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

Wey Gu (Chief Evangelist at NebulaGraph) has been leading the charge on exploring how to combine LLMs with graph databases - graph databases enable more sophisticated forms of data retrieval that exploit relationships between data.
​In this webinar, we first give an overview of the basics of graph stores, and then talk about how they can be used in RAG (and Llamaindex). We then chat about general questions, such as how they compare with vector db's, their limitations, and how they can be further exploited for retrieval-augmented systems.

[@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 ?

[@clin_dev_1]

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]