Session Track: AI Engineering
Session Time:
Session description
This session focuses on the implementation of GraphReader, a graph-based retrieval system designed to enhance RAG accuracy and performance by structuring long documents into explorable knowledge graphs. The talk will focus on: - GraphReader: How it aims to enable AI agents to retrieve structured information from a document-structured knowledge graph, optimising retrieval with an agentic approach and improving answers to complex queries. - Knowledge Graph Structure: The system breaks documents into smaller text chunks, extracting atomic facts and linking them to key concepts for better information retrieval. - Implementation Details: The GraphReader is built using Neo4j (for graph storage) and LangChain/LangGraph (for defining agent workflows). Code snippets for setting up the Neo4j database, extracting atomic facts, and constructing the knowledge graph. - Agent Exploration Process: The GraphReader agent follows a structured workflow to traverse the graph, starting with key elements, gathering atomic facts, reading text chunks, and optimising retrieval with an agentic approach before providing answers. - Performance Optimisation: The implementation leverages constraints for faster retrieval and indexing, along with a structured prompting approach to guide the AI in selecting relevant facts and key elements.
Data Scientist, Deloitte
Jayita Bhattacharyya is passionate about the AI and ML space, and is keen to adopt new technologies for solving real-world problems. She is currently focusing on generative AI. Along with the team, we help customers incorporate AI into software engineering.
Data Scientist, Deloitte
Soumya Ranjan Das is a data scientist at Deloitte in Bangalore, specializing in generative AI applications for healthcare. With a keen interest in philosophy, history, and geopolitics, he aims to drive innovative AI solutions and strategic advancements in the industry.