RT Avi Chawla (@_avichawla)Build human-like memory for your Agents (open-source)!Every agentic and RAG system struggles with real-time knowledge updates and fast data retrieval.Zep solves these issues with its continuously evolving and temporally-aware Knowledge Graph.Like humans, Zep organizes an Agent's memories into episodes, extracts entities and their relationships from these episodes, and stores them in a knowledge graph:(refer to the image below as you read)1) Episode Subgraph: Captures raw data with ...

Agentic System Challenges - Agentic 和 RAG 系统在实时知识更新和快速数据检索方面面临挑战 [1] Zep's Solution - Zep 通过其不断发展和时间感知的知识图谱来解决这些问题 [1] - Zep 像人类一样组织信息 [1]

Build human-like memory for your Agents (open-source)!Every agentic and RAG system struggles with real-time knowledge updates and fast data retrieval.Zep solves these issues with its continuously evolving and temporally-aware Knowledge Graph.Like humans, Zep organizes an Agent's memories into episodes, extracts entities and their relationships from these episodes, and stores them in a knowledge graph:(refer to the image below as you read)1) Episode Subgraph: Captures raw data with timestamps, retaining ever ...

Graph RAG Overview - Graph RAG aims to enhance LLMs by incorporating knowledge graphs, addressing limitations like lack of domain knowledge, unverifiable answers, hallucinations, and biases [1][3][4][5][9][10] - Graph RAG leverages knowledge graphs (collections of nodes, relationships, and properties) to provide more relevant, contextual, and explainable results compared to basic RAG systems using vector databases [8][9][10][12][13][14] - Microsoft research indicates Graph RAG can achieve better results with lower token costs, supported by studies showing improvements in capabilities and analyst trends [15][16] Knowledge Graph Construction - Knowledge graph construction involves structuring unstructured information, extracting entities and relationships, and enriching the graph with algorithms [19][20][21][22] - Lexical graphs represent documents and elements (chunks, sections, paragraphs) with relationships based on document structure, temporal sequence, and similarity [25][26] - Entity extraction utilizes LLMs with graph schemas to identify entities and relationships from text, potentially integrating with existing knowledge graphs or structured data like CRM systems [27][28][29][30] - Graph algorithms (clustering, link prediction, page rank) enrich the knowledge graph, enabling cross-document topic identification and summarization [20][30][34] Graph RAG Retrieval and Applications - Graph RAG retrieval involves initial index search (vector, full text, hybrid) followed by traversing relationships to fetch additional context, considering user context for tailored results [32][33] - Modern LLMs are increasingly trained on graph processing, allowing them to effectively utilize node-relationship-node patterns provided as context [34] - Tools and libraries are available for knowledge graph construction from various sources (PDFs, YouTube transcripts, web articles), with open-source options for implementation [35][36][39][43][45] - Agentic approaches in Graph RAG break down user questions into tasks, using domain-specific retrievers and tools in sequence or loops to generate comprehensive answers and visualizations [42][44] - Industry leaders are adopting Graph RAG for production applications, such as LinkedIn's customer support, which saw a 286% reduction in median per-issue resolution time [17][18]

Problem Statement & Solution - Current memory frameworks struggle with relevance, leading to inaccurate responses or hallucinations due to the storage of arbitrary facts [3][4][5] - Semantic similarity does not equate to business relevance, as vector databases lack causal or relational understanding [7] - The industry needs domain-aware memory solutions instead of relying solely on better semantic search [8] - Zep offers a solution by enabling developers to model memory after their specific business domain, creating more cogent and capable memory [1][2] Zep's Implementation & Features - Zep allows developers to define custom entities and edges within its graph framework, tailoring memory to specific business objects [1][9] - Developers can use Pydantic, Zod, or Go structs to define business rules for these entities and their fields [9][10] - Zep's SDK allows defining entity types with descriptions and business rules for fields, enabling precise control over data stored [10] - Zep allows building tools for agents to retrieve financial snapshots by running multiple searches concurrently and filtering by specific node types [10][11] - Zep's front end provides a knowledge graph visualization, allowing users to see the relationships and fields defined for each entity [12] Demonstration & Use Case - A finance coach application demonstrates Zep's ability to store explicit business objects like financial goals, debts, and income sources [8][9] - The application captures relevant information, such as a $5,000 monthly rent, and stores it as a debt account entity with defined fields [11][12]

[Music] to quickly introduce myself. My name is Mitesh. I lead the develop advocate team at Nvidia.And the goal of my team is to uh create technical workflows, notebooks uh for different applications and then we release that codebase uh on GitHub. So developers in general which is me and you all of us together we can harness that uh that knowledge and take it further for the application or use case that you're working on. So that is what my uh my team does including myself.In today's talk, I'm I'm I'm going ...

[Music] So, as you come in, we have here a server set up with everything you'll need. If you want to follow along, you should have gotten a post-it note. If you don't, just raise your hand and my colleague Alex over here will come find you and we'll provide you with one.Uh, basically what you're going to do is you're just going to go, if you have a number 160 or below, you go to this link here, the QR code on top as well. Um, and if you have a number that's 2011 or above, you go to the second link or the QR ...

Knowledge Graph Architecture & Agentic Workflows - Knowledge graphs can enhance agentic workflows by enabling reasoning and question decomposition, moving beyond simple vector searches [4] - Knowledge graphs facilitate the expression of simple data models to agents, aiding in accurate information retrieval and expansion with more data [5] - The integration of knowledge graphs allows for more precise question answering through a more expressive data model [22] Data Modeling & Entity Extraction - Data modeling should focus on defining key entities and their relationships, such as people, skills, and activities [17] - Entity extraction from unstructured documents, like resumes, can be used to create a graph database representing these relationships [18] - Pydantic classes and Langchain can be used for entity extraction workflows to decompose documents and extract JSON data containing skills and accomplishments [19][20] Benefits of Graph Databases - Graph databases enable flexible queries and high performance for complex traversals across skills, systems, domains, and accomplishments [30] - Graph databases allow for easy addition of new data and relationships, which is crucial for rapid iteration and adaptation in agentic systems [37] - Graph databases facilitate the creation of tools to find collaborators based on shared projects and domains [39] Practical Application: Employee Skills Analysis - The presentation uses an employee graph example to demonstrate skills analysis, similarity searches, and identification of skill gaps [5] - Initial attempts to answer questions using only document embeddings are inaccurate, highlighting the need for entity extraction and metadata [9] - By leveraging a knowledge graph, the system can accurately answer questions about the number of developers with specific skills, such as Python, and identify similar employees based on skill sets [24][25]

Problem Statement - Data readiness is a myth, and achieving perfect data for AI is an unattainable pipe dream [1][2][3] - Fortune 500 companies lose an average of $250 million due to poor data quality [7] - Traditional semantic layers and knowledge graphs are insufficient for capturing the nuances of business language and tribal knowledge [8][9][10][11][12][13][14] Solution: Agentic Semantic Layer (PromQL) - PromQL is presented as a "day zero smart analyst" AI system that learns and improves over time through course correction and steering [17][18][19][20] - It uses a domain-specific language (DSL) for data retrieval, computation, aggregation, and semantics, decoupling LLM plan generation from execution [21][22] - The system allows for editing the AI's "brain" to correct its understanding and guide its learning [28] - It incorporates a prompt learning layer to improve the semantic graph and create a company-specific business language [31] - The semantic layer is version controlled, allowing for fallback to previous builds [33] Key Features and Benefits - Correctable, explainable, and steerable AI that improves with use [19] - Ability to handle messy data and understand business context [24][25] - Reduces months of work into immediate start, enabling faster AI deployments [37] - Self-improving and achieves 100% accuracy on complex tasks [37] Demonstrated Capabilities - The system can understand what revenue means and perform calculations [23] - It can identify and correct errors in data, such as incorrect status values [24] - It can integrate data from multiple databases and SAS applications [25][27] - It can summarize support tickets and extract sentiment [26][29] - It can learn the meaning of custom terms and relationships between tables [35][36] Customer Validation - A Fortune 500 food chain company and a high-growth fintech company achieved 100% accurate AI using PromQL [38]

Snowflake (SNOW) Update Summary Company Overview - **Company**: Snowflake Inc. (SNOW) - **Event**: Update/Briefing on June 11, 2025 - **Key Speakers**: Ruby (Head of Partner Marketing for APJ), Mike Garnan (CRO), Ash Willis (VP of Partner Alliance for APJ) Key Points Industry and Market Position - Snowflake is experiencing significant growth, with over 20,000 attendees at their recent summit, doubling their growth from the previous year [4][5] - The company is a sponsor for the LA 2028 Olympics, indicating strong brand visibility and market engagement [7] Financial Performance - Snowflake reported a billion-dollar revenue quarter, representing a **26% year-on-year growth** [18] - The company's **net revenue retention rate** is at **124%**, indicating that existing customers are expanding their contracts [18] - Remaining revenue obligation (RPO) stands at **$6.7 billion**, a **34% year-on-year increase**, suggesting strong future revenue potential [18][24] Customer Engagement and Product Adoption - Snowflake has a total of **11,200 customers**, with **451 new customers** added in Q1 [19] - Approximately **50% of customers** are actively using Snowflake's AI and ML products, showcasing strong adoption of advanced technologies [19] - The company emphasizes the importance of simplifying AI initiatives for customers, which is a key selling point [20] Strategic Focus and Partnerships - Snowflake is focusing on building a robust partner ecosystem to drive consumption and accelerate migrations from legacy systems [25][27] - The company is targeting traditional warehousing technologies like Teradata and Oracle Exadata for migration opportunities [26] - A unique compensation structure is in place where sales teams are incentivized based on consumption rather than contract bookings, aligning interests with customer success [25] AI and Innovation - Snowflake is leveraging AI to enhance productivity and drive business outcomes, with examples of AI applications improving operational efficiency [35][36] - The partnership with Spark New Zealand and Relational AI is highlighted as a strategic move to enhance decision-making capabilities through AI [75][90] Summit Insights - The recent summit showcased a strong network effect, with **70% of content delivered by customers**, emphasizing real-world applications of Snowflake's technology [40] - The event attracted a diverse audience, including business leaders and technical experts, indicating a shift towards business impact rather than just technology [39] Future Outlook - Snowflake plans to invest significantly in its partner ecosystem, including traditional resellers and systems integrators, to scale its business efficiently [48][50] - The company aims to activate its channel to potentially exceed **35% growth** in the future [52] Customer Case Studies - Spark New Zealand is leveraging AI to streamline processes, such as call summarization, which enhances data quality and operational efficiency [84][89] - Relational AI is working with Snowflake to create a relational knowledge graph, addressing knowledge silos within organizations [97][100] Additional Insights - The emphasis on AI is not about job replacement but enhancing productivity and enabling existing employees to work more efficiently [35][36] - The partnership approach is seen as crucial for future innovation, with a focus on collaborative growth and shared success [109][110] This summary encapsulates the key insights and strategic directions discussed during the Snowflake update, highlighting the company's robust growth, innovative use of AI, and commitment to building a strong partner ecosystem.