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Part 6 | Create Semantic Model & Table Relationships in Snowflake | Improve Your Cortex Analyst
5:50
YouTubeAlgoChat
Part 6 | Create Semantic Model & Table Relationships in Snowflake | Improve Your Cortex Analyst
Make your Cortex Analyst smarter by defining a semantic model and establishing relationships between your Snowflake tables. This video explains joins, dimensions, measures, metadata, and contextual relationships — crucial for accurate AI-generated insights. Snowflake semantic model Define table relationships Snowflake Data relationships LLM ...
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Pat 1: Understanding Semantics
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In Transformer architectures, token embeddings map tokens into a high-dimensional vector space where closeness in the space means closeness in semantic meaning. These vectors are dense, trained such that words sharing semantic traits cluster together, allowing the model to measure similarity through dot products (cosine similarity). This spatial arrangement is structured, preserving relationships as directional offsets in the vector space. This property allows for vector arithmetic; in the class
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