The Stock Exchange of Hong Kong Limited and the Securities and Futures Commission take no responsibility for the contents of this Application Proof, make no representation as to its accuracy or completeness and expressly disclaim any liability whatsoever for any loss howsoever arising from or in reliance upon the whole or any part of the contents of this Application Proof. Application Proof of Beijing Haizhi Technology Group Co., Ltd. 北京海致科技集團股份有限公司 (the "Company") (A joint stock company incorporated in the ...

Hello everybody and welcome to my session at a engineer code summit and I'm going to talk a bit about how you can connect the dots with graph technology and solve problems like context engineering um improving retrieval patterns and also agentic memory. So we're going to have a lot of fun. My name is Stephen Chin.I'm VP of developer relations at Neo Forj and you can find me at all the different social media outlets with my handle Steve on Java. So excited you're all here to join for the session today. And I ...

AI Agent Enhancement - Graphiti 构建了时间感知的知识图谱，为 AI Agents 提供记忆能力 [1] - Graphiti 的 MCP 服务器与 Claude/Cursor 集成，为所有 AI 交互添加了强大的记忆层 [1] Open Source & Community - 该项目是 100% 开源的，拥有超过 18k+ stars (18 thousand plus stars) [1]

Core Argument - Structured Representations, emphasizing relationships between clauses, documents, entities, and parties, are crucial in the legal field [1] - Structured Context Injection, enabled by Structured Representations, enhances context and reduces hallucinations in legal agents [1] Case Studies & Applications - The report highlights production systems built for legal use-cases, including recursive contractual clause retrieval and HITL legal reasoning news agents [1] - These systems demonstrate the significant improvement in effectiveness and reliability of legal agents through structured representations [1] Key Technologies - Structured Representations are presented as a key technology for improving legal agents [1]

Knowledge Graph & LLM Application - Knowledge graphs combined with large language models (LLMs) can be used to build AI applications, particularly with graph retrieval augmented generation (RAG) architecture [2] - Graph RAG replaces vector databases with knowledge graphs built on graph databases, enhancing retrieval strategies [3] - Using a knowledge graph provides richer retrieval strategies beyond vector semantic search, including contextualization and structured queries [4] - Property graph model implements nodes and relationships, nodes represent entities and relationships connect them [4][5] Ontology & Schema - Ontologies provide an implementation-agnostic approach to representing schemas, facilitating knowledge graph creation for both structured and unstructured data pipelines [14][17] - Ontologies describe a domain with definitions of classes and relationships, matching well with graph models [15] - Financial Industry Business Ontology (FIBO) is a public financial industry ontology example [15] - Storing ontologies in the graph can drive dynamic behavior in retrievers, allowing for on-the-fly adjustments by modifying the ontology [29][30] Retrieval Strategies - Graph captures text chunks with embeddings, creating a new search space for vector search [20] - Vector search finds vectors in proximity, which can be dereferenced back to the graph for contextualization, navigation, and enrichment [20] - Dynamic queries, driven by ontologies, can be used to create dynamic retrievers, enabling data-driven behavior [26][29]