Keysat
2d43bad6fc
Both NL-query intents counted/listed a user's ENTIRE captured sent corpus (internal, vendor, personal mail) rather than only email to a matched investor — they were missing the `EXISTS email_investor_links` gate that recent_emails and the Communications panel's query_email_activity use. Their own docstrings said "investor emails", so the behavior was wrong, not just loose. Add the matched-only gate to both, mirroring query_email_activity. The runner test now seeds an unmatched sent email and asserts it is excluded (without the fix comms_by_user returns 3 not 2, this_week 2 not 1) — the prior fixture linked every email, so the leak went uncaught. Also documents the matched-only rule in the nl-query guide, and refreshes the AGENTS.md Current state (v93 deployed; this fix pending a v94 s9pk since the intents run on the box, not the bot).
441 lines
24 KiB
Python
441 lines
24 KiB
Python
"""NL-query intents — the curated, hand-written query catalog (W2, the safe core).
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Each intent is a FIXED, reviewed, parameterized SQL query with a small set of typed
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"slots" (the blanks a question fills in: a number of days, a name, a limit). There is NO
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generic SQL/AST compiler and NO dynamically-built identifiers: every table and column name
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is hardcoded in the query text, and every value the caller (or an LLM) supplies reaches
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SQLite only as a bound `?` parameter. That is the whole trust model — a malformed or
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hostile request can change a bound value, never the query structure. Adding a capability
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means adding a reviewed entry here, not widening a language.
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Soft-delete discipline (CLAUDE.md standing rule), per table:
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- reminders / opportunities / communications carry `deleted_at` -> filter `deleted_at IS NULL`.
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- emails have NO `deleted_at`; "live" means a non-tombstoned per-mailbox sighting exists
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(`email_account_messages.deleted_at IS NULL`) — mirror the digest / query_email_activity.
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- fundraising_investors/_contacts/_funds/_commitments are a HARD-REBUILT projection of the
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grid blob with NO `deleted_at` column; the live/retired axis there is the `graveyard` flag.
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Do NOT add `deleted_at IS NULL` to those tables — the column does not exist and the clause
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would raise. Exclude `graveyard = 1` where the question means "live" investors.
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Each run_* returns {columns, rows, summary, truncated}. `summary` is a DETERMINISTIC local
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one-liner (never an LLM narrative) — results never leave the box to be summarized.
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"""
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import sqlite3
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from datetime import datetime, timedelta
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# Generous ceiling — the Matrix review room is two admins and the web app is internal, so
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# dumping the full book is acceptable (per Grant); this only guards against an unbounded
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# scan flooding a response. A list intent past this is reported truncated, never silently cut.
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MAX_ROWS = 500
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# Live, non-terminal pipeline stages in funnel order (mirrors server.PIPELINE_STAGES; 'lost'
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# is the terminal drop). Kept here so the pipeline intents have a stable rank without importing
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# the server module (helpers take a conn; they never import server — house convention).
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_STAGE_ORDER = ['lead', 'outreach', 'meeting', 'due_diligence', 'committed', 'funded']
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_STAGE_RANK_SQL = (
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"CASE stage WHEN 'lead' THEN 1 WHEN 'outreach' THEN 2 WHEN 'meeting' THEN 3 "
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"WHEN 'due_diligence' THEN 4 WHEN 'committed' THEN 5 WHEN 'funded' THEN 6 ELSE 0 END")
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# ── helpers ────────────────────────────────────────────────────────────────────────────
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def _rows(cur):
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"""Materialize a cursor as a list of plain dicts, independent of the connection's
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row_factory (works whether rows come back as tuples or sqlite3.Row)."""
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cols = [c[0] for c in cur.description]
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return [dict(zip(cols, r)) for r in cur.fetchall()]
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def like_contains(value):
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"""Build a safe LIKE pattern for a free-text contains match. Escapes the LIKE
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wildcards so a user/LLM value of '%' or '_' is treated literally — paired with
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`LIKE ? ESCAPE '\\'` in the SQL, this stops '%' from matching the entire table."""
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v = value.replace("\\", "\\\\").replace("%", "\\%").replace("_", "\\_")
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return f"%{v}%"
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def _last_activity_by_investor(conn):
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"""{fundraising_investors.id: latest activity ISO ts} across logged communications and
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captured grid-linked emails — the per-investor recency signal behind the "gone quiet"
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and "last contact" intents.
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NB: this MIRRORS server.last_activity_by_investor() and its soft-delete joins (comms via
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cm.deleted_at IS NULL; email via a live email_account_messages sighting). It is duplicated
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rather than imported only to keep this module free of a server import (the main module runs
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as __main__, so `import server` would re-execute it). Keep the two in sync; the soft-delete
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test guards this copy. If a third caller appears, extract both to a shared module."""
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out = {}
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def _bump(inv_id, ts):
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if inv_id and ts and (out.get(inv_id) is None or str(ts) > str(out[inv_id])):
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out[inv_id] = ts
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# Each leg is guarded: the comms/email tables can be absent on a minimal DB. This is a
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# narrow, intentional tolerance for optional tables — NOT the broad error-swallowing the
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# runner forbids (a failure in an intent's main query surfaces as query_failed).
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try:
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for r in conn.execute(
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"SELECT fc.investor_id AS inv, MAX(cm.communication_date) AS last_ts "
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"FROM communications cm JOIN fundraising_contacts fc ON fc.contact_id = cm.contact_id "
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"WHERE cm.deleted_at IS NULL AND fc.contact_id IS NOT NULL GROUP BY fc.investor_id"):
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_bump(r["inv"], r["last_ts"])
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except sqlite3.OperationalError:
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pass
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try:
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for r in conn.execute(
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"SELECT eil.fundraising_investor_id AS inv, MAX(e.sent_at) AS last_ts "
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"FROM email_investor_links eil JOIN emails e ON e.id = eil.email_id "
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"WHERE eil.fundraising_investor_id IS NOT NULL AND EXISTS "
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"(SELECT 1 FROM email_account_messages eam WHERE eam.email_id = e.id "
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"AND eam.deleted_at IS NULL) GROUP BY eil.fundraising_investor_id"):
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_bump(r["inv"], r["last_ts"])
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except sqlite3.OperationalError:
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pass
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return out
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def _today():
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return datetime.utcnow().date()
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def _days_since(ts):
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"""Whole days between an ISO date/datetime string and today (UTC). None if unparseable."""
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if not ts:
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return None
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try:
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d = datetime.fromisoformat(str(ts)[:10].replace("Z", "")).date()
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except ValueError:
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return None
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return (_today() - d).days
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def _own_addresses(conn):
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try:
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return {(r[0] or "").lower().strip()
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for r in conn.execute("SELECT email_address FROM email_accounts")} - {""}
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except sqlite3.OperationalError:
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return set()
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def _truncate(rows):
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"""Apply the global ceiling, returning (rows, truncated)."""
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if len(rows) > MAX_ROWS:
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return rows[:MAX_ROWS], True
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return rows, False
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# ── investor intents ─────────────────────────────────────────────────────────────────────
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def run_investors_cold(conn, slots):
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"""Live investors not contacted in `days` days — never-contacted first, then oldest."""
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days = slots["days"]
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cutoff = (_today() - timedelta(days=days)).isoformat()
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last = _last_activity_by_investor(conn)
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invs = _rows(conn.execute(
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"SELECT id, investor_name, lead, total_invested FROM fundraising_investors "
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"WHERE graveyard = 0 ORDER BY investor_name"))
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cold = []
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for inv in invs:
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ts = last.get(inv["id"])
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if ts is None or str(ts)[:10] < cutoff:
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cold.append({"investor_name": inv["investor_name"], "lead": inv["lead"],
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"total_invested": inv["total_invested"],
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"last_activity_at": ts, "days_since": _days_since(ts)})
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# never-contacted (days_since None) first, then most-stale first
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cold.sort(key=lambda r: (r["days_since"] is not None, -(r["days_since"] or 0)))
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rows, trunc = _truncate(cold)
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return {"columns": ["investor_name", "lead", "total_invested", "last_activity_at", "days_since"],
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"rows": rows, "truncated": trunc,
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"summary": f"{len(cold)} live investor(s) not contacted in {days}+ days."}
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def run_investor_lookup(conn, slots):
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"""One investor's profile: contacts (name/email/title/city), committed total, per-fund
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commitments, lead. Name matched as a contains (an LLM/user may pass a partial)."""
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pat = like_contains(slots["name"])
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invs = _rows(conn.execute(
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"SELECT id, investor_name, lead, lead_source, total_invested, follow_up, graveyard "
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"FROM fundraising_investors WHERE investor_name LIKE ? ESCAPE '\\' "
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"ORDER BY graveyard, investor_name LIMIT 25", (pat,)))
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for inv in invs:
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inv["contacts"] = _rows(conn.execute(
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"SELECT full_name, email, title, city, state, country FROM fundraising_contacts "
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"WHERE investor_id = ? ORDER BY sort_order, full_name", (inv["id"],)))
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inv["commitments"] = _rows(conn.execute(
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"SELECT f.fund_name, c.amount FROM fundraising_commitments c "
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"JOIN fundraising_funds f ON f.id = c.fund_id WHERE c.investor_id = ? AND c.amount <> 0 "
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"ORDER BY f.display_order", (inv["id"],)))
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inv.pop("id", None)
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return {"columns": ["investor_name", "lead", "lead_source", "total_invested",
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"follow_up", "graveyard", "contacts", "commitments"],
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"rows": invs, "truncated": False,
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"summary": f"{len(invs)} investor(s) matching \"{slots['name']}\"."}
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def run_investors_by_city(conn, slots):
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"""Investors with a contact located in `city` (contains match on the contact's city)."""
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pat = like_contains(slots["city"])
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rows = _rows(conn.execute(
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"SELECT i.investor_name, c.full_name AS contact, c.city, c.state, c.country, i.lead "
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"FROM fundraising_contacts c JOIN fundraising_investors i ON i.id = c.investor_id "
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"WHERE i.graveyard = 0 AND c.city LIKE ? ESCAPE '\\' "
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"ORDER BY i.investor_name, c.full_name LIMIT ?", (pat, MAX_ROWS + 1)))
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rows, trunc = _truncate(rows)
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return {"columns": ["investor_name", "contact", "city", "state", "country", "lead"],
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"rows": rows, "truncated": trunc,
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"summary": f"{len(rows)} investor contact(s) in \"{slots['city']}\"."}
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def run_investors_by_lead(conn, slots):
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"""Live investors owned by a given lead/team member (contains match on `lead`)."""
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pat = like_contains(slots["lead"])
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rows = _rows(conn.execute(
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"SELECT investor_name, lead, total_invested, follow_up FROM fundraising_investors "
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"WHERE graveyard = 0 AND lead LIKE ? ESCAPE '\\' "
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"ORDER BY total_invested DESC, investor_name LIMIT ?", (pat, MAX_ROWS + 1)))
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rows, trunc = _truncate(rows)
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return {"columns": ["investor_name", "lead", "total_invested", "follow_up"],
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"rows": rows, "truncated": trunc,
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"summary": f"{len(rows)} live investor(s) led by \"{slots['lead']}\"."}
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def run_top_investors_committed(conn, slots):
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"""Top `limit` live investors by total committed capital across all funds."""
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n = slots["limit"]
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rows = _rows(conn.execute(
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"SELECT investor_name, total_invested, lead FROM fundraising_investors "
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"WHERE graveyard = 0 AND total_invested > 0 "
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"ORDER BY total_invested DESC, investor_name LIMIT ?", (n,)))
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return {"columns": ["investor_name", "total_invested", "lead"], "rows": rows,
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"truncated": False, "summary": f"Top {len(rows)} investor(s) by committed capital."}
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def run_investors_follow_up(conn, slots):
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"""Investors we owe a follow-up to: those with an OPEN reminder, overdue first. Uses the
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W1 reminders table (the richer follow-up layer) joined to the current grid name."""
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today = _today().isoformat()
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rows = _rows(conn.execute(
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"SELECT COALESCE(i.investor_name, r.investor_name) AS investor_name, r.title, "
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"r.due_date, r.status, r.assignee_id, "
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"CASE WHEN r.due_date IS NOT NULL AND substr(r.due_date,1,10) < ? THEN 1 ELSE 0 END AS overdue "
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"FROM reminders r LEFT JOIN fundraising_investors i ON i.id = r.investor_id "
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"WHERE r.deleted_at IS NULL AND r.status = 'open' AND r.investor_id IS NOT NULL "
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"ORDER BY (r.due_date IS NULL), r.due_date ASC LIMIT ?", (today, MAX_ROWS + 1)))
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rows, trunc = _truncate(rows)
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return {"columns": ["investor_name", "title", "due_date", "status", "overdue"],
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"rows": rows, "truncated": trunc,
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"summary": f"{len(rows)} investor(s) with an open follow-up reminder."}
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# ── pipeline intents ──────────────────────────────────────────────────────────────────────
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def run_pipeline_top(conn, slots):
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"""Top `limit` live pipeline opportunities by stage (furthest along first), with the
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investor, owner, and most-recent activity."""
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n = slots["limit"]
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last = _last_activity_by_investor(conn)
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rows = _rows(conn.execute(
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"SELECT o.fundraising_investor_id AS inv_id, "
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"COALESCE(i.investor_name, o.name) AS investor_name, o.stage, o.expected_amount, "
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"o.probability, u.full_name AS owner FROM opportunities o "
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"LEFT JOIN fundraising_investors i ON i.id = o.fundraising_investor_id "
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"LEFT JOIN users u ON u.id = o.owner_id "
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"WHERE o.deleted_at IS NULL AND o.stage != 'lost' "
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f"ORDER BY {_STAGE_RANK_SQL} DESC, o.expected_amount DESC LIMIT ?", (n,)))
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for r in rows:
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r["last_activity_at"] = last.get(r.pop("inv_id"))
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return {"columns": ["investor_name", "stage", "expected_amount", "probability", "owner",
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"last_activity_at"],
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"rows": rows, "truncated": False,
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"summary": f"Top {len(rows)} live pipeline opportunit(ies) by stage."}
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def run_pipeline_totals(conn, slots):
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"""Total pipeline dollars and the split across each stage (excludes lost)."""
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rows = _rows(conn.execute(
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"SELECT stage, COUNT(*) AS count, COALESCE(SUM(expected_amount),0) AS expected_total, "
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"COALESCE(SUM(commitment_amount),0) AS committed_total FROM opportunities "
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f"WHERE deleted_at IS NULL AND stage != 'lost' GROUP BY stage ORDER BY {_STAGE_RANK_SQL}"))
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total = sum(r["expected_total"] for r in rows)
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count = sum(r["count"] for r in rows)
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return {"columns": ["stage", "count", "expected_total", "committed_total"],
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"rows": rows, "truncated": False,
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"summary": f"${total:,.0f} expected across {count} live opportunit(ies) in "
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f"{len(rows)} stage(s)."}
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# ── email / communication intents ─────────────────────────────────────────────────────────
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def run_recent_emails(conn, slots):
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"""The most recent `limit` matched investor emails, optionally one direction.
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Matched-only + soft-delete-correct (a live email_account_messages sighting), mirroring
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the Communications panel's query_email_activity."""
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n, direction = slots["limit"], slots["direction"]
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where = ["EXISTS (SELECT 1 FROM email_account_messages eam WHERE eam.email_id = e.id "
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"AND eam.deleted_at IS NULL)",
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"EXISTS (SELECT 1 FROM email_investor_links l WHERE l.email_id = e.id)"]
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params = []
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own = _own_addresses(conn)
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if direction in ("inbound", "outbound") and own:
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op = "IN" if direction == "outbound" else "NOT IN"
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where.append(f"LOWER(e.from_email) {op} ({','.join('?' for _ in own)})")
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params.extend(sorted(own))
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sql = ("SELECT e.subject, e.from_name, e.from_email, e.sent_at, "
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"(SELECT fi.investor_name FROM email_investor_links l "
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" JOIN fundraising_investors fi ON fi.id = l.fundraising_investor_id "
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" WHERE l.email_id = e.id AND l.fundraising_investor_id IS NOT NULL LIMIT 1) AS investor "
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"FROM emails e WHERE " + " AND ".join(where) + " ORDER BY e.sent_at DESC LIMIT ?")
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rows = _rows(conn.execute(sql, params + [n]))
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label = {"inbound": "received", "outbound": "sent"}.get(direction, "")
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return {"columns": ["sent_at", "subject", "from_name", "from_email", "investor"],
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"rows": rows, "truncated": False,
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"summary": f"{len(rows)} most-recent {label + ' ' if label else ''}investor email(s)."}
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def run_investor_last_contact(conn, slots):
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"""When we last had any activity with investor X (matched by name)."""
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pat = like_contains(slots["name"])
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last = _last_activity_by_investor(conn)
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invs = _rows(conn.execute(
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"SELECT id, investor_name FROM fundraising_investors "
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"WHERE investor_name LIKE ? ESCAPE '\\' ORDER BY graveyard, investor_name LIMIT 25", (pat,)))
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rows = []
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for inv in invs:
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ts = last.get(inv["id"])
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rows.append({"investor_name": inv["investor_name"], "last_activity_at": ts,
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"days_since": _days_since(ts)})
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return {"columns": ["investor_name", "last_activity_at", "days_since"], "rows": rows,
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"truncated": False, "summary": f"Last contact for {len(rows)} investor(s) "
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f"matching \"{slots['name']}\"."}
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def run_comms_by_user(conn, slots):
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"""The most recent `limit` outbound **investor** emails sent by a given user (matched by
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username or full name). MATCHED-ONLY: restricted to investor-linked email (an
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email_investor_links row exists), mirroring query_email_activity / recent_emails — NOT the
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user's entire sent corpus (internal/vendor/personal mail is captured but never surfaced
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here). Soft-delete-correct (live sighting, is_sent)."""
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n, pat = slots["limit"], like_contains(slots["user"])
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rows = _rows(conn.execute(
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"SELECT e.subject, e.sent_at, u.full_name AS sender, "
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"(SELECT fi.investor_name FROM email_investor_links l "
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" JOIN fundraising_investors fi ON fi.id = l.fundraising_investor_id "
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" WHERE l.email_id = e.id AND l.fundraising_investor_id IS NOT NULL LIMIT 1) AS investor "
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"FROM emails e JOIN email_account_messages eam ON eam.email_id = e.id "
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"AND eam.deleted_at IS NULL AND eam.is_sent = 1 "
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"JOIN email_accounts ea ON ea.id = eam.account_id JOIN users u ON u.id = ea.user_id "
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"WHERE (u.username LIKE ? ESCAPE '\\' OR u.full_name LIKE ? ESCAPE '\\') "
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"AND EXISTS (SELECT 1 FROM email_investor_links l2 WHERE l2.email_id = e.id) "
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"ORDER BY e.sent_at DESC LIMIT ?", (pat, pat, n)))
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return {"columns": ["sent_at", "subject", "sender", "investor"], "rows": rows,
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"truncated": False,
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"summary": f"{len(rows)} recent email(s) sent by \"{slots['user']}\"."}
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def run_email_counts_by_user(conn, slots):
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"""Per-user counts of outbound **investor** emails over this week / month / year-to-date.
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MATCHED-ONLY: counts only investor-linked email (an email_investor_links row exists),
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mirroring query_email_activity / recent_emails — not the user's entire sent corpus.
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Windows are calendar-based: week = since Monday, month = since the 1st, ytd = since Jan 1."""
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today = _today()
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wk = (today - timedelta(days=today.weekday())).isoformat()
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mo = today.replace(day=1).isoformat()
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yr = today.replace(month=1, day=1).isoformat()
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where = ("WHERE eam.deleted_at IS NULL AND eam.is_sent = 1 "
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"AND EXISTS (SELECT 1 FROM email_investor_links l WHERE l.email_id = e.id)")
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params = [wk, mo, yr]
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if slots.get("user"):
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pat = like_contains(slots["user"])
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where += " AND (u.username LIKE ? ESCAPE '\\' OR u.full_name LIKE ? ESCAPE '\\')"
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params.extend([pat, pat])
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rows = _rows(conn.execute(
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"SELECT u.full_name AS user, u.username, "
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"SUM(CASE WHEN substr(e.sent_at,1,10) >= ? THEN 1 ELSE 0 END) AS this_week, "
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"SUM(CASE WHEN substr(e.sent_at,1,10) >= ? THEN 1 ELSE 0 END) AS this_month, "
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"SUM(CASE WHEN substr(e.sent_at,1,10) >= ? THEN 1 ELSE 0 END) AS ytd "
|
|
"FROM users u JOIN email_accounts ea ON ea.user_id = u.id "
|
|
"JOIN email_account_messages eam ON eam.account_id = ea.id "
|
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"JOIN emails e ON e.id = eam.email_id " + where +
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" GROUP BY u.id HAVING ytd > 0 ORDER BY ytd DESC", params))
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return {"columns": ["user", "this_week", "this_month", "ytd"], "rows": rows,
|
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"truncated": False, "summary": f"Outbound email counts for {len(rows)} user(s)."}
|
|
|
|
|
|
# ── registry ──────────────────────────────────────────────────────────────────────────────
|
|
# key -> {summary, slots, run, example}. `slots` is consumed by the runner's validator and
|
|
# (later) surfaced to the local-model translator + the UI as the single source of truth for
|
|
# what is queryable. SlotSpec: {type: int|enum|text, ...constraints}.
|
|
INTENTS = {
|
|
"investors_cold": {
|
|
"summary": "Investors we haven't contacted in a while (default 90 days).",
|
|
"slots": {"days": {"type": "int", "default": 90, "min": 1, "max": 3650}},
|
|
"example": "Which investors haven't we reached out to in the last 3 months?",
|
|
"run": run_investors_cold,
|
|
},
|
|
"investor_lookup": {
|
|
"summary": "One investor's contacts, email, committed total and per-fund breakdown.",
|
|
"slots": {"name": {"type": "text", "required": True, "maxlen": 120}},
|
|
"example": "What is Acme Capital's email and how much have they committed across funds?",
|
|
"run": run_investor_lookup,
|
|
},
|
|
"investors_by_city": {
|
|
"summary": "Investors with a contact located in a given city.",
|
|
"slots": {"city": {"type": "text", "required": True, "maxlen": 80}},
|
|
"example": "Who are all the investors located in Austin?",
|
|
"run": run_investors_by_city,
|
|
},
|
|
"investors_by_lead": {
|
|
"summary": "Investors owned by a given lead / team member.",
|
|
"slots": {"lead": {"type": "text", "required": True, "maxlen": 80}},
|
|
"example": "Show me the investors led by Jonathan.",
|
|
"run": run_investors_by_lead,
|
|
},
|
|
"top_investors_committed": {
|
|
"summary": "Top investors by total committed capital.",
|
|
"slots": {"limit": {"type": "int", "default": 10, "min": 1, "max": MAX_ROWS}},
|
|
"example": "List our top 10 investors by committed capital.",
|
|
"run": run_top_investors_committed,
|
|
},
|
|
"investors_follow_up": {
|
|
"summary": "Investors we owe a follow-up to (have an open reminder), overdue first.",
|
|
"slots": {},
|
|
"example": "Which investors do we owe follow-ups to?",
|
|
"run": run_investors_follow_up,
|
|
},
|
|
"pipeline_top": {
|
|
"summary": "Top pipeline opportunities by stage, with investor, owner and last activity.",
|
|
"slots": {"limit": {"type": "int", "default": 10, "min": 1, "max": MAX_ROWS}},
|
|
"example": "List our top 10 pipeline investors by stage and last conversation.",
|
|
"run": run_pipeline_top,
|
|
},
|
|
"pipeline_totals": {
|
|
"summary": "Total pipeline dollars and the split across each stage.",
|
|
"slots": {},
|
|
"example": "What is our total pipeline in dollars, split by stage?",
|
|
"run": run_pipeline_totals,
|
|
},
|
|
"recent_emails": {
|
|
"summary": "The most recent investor emails (optionally inbound or outbound only).",
|
|
"slots": {"limit": {"type": "int", "default": 10, "min": 1, "max": 100},
|
|
"direction": {"type": "enum", "choices": ["any", "inbound", "outbound"],
|
|
"default": "any"}},
|
|
"example": "What were the last 10 investor emails we sent, and who to?",
|
|
"run": run_recent_emails,
|
|
},
|
|
"investor_last_contact": {
|
|
"summary": "When we last had any activity with a given investor.",
|
|
"slots": {"name": {"type": "text", "required": True, "maxlen": 120}},
|
|
"example": "When did we last reach out to Acme Capital?",
|
|
"run": run_investor_last_contact,
|
|
},
|
|
"comms_by_user": {
|
|
"summary": "Recent investor emails sent by a given team member.",
|
|
"slots": {"user": {"type": "text", "required": True, "maxlen": 80},
|
|
"limit": {"type": "int", "default": 10, "min": 1, "max": 100}},
|
|
"example": "What were the last investor emails sent by Grant?",
|
|
"run": run_comms_by_user,
|
|
},
|
|
"email_counts_by_user": {
|
|
"summary": "How many investor emails each user sent this week / month / year-to-date.",
|
|
"slots": {"user": {"type": "text", "required": False, "maxlen": 80}},
|
|
"example": "How many emails has Grant sent this week, this month, and year to date?",
|
|
"run": run_email_counts_by_user,
|
|
},
|
|
}
|