Technology & Software Development - Graph RAG is presented as a practical example for RAG over code, addressing limitations of naive chunking in handling codebases with long-range dependencies [1] - Graph-Code, a graph-driven RAG system, is introduced for analyzing Python codebases and enabling natural language querying [1] - Graph-Code extracts classes, functions, and relationships from code through deep code parsing [1] - Memgraph is utilized to store the codebase as a graph within the Graph-Code system [1] - Graph-Code parses pyproject files to understand external dependencies [1] - The system retrieves actual source code snippets for found functions [1]

Join the DKG Global Hackathon to take on challenges in the AI industry using blockchain + knowledge graph tech.$30K prize poolSubmission Deadline: Nov 21Register today: https://t.co/thkbJUBj77OriginTrail (@origin_trail):In the age of AI, how do we know what’s true?Do you have what it takes to build:•⁠ Grokpedia vs. Wikipedia comparison•⁠ Decentralized Community Notes•⁠ ⁠Reputation Social Graph💰$30K rewardsSponsored by @Polkadot, @umanitek & @origin_trailLink in reply. https://t.co/smoq51JtXQ ...

Agents forget everything after each task!Graphiti builds a temporal knowledge graph for Agents that provides a memory layer to all interactions.Fully open-source with 20k+ stars!Learn how to use Graphiti MCP to connect all AI apps via a common memory layer (100% local): https://t.co/cpAZFJcrufAvi Chawla (@_avichawla):Big update for Claude Desktop and Cursor users!Now you can connect all AI apps via a common memory layer in a minute.I used the Graphiti MCP server that runs 100% locally to cross-operate acros ...

Core Insights - The sales intelligence market is crowded, with services that help identify prospects, provide background information, and automate follow-ups [1] - Sumble, a startup from San Francisco, aims to provide contextual information by aggregating data from various online sources [2] Company Overview - Sumble was founded by Anthony Goldbloom and Ben Hamner, who previously created the data science community Kaggle [3] - The startup utilizes a knowledge graph supported by large language models to connect diverse data points, offering insights into a company's technographic data, organizational structure, and potential contacts [3] Market Position and Growth - Despite the competitive landscape, Sumble has successfully signed 17 enterprise customers since its launch in April 2024, including notable companies like Snowflake and Figma [4] - The startup has experienced significant growth, with a reported 550% year-over-year revenue increase, although specific revenue figures were not disclosed [4] User Engagement and Funding - Sumble's user base has grown rapidly within companies, often expanding from a few users to hundreds in a short period, primarily through word of mouth and internal communication channels like Slack [5] - The company recently emerged from stealth mode with $38.5 million in funding, including an $8.5 million seed round and a $30 million Series A led by prominent investors [5]

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]

Core Functionality - Zep aims to build human-like memory for agents, addressing real-time knowledge updates and fast data retrieval challenges in agentic and RAG systems [1] - Zep organizes agent memories into episodes, extracts entities and relationships, and stores them in a knowledge graph [1] - The system features an Episode Subgraph for capturing raw, timestamped data, a Semantic Entity Subgraph for extracting and versioning entities and facts, and a Community Subgraph for grouping related entities [1][2] Performance Metrics - Zep delivers up to 1850% (18.5 times) higher accuracy with 90% lower latency compared to tools like MemGPT [2] Open Source Nature - Zep is fully open-source [2]

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 ...