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Presentations

Self-contained HTML decks for briefing a team. Each one opens in its own tab, needs no build step, and works offline apart from its web fonts.


Historical Datasets

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How build_historical_dataset returns one row per entity per period of constant feature values: the window (start, end, duration, delay), what INNER and FULL OUTER change, the SQL chosen from EXPLAIN plans, and what testing on a live feature store showed. Every number comes from a run on Vantage 20, on a store built from the demos/ notebooks.

Audience — data scientists and architects new to the feature.

See also the reference page Historical datasets.


Consumer Agent Field Guide

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How the consumer agent is designed, what it is allowed to answer, and how to build single- and multi-turn golden evaluation sets for it.

Audience — data scientists and engineers who will use, extend or evaluate the agent.

Section Covers
01 The agent Scope and boundaries, the LangGraph pipeline, the 16-intent roster, the certainty boundary between a governed feature and a free column, cross-turn memory, the three ways to run it
02 Prerequisites What must exist in the store per skill — FS_FOLLOW_UP for freshness, valid-time catalogs and data for time travel, the business dictionary and vector index for definitions and search, tdstone2 for drilling into in-database Python
03 Skills SKILL.md anatomy, install_skills() into another agent, and the TDFS4DS_SKILLS_FOLDER plugin path
04 Evaluation The five scored dimensions, the two grounding sources the judge reads, and the anatomy of single-turn, multi-turn and fact-first scenarios
05 Golden set Six steps with runnable code, starting from the prerequisite of a populated store
06 Discipline Versioning the scenario set, and what the scores do not prove

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