Files
breakpilot-compliance/ai-compliance-sdk/cmd/iace-audit/propose.go
T
Benjamin Admin 8440ddfecb feat(ai-sdk): runnable iace-audit propose CLI + live LLM wiring (P2 slice 3)
Makes the offline proposer runnable end-to-end.

- BuildProposerInput (proposer_input.go): non-test engine->hazards path. The
  PatternMatch->Hazard converter is lifted out of the GT test files into
  production scope so both the tests and the CLI share one pipeline.
- iace-audit propose <narrative.json> [<ground-truth.json>]: detect candidates ->
  GT-screen survivors (when a ground truth is given) -> judge (HeuristicJudge by
  default, LLMJudge over ollama when IACE_PROPOSE_LLM=1) -> write the human-review
  queue to audit-reports/proposals.{md,json}. Propose-only.

Smoke run on a dishwasher narrative: 32 fired -> 3 candidates -> queue with a
confident duplicate, a confident distinct, and one punted to the LLM judge; GT
wall recall-safe. Live qwen is opt-in via env; the heuristic default keeps the
tool runnable (and CI deterministic) without a model. Proposal types 2-4
(foreign-framing gates, vocab->tag, coverage blind spots) remain for slice 4.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
2026-06-26 10:27:01 +02:00

142 lines
4.1 KiB
Go

package main
import (
"context"
"encoding/json"
"fmt"
"os"
"strconv"
"github.com/breakpilot/ai-compliance-sdk/internal/iace"
"github.com/breakpilot/ai-compliance-sdk/internal/llm"
)
type narrativeInput struct {
MachineType string `json:"machine_type"`
Narrative string `json:"narrative"`
MachineTypes []string `json:"machine_types,omitempty"`
}
// cmdPropose — Method P: offline dedup-candidate proposer.
//
// iace-audit propose <narrative.json> [<ground-truth.json>]
//
// Detect near-duplicate patterns, screen survivors against a ground truth (if
// given), judge them (heuristic by default, LLM when enabled), and write the
// human-review queue to audit-reports/proposals.{md,json}. Propose-only — it
// writes a report and never mutates the pattern library.
//
// Env:
//
// IACE_PROPOSE_THRESHOLD candidate score threshold (default 0.30)
// IACE_PROPOSE_LLM=1 use the offline LLM judge instead of the heuristic
// OLLAMA_URL ollama base URL (default http://localhost:11434)
// SELF_HOSTED_LLM_MODEL model name (default qwen2.5:32b-instruct)
func cmdPropose(args []string) {
if len(args) < 1 {
fmt.Fprintln(os.Stderr, "propose: usage: iace-audit propose <narrative.json> [<ground-truth.json>]")
os.Exit(2)
}
var in narrativeInput
must(readJSONFile(args[0], &in))
if in.Narrative == "" {
fmt.Fprintln(os.Stderr, "propose: narrative is empty")
os.Exit(2)
}
var gt *iace.GroundTruth
if len(args) >= 2 {
var g iace.GroundTruth
must(readJSONFile(args[1], &g))
gt = &g
}
threshold := envFloat("IACE_PROPOSE_THRESHOLD", 0.30)
hazards, mits, fired := iace.BuildProposerInput(in.Narrative, in.MachineType, in.MachineTypes)
candidates := iace.FindDedupCandidates(fired, threshold)
byID := make(map[string]iace.PatternMatch, len(fired))
for _, pm := range fired {
byID[pm.PatternID] = pm
}
judge := selectJudge(in.MachineType)
ctx := context.Background()
var proposals []iace.JudgedProposal
blocked := 0
for _, c := range candidates {
var sr iace.ScreenResult
if gt != nil {
sr = iace.ScreenSupersession(gt, hazards, mits, c.KeepHazardName, c.DropName)
if sr.RecallAfter < sr.RecallBefore || sr.DistinctGT {
blocked++
continue
}
}
v, conf, rat := judge.Judge(ctx, c, byID[c.KeepPattern], byID[c.DropPattern])
proposals = append(proposals, iace.JudgedProposal{
Candidate: c, Screen: sr, Verdict: v, Confidence: conf, Rationale: rat, Judge: judge.Name(),
})
}
writeText("audit-reports/proposals.md", iace.RenderProposalQueue(in.MachineType, proposals))
writeJSON("audit-reports/proposals.json", proposals)
printSummary("Method P — Dedup Proposer ("+judge.Name()+")", map[string]int{
"fired_patterns": len(fired),
"candidates": len(candidates),
"in_queue": len(proposals),
"gt_blocked": blocked,
})
if gt == nil {
fmt.Fprintln(os.Stderr, "note: no ground truth provided — GT wall NOT applied (candidates not recall-screened)")
}
}
func selectJudge(machineClass string) iace.CandidateJudge {
if os.Getenv("IACE_PROPOSE_LLM") != "1" {
return iace.HeuristicJudge{}
}
base := envStr("OLLAMA_URL", "http://localhost:11434")
model := envStr("SELF_HOSTED_LLM_MODEL", "qwen2.5:32b-instruct")
reg := llm.NewProviderRegistry("ollama", "")
reg.Register(llm.NewOllamaAdapter(base, model))
fmt.Printf("using LLM judge (ollama %s, model %s)\n", base, model)
return iace.LLMJudge{Completer: iace.NewRegistryCompleter(reg, model), MachineClass: machineClass}
}
func readJSONFile(path string, v any) error {
raw, err := os.ReadFile(path)
if err != nil {
return err
}
return json.Unmarshal(raw, v)
}
func writeText(path, content string) {
_ = os.MkdirAll("audit-reports", 0o755)
if err := os.WriteFile(path, []byte(content), 0o644); err != nil {
fmt.Fprintln(os.Stderr, "warn: could not write", path, err)
return
}
fmt.Println("→ wrote", path)
}
func envStr(key, def string) string {
if v := os.Getenv(key); v != "" {
return v
}
return def
}
func envFloat(key string, def float64) float64 {
if v := os.Getenv(key); v != "" {
if f, err := strconv.ParseFloat(v, 64); err == nil {
return f
}
}
return def
}