Summary
The video introduces Light Rag, a retrieval-augmented generation system combining knowledge graphs with embedding-based retrieval. It outlines steps to set up the system, including cloning the repo, defining model names, and selecting embedding models. The video demonstrates configuring the context window, creating embedding models, indexing, querying the system, and visualizing graphs to explore entity relationships. Additionally, it discusses alternative systems, local model support, performance comparison, and future video plans.
Introduction to Light Rag System
Introduction to a retrieval-augmented generation system called Light Rag, which combines knowledge graphs with embedding-based retrieval. Mentions the alternative to GraphRack from Microsoft and support for local models.
Setting Up Light Rag System
Steps to set up the Light Rag system including cloning the repo, creating a virtual environment, installing necessary components, defining the model name, and selecting embedding models.
Configuring Context Window and Models
Details on configuring the context window, creating the embedding models, and setting parameters for the model file in the Light Rag system.
Running Light Rag System
Instructions on starting the server for the system, downloading necessary components, and running the system using the created models.
Indexing and Querying
Information on indexing, creating an index, querying the system, and running examples on the Light Rag system.
Visualization and Demonstration
Steps to visualize the graph created, demonstrating the usage of the system, and exploring relationships between entities in the system.
Other Demos and Conclusion
Overview of other demos available, comparison of Light Rag performance, mention of MIT license, and future video plans on Light Rag.
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