The Observation Theory EncyclopediaFrom TSKAboutBy kindBy chapterBy Lean fileLedgerProvenance

retrieval-augmented pipeline concept

DefinitionA system that answers a question by chunking documents, embedding the chunks, indexing them, retrieving the nearest to the embedded question, and handing them to a generator. A chain of observers. Chapter 12. Also retrieval-augmented, pipeline.
ExampleChunker, encoder, index, generator: a query becomes a vector, the vector a candidate list, and the list with the question an answer.
BookData Mining as Observation, draft 0.2, commit f3914f0; entry id retrieval-augmented-pipeline, kind concept.
Statusno ledger row names this entry. Corrections: none recorded.
Defining equation

Book equation 12.1.

Assumptions and scope
  • A system that chunks documents, embeds the chunks, indexes them, retrieves the nearest to the embedded question, and hands them to a generator. Whatever one stage declares the same, every later stage and the pipeline declare the same, and for linear stages the rank of the composition is at most the rank of any stage, so the pipeline’s read subspace is no larger than its narrowest stage’s.
  • Both hold for every stage, differentiable or not. The benchmark score of the whole is a certificate whose false-clear rate on deployment slices is the chapter’s measurement.
Prior artnone recorded
Evidencelean/DataMiningAsObservation/Pipeline.lean
Reviewednot yet reviewed; generated 2026-09-10 from records at the commits on the provenance page.
chunkerencoderindexgeneratortwo inputsone outputidentified here, for every stage after
Chunker, encoder, index, and generator, each reading the one before.

Equation

Book equation 12.1.

\[\begin{gathered} x\sim_{\text{stage}} x'\ \Longrightarrow\ x\sim_{\text{pipeline}} x'\quad\text{for every stage, differentiable or not}, \\ \operatorname{rank}P_{\text{run}}(x)\le\min_{\text{stages in the run}}\operatorname{rank}J_{\text{stage}}(x)\quad\text{on a differentiable run of stages, at each row.} \end{gathered}\]

Conditions

Conditions are curated in entries.toml rather than read from a record.

Ledger

none

First stated

Chapter 12 of Data Mining as Observation, with the program’s streaming-retrieval staleness row OT-11 and the legal-citation retrieval flip.

Measurements

none

Failures and corrections

none

Invariance envelope

none declared

Machine checked

lean/DataMiningAsObservation/Pipeline.lean, theorems quotient_inherited, quotient_inherited_chain, rank_comp_le_first, rank_comp_le_second, at observation-data-mining f3914f0; what the check covers is stated in the book’s appendix C.

Used in

Data Mining as Observation chapters 0, 1, 2, 3, 8, 10, 11, 12, 13, 14.

Related

quotient; read subspace; deployment mismatch; freshness.

See also

Book equations stated beside the entry’s terms, not defining it: 13.1.

Ledger rows that cite the entry’s records without naming it: OT-11, GO-B-legal (035→036).

Sources-table rows that share a record with the entry without naming it: chapter 12 section 12.3, chapter 12 section 12.6.

Status

Generated 2026-09-10 by encyclopedia/generate.py; book at observation-data-mining f3914f0; the commit of every record is listed in the encyclopedia’s provenance.

← residualizationRobin Hood index →