Keysat
c898ad8530
The currency-anchored amount regexes treated a single-letter magnitude suffix (k/m/b) as optional but unbounded, so "$5,000,000 but" scrubbed to "[AMOUNT_1]ut" — the 'b' of "but" was consumed as a 'billion' suffix. Add a word boundary after _MAG on the three currency-anchored _AMOUNT_RES patterns (range, symbol, ISO-code); the worded-amount pattern is unaffected. Money still tokenizes in every case ($5m/$5b/$3-5M/USD 5,000,000); only the OUTBOUND to-Claude text stops losing the leading letter of the following word. Round-trips were already lossless. Regression-locked by a round-5 section in test_scrub_leak.py; full redaction suite (scrub_leak + reidentification + grounding_boundary) green. Packaged as StartOS v0.1.0:57. Reported by the Spark gateway dev; gateway re-vendored scrub.py verbatim for parity (same golden-file leak test gates both sides). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
412 lines
21 KiB
Python
412 lines
21 KiB
Python
"""Redaction / re-hydration boundary — the privacy gate between Ten31's sovereign
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data and the Claude API. Implements docs/redaction-rehydration.md, hardened against an
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adversarial leak-hunt (see docs/spark-control-scrub-endpoints.md for the gateway twin).
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Defense in depth — NO single layer is trusted as "leak-proof":
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1. MINIMIZE-FIRST (caller): a local-Qwen summary strips most identity before scrub runs.
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2. PRE-NEUTRALIZE: any pre-existing [TYPE_N]-shaped string in the input is tokenized
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first, so every placeholder that reaches Claude is one WE minted (no injection).
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3. TIER-1 DROP: labelled/structured account-wire-SSN-IBAN-passport data, separator
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tolerant, excised entirely (never tokenized, never in the map).
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4. KNOWN-ENTITY tokenize: the LP identities we own (dictionary from the canonical
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layer), matched UNICODE-FOLDED (accents/case) with hyphenated-surname extension.
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5. STRUCTURED-PII tokenize/bucket: emails, URLs (incl. scheme-less/social), phones
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(intl + extensions), amounts (currency words/codes/symbols + worded + ranges),
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dates (ISO + worded + numeric + quarter), street addresses, bare long digit runs.
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6. NER BACKSTOP (ner_fn, on-infra local Qwen): tokenizes residual unknown person/org/
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location names the dictionary can't know. Unknown names are the largest residual,
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so callers in production pass ner_fn and FAIL CLOSED if it is unreachable.
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The pseudonym map ({token: real_value}) is the de-anonymization key: local-only, NEVER
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sent to Claude, NEVER written to interaction_log (only counts).
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"""
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import json
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import re
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import sqlite3
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import unicodedata
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import uuid
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from datetime import datetime, timezone
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TOKEN_TYPES = ("PERSON", "ORG", "FUND", "EMAIL", "PHONE", "URL", "ADDR", "AMOUNT", "DATE", "LOC", "MISC")
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_TOKEN_RE = re.compile(r"\[(?:" + "|".join(TOKEN_TYPES) + r")_\d+\]")
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# ── Tier-1: NEVER-SEND (dropped, not tokenized). Separator-tolerant + label-anchored. ──
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# Separators allow space/dot/dash/SLASH/COMMA so grouped account/SSN forms can't bypass.
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_SEP = r"[\s.\-/,]"
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_LABEL = (r"(?:acct|account|a/c|wire|routing|aba|sort\s?code|ssn|social\s?security|tax\s?id|"
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r"ein|policy|member|ref)")
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TIER1_PATTERNS = [
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("ssn", re.compile(r"\b\d{3}" + _SEP + r"\d{2}" + _SEP + r"\d{4}\b")),
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("ssn", re.compile(r"(?i)\b(?:ssn|social\s?security|tax\s?id|ein)\b[^\d]{0,12}\(?\d{3}\)?" + _SEP + r"{0,3}\d{2}" + _SEP + r"{0,3}\d{4}\b")),
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("iban", re.compile(r"\b[A-Z]{2}\d{2}(?:\s?[A-Z0-9]){11,30}\b")), # IBAN >=15 chars; excludes 12-char ISIN
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("swift", re.compile(r"(?i)\b(?:swift|bic)\b[^A-Za-z0-9]{0,8}[A-Z]{4}[A-Z]{2}[A-Z0-9]{2,5}\b")),
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("passport", re.compile(r"(?i)\bpassport\b(?:\s?(?:no|number|num|#)\.?)?[^\dA-Za-z]{0,6}[A-Za-z]{0,2}[\s\-]?\d{6,9}\b")),
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("labeled_account", re.compile(r"(?i)\b" + _LABEL + r"\b[^\dA-Za-z]{0,14}[#:]?\s*[\dXx](?:[\dXx]" + _SEP + r"?){5,}\b")),
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# labelled identifier with a LETTER prefix or an intervening 'no/number/id/ref/to' word
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# (e.g. 'acct A123456789012', 'member ID: X4451200931', 'Wire to GB123456789012') — these
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# slip the digit-led rule above, the bare-digit catch, and the IBAN floor.
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("labeled_account", re.compile(r"(?i)\b" + _LABEL + r"\b(?:[\s.:#\-]{0,3}(?:no|number|num|id|ref|to)\b)?[\s.:#\-]{0,4}[A-Za-z]{0,4}\d[\dA-Za-z]{4,}\b")),
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]
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# ── structured PII (Tier-2) ────────────────────────────────────────────────────
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_EMAIL_RE = re.compile(r"\b[A-Za-z0-9._%+\-]+@[A-Za-z0-9.\-]+\.[A-Za-z]{2,}\b")
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_URL_RE = re.compile(
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r"\bhttps?://[^\s)\]]+"
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r"|\bwww\.[^\s)\]]+"
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r"|\b(?:[a-z0-9\-]+\.)?(?:linkedin|twitter|github|facebook|instagram|x|substack|medium)\.com/[^\s)\]]+",
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re.IGNORECASE)
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# Phones: NANP (3-3-4, optional +1, optional extension) OR E.164/international (leading +).
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# Tightened so plain 4-4 year ranges ('2019-2024') don't match.
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_PHONE_RE = re.compile(
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r"(?<![\w.])(?:"
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r"(?:\+?1[\s.\-]?)?(?:\(\d{3}\)[\s.\-]?|\d{3}[\s.\-])\d{3}[\s.\-]\d{4}"
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r"|\+\d{1,3}(?:[\s.\-]?\d){7,14}"
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r")(?:\s?(?:x|ext\.?|extension)\s?\d{1,6})?(?![\w])")
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# Amounts: ONLY currency-anchored (symbol / code / currency-word), so non-money quantities
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# ('3m tall', 'ten million tokens', '250k followers') are NOT eaten. Bare magnitudes without
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# a currency cue are left to minimize-first + NER, which strip real money amounts.
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_NUMWORD = (r"(?:one|two|three|four|five|six|seven|eight|nine|ten|eleven|twelve|thirteen|"
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r"fourteen|fifteen|sixteen|seventeen|eighteen|nineteen|twenty|thirty|forty|fifty|"
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r"sixty|seventy|eighty|ninety|hundred|couple|few|several|half|a)")
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_MAG = r"(?:mm|bn|tn|thousand|million|billion|trillion|k|m|b)" # longest-first so 'MM' isn't split into 'M'
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_AMOUNT_RES = [
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re.compile(r"[$€£]\s?\d[\d,. ]*\d?\s?-\s?[$€£]?\s?\d[\d,. ]*\d?(?:\s?" + _MAG + r"\b)?", re.IGNORECASE), # $3-5M range
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re.compile(r"[$€£]\s?\d[\d,]*(?:\.\d+)?(?:\s?" + _MAG + r"\b)?", re.IGNORECASE), # $5,000,000 / $5m
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re.compile(r"\b(?:USD|EUR|GBP|CHF|CAD|AUD)\s?[$€£]?\s?\d[\d,]*(?:\.\d+)?(?:\s?" + _MAG + r"\b)?", re.IGNORECASE),
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re.compile(r"\b\d[\d,]*(?:\.\d+)?\s?(?:dollars?|euros?|pounds?)\b", re.IGNORECASE), # 5,000,000 dollars
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re.compile(r"(?i)\b(?:" + _NUMWORD + r"[\s\-]+){1,4}" + _MAG + r"\s+(?:dollars?|euros?|pounds?)\b"), # five million dollars
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]
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_MONTHS = (r"(?:jan|feb|mar|apr|may|jun|jul|aug|sep|sept|oct|nov|dec)[a-z]*\.?")
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_DATE_RES = [
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re.compile(r"\b(?:19|20)\d{2}-\d{2}-\d{2}\b"), # ISO
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re.compile(r"(?i)\b" + _MONTHS + r"\s+\d{1,2}(?:st|nd|rd|th)?,?\s+(?:19|20)?\d{2}\b"), # March 12, 1986
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re.compile(r"(?i)\b\d{1,2}(?:st|nd|rd|th)?\s+" + _MONTHS + r",?\s+(?:19|20)?\d{2}\b"), # 12 March 1986
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re.compile(r"\b(?:0?[1-9]|1[0-2])[/.\-](?:0?[1-9]|[12]\d|3[01])[/.\-](?:19|20)?\d{2}\b"), # 3/12/86 (valid m/d only)
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re.compile(r"(?i)\bQ[1-4][\s\-]?(?:19|20)\d{2}\b"), # Q1 1986
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re.compile(r"(?i)\b" + _MONTHS + r"\s+(?:19|20)\d{2}\b"), # March 1986
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]
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# Addresses: US number-first, PO Box, and European -strasse/-gasse + 'Rue/Calle/Via X N'.
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# Comprehensive international address detection relies on the NER LOC backstop + minimize-first.
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_ADDR_RE = re.compile(
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r"\bP\.?\s?O\.?\s?Box\s+\d+"
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r"|\b\d{1,6}\s+(?:[A-Z][A-Za-z'.]+\s?){1,4}"
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r"(?:Street|St|Avenue|Ave|Road|Rd|Lane|Ln|Boulevard|Blvd|Drive|Dr|Court|Ct|Way|Place|Pl|Square|Sq|Terrace|Ter)\b\.?"
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r"(?:,?\s+[A-Z][A-Za-z]+)*"
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r"|\b[A-Z][A-Za-z]*(?:strasse|straße|gasse|weg)\s+\d{1,5}"
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r"|\b(?:Rue|Calle|Via|Avenida)\s+(?:[A-Z][A-Za-z'.]+\s?){1,3}\d{1,5}",
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re.IGNORECASE)
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_ZIP_RE = re.compile(r"\b[A-Z]{2}\s+\d{5}(?:-\d{4})?\b")
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# bare long unlabeled run -> reversible [MISC]. Not glued to letters (so an ISIN/ticker like
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# US0378331005 stays intact substance), and a trailing sentence period doesn't block it.
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_BARE_DIGITS_RE = re.compile(r"(?<![\dA-Za-z.\-])\d{9,}(?![A-Za-z]|\.?\d)")
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_WORDX = r"[^\W_]" # unicode word char without underscore
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def _fold(s):
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"""1:1 length-preserving fold: strip diacritics per char + casefold, so 'Jonathán'
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matches a stored ASCII 'Jonathan'. Length preserved so match spans map to the original."""
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out = []
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for ch in s:
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d = unicodedata.normalize("NFKD", ch)
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base = "".join(c for c in d if not unicodedata.combining(c))
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out.append((base[0] if base else ch).lower())
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return "".join(out)
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def _bucket_amount(s):
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num = re.sub(r"[^\d.]", "", s)
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try:
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v = float(num)
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except ValueError:
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return "~$?"
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low = s.lower()
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if "billion" in low or re.search(r"\d\s?bn?\b", low):
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v *= 1_000_000_000
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elif "million" in low or re.search(r"\d\s?mm?\b", low):
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v *= 1_000_000
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elif "thousand" in low or re.search(r"\d\s?k\b", low):
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v *= 1_000
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if v >= 1_000_000_000:
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return f"~${round(v/1_000_000_000)}B"
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if v >= 1_000_000:
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return f"~${round(v/1_000_000)}M"
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if v >= 1_000:
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return f"~${round(v/1_000)}k"
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return "~$<1k"
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def _bucket_date(s):
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iso = re.match(r"((?:19|20)\d{2})-(\d{2})-\d{2}", s)
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if iso:
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return f"Q{(int(iso.group(2))-1)//3 + 1} {iso.group(1)}"
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q = re.search(r"(?i)Q([1-4])[\s\-]?((?:19|20)\d{2})", s)
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if q:
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return f"Q{q.group(1)} {q.group(2)}"
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y = re.search(r"\b((?:19|20)\d{2})\b", s)
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if y:
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return y.group(1)
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yy = re.search(r"[/.\-](\d{2})\b", s) # 2-digit year fallback
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if yy:
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return "19" + yy.group(1) if int(yy.group(1)) > 30 else "20" + yy.group(1)
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return "(period)"
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class ScrubState:
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"""Local pseudonym map for ONE task: same surface string -> same token (injective).
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The map is the de-anon key — local-only, never sent/serialized to a third party."""
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def __init__(self):
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self.token_map = {}
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self._by_value = {}
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self._counters = {t: 0 for t in TOKEN_TYPES}
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self.tier1_dropped = []
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def token_for(self, ttype, surface):
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key = (ttype, surface)
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tok = self._by_value.get(key)
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if tok is None:
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self._counters[ttype] += 1
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tok = f"[{ttype}_{self._counters[ttype]}]"
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self._by_value[key] = tok
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self.token_map[tok] = surface
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return tok
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def _flatten_known(known_entities):
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if not known_entities:
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return []
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type_by_key = {"persons": "PERSON", "orgs": "ORG", "funds": "FUND", "emails": "EMAIL", "locations": "LOC"}
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out = []
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for key, ttype in type_by_key.items():
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for s in known_entities.get(key, []) or []:
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s = (s or "").strip()
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if s:
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out.append((s, ttype))
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return out
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def _match_known(text, known_list, state):
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"""Tokenize known entities, matched UNICODE-FOLDED + case-insensitive, longest-first,
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extending over hyphen/apostrophe compounds so a known half of a double-barrelled
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surname pulls in the whole token. Operates by span so we can fold for matching but
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replace the ORIGINAL surface (preserved for rehydrate)."""
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if not known_list:
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return text
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folded = _fold(text)
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pairs = sorted(((_fold(unicodedata.normalize("NFKC", s)), t) for s, t in known_list),
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key=lambda x: len(x[0]), reverse=True)
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type_by_folded = {}
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for fs, t in pairs:
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type_by_folded.setdefault(fs, t)
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alt = "|".join(re.escape(fs) for fs, _ in pairs if fs)
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if not alt:
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return text
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rx = re.compile(r"(?<![0-9A-Za-z])(?:" + alt + r")(?![0-9A-Za-z])")
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spans = []
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for m in rx.finditer(folded):
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st, en = m.start(), m.end()
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ttype = type_by_folded.get(folded[st:en], "MISC")
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# extend over hyphen/apostrophe compounds on both sides
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while st > 1 and folded[st - 1] in "-'’" and re.match(_WORDX, folded[st - 2] or ""):
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k = st - 2
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while k >= 0 and (re.match(_WORDX, folded[k]) or folded[k] in "-'’"):
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k -= 1
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st = k + 1
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while en < len(folded) - 1 and folded[en] in "-'’" and re.match(_WORDX, folded[en + 1] or ""):
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k = en + 1
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while k < len(folded) and (re.match(_WORDX, folded[k]) or folded[k] in "-'’"):
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k += 1
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en = k
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spans.append((st, en, ttype))
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if not spans:
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return text
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# merge overlaps, replace right-to-left in the ORIGINAL
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spans.sort()
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merged = [spans[0]]
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for st, en, tt in spans[1:]:
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ps, pe, ptt = merged[-1]
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if st <= pe:
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merged[-1] = (ps, max(pe, en), ptt)
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else:
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merged.append((st, en, tt))
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for st, en, tt in reversed(merged):
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surface = text[st:en]
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text = text[:st] + state.token_for(tt, surface) + text[en:]
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return text
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def scrub(text, known_entities=None, bucket=False, state=None, ner_fn=None):
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"""De-identify `text`. Returns (outbound_text, token_map, audit). Pass ner_fn (a
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local-model NER callable text->[(surface,type)]) in production to catch unknown
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names; without it the dictionary+regex path leaves unknown free-text names as
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residual (callers should minimize-first and/or fail closed)."""
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if text is None:
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text = ""
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st = state or ScrubState()
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# NFKC-normalize so decomposed (NFD) names and ligatures align with the dictionary
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# (else 'Reyés' in NFD or 'Steffen' with a ligature would miss and leak), and strip
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# zero-width characters that could split a known name ('Rey<U+200B>es').
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s = unicodedata.normalize("NFKC", str(text))
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s = re.sub(r"[\u200b\u200c\u200d\u2060\ufeff]", "", s)
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# 1) PRE-NEUTRALIZE pre-existing [TYPE_N] strings so they can't collide with our tokens.
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s = _TOKEN_RE.sub(lambda m: st.token_for("MISC", m.group(0)), s)
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# 2) TIER-1 DROP (labelled/structured; separator tolerant). Neutral marker, no value.
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for label, pat in TIER1_PATTERNS:
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def _drop(_m, _label=label):
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st.tier1_dropped.append(_label)
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return "[redacted]"
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s = pat.sub(_drop, s)
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# 3) KNOWN ENTITIES (unicode-folded, hyphen-extended).
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s = _match_known(s, _flatten_known(known_entities), st)
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# 4) STRUCTURED PII. Order matters: emails/urls/addresses, then DATES and AMOUNTS
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# (so dashed ISO dates / ranges aren't swallowed by the permissive phone matcher),
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# then PHONES, then any bare long digit run left over.
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s = _EMAIL_RE.sub(lambda m: st.token_for("EMAIL", m.group(0)), s)
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s = _URL_RE.sub(lambda m: st.token_for("URL", m.group(0)), s)
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s = _ZIP_RE.sub(lambda m: st.token_for("LOC", m.group(0)), s) # state+ZIP before ADDR (which would eat the state)
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s = _ADDR_RE.sub(lambda m: st.token_for("ADDR", m.group(0)), s)
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for date_re in _DATE_RES:
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if bucket:
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s = date_re.sub(lambda m: _bucket_date(m.group(0)), s)
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else:
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s = date_re.sub(lambda m: st.token_for("DATE", m.group(0)), s)
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for amt_re in _AMOUNT_RES:
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if bucket:
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s = amt_re.sub(lambda m: _bucket_amount(m.group(0)), s)
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else:
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s = amt_re.sub(lambda m: st.token_for("AMOUNT", m.group(0)), s)
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s = _PHONE_RE.sub(lambda m: st.token_for("PHONE", m.group(0)), s)
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# bare long unlabeled digit runs -> reversible [MISC] (never leak digits to Claude;
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# don't DROP, since these may be substance like share counts / security ids).
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s = _BARE_DIGITS_RE.sub(lambda m: st.token_for("MISC", m.group(0)), s)
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# 5) NER BACKSTOP for unknown names (production: local Qwen). Tokenize what it finds.
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# A connection failure here propagates so the caller can FAIL CLOSED rather than
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# emit name-blind. Sort longest-first so a full name is tokenized before its parts.
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if ner_fn is not None:
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for surface, ntype in sorted((ner_fn(s) or []), key=lambda e: len(e[0] or ""), reverse=True):
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surface = (surface or "").strip()
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if not surface or _TOKEN_RE.search(surface):
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continue
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tt = ntype if ntype in TOKEN_TYPES else "PERSON"
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s = re.sub(r"(?<![0-9A-Za-z])" + re.escape(surface) + r"(?![0-9A-Za-z])",
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lambda m: st.token_for(tt, m.group(0)), s)
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audit = {
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"token_count": len(st.token_map),
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"tokens_by_type": _counts_by_type(st.token_map),
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"tier1_dropped_count": len(st.tier1_dropped),
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"tier1_dropped_kinds": sorted(set(st.tier1_dropped)),
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"bucketed": bool(bucket),
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"outbound_chars": len(s),
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}
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return s, dict(st.token_map), audit
|
||
|
||
|
||
def _counts_by_type(token_map):
|
||
out = {}
|
||
for tok in token_map:
|
||
m = re.match(r"\[([A-Z]+)_\d+\]", tok)
|
||
if m:
|
||
out[m.group(1)] = out.get(m.group(1), 0) + 1
|
||
return out
|
||
|
||
|
||
def rehydrate(text, token_map):
|
||
"""Substitute real values back in via a SINGLE non-overlapping pass (one alternation,
|
||
longest tokens first) so an inserted value that is itself token-shaped can't be
|
||
re-substituted by a later pass. Tier-1 drops are not restorable — excluded by design."""
|
||
s = str(text or "")
|
||
if not token_map:
|
||
return s
|
||
rx = re.compile("|".join(re.escape(t) for t in sorted(token_map, key=len, reverse=True)))
|
||
return rx.sub(lambda m: token_map[m.group(0)], s)
|
||
|
||
|
||
def residual_tokens(text):
|
||
return _TOKEN_RE.findall(str(text or ""))
|
||
|
||
|
||
# ── known-entity dictionary from the CRM (read-only) ───────────────────────────
|
||
|
||
def build_known_entities(db_path):
|
||
"""Deterministic dictionary of OUR entities to tokenize, read-only from the CRM.
|
||
Includes full names AND every name part (so mid-prose surnames are caught) + email
|
||
local-parts. RAISES on read failure — callers must fail closed, never run name-blind."""
|
||
persons, orgs, funds, emails = set(), set(), set(), set()
|
||
conn = sqlite3.connect(f"file:{db_path}?mode=ro", uri=True)
|
||
conn.row_factory = sqlite3.Row
|
||
|
||
def _add_person(name):
|
||
name = (name or "").strip()
|
||
if len(name) >= 2:
|
||
persons.add(name)
|
||
for part in re.split(r"[\s'’\-]+", name):
|
||
if len(part) >= 2 and not part.isdigit(): # index every part incl. short surnames (Wu, Li)
|
||
persons.add(part)
|
||
|
||
def _safe(q, fn):
|
||
try:
|
||
for r in conn.execute(q):
|
||
fn(r)
|
||
except sqlite3.OperationalError:
|
||
pass
|
||
|
||
# No `deleted_at` filter: tokenizing a soft-deleted name is desirable, and the live
|
||
# contacts/canonical schemas vary on that column — filtering on it silently zeroed the
|
||
# whole dictionary (a missing-column OperationalError swallowed by _safe).
|
||
_safe("SELECT display_name, primary_email FROM canonical_entities WHERE entity_kind='person'",
|
||
lambda r: (_add_person(r["display_name"]), r["primary_email"] and emails.add(r["primary_email"].strip().lower())))
|
||
_safe("SELECT first_name, last_name, email FROM contacts",
|
||
lambda r: (_add_person(f"{r['first_name'] or ''} {r['last_name'] or ''}"),
|
||
r["email"] and emails.add(r["email"].strip().lower())))
|
||
_safe("SELECT full_name, email FROM fundraising_contacts",
|
||
lambda r: (_add_person(r["full_name"]), r["email"] and emails.add(r["email"].strip().lower())))
|
||
_safe("SELECT display_name FROM canonical_entities WHERE entity_kind IN ('organization','investor','lp')",
|
||
lambda r: r["display_name"] and orgs.add(r["display_name"].strip()))
|
||
_safe("SELECT name FROM organizations", lambda r: r["name"] and orgs.add(r["name"].strip()))
|
||
_safe("SELECT investor_name FROM fundraising_investors", lambda r: r["investor_name"] and orgs.add(r["investor_name"].strip()))
|
||
_safe("SELECT fund_name FROM fundraising_funds", lambda r: r["fund_name"] and funds.add(r["fund_name"].strip()))
|
||
conn.close()
|
||
|
||
for e in list(emails):
|
||
lp = e.split("@")[0]
|
||
if len(lp) >= 3 and not lp.isdigit():
|
||
persons.add(lp)
|
||
return {"persons": sorted(persons, key=len, reverse=True),
|
||
"orgs": sorted(orgs, key=len, reverse=True),
|
||
"funds": sorted(funds, key=len, reverse=True),
|
||
"emails": sorted(emails, key=len, reverse=True)}
|
||
|
||
|
||
# ── audit logging (metadata only — never the map or real values) ───────────────
|
||
|
||
def _now():
|
||
return datetime.now(timezone.utc).replace(tzinfo=None).isoformat() + "Z"
|
||
|
||
|
||
def log_scrub(conn, actor_id, audit, task=None, session_id=None, target_id=None, source="mcp"):
|
||
payload = {"task": task, "session_id": session_id,
|
||
"token_count": audit.get("token_count"), "tokens_by_type": audit.get("tokens_by_type"),
|
||
"tier1_dropped_count": audit.get("tier1_dropped_count"),
|
||
"tier1_dropped_kinds": audit.get("tier1_dropped_kinds"),
|
||
"bucketed": audit.get("bucketed"), "outbound_chars": audit.get("outbound_chars")}
|
||
conn.execute(
|
||
"""INSERT INTO interaction_log (id, ts, actor_type, actor_id, action, target_type, target_id, payload, source, created_at)
|
||
VALUES (?,?, 'agent', ?, 'redaction.scrub', 'canonical_entity', ?, ?, ?, ?)""",
|
||
(str(uuid.uuid4()), _now(), actor_id, target_id, json.dumps(payload), source, _now()))
|
||
|
||
|
||
def log_rehydrate(conn, actor_id, tokens_rehydrated, residual, human_decision="pending",
|
||
reviewer_id=None, task=None, session_id=None, source="mcp"):
|
||
payload = {"task": task, "session_id": session_id, "tokens_rehydrated": tokens_rehydrated,
|
||
"residual_placeholders": residual, "human_decision": human_decision, "reviewer_id": reviewer_id}
|
||
conn.execute(
|
||
"""INSERT INTO interaction_log (id, ts, actor_type, actor_id, action, target_type, target_id, payload, source, created_at)
|
||
VALUES (?,?, 'agent', ?, 'redaction.rehydrate', 'canonical_entity', NULL, ?, ?, ?)""",
|
||
(str(uuid.uuid4()), _now(), actor_id, json.dumps(payload), source, _now()))
|