LlamaIndex Webinar: Graph Databases, Knowledge Graphs, and RAG with Wey (NebulaGraph)

[@BuyNLarge_]

🎯 Key Takeaways for quick navigation: 00:00 *🌐 Introduction to Graph Databases and RAG Systems* - Overview of how graph databases integrate with retrieval augmented generation (RAG) systems and their role in enhancing QA and other applications. - Graph databases structure information into nodes and relationships, offering unique advantages for complex queries. - Discussion on the potential of graph databases for creating more efficient and context-aware RAG systems. 02:07 *🤔 What is a Graph?* - Exploring the concept of graph databases through historical context and their application in modern technology. - Introduction to Knowledge Graphs and their significance in improving search outcomes. - Explanation of why graph databases are preferred for certain types of data retrieval and analysis over traditional databases. 05:33 *🚀 NebulaGraph and Performance* - Introduction to NebulaGraph, an open-source project designed for handling hyperscale graph data. - Discussion on the performance and scalability benefits of using NebulaGraph for large-scale graph databases. 08:55 *🛠️ Integrating Graphs into RAG with LlamaIndex* - Detailed walkthrough of integrating knowledge graphs into the retrieval augmented generation (RAG) process with LlamaIndex. - The creation and utilization of a graph store to enhance RAG systems by providing context-rich, interconnected data. - How graph stores contribute to the simplification and efficiency of data indexing and querying in RAG systems. 13:27 *💡 Knowledge Graphs in RAG Paradigm* - Discussion on the hypothesis and initial findings from incorporating knowledge graphs into the RAG framework. - Presentation of the process for creating knowledge graphs from unstructured data and integrating them into RAG workflows for improved query responses. 18:16 *🌟 Practical Applications and QA Systems* - Exploration of practical applications of graph databases across various industries and use cases, such as fraud detection and user behavior analysis. - Introduction to graph algorithms and their role in enhancing the analysis and interpretation of graph data. - Discussion on the future integration of graphQA with LlamaIndex and the benefits of knowledge graphs in QA systems. 25:51 *🧠 Graph Algorithms and Their Applications* - Discussion on the utility of graph algorithms like PageRank and their role in identifying key nodes within a graph. - Graph algorithms can significantly influence machine learning models by providing structured, meaningful clustering information. - Example given on real-time fraud detection using Graph Neural Networks (GNN) and NebulaGraph. 28:05 *🔄 Differentiating Database Types* - Exploration of practical differences between graph databases, SQL databases, and vector stores. - Graph databases excel in handling multi-hop queries and complex relationships, offering advantages over traditional SQL in certain scenarios. - Discussion on analytical tasks and machine critical transactions, with emphasis on the suitability of graph databases for certain tasks despite potential trade-offs. 30:34 *⚖️ Vector vs. Graph Databases in LLM Retrieval Augmented Generation* - Comparison between vector databases and graph databases within the context of LLMs and retrieval augmented generation. - Discussion on trade-offs, including the loss of structural information in vector-based semantic search versus graph-based searches. - Speculation on combining vector and graph databases for enhanced retrieval augmented generation results, showcasing a promising direction for future exploration. 35:02 *🌟 Future Directions in LLMs, Graphs, and RAG* - Discussion on potential future explorations combining LLMs, graphs, and retrieval augmented generation, including the breakdown of complex queries and the use of domain-specific knowledge graphs. - Ambition to introduce vector search capabilities into NebulaGraph to enable more nuanced embeddings and semantic searches. - Vision for a complex RAG workflow that leverages both graph and vector databases for nuanced and efficient query processing. Made with HARPA AI

[@jonclement]

Any links to the presentation? I heard it is "up" somewhere.

[@DANDARE120]

What if we want to use other graph dbs like neo4j or neptune db ?

[@healthtourguide]

Thx for the content! I have tried to use llamaindex to create graphs for chinese medical content but the quality wasnt that good. Right now my solution is to manually turn the text to a cypher query via a chain of 2 one shot prompts. Am I allowed to do this customization on llamaindex?

[@andy111007]

same here, terrible results

[@satyamgupta2182]

@@andy111007 , @clin_dev_1 Did you come across any resources that can improve it?

[@out-of-sight]