Videos

Information retrieval and knowledge extraction is a growing field, which has witnessed a notable shift with the advent of Large Language Models (LLMs) and knowledge graphs, particularly within the context of multi-hop question answering. In this session, we’ll walk you through a project that leverages the robust capabilities of Neo4j Vector Index and Neo4j Graph Database to implement a retrieval-augmented generation system, aiming to provide precise and contextually rich answers to user queries. Guest: Saurav Joshi GenAI Landing https://bit.ly/4eRHS6g Blog: https://medium.com/neo4j/enhanced-qa-integrating-unstructured-and-graph-knowledge-using-neo4j-and-langchain-6abf6fc24c27 Code: https://github.com/sauravjoshi23/towards-agi/tree/main/retrieval%20augmented%20generation/integrated-qa-neo4j-langchain-semi-structured-data Google Gemini: https://deepmind.google/technologies/gemini/ Multi Modal RAG: https://blog.llamaindex.ai/multimodal-rag-pipeline-with-llamaindex-and-neo4j-a2c542eb0206 Slides: https://docs.google.com/presentation/d/1RaTCegq1nOAwRS821kbI29k3dv–NHxHvd2WwE3aHRQ 0:00 Intro 9:55 Enhanced QA 10:33 Neo4j x LangChain 12:30 Benefits of Knowledge Graphs 14:54 RAG Workflow 19:58 Vector Search 22:28 Graph Search 28:30 Q&A 32:50 Code: RAG 42:45 Code: Knowledge Graph 47:05 Code: RAG (continued) 50:55 Q&A 1:03:33 WrapUp #neo4j #graphdatabase #langchain #llm #knowledgegraph #rag #largelanguagemodels #chatbot