Context Engineering & AI - Context engineering is evolving from simple prompt engineering to a dynamic approach that feeds AI with wider context for better results [3] - Context engineering enables selective curation of information relevant to specific domains, especially important in enterprise environments [4] - Structuring input in context engineering improves signal over noise, addressing a major problem with current AI models [5] - Memory, both short-term and long-term, is crucial for AI, enabling collaboration, remembering conversation history, and effective long-term operations [10][11][12] Knowledge Graphs & Graph RAG - Knowledge graphs provide structured information that complements AI's ability to create and pull from different sources [17] - Graph RAG, which uses graphs as part of the retrieval process, provides more relevant results than vector similarity search by incorporating relationships, nodes, and community groupings [22][23] - Graph RAG enables explainable AI and allows for the implementation of role-based access control, ensuring that only authorized individuals can access specific information [25] Neo4j Solutions & Resources - Neo4j offers a knowledge graph builder, a web application that allows users to upload files and generate knowledge graphs [28] - Neo4j's MCP server is an open-source extension that enables querying knowledge graphs using Cypher, a graph query language [46] - Neo4j provides resources like Graph Academy (free learning resources) and Nodes AI (virtual conference) for learning about graph technology and AI applications [53][54]

RT Avi Chawla (@_avichawla)RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant chunks, but this task needs the full context.Graph RAG solves this.The following visual depicts how it differs from naive RAG.The core idea is to:- Create a graph (entities & relationships) from documents.- Trave ...

If you found it insightful, reshare it with your network.Find me → @_avichawlaEvery day, I share tutorials and insights on DS, ML, LLMs, and RAGs. https://t.co/CVUW8FVKgjAvi Chawla (@_avichawla):RAG vs. Graph RAG, explained visually!RAG has many issues.For instance, imagine you want to summarize a biography, and each chapter of the document covers a specific accomplishment of a person (P).This is difficult with naive RAG since it only retrieves the top-k relevant https://t.co/Ad5ztdo7Lz ...

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]

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]