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]

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]

Enterprise knowledge bases are filled with "dense mapping," thousands of documents where similar terms appear repeatedly, causing traditional vector retrieval to return the wrong version or irrelevant information. When our customers kept hitting this wall with their RAG systems, we knew we needed a fundamentally different approach. In this talk, I'll share Writer's journey developing a graph-based RAG architecture that achieved 86.31% accuracy on the RobustQA benchmark while maintaining sub-second response ...

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] My name is Steve. Uh Steve Ruiz. I am from a company that I started called Teal Draw. Teal Draw started as a um well, a couple things. started as like a a digital ink library that then uh Christopher had me implement in Excaladra. When I was working on that, I was like, you know, there should probably be like a kind of a a really good SDK for building these types of things. And I'd already worked on a couple of projects that uh we're kind of going in that direction. So, I did turned out if you build ...

[Music] Thank you so much for the intro. I'm so excited to be here uh talking about like figure out like how do we like AI and human like work in the world of white bowling and I built excro and if you've don't know about it like you'll see like many thing about it and one of the expectation you probably have uh about speaker at the AI engineer conference is that I talk about AI on every single sentence for the entire talk. So I'm just going to give you a warning.I'm only going to do it for the second half ...

AI models are getting tasked to do increasingly complex and industry specific tasks where different retrieval approaches provide distinct advantages in accuracy, explainability, and cost to execute. GraphRAG retrieval models have become a powerful tool to solve domain specific problems where answers require logical reasoning and correlation that can be aided by graph relationships and proximity algorithms. We will demonstrate how an agent architecture combining RAG and GraphRAG retrieval patterns can bridge ...

Bad designs are still bad. AI doesn’t make it good. The novelty of AI makes the bad things tolerable, for a short time. Building great designs and experiences with AI have the same first principles pre-AI. When people use software, they want it to feel responsive, safe, accessible and delightful. We’ll go over the big and small details that goes into software that people want to use, not forced to use. About John Pham I'm John Pham, an engineer and a self-taught designer. I seek the dopamine hits of buildin ...

Overview - The document discusses building production voice applications [1] - It shares learnings from working with customers in the voice application domain [1] Authorship - The content is associated with tokisherbakov (Twitter handle) and akotha7 (LinkedIn profile) [1]