Summary of "Hypermode Live: Knowledge Graphs + AI"
Video Summary
The video "Hypermode Live: Knowledge Graphs + AI" features discussions on the integration of knowledge graphs with AI, particularly focusing on a hackathon launched by Hypermode and Neo4j.
Key Concepts and Features Discussed:
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Knowledge Graphs and AI Integration:
- Knowledge graphs can complement large language models (LLMs) by providing memory and contextual information that LLMs often lack.
- The combination of graphs and LLMs can enhance the performance of AI applications.
- Hackathon Announcement:
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Modus Framework:
- Modus is a serverless framework designed for building model-native applications.
- It supports multiple programming languages and allows users to create functions that interact with various data sources, including knowledge graphs.
- The framework utilizes WebAssembly for efficient function execution.
- Hands-On Examples:
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Best Practices for Document Chunking:
- When preparing documents for vectorization, it is crucial to maintain semantic coherence by using natural document structures (e.g., paragraphs, sections) as boundaries for chunks.
- Different chunking strategies can be employed, including fixed-size chunking, recursive chunking, and semantic chunking based on vector similarity.
Resources and Guides:
- The video includes links to the Modus GitHub repository for code samples and further resources.
- Participants in the hackathon can find starter code and guidelines on the Devpost platform.
Main Speakers:
- Will (Hypermode)
- Michael Hunger (Neo4j)
- Raphael (Hypermode)
This session serves as both an informative discussion on the potential of knowledge graphs in AI and a practical guide for developers looking to participate in the hackathon.
Category
Technology