Config suggestions (the healing loop)

from goldenmatch import dedupe_df
from goldenmatch.core.suggest import review_config, apply_suggestion

# 1. Zero-config first pass — good results, no rules.
result = dedupe_df(df)

# 2. You get the config it chose.
config = result.config

# 3. The healer reviews results vs the config and ranks self-verified tweaks.
suggestions = review_config(df, config)   # verify=True by default

# 4. Apply the ones you want.
for s in suggestions:
    print(s.kind, "->", s.rationale)      # every suggestion explains itself
    config = apply_suggestion(config, s)

# 5. Re-run; results improve. Repeat until review_config returns nothing.
result = dedupe_df(df, config=config)

from goldenmatch import dedupe_df

# Default run: free trigger gates a cheap attach. result.suggestions is
# [] on a healthy result; on a RED/YELLOW or score-dip result it carries
# raw candidate suggestions (cheap, unverified) you can inspect.
result = dedupe_df(df)
for s in result.suggestions:        # serialized dicts: id/kind/target/rationale/verified/patch
    print(s["kind"], "->", s["rationale"])

# Opt in to the expensive verified path (each candidate simulated through the gate):
result = dedupe_df(df, suggest=True)

# Or run the full apply-and-re-run loop and read the trail of what changed:
result = dedupe_df(df, heal=True)
for step in result.heal_trail or []:
    print("applied", step["kind"], "->", step["rationale"])
result.config   # the healed config, ready to persist

import { dedupe } from "goldenmatch";
import { enableSuggestWasm } from "goldenmatch/core/suggest-wasm";

// The WASM kernel is OPT-IN — the analog of `pip install goldenmatch[native]`.
// Until you enable it the healer is gracefully empty (see below).
enableSuggestWasm();

const free = await dedupe(rows);          // free.suggestions: verified=false
const verified = await dedupe(rows, { suggest: true });  // verified=true
const healed = await dedupe(rows, { heal: true });       // healed.config + healTrail