how a website can recover from them. There are also three categories of traffic drops due to core updates - Broad Site Quality Reassessments, Searcher Intent Shift and Relevance, which Callum insightfully explains and suggests alternative strategies to help websites with ranking issues. Callum also highlights how to deal with Google's changing understanding of intent and the shift in SERPs. As always, the show ends with passing the baton… Callum passes the virtual baton to next week's super groovy guest, Alex Sanfilippo. What you'll learn from Callum Scott 00:00 Callum Scott and Jason Barnard 01:08 Callum Scott's Generative AI Result on Google 01:30 Kalicube Support Group 01:38 Blue Orchid Digital Ltd Brand SERP 03:22 When Did Google Start Relying More on Quality Signals Than on Pagerank or Word Count? 04:51 How Has AI Affected Google's Categorization of Website Quality? 05:53 What Significant Algorithm Changes Did Google Make in 2017? 06:26 How Did the Shift Towards Machine Learning Unfold Within the Google Search Team Between 2014 and 2017? 07:34 Understanding the Role of Features in Machine Learning for E-E-A-T 08:25 How Does Google's Confidence in Displaying a Knowledge Panel Affect Users' Trust in Their Search Results? 09:51 What are Some Examples of Traffic Drops, Causes and Recoveries? 10:55 Three Categories of Traffic Drops Due to Core Updates 11:04 First Category: Broad Site Quality Reassessments 12:16 How Does the Persistence of Low-quality Content Affect Google's Focus and Resource Allocation for a Website? 12:48 Second Category: Searcher Intent Shift 14:29 How Feasible is it for a Single Website to be Ranked for Both Informational and Transactional Intents? 16:01 How to Deal with the Change in Google's Understanding of Intent and the Shift in SERPs 18:12 Third Category: Relevance 19:30 What is the Best Alternative Strategy for Websites with Ranking Issues? 21:25 What are the Challenges of Convincing Clients to Address Traffic Drops by Individually Prioritizing Pages and Queries? 23:18 Traffic Drop Following a Core Update: Wait or Act Immediately 24:28 How to Convince Clients Not to Panic When Traffic Drops? 27:36 How Can Branded Search Help to Mitigate a Traffic Drop 29:35 Padding the Baton: Callum (Callie) Scott to Alex Sanfilippo This episode was recorded live on video August 29th 2023

Investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems. That's the conclusion of the latest research that Juan Sequeda, Dean Allemang and Bryon Jacob have recently presented. In this episode, we will dive into the details of this research and understand why to succeed in this AI world, enterprises must treat the business context and semantics as a first-class citizen.

The EDGE of the WEB team ventured across the country to attend the inaugural BrightonSEO U.S conference in beautiful San Diego! This special podcast was filmed LIVE in front of an audience with 5 of the industry's best as panelists. Witness industry experts collaborate in forecasting the unpredictable future of SEO. The panel evaluates the industry's most disruptive topics, including Content at Scale, E-E-A-T, AI Generated Content, SGE, Google's Knowledge Graph, and beyond, offering insights that light the path ahead. Do not miss this very special feature of The EDGE of the Web as we discover the true trajectory of our industry, and SEO's unite to scale the expansive future ahead!   *Thanks to our panelists!* Mordy Oberstein Cindy Krum Julie McCoy Ola King JR Oakes Key Segments: [00:07:20] Panel Segments [00:06:24] Title Sponsor: SE Ranking [00:07:44] How Can We Maintain Creative Control When Using AI To Operate At Scale?  [00:14:52] The Journey Ahead For SEO's As In Relation To Content [00:27:56] How Ca n We Ensure The Accuracy And Reliability Of AI Generated Content?  [00:35:35] The Expanding Google Knowledge Graph [00:45:34] How Is Search Generative AI Going To Transform The Way We Search For Information? [00:55:34] How Will SGE Change Organic Links On The SERP? [01:00:00] EDGE of The Web Sponsor: SE Ranking [01:04:27] How Can SEO Tools Gauge The Success Of SEO Campaigns In The Context Of SGE? [01:11:52] The Future Of SEO In The Next 18 Months   Thanks to Our Sponsor! SE Ranking: edgeofthewebradio.com/seranking  Follow Our Panelists  Cindy Krum Julie McCoy Ola King JR Oakes Mordy Oberstein

Beatrice Gamba talks with Jason Barnard about smashing SEO strategies in the era of AI. Beatrice Gamba is Head of Agency and SEO Strategist at WordLift. Born and raised in Rome, Beatrice joined the WordLift team in Rome in 2016, first as Project Manager for its side- company InsideOut10, then as Digital Project Manager and now as Head of Agency. She has more than eight years of experience in tech and digital companies. After graduating in Economics, she moved to Berlin where she worked for Zalando and dealt with clients from all over Europe, always ensuring a high-quality service. Beatrice strives every day to create value with innovative, AI-driven digital strategies and to ensure that she achieves the best results with detail-oriented management and the most effective resources. Companies are increasingly using AI models to improve their SEO strategies, which is seen as the next big thing in SEO. AI is helping organisations tailor their content to how users search online. Using powerful AI tools, they can understand what users are really searching for and analyse this data to make SEO ever more effective. This means a significant shift from focusing on keywords to understanding user intent. In this brilliant episode, Beatrice Gamba shares her groovy expertise on AI-SEO. She highlights the importance of the internal Knowledge Graph, the shift from keywords to user intent in SEO strategies and the use of conversational question and answer formats for content. Beatrice also explores the use of AI to improve SEO and competition with Generative AI, optimizing for the new SERPs on Google and Bing and the significance of Knowledge Graphs and Structured Data to improve Brand Search results. As always, the show ends with passing the baton… Beatrice passes the virtual baton to next week's incredible guest, Callum (Callie) Scott. What you'll learn from Beatrice Gamba 00:00 Beatrice Gamba and Jason Barnard 01:10 Beatrice Gamba's Brand SERP 02:58 How Can SEOs Navigate the Evolution to AI-Optimized Content for Increased Online Visibility? 04:28 How to Optimise Your Content for the New Google and Bing Conversational Search Experience 07:00 What is an Internal Knowledge Graph? 09:34 What are the Three Sections of the SERP?  09:47 Why is Mastering Knowledge and Recommendations Key for a Successful Generative Experience on SERPs? 10:29 How Can AI, User Intent, and Structured Content Revolutionize SEO Strategies? 12:14 Can the Use of ChatGPT for FAQ Content Pass Google's Quality Assessment? 13:27 How Can Structured Data and External Links Enhance Your Online Authority and Expertise?  14:46 Which AI Tools Can Support Your SEO Strategy in the Evolving Digital Landscape? 16:41 How Can AI Improve Your Link Building Strategies? 18:06 Revealing strategies and Tools for Leveraging AI to Build Effective Links 19:31 How Can AI Revolutionize Crawling and Indexing Techniques? 21:34 Can AI Responses Reflect Your Tone of Voice? 22:37 Is It Necessary to Manually Revise Answers from AI Tools? 24:35 How Can Structured Data and Knowledge Graphs Future-Proof Your SEO Strategy? 27:22 How Can AI-SEO Help with Branded Search Strategies? 29:23 Passing the Baton: Beatrice Gamba to Callum (Callie) Scott This episode was recorded live on video August 22nd 2023

Beatrice Gamba talks with Jason Barnard about smashing SEO strategies in the era of AI. Beatrice Gamba is Head of Agency and SEO Strategist at WordLift. Born and raised in Rome, Beatrice joined the WordLift team in Rome in 2016, first as Project Manager for its side- company InsideOut10, then as Digital Project Manager and now as Head of Agency. She has more than eight years of experience in tech and digital companies. After graduating in Economics, she moved to Berlin where she worked for Zalando and dealt with clients from all over Europe, always ensuring a high-quality service. Beatrice strives every day to create value with innovative, AI-driven digital strategies and to ensure that she achieves the best results with detail-oriented management and the most effective resources. Companies are increasingly using AI models to improve their SEO strategies, which is seen as the next big thing in SEO. AI is helping organisations tailor their content to how users search online. Using powerful AI tools, they can understand what users are really searching for and analyse this data to make SEO ever more effective. This means a significant shift from focusing on keywords to understanding user intent. In this brilliant episode, Beatrice Gamba shares her groovy expertise on AI-SEO. She highlights the importance of the internal Knowledge Graph, the shift from keywords to user intent in SEO strategies and the use of conversational question and answer formats for content. Beatrice also explores the use of AI to improve SEO and competition with Generative AI, optimizing for the new SERPs on Google and Bing and the significance of Knowledge Graphs and Structured Data to improve Brand Search results. As always, the show ends with passing the baton… Beatrice passes the virtual baton to next week's incredible guest, Callum (Callie) Scott. What you'll learn from Beatrice Gamba 00:00 Beatrice Gamba and Jason Barnard 01:10 Beatrice Gamba's Brand SERP 02:58 How Can SEOs Navigate the Evolution to AI-Optimized Content for Increased Online Visibility? 04:28 How to Optimise Your Content for the New Google and Bing Conversational Search Experience 07:00 What is an Internal Knowledge Graph? 09:34 What are the Three Sections of the SERP?  09:47 Why is Mastering Knowledge and Recommendations Key for a Successful Generative Experience on SERPs? 10:29 How Can AI, User Intent, and Structured Content Revolutionize SEO Strategies? 12:14 Can the Use of ChatGPT for FAQ Content Pass Google's Quality Assessment? 13:27 How Can Structured Data and External Links Enhance Your Online Authority and Expertise?  14:46 Which AI Tools Can Support Your SEO Strategy in the Evolving Digital Landscape? 16:41 How Can AI Improve Your Link Building Strategies? 18:06 Revealing strategies and Tools for Leveraging AI to Build Effective Links 19:31 How Can AI Revolutionize Crawling and Indexing Techniques? 21:34 Can AI Responses Reflect Your Tone of Voice? 22:37 Is It Necessary to Manually Revise Answers from AI Tools? 24:35 How Can Structured Data and Knowledge Graphs Future-Proof Your SEO Strategy? 27:22 How Can AI-SEO Help with Branded Search Strategies? 29:23 Passing the Baton: Beatrice Gamba to Callum (Callie) Scott This episode was recorded live on video August 22nd 2023

RAG, Retrieval Augemented Generation, is the term you now constantly hear in conjunction with LLM that provides context. But how does it actually work? And what's the relationship with Vector Databases and Knowledge Graphs? This will be a geeky AI episode with Mike Dillinger.

I had an insightful conversation with Jason Barnard, The Brand SERP Guy, about Google's biggest algorithm update ever - the Killer Whale update. We discussed how Google made massive changes to its Knowledge Graph in July 2022, focusing on people entities and EEAT credibility signals. This then rolled out into core updates in August and September 2022. Jason explained how the changes affect personal brands and how to prepare, including sculpting your online entity so Google understands you correctly. He also shared tips on monitoring brand SERPs to optimize your digital marketing strategy. This update shows the growing importance of brand signals and entities. Don't miss Jason's follow-up webinar on this topic here: https://kalicu.be/killer-whale/ https://www.youtube.com/watch?v=RhJlCP-gymY Or watch the video version of this podcast: https://www.youtube.com/watch?v=nEe2SMzCFfo

Beatrice Gamba, Senior SEO Strategist at WordLift, discusses the Knowledge Graph. The Knowledge Graph is a powerful tool for search engine optimization and can help you improve the relevance of your content by adding more meaning to it. In fact, it's so powerful that simply submitting a Knowledge Graph can be considered an SEO strategy. Today, Beatrice talks about Knowledge Graph SEO. Show NotesConnect With: Beatrice Gamba: Website // LinkedInThe Voices of Search Podcast: Email // LinkedIn // TwitterBenjamin Shapiro: Website // LinkedIn // TwitterSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Knowledge graphs let people and computers work from the same body of facts to create uniquely informative and powerful experiences. Katariina Kari and her colleagues at IKEA use ontologies and knowledge graphs to drive applications like recommendation systems and to streamline back-end processes like image recognition. Katariina balances her engineering expertise with a deep appreciation for the humans who create and use AI applications like knowledge graphs. https://ellessmedia.com/csi/katariina-kari/

Want to help define the AI Engineer stack? >800 folks have weighed in on the top tools, communities and builders for the first State of AI Engineering survey, which we will present for the first time at next week's AI Engineer Summit. Join us online!This post had robust discussion on HN and Twitter.In October 2022, Robust Intelligence hosted an internal hackathon to play around with LLMs which led to the creation of two of the most important AI Engineering tools: LangChain

neo4cyclone: https://github.com/javixeneize/neo4cycloneNeomodel: Python OGM for Neo4j Extends Version Support and Moves to Neo4j Labs: https://neo4j.com/developer-blog/neomodel-python-ogm-neo4j-labs/Getting From Denmark Hill to Gatwick Airport With Quantified Path Patterns: https://neo4j.com/developer-blog/denmark-hill-to-gatwick-airport-quantified-path-patterns/Graphs for DFIR (Digital Forensics and Incident Response): A Roadmap:  http://www.ds4n6.io/blog/23050801.htmlLeverage LLMs for Graph Data Science Pipelines: 4 Steps to Avoid Pitfalls of ChatGPT: https://www.graphable.ai/blog/data-science-pipeline-steps/How To Verify Database Connection From a Spring Boot Application: https://dzone.com/articles/how-to-verify-database-connection-from-a-spring-boLangChain Library Adds Full Support for Neo4j Vector Index: https://neo4j.com/developer-blog/langchain-library-full-support-neo4j-vector-index/Explore OpenAI vector embedding with Neo4j, LangChain, and Wikipedia: https://medium.com/@therobbrennan/explore-openai-vector-embedding-with-neo4j-6ea2a40693d9Exploring the Intersection of Neo4j and Large Language Models: https://medium.com/neo4j/exploring-the-intersection-of-neo4j-and-large-language-models-6fda9ac72ef8Construct Knowledge Graphs From Unstructured Text: https://medium.com/neo4j/construct-knowledge-graphs-from-unstructured-text-877be33300a2Knowledge Graph Construction Demo from raw text using an LLM: https://www.youtube.com/watch?v=Hg4ahTQlBm0Azure OpenAI Neo4j Demo: https://www.youtube.com/watch?v=3PO-erAP6R4Sebastian Dashner - Applications with graph databases (Neo4j & Quarkus): https://www.youtube.com/watch?v=K0RUYdliUW8QCon SF 2023 (Oct 2)GraphTalk Milan (Oct 5)Road to NODES: Neo4j GDS w/ Generative AI (Oct 5)Analyzing the Physical World (Oct 10)GraphSummit Frankfurt (Oct 10)Conference (Pittsburgh, PA): NACIS 2023 Building QGIS Plugins w/ Python (Oct 11)Meetup (virtual/in-person, Austin, TX): Airplane Route Optimization using Neo4j (Oct 11)Conference (Raleigh, NC): ATO 2023 Building Open Source GIS Plugins w/ QGIS, Python, and Neo4j (Oct 15)Conference (Baltimore, MD): FOSS4G Building Open Source GIS Plugins with QGIS, Python, and Neo4j (Oct 23)Conference (Arlington, VA): GraphSummit for Government (Oct 25)Conference (virtual): Nodes 2023 (Oct 25)Conference (Spain): Madrid Tech Show (Oct 30)

Summary A significant amount of time in data engineering is dedicated to building connections and semantic meaning around pieces of information. Linked data technologies provide a means of tightly coupling metadata with raw information. In this episode Brian Platz explains how JSON-LD can be used as a shared representation of linked data for building semantic data products. Announcements Hello and welcome to the Data Engineering Podcast, the show about modern data management This episode is brought to you by Datafold – a testing automation platform for data engineers that finds data quality issues before the code and data are deployed to production. Datafold leverages data-diffing to compare production and development environments and column-level lineage to show you the exact impact of every code change on data, metrics, and BI tools, keeping your team productive and stakeholders happy. Datafold integrates with dbt, the modern data stack, and seamlessly plugs in your data CI for team-wide and automated testing. If you are migrating to a modern data stack, Datafold can also help you automate data and code validation to speed up the migration. Learn more about Datafold by visiting dataengineeringpodcast.com/datafold (https://www.dataengineeringpodcast.com/datafold) Introducing RudderStack Profiles. RudderStack Profiles takes the SaaS guesswork and SQL grunt work out of building complete customer profiles so you can quickly ship actionable, enriched data to every downstream team. You specify the customer traits, then Profiles runs the joins and computations for you to create complete customer profiles. Get all of the details and try the new product today at dataengineeringpodcast.com/rudderstack (https://www.dataengineeringpodcast.com/rudderstack) You shouldn't have to throw away the database to build with fast-changing data. You should be able to keep the familiarity of SQL and the proven architecture of cloud warehouses, but swap the decades-old batch computation model for an efficient incremental engine to get complex queries that are always up-to-date. With Materialize, you can! It's the only true SQL streaming database built from the ground up to meet the needs of modern data products. Whether it's real-time dashboarding and analytics, personalization and segmentation or automation and alerting, Materialize gives you the ability to work with fresh, correct, and scalable results — all in a familiar SQL interface. Go to dataengineeringpodcast.com/materialize (https://www.dataengineeringpodcast.com/materialize) today to get 2 weeks free! If you're a data person, you probably have to jump between different tools to run queries, build visualizations, write Python, and send around a lot of spreadsheets and CSV files. Hex brings everything together. Its powerful notebook UI lets you analyze data in SQL, Python, or no-code, in any combination, and work together with live multiplayer and version control. And now, Hex's magical AI tools can generate queries and code, create visualizations, and even kickstart a whole analysis for you – all from natural language prompts. It's like having an analytics co-pilot built right into where you're already doing your work. Then, when you're ready to share, you can use Hex's drag-and-drop app builder to configure beautiful reports or dashboards that anyone can use. Join the hundreds of data teams like Notion, AllTrails, Loom, Mixpanel and Algolia using Hex every day to make their work more impactful. Sign up today at dataengineeringpodcast.com/hex (https://www.dataengineeringpodcast.com/hex) to get a 30-day free trial of the Hex Team plan! Your host is Tobias Macey and today I'm interviewing Brian Platz about using JSON-LD for building linked-data products Interview Introduction How did you get involved in the area of data management? Can you describe what the term "linked data product" means and some examples of when you might build one? What is the overlap between knowledge graphs and "linked data products"? What is JSON-LD? What are the domains in which it is typically used? How does it assist in developing linked data products? what are the characteristics that distinguish a knowledge graph from What are the layers/stages of applications and data that can/should incorporate JSON-LD as the representation for records and events? What is the level of native support/compatibiliity that you see for JSON-LD in data systems? What are the modeling exercises that are necessary to ensure useful and appropriate linkages of different records within and between products and organizations? Can you describe the workflow for building autonomous linkages across data assets that are modelled as JSON-LD? What are the most interesting, innovative, or unexpected ways that you have seen JSON-LD used for data workflows? What are the most interesting, unexpected, or challenging lessons that you have learned while working on linked data products? When is JSON-LD the wrong choice? What are the future directions that you would like to see for JSON-LD and linked data in the data ecosystem? Contact Info LinkedIn (https://www.linkedin.com/in/brianplatz/) Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don't forget to check out our other shows. Podcast.__init__ (https://www.pythonpodcast.com) covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast (https://www.themachinelearningpodcast.com) helps you go from idea to production with machine learning. Visit the site (https://www.dataengineeringpodcast.com) to subscribe to the show, sign up for the mailing list, and read the show notes. If you've learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com (mailto:hosts@dataengineeringpodcast.com)) with your story. To help other people find the show please leave a review on Apple Podcasts (https://podcasts.apple.com/us/podcast/data-engineering-podcast/id1193040557) and tell your friends and co-workers Links Fluree (https://flur.ee/) JSON-LD (https://json-ld.org/) Knowledge Graph (https://en.wikipedia.org/wiki/Knowledge_graph) Adjacency List (https://en.wikipedia.org/wiki/Adjacency_list) RDF == Resource Description Framework (https://www.w3.org/RDF/) Semantic Web (https://en.wikipedia.org/wiki/Semantic_Web) Open Graph (https://ogp.me/) Schema.org (https://schema.org/) RDF Triple (https://en.wikipedia.org/wiki/Semantic_triple) IDMP == Identification of Medicinal Products (https://www.fda.gov/industry/fda-data-standards-advisory-board/identification-medicinal-products-idmp) FIBO == Financial Industry Business Ontology (https://spec.edmcouncil.org/fibo/) OWL Standard (https://www.w3.org/OWL/) NP-Hard (https://en.wikipedia.org/wiki/NP-hardness) Forward-Chaining Rules (https://en.wikipedia.org/wiki/Forward_chaining) SHACL == Shapes Constraint Language) (https://www.w3.org/TR/shacl/) Zero Knowledge Cryptography (https://en.wikipedia.org/wiki/Zero-knowledge_proof) Turtle Serialization (https://www.w3.org/TR/turtle/) The intro and outro music is from The Hug (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/Love_death_and_a_drunken_monkey/04_-_The_Hug) by The Freak Fandango Orchestra (http://freemusicarchive.org/music/The_Freak_Fandango_Orchestra/) / CC BY-SA (http://creativecommons.org/licenses/by-sa/3.0/)

As alluded to on the pod, LangChain has just launched LangChain Hub: “the go-to place for developers to discover new use cases and polished prompts.” It's available to everyone with a LangSmith account, no invite code necessary. Check it out!In 2023, LangChain has speedrun the race from 2:00 to 4:00 to 7:00 Silicon Valley Time. From the back to back $10m Benchmark seed and (rumored) $20-25m Sequoia Series A in April, to back to back critiques of “LangChain is Pointless” and “The Problem with LangChain” in July, to teaching with Andrew Ng and keynoting at basically every AI conference this fall (including ours), it has been an extreme rollercoaster for Harrison and his growing team creating one of the most popular (>60k stars at time of writing) building blocks for AI Engineers.LangChain's OriginsThe first commit to LangChain shows its humble origins as a light wrapper around Python's formatter.format for prompt templating. But as Harrison tells the story, even his first experience with text-davinci-002 in early 2022 was focused on chatting with data from their internal company Notion and Slack, what is now known as Retrieval Augmented Generation (RAG). As the Generative AI meetup scene came to life post Stable Diffusion, Harrison saw a need for common abstractions for what people were building with text LLMs at the time:* LLM Math, aka Riley Goodside's “You Can't Do Math” REPL-in-the-loop (PR #8)* Self-Ask With Search, Ofir Press' agent pattern (PR #9) (later ReAct, PR #24)* NatBot, Nat Friedman's browser controlling agent (PR #18)* Adapters for OpenAI, Cohere, and HuggingFaceHubAll this was built and launched in a few days from Oct 16-25, 2022. Turning research ideas/exciting usecases into software quickly and often has been in the LangChain DNA from Day 1 and likely a big driver of LangChain's success, to date amassing the largest community of AI Engineers and being the default launch framework for every big name from Nvidia to OpenAI:Dancing with GiantsBut AI Engineering is built atop of constantly moving tectonic shifts: * ChatGPT launched in November (“The Day the AGI Was Born”) and the API released in March. Before the ChatGPT API, OpenAI did not have a chat endpoint. In order to build a chatbot with history, you had to make sure to chain all messages and prompt for completion. LangChain made it easy to do that out of the box, which was a huge driver of usage. * Today, OpenAI has gone all-in on the chat API and is deprecating the old completions models, essentially baking in the chat pattern as the default way most engineers should interact with LLMs… and reducing (but not eliminating) the value of ConversationChains.* And there have been more updates since: Plugins released in API form as Functions in June (one of our top pods ever… reducing but not eliminating the value of OutputParsers) and Finetuning in August (arguably reducing some need for Retrieval and Prompt tooling). With each update, OpenAI and other frontier model labs realign the roadmaps of this nascent industry, and Harrison credits the modular design of LangChain in staying relevant. LangChain has not been merely responsive either: LangChain added Agents in November, well before they became the hottest topic of the AI Summer, and now Agents feature as one of LangChain's top two usecases. LangChain's problem for podcasters and newcomers alike is its sheer scope - it is the world's most complete AI framework, but it also has a sprawling surface area that is difficult to fully grasp or document in one sitting. This means it's time for the trademark Latent Space move (ChatGPT, GPT4, Auto-GPT, and Code Interpreter Advanced Data Analysis GPT4.5): the executive summary!What is LangChain?As Harrison explains, LangChain is an open source framework for building context-aware reasoning applications, available in Python and JS/TS.It launched in Oct 2022 with the central value proposition of “composability”, aka the idea that every AI engineer will want to switch LLMs, and combine LLMs with other things into “chains”, using a flexible interface that can be saved via a schema.Today, LangChain's principal offerings can be grouped as:* Components: isolated modules/abstractions* Model I/O* Models (for LLM/Chat/Embeddings, from OpenAI, Anthropic, Cohere, etc)* Prompts (Templates, ExampleSelectors, OutputParsers)* Retrieval (revised and reintroduced in March)* Document Loaders (eg from CSV, JSON, Markdown, PDF)* Text Splitters (15+ various strategies for chunking text to fit token limits)* Retrievers (generic interface for turning an unstructed query into a set of documents - for self-querying, contextual compression, ensembling)* Vector Stores (retrievers that search by similarity of embeddings)* Indexers (sync documents from any source into a vector store without duplication)* Memory (for long running chats, whether a simple Buffer, Knowledge Graph, Summary, or Vector Store)* Use-Cases: compositions of Components* Chains: combining a PromptTemplate, LLM Model and optional OutputParser* with Router, Sequential, and Transform Chains for advanced usecases* savable, sharable schemas that can be loaded from LangChainHub* Agents: a chain that has access to a suite of tools, of nondeterministic length because the LLM is used as a reasoning engine to determine which actions to take and in which order. Notable 100LOC explainer here.* Tools (interfaces that an agent can use to interact with the world - preset list here. Includes things like ChatGPT plugins, Google Search, WolframAlpha. Groups of tools are bundled up as toolkits)* AgentExecutor (the agent runtime, basically the while loop, with support for controls, timeouts, memory sharing, etc)* LangChain has also added a Callbacks system for instrumenting each stage of LLM, Chain, and Agent calls (which enables LangSmith, LangChain's first cloud product), and most recently an Expression Language, a declarative way to compose chains.LangChain the company incorporated in January 2023, announced their seed round in April, and launched LangSmith in July. At time of writing, the company has 93k followers, their Discord has 31k members and their weekly webinars are attended by thousands of people live.The full-featuredness of LangChain means it is often the first starting point for building any mainstream LLM use case, because they are most likely to have working guides for the new developer. Logan (our first guest!) from OpenAI has been a notable fan of both LangChain and LangSmith (they will be running the first LangChain + OpenAI workshop at AI Eng Summit). However, LangChain is not without its critics, with Aravind Srinivas, Jim Fan, Max Woolf, Mckay Wrigley and the general Reddit/HN community describing frustrations with the value of their abstractions, and many are attempting to write their own (the common experience of adding and then removing LangChain is something we covered in our Agents writeup). Harrison compares this with the timeless ORM debate on the value of abstractions.LangSmithLast month, Harrison launched LangSmith, their LLM observability tool and first cloud product. LangSmith makes it easy to monitor all the different primitives that LangChain offers (agents, chains, LLMs) as well as making it easy to share and evaluate them both through heuristics (i.e. manually written ones) and “LLM evaluating LLM” flows. The top HN comment in the “LangChain is Pointless” thread observed that orchestration is the smallest part of the work, and the bulk of it is prompt tuning and data serialization. When asked this directly our pod, Harrison agreed:“I agree that those are big pain points that get exacerbated when you have these complex chains and agents where you can't really see what's going on inside of them. And I think that's partially why we built Langsmith…” (48min mark)You can watch the full launch on the LangChain YouTube:It's clear that the target audience for LangChain is expanding to folks who are building complex, production applications rather than focusing on the simpler “Q&A your docs” use cases that made it popular in the first place. As the AI Engineer space matures, there will be more and more tools graduating from supporting “hobby” projects to more enterprise-y use cases. In this episode we run through some of the history of LangChain, how it's growing from an open source project to one of the highest valued AI startups out there, and its future. We hope you enjoy it!Show Notes* LangChain* LangChain's Berkshire Hathaway Homepage* Abstractions tweet* LangSmith* LangSmith Cookbooks repo* LangChain Retrieval blog* Evaluating CSV Question/Answering blog and YouTube* MultiOn Partner blog* Harvard Sports Analytics Collective* Evaluating RAG Webinar* awesome-langchain:* LLM Math Chain* Self-Ask* LangChain Hub UI* “LangChain is Pointless”* Harrison's links* sports - estimating player compatibility in the NBA* early interest in prompt injections* GitHub* TwitterTimestamps* [00:00:00] Introduction* [00:00:48] Harrison's background and how sports led him into ML* [00:04:54] The inspiration for creating LangChain - abstracting common patterns seen in other GPT-3 projects* [00:05:51] Overview of LangChain - a framework for building context-aware reasoning applications* [00:10:09] Components of LangChain - modules, chains, agents, etc.* [00:14:39] Underappreciated parts of LangChain - text splitters, retrieval algorithms like self-query* [00:18:46] Hiring at LangChain* [00:20:27] Designing the LangChain architecture - balancing flexibility and structure* [00:24:09] The difference between chains and agents in LangChain* [00:25:08] Prompt engineering and LangChain* [00:26:16] Announcing LangSmith* [00:30:50] Writing custom evaluators in LangSmith* [00:33:19] Reducing hallucinations - fixing retrieval vs generation issues* [00:38:17] The challenges of long context windows* [00:40:01] LangChain's multi-programming language strategy* [00:45:55] Most popular LangChain blog posts - deep dives into specific topics* [00:50:25] Responding to LangChain criticisms* [00:54:11] Harrison's advice to AI engineers* [00:55:43] Lightning RoundTranscriptAlessio: Hey everyone, welcome to the Latent Space Podcast. This is Alessio, partner and CTO at Residence at Decibel Partners, and I'm joined by my co-host Swyx, founder of Smol.ai. [00:00:19]Swyx: Welcome. Today we have Harrison Chase in the studio with us. Welcome Harrison. [00:00:23]Harrison: Thank you guys for having me. I'm excited to be here. [00:00:25]Swyx: It's been a long time coming. We've been asking you for a little bit and we're really glad that you got some time to join us in the studio. Yeah. [00:00:32]Harrison: I've been dodging you guys for a while. [00:00:34]Swyx: About seven months. You pulled me in here. [00:00:37]Alessio: About seven months. But it's all good. I totally understand. [00:00:38]Swyx: We like to introduce people through the official backgrounds and then ask you a little bit about your personal side. So you went to Harvard, class of 2017. You don't list what you did in Harvard. Was it CS? [00:00:48]Harrison: Stats and CS. [00:00:50]Swyx: That's awesome. I love me some good stats. [00:00:52]Harrison: I got into it through stats, through doing sports analytics. And then there was so much overlap between stats and CS that I found myself doing more and more of that. [00:00:59]Swyx: And it's interesting that a lot of the math that you learn in stats actually comes over into machine learning which you applied at Kensho as a machine learning engineer and Robust Intelligence, which seems to be the home of a lot of AI founders.Harrison: It does. Yeah. Swyx: And you started LangChain, I think around November 2022 and incorporated in January. Yeah. [00:01:19]Harrison: I was looking it up for the podcast and the first tweet was on, I think October 24th. So just before the end of November or end of October. [00:01:26]Swyx: Yeah. So that's your LinkedIn. What should people know about you on the personal side that's not obvious on LinkedIn? [00:01:33]Harrison: A lot of how I got into this is all through sports actually. Like I'm a big sports fan, played a lot of soccer growing up and then really big fan of the NBA and NFL. And so freshman year at college showed up and I knew I liked math. I knew I liked sports. One of the clubs that was there was the Sports Analytics Collective. And so I joined that freshman year, I was doing a lot of stuff in like Excel, just like basic stats, but then like wanted to do more advanced stuff. So learn to code, learn kind of like data science and machine learning through that way. Kind of like just kept on going down that path. I think sports is a great entryway to data science and machine learning. There's a lot of like numbers out there. People like really care. Like I remember, I think sophomore, junior year, I was in the Sports Collective and the main thing we had was a blog. And so we wrote a blog. It wasn't me. One of the other people in the club wrote a blog predicting the NFL season. I think they made some kind of like with stats and I think their stats showed that like the Dolphins would end up beating the Patriots and New England got like pissed about it, of course. So people like really care and they'll give you feedback about whether you're like models doing well or poorly. And so you get that. And then you also get like instantaneous kind of like, well, not instantaneous, but really quick feedback. Like if you predict a game, the game happens that night. Like you don't have to wait a year to see what happens. So I think sports is a great kind of like entryway for kind of like data science. [00:02:43]Alessio: There was actually my first article on the Twilio blog with a Python script to like predict pricing of like Daily Fantasy players based on my past week performance. Yeah, I don't know. It's a good getaway drug. [00:02:56]Swyx: And on my end, the way I got into finance was through sports betting. So maybe we all have some ties in there. Was like Moneyball a big inspiration? The movie? [00:03:06]Harrison: Honestly, not really. I don't really like baseball. That's like the big thing. [00:03:10]Swyx: Let's call it a lot of stats. Cool. Well, we can dive right into LangChain, which is what everyone is excited about. But feel free to make all the sports analogies you want. That really drives home a lot of points. What was your GPT aha moment? When did you start working on GPT itself? Maybe not LangChain, just anything to do with the GPT API? [00:03:29]Harrison: I think it probably started around the time we had a company hackathon. I think that was before I launched LangChain. I'm trying to remember the exact sequence of events, but I do remember that at the hackathon I worked with Will, who's now actually at LangChain as well, and then two other members of Robust. And we made basically a bot where you could ask questions of Notion and Slack. And so I think, yeah, RAG, basically. And I think I wanted to try that out because I'd heard that it was getting good. I'm trying to remember if I did anything before that to realize that it was good. So then I would focus on that on the hackathon. I can't remember or not, but that was one of the first times that I built something [00:04:06]Swyx: with GPT-3. There wasn't that much opportunity before because the API access wasn't that widespread. You had to get into some kind of program to get that. [00:04:16]Harrison: DaVinci-002 was not terrible, but they did an upgrade to get it to there, and they didn't really publicize that as much. And so I think I remember playing around with it when the first DaVinci model came out. I was like, this is cool, but it's not amazing. You'd have to do a lot of work to get it to do something. But then I think that February or something, I think of 2022, they upgraded it and it was it got better, but I think they made less of an announcement around it. And so I just, yeah, it kind of slipped under the radar for me, at least. [00:04:45]Alessio: And what was the step into LangChain? So you did the hackathon, and then as you were building the kind of RAG product, you felt like the developer experience wasn't that great? Or what was the inspiration? [00:04:54]Harrison: No, honestly, so around that time, I knew I was going to leave my previous job. I was trying to figure out what I was going to do next. I went to a bunch of meetups and other events. This was like the September, August, September of that year. So after Stable Diffusion, but before ChatGPT. So there was interest in generative AI as a space, but not a lot of people hacking on language models yet. But there were definitely some. And so I would go to these meetups and just chat with people and basically saw some common abstractions in terms of what they were building, and then thought it would be a cool side project to factor out some of those common abstractions. And that became kind of like LangChain. I looked up again before this, because I remember I did a tweet thread on Twitter to announce LangChain. And we can talk about what LangChain is. It's a series of components. And then there's some end-to-end modules. And there was three end-to-end modules that were in the initial release. One was NatBot. So this was the web agent by Nat Friedman. Another was LLM Math Chain. So it would construct- [00:05:51]Swyx: GPT-3 cannot do math. [00:05:53]Harrison: Yeah, exactly. And then the third was Self-Ask. So some type of RAG search, similar to React style agent. So those were some of the patterns in terms of what I was seeing. And those all came from open source or academic examples, because the people who were actually working on this were building startups. And they were doing things like question answering over your databases, question answering over SQL, things like that. But I couldn't use their code as kind of like inspiration to factor things out. [00:06:18]Swyx: I talked to you a little bit, actually, roundabout, right after you announced LangChain. I'm honored. I think I'm one of many. This is your first open source project. [00:06:26]Harrison: No, that's not actually true. I released, because I like sports stats. And so I remember I did release some really small, random Python package for scraping data from basketball reference or something. I'm pretty sure I released that. So first project to get a star on GitHub, let's say that. [00:06:45]Swyx: Did you reference anything? What was the inspirations, like other frameworks that you look to when open sourcing LangChain or announcing it or anything like that? [00:06:53]Harrison: I mean, the only main thing that I looked for... I remember reading a Hacker News post a little bit before about how a readme on the project goes a long way. [00:07:02]Swyx: Readme's help. [00:07:03]Harrison: Yeah. And so I looked at it and was like, put some status checks at the top and have the title and then one or two lines and then just right into installation. And so that's the main thing that I looked at in terms of how to structure it. Because yeah, I hadn't done open source before. I didn't really know how to communicate that aspect of the marketing or getting people to use it. I think I had some trouble finding it, but I finally found it and used that as a lot [00:07:25]Swyx: of the inspiration there. Yeah. It was one of the subjects of my write-up how it was surprising to me that significant open source experience actually didn't seem to matter in the new wave of AI tooling. Most like auto-GPTs, Torrents, that was his first open source project ever. And that became auto-GPT. Yeah. I don't know. To me, it's just interesting how open source experience is kind of fungible or not necessary. Or you can kind of learn it on the job. [00:07:49]Alessio: Overvalued. [00:07:50]Swyx: Overvalued. Okay. You said it, not me. [00:07:53]Alessio: What's your description of LangChain today? I think when I built the LangChain Hub UI in January, there were a few things. And I think you were one of the first people to talk about agents that were already in there before it got hot now. And it's obviously evolved into a much bigger framework today. Run people through what LangChain is today, how they should think about it, and all of that. [00:08:14]Harrison: The way that we describe it or think about it internally is that LangChain is basically... I started off saying LangChain's a framework for building LLM applications, but that's really vague and not really specific. And I think part of the issue is LangChain does do a lot, so it's hard to be somewhat specific. But I think the way that we think about it internally, in terms of prioritization, what to focus on, is basically LangChain's a framework for building context-aware reasoning applications. And so that's a bit of a mouthful, but I think that speaks to a lot of the core parts of what's in LangChain. And so what concretely that means in LangChain, there's really two things. One is a set of components and modules. And these would be the prompt template abstraction, the LLM abstraction, chat model abstraction, vector store abstraction, text splitters, document loaders. And so these are combinations of things that we build and we implement, or we just have integrations with. So we don't have any language models ourselves. We don't have any vector stores ourselves, but we integrate with a lot of them. And then the text splitters, we have our own logic for that. The document loaders, we have our own logic for that. And so those are the individual modules. But then I think another big part of LangChain, and probably the part that got people using it the most, is the end-to-end chains or applications. So we have a lot of chains for getting started with question answering over your documents, chat question answering, question answering over SQL databases, agent stuff that you can plug in off the box. And that basically combines these components in a series of specific ways to do this. So if you think about a question answering app, you need a lot of different components kind of stacked. And there's a bunch of different ways to do question answering apps. So this is a bit of an overgeneralization, but basically, you know, you have some component that looks up an embedding from a vector store, and then you put that into the prompt template with the question and the context, and maybe you have the chat history as well. And then that generates an answer, and then maybe you parse that out, or you do something with the answer there. And so there's just this sequence of things that you basically stack in a particular way. And so we just provide a bunch of those assembled chains off the shelf to make it really easy to get started in a few lines of code. [00:10:09]Alessio: And just to give people context, when you first released LangChain, OpenAI did not have a chat API. It was a completion-only API. So you had to do all the human assistant, like prompting and whatnot. So you abstracted a lot of that away. I think the most interesting thing to me is you're kind of the Switzerland of this developer land. There's a bunch of vector databases that are killing each other out there to get people to embed data in them, and you're like, I love you all. You all are great. How do you think about being an opinionated framework versus leaving a lot of choice to the user? I mean, in terms of spending time into this integration, it's like you only have 10 people on the team. Obviously that takes time. Yeah. What's that process like for you all? [00:10:50]Harrison: I think right off the bat, having different options for language models. I mean, language models is the main one that right off the bat we knew we wanted to support a bunch of different options for. There's a lot to discuss there. People want optionality between different language models. They want to try it out. They want to maybe change to ones that are cheaper as new ones kind of emerge. They don't want to get stuck into one particular one if a better one comes out. There's some challenges there as well. Prompts don't really transfer. And so there's a lot of nuance there. But from the bat, having this optionality between the language model providers was a big important part because I think that was just something we felt really strongly about. We believe there's not just going to be one model that rules them all. There's going to be a bunch of different models that are good for a bunch of different use cases. I did not anticipate the number of vector stores that would emerge. I don't know how many we supported in the initial release. It probably wasn't as big of a focus as language models was. But I think it kind of quickly became so, especially when Postgres and Elastic and Redis started building their vector store implementations. We saw that some people might not want to use a dedicated vector store. Maybe they want to use traditional databases. I think to your point around what we're opinionated about, I think the thing that we believe most strongly is it's super early in the space and super fast moving. And so there's a lot of uncertainty about how things will shake out in terms of what role will vector databases play? How many will there be? And so I think a lot of it has always kind of been this optionality and ability to switch and not getting locked in. [00:12:19]Swyx: There's other pieces of LangChain which maybe don't get as much attention sometimes. And the way that you explained LangChain is somewhat different from the docs. I don't know how to square this. So for example, you have at the top level in your docs, you have, we mentioned ModelIO, we mentioned Retrieval, we mentioned Chains. Then you have a concept called Agents, which I don't know if exactly matches what other people call Agents. And we also talked about Memory. And then finally there's Callbacks. Are there any of the less understood concepts in LangChain that you want to give some air to? [00:12:53]Harrison: I mean, I think buried in ModelIO is some stuff around like few-shot example selectors that I think is really powerful. That's a workhorse. [00:13:01]Swyx: Yeah. I think that's where I start with LangChain. [00:13:04]Harrison: It's one of those things that you probably don't, if you're building an application, you probably don't start with it. You probably start with like a zero-shot prompt. But I think that's a really powerful one that's probably just talked about less because you don't need it right off the bat. And for those of you who don't know, that basically selects from a bunch of examples the ones that are maybe most relevant to the input at hand. So you can do some nice kind of like in-context learning there. I think that's, we've had that for a while. I don't think enough people use that, basically. Output parsers also used to be kind of important, but then function calling. There's this interesting thing where like the space is just like progressing so rapidly that a lot of things that were really important have kind of diminished a bit, to be honest. Output parsers definitely used to be an understated and underappreciated part. And I think if you're working with non-OpenAI models, they still are, but a lot of people are working with OpenAI models. But even within there, there's different things you can do with kind of like the function calling ability. Sometimes you want to have the option of having the text or the application you're building, it could return either. Sometimes you know that it wants to return in a structured format, and so you just want to take that structured format. Other times you're extracting things that are maybe a key in that structured format, and so you want to like pluck that key. And so there's just like some like annoying kind of like parsing of that to do. Agents, memory, and retrieval, we haven't talked at all. Retrieval, there's like five different subcomponents. You could also probably talk about all of those in depth. You've got the document loaders, the text splitters, the embedding models, the vector stores. Embedding models and vector stores, we don't really have, or sorry, we don't build, we integrate with those. Text splitters, I think we have like 15 or so. Like I think there's an under kind of like appreciated amount of those. [00:14:39]Swyx: And then... Well, it's actually, honestly, it's overwhelming. Nobody knows what to choose. [00:14:43]Harrison: Yeah, there is a lot. [00:14:44]Swyx: Yeah. Do you have personal favorites that you want to shout out? [00:14:47]Harrison: The one that we have in the docs is the default is like the recursive text splitter. We added a playground for text splitters the other week because, yeah, we heard a lot that like, you know, and like these affect things like the chunk overlap and the chunks, they affect things in really subtle ways. And so like I think we added a playground where people could just like choose different options. We have like, and a lot of the ideas are really similar. You split on different characters, depending on kind of like the type of text that you have marked down, you might want to split on differently than HTML. And so we added a playground where you can kind of like choose between those. I don't know if those are like underappreciated though, because I think a lot of people talk about text splitting as being a hard part, and it is a really important part of creating these retrieval applications. But I think we have a lot of really cool retrieval algorithms as well. So like self query is maybe one of my favorite things in LangChain, which is basically this idea of when you have a user question, the typical kind of like thing to do is you embed that question and then find the document that's most similar to that question. But oftentimes questions have things that just, you don't really want to look up semantically, they have some other meaning. So like in the example that I use, the example in the docs is like movies about aliens in the year 1980. 1980, I guess there's some semantic meaning for that, but it's a very particular thing that you care about. And so what the self query retriever does is it splits out the metadata filter and most vector stores support like a metadata filter. So it splits out this metadata filter, and then it splits out the semantic bit. And that's actually like kind of tricky to do because there's a lot of different filters that you can have like greater than, less than, equal to, you can have and things if you have multiple filters. So we have like a pretty complicated like prompt that does all that. That might be one of my favorite things in LangChain, period. Like I think that's, yeah, I think that's really cool. [00:16:26]Alessio: How do you think about speed of development versus support of existing things? So we mentioned retrieval, like you got, or, you know, text splitting, you got like different options for all of them. As you get building LangChain, how do you decide which ones are not going to keep supporting, you know, which ones are going to leave behind? I think right now, as you said, the space moves so quickly that like you don't even know who's using what. What's that like for you? [00:16:50]Harrison: Yeah. I mean, we have, you know, we don't really have telemetry on what people are using in terms of what parts of LangChain, the telemetry we have is like, you know, anecdotal stuff when people ask or have issues with things. A lot of it also is like, I think we definitely prioritize kind of like keeping up with the stuff that comes out. I think we added function calling, like the day it came out or the day after it came out, we added chat model support, like the day after it came out or something like that. That's probably, I think I'm really proud of how the team has kind of like kept up with that because this space is like exhausting sometimes. And so that's probably, that's a big focus of ours. The support, I think we've like, to be honest, we've had to get kind of creative with how we do that. Cause we have like, I think, I don't know how many open issues we have, but we have like 3000, somewhere between 2000 and 3000, like open GitHub issues. We've experimented with a lot of startups that are doing kind of like question answering over your docs and stuff like that. And so we've got them on the website and in the discord and there's a really good one, dosu on the GitHub that's like answering issues and stuff like that. And that's actually something we want to start leaning into more heavily as a company as well as kind of like building out an AI dev rel because we're 10 people now, 10, 11 people now. And like two months ago we were like six or something like that. Right. So like, and to have like 2,500 open issues or something like that, and like 300 or 400 PRs as well. Cause like one of the amazing things is that like, and you kind of alluded to this earlier, everyone's building in the space. There's so many different like touch points. LangChain is lucky enough to kind of like be a lot of the glue that connects it. And so we get to work with a lot of awesome companies, but that's also a lot of like work to keep up with as well. And so I don't really have an amazing answer, but I think like the, I think prioritize kind of like new things that, that come out. And then we've gotten creative with some of kind of like the support functions and, and luckily there's, you know, there's a lot of awesome people working on all those support coding, question answering things that we've been able to work with. [00:18:46]Swyx: I think there is your daily rhythm, which I've seen you, you work like a, like a beast man, like mad impressive. And then there's sometimes where you step back and do a little bit of high level, like 50,000 foot stuff. So we mentioned, we mentioned retrieval. You did a refactor in March and there's, there's other abstractions that you've sort of changed your mind on. When do you do that? When do you do like the, the step back from the day to day and go, where are we going and change the direction of the ship? [00:19:11]Harrison: It's a good question so far. It's probably been, you know, we see three or four or five things pop up that are enough to make us think about it. And then kind of like when it reaches that level, you know, we don't have like a monthly meeting where we sit down and do like a monthly plan or something. [00:19:27]Swyx: Maybe we should. I've thought about this. Yeah. I'd love to host that meeting. [00:19:32]Harrison: It's really been a lot of, you know, one of the amazing things is we get to interact with so many different people. So it's been a lot of kind of like just pattern matching on what people are doing and trying to see those patterns before they punch us in the face or something like that. So for retrieval, it was the pattern of seeing like, Hey, yeah, like a lot of people are using vector sort of stuff. But there's also just like other methods and people are offering like hosted solutions and we want our abstractions to work with that as well. So we shouldn't bake in this paradigm of doing like semantic search too heavily, which sounds like basic now, but I think like, you know, to start a lot of it was people needed help doing these things. But then there was like managed things that did them, hybrid retrieval mechanisms, all of that. I think another example of this, I mean, Langsmith, which we can maybe talk about was like very kind of like, I think we worked on that for like three or four months before announcing it kind of like publicly, two months maybe before giving it to kind of like anyone in beta. But this was a lot of debugging these applications as a pain point. We hear that like just understanding what's going on is a pain point. [00:20:27]Alessio: I mean, you two did a webinar on this, which is called Agents vs. Chains. It was fun, baby. [00:20:32]Swyx: Thanks for having me on. [00:20:33]Harrison: No, thanks for coming. [00:20:34]Alessio: That was a good one. And on the website, you list like RAG, which is retrieval of bank debt generation and agents as two of the main goals of LangChain. The difference I think at the Databricks keynote, you said chains are like predetermined steps and agents is models reasoning to figure out what steps to take and what actions to take. How should people think about when to use the two and how do you transition from one to the other with LangChain? Like is it a path that you support or like do people usually re-implement from an agent to a chain or vice versa? [00:21:05]Swyx: Yeah. [00:21:06]Harrison: You know, I know agent is probably an overloaded term at this point, and so there's probably a lot of different definitions out there. But yeah, as you said, kind of like the way that I think about an agent is basically like in a chain, you have a sequence of steps. You do this and then you do this and then you do this and then you do this. And with an agent, there's some aspect of it where the LLM is kind of like deciding what to do and what steps to do in what order. And you know, there's probably some like gray area in the middle, but you know, don't fight me on this. And so if we think about those, like the benefits of the chains are that they're like, you can say do this and you just have like a more rigid kind of like order and the way that things are done. They have more control and they don't go off the rails and basically everything that's bad about agents in terms of being uncontrollable and expensive, you can control more finely. The benefit of agents is that I think they handle like the long tail of things that can happen really well. And so for an example of this, let's maybe think about like interacting with a SQL database. So you can have like a SQL chain and you know, the first kind of like naive approach at a SQL chain would be like, okay, you have the user question. And then you like write the SQL query, you do some rag, you pull in the relevant tables and schemas, you write a SQL query, you execute that against the SQL database. And then you like return that as the answer, or you like summarize that with an LLM and return that to the answer. And that's basically the SQL chain that we have in LangChain. But there's a lot of things that can go wrong in that process. Starting from the beginning, you may like not want to even query the SQL database at all. Maybe they're saying like, hi, or something, or they're misusing the application. Then like what happens if you have some step, like a big part of the application that people with LangChain is like the context aware part. So there's generally some part of bringing in context to the language model. So if you bring in the wrong context to the language model, so it doesn't know which tables to query, what do you do then? If you write a SQL query, it's like syntactically wrong and it can't run. And then if it can run, like what if it returns an unexpected result or something? And so basically what we do with the SQL agent is we give it access to all these different tools. So it has another tool, it can run the SQL query as another, and then it can respond to the user. But then if it kind of like, it can decide which order to do these. And so it gives it flexibility to handle all these edge cases. And there's like, obviously downsides to that as well. And so there's probably like some safeguards you want to put in place around agents in terms of like not letting them run forever, having some observability in there. But I do think there's this benefit of, you know, like, again, to the other part of what LangChain is like the reasoning part, like each of those steps individually involves some aspect of reasoning, for sure. Like you need to reason about what the SQL query is, you need to reason about what to return. But there's then there's also reasoning about the order of operations. And so I think to me, the key is kind of like giving it an appropriate amount to reason about while still keeping it within checks. And so to the point, like, I would probably recommend that most people get started with chains and then when they get to the point where they're hitting these edge cases, then they think about, okay, I'm hitting a bunch of edge cases where the SQL query is just not returning like the relevant things. Maybe I should add in some step there and let it maybe make multiple queries or something like that. Basically, like start with chain, figure out when you're hitting these edge cases, add in the reasoning step to that to handle those edge cases appropriately. That would be kind of like my recommendation, right? [00:24:09]Swyx: If I were to rephrase it, in my words, an agent would be a reasoning node in a chain, right? Like you start with a chain, then you just add a reasoning node, now it's an agent. [00:24:17]Harrison: Yeah, the architecture for your application doesn't have to be just a chain or just an agent. It can be an agent that calls chains, it can be a chain that has an agent in different parts of them. And this is another part as well. Like the chains in LangChain are largely intended as kind of like a way to get started and take you some amount of the way. But for your specific use case, in order to kind of like eke out the most performance, you're probably going to want to do some customization at the very basic level, like probably around the prompt or something like that. And so one of the things that we've focused on recently is like making it easier to customize these bits of existing architectures. But you probably also want to customize your architectures as well. [00:24:52]Swyx: You mentioned a bit of prompt engineering for self-ask and then for this stuff. There's a bunch of, I just talked to a prompt engineering company today, PromptOps or LLMOps. Do you have any advice or thoughts on that field in general? Like are you going to compete with them? Do you have internal tooling that you've built? [00:25:08]Harrison: A lot of what we do is like where we see kind of like a lot of the pain points being like we can talk about LangSmith and that was a big motivation for that. And like, I don't know, would you categorize LangSmith as PromptOps? [00:25:18]Swyx: I don't know. It's whatever you want it to be. Do you want to call it? [00:25:22]Harrison: I don't know either. Like I think like there's... [00:25:24]Swyx: I think about it as like a prompt registry and you store them and you A-B test them and you do that. LangSmith, I feel like doesn't quite go there yet. Yeah. It's obviously the next step. [00:25:34]Harrison: Yeah, we'll probably go. And yeah, we'll do more of that because I think that's definitely part of the application of a chain or agent is you start with a default one, then you improve it over time. And like, I think a lot of the main new thing that we're dealing with here is like language models. And the main new way to control language models is prompts. And so like a lot of the chains and agents are powered by this combination of like prompt language model and then some output parser or something doing something with the output. And so like, yeah, we want to make that core thing as good as possible. And so we'll do stuff all around that for sure. [00:26:05]Swyx: Awesome. We might as well go into LangSmith because we're bringing it up so much. So you announced LangSmith I think last month. What are your visions for it? Is this the future of LangChain and the company? [00:26:16]Harrison: It's definitely part of the future. So LangSmith is basically a control center for kind of like your LLM application. So the main features that it kind of has is like debugging, logging, monitoring, and then like testing and evaluation. And so debugging, logging, monitoring, basically you set three environment variables and it kind of like logs all the runs that are happening in your LangChain chains or agents. And it logs kind of like the inputs and outputs at each step. And so the main use case we see for this is in debugging. And that's probably the main reason that we started down this path of building it is I think like as you have these more complex things, debugging what's actually going on becomes really painful whether you're using LangChain or not. And so like adding this type of observability and debuggability was really important. Yeah. There's a debugging aspect. You can see the inputs, outputs at each step. You can then quickly enter into like a playground experience where you can fiddle around with it. The first version didn't have that playground and then we'd see people copy, go to open AI playground, paste in there. Okay. Well, that's a little annoying. And then there's kind of like the monitoring, logging experience. And we recently added some analytics on like, you know, how many requests are you getting per hour, minute, day? What's the feedback like over time? And then there's like a testing debugging, sorry, testing and evaluation component as well where basically you can create datasets and then test and evaluate these datasets. And I think importantly, all these things are tied to each other and then also into LangChain, the framework. So what I mean by that is like we've tried to make it as easy as possible to go from logs to adding a data point to a dataset. And because we think a really powerful flow is you don't really get started with a dataset. You can accumulate a dataset over time. And so being able to find points that have gotten like a thumbs up or a thumbs down from a user can be really powerful in terms of creating a good dataset. And so that's maybe like a connection between the two. And then the connection in the other way is like all the runs that you have when you test or evaluate something, they're logged in the same way. So you can debug what exactly is going on and you don't just have like a final score. You have like this nice trace and thing where you can jump in. And then we also want to do more things to hook this into a LangChain proper, the framework. So I think like some of like the managing the prompts will tie in here already. Like we talked about example selectors using datasets as a few short examples is a path that we support in a somewhat janky way right now, but we're going to like make better over time. And so there's this connection between everything. Yeah. [00:28:42]Alessio: And you mentioned the dataset in the announcement blog post, you touched on heuristic evaluation versus LLMs evaluating LLMs. I think there's a lot of talk and confusion about this online. How should people prioritize the two, especially when they might start with like not a good set of evals or like any data at all? [00:29:01]Harrison: I think it's really use case specific in the distinction that I draw between heuristic and LLM. LLMs, you're using an LLM to evaluate the output heuristics, you have some common heuristic that you can use. And so some of these can be like really simple. So we were doing some kind of like measuring of an extraction chain where we wanted it to output JSON. Okay. One evaluation can be, can you use JSON.loads to load it? And like, right. And that works perfectly. You don't need an LLM to do that. But then for like a lot of like the question answering, like, is this factually accurate? And you have some ground truth fact that you know it should be answering with. I think, you know, LLMs aren't perfect. And I think there's a lot of discussion around the pitfalls of using LLMs to evaluate themselves. And I'm not saying they're perfect by any means, but I do think they're, we've found them to be kind of like better than blue or any of those metrics. And the way that I also like to use those is also just like guide my eye about where to look. So like, you know, I might not trust the score of like 0.82, like exactly correct, but like I can look to see like which data points are like flagged as passing or failing. And sometimes the evaluators messing up, but it's like good to like, you know, I don't have to look at like a hundred data points. I can focus on like 10 or something like that. [00:30:10]Alessio: And then can you create a heuristic once in Langsmith? Like what's like your connection to that? [00:30:16]Harrison: Yeah. So right now, all the evaluation, we actually do client side. And part of this is basically due to the fact that a lot of the evaluation is really application specific. So we thought about having evaluators, you could just click off and run in a server side or something like that. But we still think it's really early on in evaluation. We still think there's, it's just really application specific. So we prioritized instead, making it easy for people to write custom evaluators and then run them client side and then upload the results so that they can manually inspect them because I think manual inspection is still a pretty big part of evaluation for better or worse. [00:30:50]Swyx: We have this sort of components of observability. We have cost, latency, accuracy, and then planning. Is that listed in there? [00:30:57]Alessio: Well, planning more in the terms of like, if you're an agent, how to pick the right tool and whether or not you are picking the right tool. [00:31:02]Swyx: So when you talk to customers, how would you stack rank those needs? Are they cost sensitive? Are they latency sensitive? I imagine accuracy is pretty high up there. [00:31:13]Harrison: I think accuracy is definitely the top that we're seeing right now. I think a lot of the applications, people are, especially the ones that we're working with, people are still struggling to get them to work at a level where they're reliable [00:31:24]Swyx: enough. [00:31:25]Harrison: So that's definitely the first. Then I think probably cost becomes the next one. I think a few places where we've started to see this be like one of the main things is the AI simulation that came out. [00:31:36]Swyx: Generative agents. Yeah, exactly. [00:31:38]Harrison: Which is really fun to run, but it costs a lot of money. And so one of our team members, Lance, did an awesome job hooking up like a local model to it. You know, it's not as perfect, but I think it helps with that. Another really big place for this, we believe, is in like extraction of structured data from unstructured data. And the reason that I think it's so important there is that usually you do extraction of some type of like pre-processing or indexing process over your documents. I mean, there's a bunch of different use cases, but one use case is for that. And generally that's over a lot of documents. And so that starts to rack up a bill kind of quickly. And I think extraction is also like a simpler task than like reasoning about which tools to call next in an agent. And so I think it's better suited for that. Yeah. [00:32:15]Swyx: On one of the heuristics I wanted to get your thoughts on, hallucination is one of the big problems there. Do you have any recommendations on how people should reduce hallucinations? [00:32:25]Harrison: To reduce hallucinations, we did a webinar on like evaluating RAG this past week. And I think there's this great project called RAGOS that evaluates four different things across two different spectrums. So the two different spectrums are like, is the retrieval part right? Or is the generation, or sorry, like, is it messing up in retrieval or is it messing up in generation? And so I think to fix hallucination, it probably depends on where it's messing up. If it's messing up in generation, then you're getting the right information, but it's still hallucinating. Or you're getting like partially right information and hallucinating some bits, a lot of that's prompt engineering. And so that's what we would recommend kind of like focusing on the prompt engineering part. And then if you're getting it wrong in the, if you're just not retrieving the right stuff, then there's a lot of different things that you can probably do, or you should look at on the retrieval bit. And honestly, that's where it starts to become a bit like application specific as well. Maybe there's some temporal stuff going on. Maybe you're not parsing things correctly. Yeah. [00:33:19]Swyx: Okay. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:33:35]Harrison: Yeah. Yeah. [00:33:37]Swyx: Yeah. [00:33:38]Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:33:56]Swyx: Yeah. Yeah. [00:33:58]Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:34:04]Swyx: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. Yeah. [00:34:17]Harrison: Yeah. Yeah. Yeah. Yeah. Yeah. Yeah, I mean, there's probably a larger discussion around that, but openAI definitely had a huge headstart, right? And that's... Clawds not even publicly available yet, I don't think. [00:34:28]Swyx: The API? Yeah. Oh, well, you can just basically ask any of the business reps and they'll give it to you. [00:34:33]Harrison: You can. But it's still a different signup process. I think there's... I'm bullish that other ones will catch up especially like Anthropic and Google. The local ones are really interesting. I think we're seeing a big... [00:34:46]Swyx: Lama Two? Yeah, we're doing the fine-tuning hackathon tomorrow. Thanks for promoting that. [00:34:50]Harrison: No, thanks for it. I'm really excited about that stuff. I mean, that's something that like we've been, you know, because like, as I said, like the only thing we know is that the space is moving so fast and changing so rapidly. And like, local models are, have always been one of those things that people have been bullish on. And it seems like it's getting closer and closer to kind of like being viable. So I'm excited to see what we can do with some fine-tuning. [00:35:10]Swyx: Yeah. I have to confess, I did not know that you cared. It's not like a judgment on Langchain. I was just like, you know, you write an adapter for it and you're done, right? Like how much further does it go for Langchain? In terms of like, for you, it's one of the, you know, the model IO modules and that's it. But like, you seem very personally, very passionate about it, but I don't know what the Langchain specific angle for this is, for fine-tuning local models, basically. Like you're just passionate about local models and privacy and all that, right? And open source. [00:35:41]Harrison: Well, I think there's a few different things. Like one, like, you know, if we think about what it takes to build a really reliable, like context-aware reasoning application, there's probably a bunch of different nodes that are doing a bunch of different things. And I think it is like a really complex system. And so if you're relying on open AI for every part of that, like, I think that starts to get really expensive. Also like, probably just like not good to have that much reliability on any one thing. And so I do think that like, I'm hoping that for like, you know, specific parts at the end, you can like fine-tune a model and kind of have a more specific thing for a specific task. Also, to be clear, like, I think like, I also, at the same time, I think open AI is by far the easiest way to get started. And if I was building anything, I would absolutely start with open AI. So. [00:36:27]Swyx: It's something I think a lot of people are wrestling with. But like, as a person building apps, why take five vendors when I can take one vendor, right? Like, as long as I trust Azure, I'm just entrusting all my data to Azure and that's it. So I'm still trying to figure out the real case for local models in production. And I don't know, but fine-tuning, I think, is a good one. That's why I guess open AI worked on fine-tuning. [00:36:49]Harrison: I think there's also like, you know, like if there is, if there's just more options available, like prices are going to go down. So I'm happy about that. So like very selfishly, there's that aspect as well. [00:37:01]Alessio: And in the Lancsmith announcement, I saw in the product screenshot, you have like chain, tool and LLM as like the three core atoms. Is that how people should think about observability in this space? Like first you go through the chain and then you start dig down between like the model itself and like the tool it's using? [00:37:19]Harrison: We've added more. We've added like a retriever logging so that you can see like what query is going in and what are the documents you're getting out. Those are like the three that we started with. I definitely think probably the main ones, like basically the LLM. So the reason I think the debugging in Lancsmith and debugging in general is so needed for these LLM apps is that if you're building, like, again, let's think about like what we want people to build in with LangChain. These like context aware reasoning applications. Context aware. There's a lot of stuff in the prompt. There's like the instructions. There's any previous messages. There's any input this time. There's any documents you retrieve. And so there's a lot of like data engineering that goes into like putting it into that prompt. This sounds silly, but just like making sure the data shows up in the right format is like really important. And then for the reasoning part of it, like that's obviously also all in the prompt. And so being able to like, and there's like, you know, the state of the world right now, like if you have the instructions at the beginning or at the end can actually make like a big difference in terms of whether it forgets it or not. And so being able to kind of like. [00:38:17]Swyx: Yeah. And it takes on that one, by the way, this is the U curve in context, right? Yeah. [00:38:21]Harrison: I think it's real. Basically I've found long context windows really good for when I want to extract like a single piece of information about something basically. But if I want to do reasoning over perhaps multiple pieces of information that are somewhere in like the retrieved documents, I found it not to be that great. [00:38:36]Swyx: Yeah. I have said that that piece of research is the best bull case for Lang chain and all the vector companies, because it means you should do chains. It means you should do retrieval instead of long context, right? People are trying to extend long context to like 100K, 1 million tokens, 5 million tokens. It doesn't matter. You're going to forget. You can't trust it. [00:38:54]Harrison: I expect that it will probably get better over time as everything in this field. But I do also think there'll always be a need for kind of like vector stores and retrieval in some fashions. [00:39:03]Alessio: How should people get started with Langsmith Cookbooks? Wanna talk maybe a bit about that? [00:39:08]Swyx: Yeah. [00:39:08]Harrison: Again, like I think the main thing that even I find valuable about Langsmith is just like the debugging aspect of it. And so for that, it's very simple. You can kind of like turn on three environment variables and it just logs everything. And you don't look at it 95% of the time, but that 5% you do when something goes wrong, it's quite handy to have there. And so that's probably the easiest way to get started. And we're still in a closed beta, but we're letting people off the wait list every day. And if you really need access, just DM me and we're happy to give you access there. And then yeah, there's a lot that you can do with Langsmith that we've been talking about. And so Will on our team has been leading the charge on a really great like Langsmith Cookbooks repo that covers everything from collecting feedback, whether it's thumbs up, thumbs down, or like multi-scale or comments as well, to doing evaluation, doing testing. You can also use Langsmith without Langchain. And so we've got some notebooks on that in there. But we have Python and JavaScript SDKs that aren't dependent on Langchain in any way. [00:40:01]Swyx: And so you can use those. [00:40:01]Harrison: And then we'll also be publishing a notebook on how to do that just with the REST APIs themselves. So yeah, definitely check out that repo. That's a great resource that Will's put together. [00:40:10]Swyx: Yeah, awesome. So we'll zoom out a little bit from Langsmith and talk about Langchain, the company. You're also a first-time founder. Yes. And you've just hired your 10th employee, Julia, who I know from my data engineering days. You mentioned Will Nuno, I think, who maintains Langchain.js. I'm very interested in like your multi-language strategy, by the way. Ankush, your co-founder, Lance, who did AutoEval. What are you staffing up for? And maybe who are you hiring? [00:40:34]Harrison: Yeah, so 10 employees, 12 total. We've got three more joining over the next three weeks. We've got Julia, who's awesome leading a lot of the product, go-to-market, customer success stuff. And then we've got Bri, who's also awesome leading a lot of the marketing and ops aspects. And then other than that, all engineers. We've staffed up a lot on kind of like full stack infra DevOps, kind of like as we've started going into the hosted platform. So internally, we're split about 50-50 between the open source and then the platform stuff. And yeah, we're looking to hire particularly on kind of like the things, we're actually looking to hire across most fronts, to be honest. But in particular, we probably need one or two more people on like open source, both Python and JavaScript and happy to dive into the multi-language kind of like strategy there. But again, like strong focus there on engineering, actually, as opposed to maybe like, we're not a research lab, we're not a research shop. [00:41:48]Swyx: And then on the platform side, [00:41:49]Harrison: like we definitely need some more people on the infra and DevOps side. So I'm using this as an opportunity to tell people that we're hiring and that you should reach out if that sounds like you. [00:41:58]Swyx: Something like that, jobs, whatever. I don't actually know if we have an official job. [00:42:02]Harrison: RIP, what happened to your landing page? [00:42:04]Swyx: It used to be so based. The Berkshire Hathaway one? Yeah, so what was the story, the quick story behind that? Yeah, the quick story behind that is we needed a website [00:42:12]Harrison: and I'm terrible at design. [00:42:14]Swyx: And I knew that we couldn't do a good job. [00:42:15]Harrison: So if you can't do a good job, might as well do the worst job possible. Yeah, and like lean into it. And have some fun with it, yeah. [00:42:21]Swyx: Do you admire Warren Buffett? Yeah, I admire Warren Buffett and admire his website. And actually you can still find a link to it [00:42:26]Harrison: from our current website if you look hard enough. So there's a little Easter egg. Before we dive into more of the open source community things, [00:42:33]Alessio: let's dive into the language thing. How do you think about parity between the Python and JavaScript? Obviously, they're very different ecosystems. So when you're working on a LangChain, is it we need to have the same abstraction in both language or are you to the needs? The core stuff, we want to have the same abstractions [00:42:50]Harrison: because we basically want to be able to do serialize prompts, chains, agents, all the core stuff as tightly as possible and then use that between languages. Like even, yeah, like even right now when we log things to LangChain, we have a playground experience where you can run things that runs in JavaScript because it's kind of like in the browser. But a lot of what's logged is like Python. And so we need that core equivalence for a lot of the core things. Then there's like the incredibly long tail of like integrations, more researchy things. So we want to be able to do that. Python's probably ahead on a lot of like the integrations front. There's more researchy things that we're able to include quickly because a lot of people release some of their code in Python and stuff like that. And so we can use that. And there's just more of an ecosystem around the Python project. But the core stuff will have kind of like the same abstractions and be translatable. That didn't go exactly where I was thinking. So like the LangChain of Ruby, the LangChain of C-sharp, [00:43:44]Swyx: you know, there's demand for that. I mean, I think that's a big part of it. But you are giving up some real estate by not doing it. Yeah, it comes down to kind of like, you know, ROI and focus. And I think like we do think [00:43:58]Harrison: there's a strong JavaScript community and we wanted to lean into that. And I think a lot of the people that we brought on early, like Nuno and Jacob have a lot of experience building JavaScript tooling in that community. And so I think that's a big part of it. And then there's also like, you know, building JavaScript tooling in that community. Will we do another language? Never say never, but like... [00:44:21]Swyx: Python JS for now. Yeah. Awesome. [00:44:23]Alessio: You got 83 articles, which I think might be a record for such a young company. What are like the hottest hits, the most popular ones? [00:44:32]Harrison: I think the most popular ones are generally the ones where we do a deep dive on something. So we did something a few weeks ago around evaluating CSV q

Spring Boot: Up and Running by Mark Heckler: https://bit.ly/springbootbookNODES 2023: https://neo4j.com/nodes-2023Building an Educational Chatbot for GraphAcademy with Neo4j Using LLMs and Vector Search: https://medium.com/neo4j/building-an-educational-chatbot-for-graphacademy-with-neo4j-f707c4ce311bLivestream: Going Meta Ep 20Build a Chatbot for Clinical Trials across Multiple Data Sources: https://medium.com/star-gazers/build-a-chatbot-for-clinical-trials-across-multiple-data-sources-f121211cec98GraphAcademy course - Introduction to Neo4j and GraphQL: https://graphacademy.neo4j.com/courses/graphql-basicsLondon Data GraphQL (Sep 7)GraphQL Conf (Sep 19)Creating a Custom Connector in Confluent Cloud to Sink Data to Aura for Real-Time Analysis (Part 2): https://neo4j.com/developer-blog/confluent-cloud-neo4j-auradb-connector-2/Build a Movie Database with Neo4j's Knowledge Graph Sandbox: https://thenewstack.io/build-a-movie-database-with-neo4js-knowledge-graph-sandbox/Knowledge Graph from text using LLM: https://www.youtube.com/watch?v=Hg4ahTQlBm0SDN custom queries/projections: https://www.javacodegeeks.com/2023/08/java-application-with-neo4j-how-to-use-spring-custom-queries-and-projections.htmlNeo4j Live - Movie Recommendations: https://youtube.com/live/wndOSi3i5OYUnderstanding HashGNN in GDS: https://www.youtube.com/watch?v=fccFuyjNEcMGoogle Datacloud Demo: https://www.youtube.com/watch?v=9dAnPoFV80cNeo4j Metadata management of NoSQL sources: https://www.youtube.com/watch?v=ZExin7j8ysERoad to NODES training:Interactive Dashboarding with NeoDash (Sep 20)Graphing Relational Database Models (Sep 27)Meetup (virtual/in-person, Austin, TX): Airplane Route Optimization using Neo4j (Sep 13)Meetup (Singapore): Intersection of Graph Databases and AI (Sep 20)Meetup (Australia): Graph Database Melbourne (Sep 21)Meetup (Australia): Exploring the Intersection of Graph Data Science and AI (Sep 28)Pycon India (Sep 28 - 30): https://in.pycon.org/2023/Conference: PyData Amsterdam (Sep 13)Conference: Big Data London (Sep 19)Conference: Big Data Paris (Sep 25)Conference (Germany): JUG Saxony Day 2023 Dogfooding the Graph Ecosystem (Sep 28)

Sara Taher is a strong presence in the SEO industry, but oh, so humble.   Erin and Sara dive into the importance of prioritizing user experience, as it is a key connection for navigating SEO intent. We're migrating from volume concepts of SEO to intent oriented matches.  But clearly, we've heard the call of AI for SEO.  How do they really work together?   You've heard it time and time again, quality over quantity, but let's hear Sara very clearly……it's about the user.  If you're not reading their signals, you're not connected. Key Segments: [00:00:36] Introducing Sara Taher [00:01:48] Sara's History In Digital Marketing  [00:03:35] How Does Entity SEO Differ From Traditional SEO? [00:05:30] Examples of Entity SEO  [00:11:12] How Important is Google's Knowledge Graph? [00:11:56] EDGE of the Web Title Sponsor: Site Strategics [00:12:42] Looking Into The User Experience [00:19:04] EDGE of the Web Title Sponsor: Brightlocal [00:20:02] How Can You Integrate Entity SEO Into Your Digital Marketing Strategy? [00:23:43] Misconceptions About Entity SEO [00:30:25]  EDGE of the Web Sponsor: Wix  [00:31:03] AI Tools For Entity SEO Thanks to our Sponsors! Site Strategics https://edgeofthewebradio.com/site Brightlocal: https://edgeofthewebradio.com/brightlocal  Wix: http://edgeofthewebradio.com/wix Follow our Guest https://twitter.com/SaraTaherSEO  https://www.linkedin.com/in/sara-seo-specialist