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Adds bounded AI support to the IHF governance loop. All AI outputs are attributed (model_ref), reviewable (AgentReviewRecord), and reversible. No autonomous decisions; no silent requirement promotion. - T01: Schema — agent_proposals, agent_review_records, confidence_annotations (migration 1743379200) - T02: AgentProposalsController (index/show/accept/reject, idempotent review guard), global nav "Agent" link - T03: SummarizeClusterAction — Claude API cluster summary on widget show - T04: DraftRequirementAction — AI requirement draft; acceptance creates RequirementCandidate (human-gated) - T05: DetectDuplicatesAction — duplicate_flag proposal on candidate show - T06: DetectPolicySensitivityAction — policy_flag with ConfidenceAnnotations per concern scope - T07: ProposeImplementationAction — impl_proposal from decision show - T08: AgentAuditDashboardAction — autoRefresh; KPI row, unreviewed queue, recent proposals, attribution log matrix - T09: integration tests, SCOPE.md updated, phase5-summary.md, flake.nix adds http-conduit/aeson/string-conversions Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
129 lines
5.6 KiB
Haskell
129 lines
5.6 KiB
Haskell
module Application.Helper.Controller where
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import IHP.ControllerPrelude
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import Generated.Types
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import Data.Time.Clock (addUTCTime)
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import Data.List (sortBy)
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-- Phase 5: Anthropic API
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import Network.HTTP.Conduit (newManager, tlsManagerSettings, parseRequest, httpLbs, responseBody, method, requestHeaders, requestBody, RequestBodyLBS(..))
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import Data.Aeson (object, (.=), encode, eitherDecode, Value)
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import Data.Aeson.Lens (key, _String, nth)
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import Control.Lens ((^?))
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import Data.String.Conversions (cs)
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import System.Environment (lookupEnv)
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-- Here you can add functions which are available in all your controllers
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-- | Returns the set of widget IDs that are currently in regression.
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--
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-- A regression is defined as: a widget that has an OutcomeSignal(improved)
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-- for any deployment, followed by a new Annotation(severity IN high/critical)
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-- created more than 1 day after the signal's observed_at (grace period).
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regressedWidgetIds :: [OutcomeSignal] -> [Annotation] -> [Id Widget]
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regressedWidgetIds signals annotations =
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[ wid
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| wid <- nub (map (.widgetId) signals)
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, isInRegression signals annotations wid
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]
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isInRegression :: [OutcomeSignal] -> [Annotation] -> Id Widget -> Bool
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isInRegression signals annotations wid =
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let improvedSignals = filter (\s -> s.widgetId == wid && s.signalType == "improved") signals
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highAnns = filter (\a -> a.widgetId == wid
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&& a.severity `elem` ["high", "critical"]
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&& isNothing a.retractedAt) annotations
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graceEnd sig = addUTCTime (24 * 3600) sig.observedAt
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in any (\sig -> any (\ann -> ann.createdAt > graceEnd sig) highAnns) improvedSignals
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-- | Computes the number of completed improvement cycles per widget.
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--
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-- A cycle is counted when:
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-- 1. A RequirementCandidate for the widget was accepted
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-- 2. A DecisionRecord exists for that requirement/candidate
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-- 3. A DeploymentRecord exists for that decision
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-- 4. A new RequirementCandidate was subsequently created for the same widget
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--
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-- Returns a list of (widgetId, cycleCount) for widgets with cycleCount >= 1,
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-- sorted descending by cycleCount.
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widgetCycleCounts
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:: [RequirementCandidate]
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-> [Requirement]
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-> [DecisionRecord]
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-> [DeploymentRecord]
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-> [(Id Widget, Int)]
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widgetCycleCounts candidates requirements decisions deployments =
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sortBy (\(_, a) (_, b) -> compare b a)
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[ (wid, cycleCount wid)
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| wid <- nub (map (.sourceWidgetId) candidates)
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, cycleCount wid >= 1
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]
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where
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-- A completed cycle: accepted candidate → requirement → decision → deployment
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completedCycleDeploymentTimes wid =
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[ dr.deployedAt
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| c <- filter (\x -> x.sourceWidgetId == wid && x.status == "accepted") candidates
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, req <- filter (\x -> x.sourceCandidateId == c.id) requirements
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, dec <- filter (\x -> x.requirementId == Just req.id) decisions
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, dr <- filter (\x -> x.decisionId == dec.id) deployments
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]
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cycleCount wid =
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let deplTimes = completedCycleDeploymentTimes wid
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-- For each completed cycle, check if a subsequent candidate was created
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widCandidates = filter (\x -> x.sourceWidgetId == wid) candidates
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in length
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[ ()
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| deplTime <- deplTimes
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, any (\c -> c.createdAt > deplTime) widCandidates
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]
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-- | Call the Anthropic Messages API.
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--
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-- Returns the text content of the first content block, or an error message.
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-- API key read from IHP_ANTHROPIC_API_KEY env var.
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-- On any error (missing key, HTTP failure, unexpected JSON) returns Left with a description.
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callClaudeApi
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:: Text -- ^ system prompt
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-> Text -- ^ user message
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-> Int -- ^ max_tokens
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-> IO (Either Text Text)
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callClaudeApi systemPrompt userMessage maxTokens = do
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mApiKey <- lookupEnv "IHP_ANTHROPIC_API_KEY"
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case mApiKey of
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Nothing -> pure (Left "IHP_ANTHROPIC_API_KEY is not set")
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Just apiKey -> do
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let url = "https://api.anthropic.com/v1/messages"
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let body = object
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[ "model" .= ("claude-sonnet-4-6" :: Text)
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, "max_tokens" .= maxTokens
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, "system" .= systemPrompt
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, "messages" .= [ object
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[ "role" .= ("user" :: Text)
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, "content" .= userMessage
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] ]
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]
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let reqBody = RequestBodyLBS (encode body)
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manager <- newManager tlsManagerSettings
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initReq <- parseRequest url
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let req = initReq
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{ method = "POST"
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, requestHeaders =
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[ ("content-type", "application/json")
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, ("x-api-key", cs apiKey)
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, ("anthropic-version", "2023-06-01")
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]
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, requestBody = reqBody
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}
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resp <- httpLbs req manager
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let respBody = responseBody resp
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case eitherDecode respBody of
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Left err -> pure (Left ("JSON parse error: " <> cs err))
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Right val ->
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case val ^? key "content" . nth 0 . key "text" . _String of
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Just txt -> pure (Right txt)
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Nothing ->
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case val ^? key "error" . key "message" . _String of
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Just msg -> pure (Left ("API error: " <> msg))
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Nothing -> pure (Left "Unexpected API response shape")
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