Scott KarpandClaude Sonnet 4.6
80ec364da6
Shoresh Engine (Haskell, Step 5) - 12 modules: Types, Lexicon, Binyanim, Teamim, Gematria, Graph, KetivQere, Smikhut, Havdalah, Kriah, KiTov, Firmware - 33 EUnit tests, 0 failures - Firmware module verifies L1 SHA-256 hash at startup Talmudic Engine (SWI-Prolog, Step 6) - 15 modules: halt, reasoning, specification, interface - 57 PlUnit tests, 0 failures - Three cardinal halt conditions: avodah_zarah, shfichut_damim, gilui_arayot Vav Pipeline (Elixir, Steps 8-11) - 14 modules: BereishitCycle, Shema, Cloud, Manna, L1Reader, Specification, HaltConditions, ShabbatTimer, CycleLog, Chatzotzrot, ShoreshEngineClient, TalmudicEngineClient, Application - 46 ExUnit tests, 0 failures - Nishma-first architecture: hears before it speaks Sanhedrin Framework (Erlang/OTP, Step 12) - 8 modules: sanhedrin_app, sanhedrin_sup, escalation, panel_sup, panel, judge, clerk, halt_monitor - 46 EUnit tests, 0 failures (includes fix: rejected outcome now reachable) - Panel sizes: 1/3/23/71 — unanimous capital guilty → acquit (Sanhedrin 17a) - Minority opinions append-only, never deleted Korban Processor (Python, Step 13) - 6 modules: korban, shechitah, semichah, zerikah, haktarah, router Builder tooling: AGENTS.md, opencode.json, memory/, src/tools/ Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
54 lines
1.9 KiB
Haskell
54 lines
1.9 KiB
Haskell
{-# LANGUAGE OverloadedStrings #-}
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-- | Havdalah — separation and distinction.
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--
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-- Architecture §3.1:
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-- havdalah :: Input -> [(Category, Content)]
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-- "Separate an input into distinguished categories"
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--
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-- Havdalah is the act of making distinctions. In the processing
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-- pipeline, it takes the output of vayomer (structured symbolic output)
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-- and separates it into categories that can be named (kriah).
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--
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-- Te'amim inform the separation — the text's own divisions guide havdalah.
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module Shoresh.Havdalah
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( havdalah
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, separateByDomain
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, separateByTeamim
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) where
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import Shoresh.Types
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import Shoresh.Teamim (ParsedVerse(..), AnnotatedWord(..), primaryDivision)
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import Data.Text (Text)
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import qualified Data.Text as T
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import Data.Map.Strict (Map)
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import qualified Data.Map.Strict as Map
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-- | Separate input into distinguished categories.
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-- Uses both semantic domain analysis and te'amim structure.
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havdalah :: [(Shoresh, SemanticDomain)] -> [(Text, [(Shoresh, SemanticDomain)])]
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havdalah items =
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let grouped = Map.toList $ Map.fromListWith (++)
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[ (domainName d, [(sh, d)]) | (sh, d) <- items ]
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in grouped
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where
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domainName :: SemanticDomain -> Text
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domainName d = T.pack (show d)
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-- | Separate roots by their semantic domain.
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separateByDomain :: [ShoreshEntry] -> Map SemanticDomain [ShoreshEntry]
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separateByDomain entries =
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Map.fromListWith (++) [ (sePrimaryDomain e, [e]) | e <- entries ]
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-- | Separate using te'amim structure.
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-- The te'amim parse tree tells us where the text itself
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-- draws its divisions. Havdalah follows the text's own structure.
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separateByTeamim :: ParsedVerse -> ([Text], [Text])
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separateByTeamim verse =
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let (left, right) = primaryDivision verse
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in (collectWords left, collectWords right)
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where
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collectWords :: ParsedVerse -> [Text]
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collectWords (Leaf w) = [awWord w]
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collectWords (Branch _ l r) = collectWords l ++ collectWords r
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