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]

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 ...

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Graph RAG Applications & Performance - Graph RAG is a key enabler for building effective AI applications, especially when paired with agents [1] - Graph RAG excels at semantic understanding and can perform global queries over a code repository [2][3] - Graph RAG can be used for code translation from Python to Rust, outperforming direct LLM translation [4][9] - Graph RAG can be applied to large codebases like Doom (100,000 lines of code, 231 files) for documentation and feature development [10][12][13] - Graph RAG, when combined with GitHub Copilot coding agent, enables complex multi-file modifications, such as adding jump capability to Doom [18][20] Benchmark QED & Lazy Graph - Benchmark QED is a new open-source tool for measuring and evaluating Graph RAG systems, focusing on local and global quality metrics [21][22] - Benchmark QED includes AutoQ (query generation), AutoE (evaluation using LLM as a judge), and AutoD (dataset summarization and sampling) [22] - Lazy Graph RAG demonstrates dominant performance against vector RAG on data local questions, winning 92%, 90%, and 91% of the time against 8K, 120K, and 1 million token context windows respectively [29][30] - Lazy Graph RAG can achieve performance at a tenth of the cost compared to using a 1 million token context window [32] - Lazy Graph RAG is being incorporated into Azure AI and Microsoft Discovery Platform [34]

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]