1aff7ea2fc
The 2026-07-05 Weston call (CA3e88ce) showed the model guessing geography: it
recommended Boca Raton for a Weston caller (real answer: Pembroke Pines, ~9 mi),
invented distances ('Weston to Hialeah is twenty miles'), and confirmed a
nonexistent Miami Lakes office when the caller insisted.
- practice.py: office lat/lon + ~70-place South Florida gazetteer + haversine;
geography_block() renders a full distance table (closest first) from the
caller's area to every office. 'Westin' aliased to Weston (STT spelling).
- callstate.py: extractor gains caller_area; CallStateGroomer appends the
GEOGRAPHY block to the system message — zero-lag via a synchronous gazetteer
scan of user turns until the extractor catches up.
- bot.py: DISTANCES prompt rule (answer only from GEOGRAPHY, never estimate),
only-these-8-offices guard, caller-area cities added to Whisper hotwords
('Weston' was heard as 'Western Florida').
Verified: staged replay of the failed exchanges on activeblue-avc:latest —
correct office, correct distances, Miami Lakes denied (5/5 + 3/3 runs).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
354 lines
15 KiB
Python
354 lines
15 KiB
Python
"""Advanced Vision Care practice facts + the phone agent's tools.
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Facts sourced from advancedvisioncareflorida.com (8 locations across Broward,
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Miami-Dade, Palm Beach). NOTE: the website does NOT publish office hours, so we do
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NOT assert hours — the agent must offer to have staff confirm them instead of
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inventing them. Fill HOURS in if/when you have them.
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"""
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import json
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import os
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import re
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from datetime import datetime, timezone
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from loguru import logger
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# ─────────────────────────────────────────────────────────────────────────────
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# Real facts from advancedvisioncareflorida.com
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# ─────────────────────────────────────────────────────────────────────────────
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# "latlon" is block-level approximate (good enough for nearest-office ranking and
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# "about X miles" answers; never used for navigation).
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LOCATIONS = [
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# Broward County
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{"city": "Hollywood / Fort Lauderdale", "address": "2873 Stirling Rd, Fort Lauderdale, FL 33312", "phone": "(954) 983-4969", "latlon": (26.047, -80.183)},
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{"city": "Tamarac", "address": "5865 N University Dr, Tamarac, FL 33321", "phone": "(954) 720-2720", "latlon": (26.198, -80.253)},
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{"city": "Pembroke Pines", "address": "246 S Flamingo Rd, Pembroke Pines, FL 33027", "phone": "(954) 443-1230", "latlon": (26.005, -80.312)},
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{"city": "Lauderdale Lakes", "address": "3682 W Oakland Park Blvd, Lauderdale Lakes, FL 33311", "phone": "(954) 730-8087", "latlon": (26.166, -80.198)},
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# Miami-Dade County
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{"city": "Hialeah", "address": "1770 W 32nd Pl, Hialeah, FL 33012", "phone": "(305) 885-4477", "latlon": (25.876, -80.300)},
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{"city": "Kendall", "address": "11605 N Kendall Dr, Miami, FL 33176", "phone": "(305) 982-8927", "latlon": (25.686, -80.381)},
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{"city": "Miami Gardens", "address": "4771 NW 183rd St, Miami Gardens, FL 33055", "phone": "(305) 390-2467", "latlon": (25.942, -80.272)},
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# Palm Beach County
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{"city": "Boca Raton", "address": "21673 State Road 7, Boca Raton, FL 33428", "phone": "(561) 470-2310", "latlon": (26.358, -80.202)},
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]
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PRACTICE_FACTS = {
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"name": "Advanced Vision Care",
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"locations": LOCATIONS,
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"insurance": [
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"CarePlus", "Doctors Health", "Florida Blue Medicare", "Optum", "Spectera",
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"Sunshine Health", "VSP", "WellCare",
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],
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"services": (
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"routine and medical eye exams, contact lens exams, pediatric eye exams, "
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"and LASIK consultations"
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),
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# Website does not publish hours — leave None so the agent won't invent them.
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"hours": None,
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}
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REQUESTS_LOG = os.path.join(os.path.dirname(os.path.abspath(__file__)), "appointment_requests.jsonl")
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# Expand street abbreviations so the TTS speaks "North Kendall Drive", not "N … D-R".
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_ABBREV = {
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"NW": "Northwest", "NE": "Northeast", "SW": "Southwest", "SE": "Southeast",
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"N": "North", "S": "South", "E": "East", "W": "West",
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"Dr": "Drive", "Rd": "Road", "Blvd": "Boulevard", "St": "Street",
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"Ave": "Avenue", "Pl": "Place", "Ln": "Lane", "Ct": "Court", "Hwy": "Highway",
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"FL": "Florida",
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}
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def _spoken_address(addr: str) -> str:
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"""Expand directional + street-type abbreviations for natural speech."""
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return re.sub(
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r"\b(" + "|".join(re.escape(k) for k in _ABBREV) + r")\b",
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lambda m: _ABBREV[m.group(1)],
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addr,
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)
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def practice_summary() -> str:
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"""Compact facts block for the system prompt."""
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f = PRACTICE_FACTS
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loc_lines = "\n".join(f" - {l['city']}: {_spoken_address(l['address'])} — {l['phone']}" for l in f["locations"])
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hours = f["hours"] or (
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"NOT published — do not state specific hours; offer to have the office confirm."
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)
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return (
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f"Practice name: {f['name']}\n"
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f"Locations ({len(f['locations'])} offices across South Florida):\n{loc_lines}\n"
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f"Insurance accepted (these EXACT plans only): {', '.join(f['insurance'])}.\n"
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f"Services: {f['services']}\n"
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f"Hours: {hours}\n"
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)
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def _find_location(name: str):
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"""Loose match a caller's city/location text to a known office."""
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if not name:
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return None
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n = name.lower()
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for l in LOCATIONS:
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if n in l["city"].lower() or l["city"].lower() in n:
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return l
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return None
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# ─────────────────────────────────────────────────────────────────────────────
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# Geography — deterministic nearest-office answers (no LLM, no external API).
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#
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# Added after the 2026-07-05 Weston call: the model guessed which office was close
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# ("Boca Raton is relatively close", "Weston to Hialeah is twenty miles" — invented)
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# and hallucinated a Miami Lakes office. Instead we precompute distances from the
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# caller's stated area to every office and inject them via CallStateGroomer, so the
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# model only ever repeats numbers it was handed.
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#
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# Coordinates are approximate residential centers — fine for ranking offices and
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# "about X miles", which is all a phone answer needs.
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# ─────────────────────────────────────────────────────────────────────────────
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GAZETTEER = {
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# Broward
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"weston": (26.080, -80.390),
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"davie": (26.076, -80.277),
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"southwest ranches": (26.059, -80.337),
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"cooper city": (26.057, -80.272),
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"plantation": (26.127, -80.250),
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"sunrise": (26.157, -80.300),
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"lauderhill": (26.164, -80.208),
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"tamarac": (26.203, -80.250),
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"north lauderdale": (26.217, -80.226),
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"margate": (26.245, -80.206),
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"coconut creek": (26.284, -80.184),
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"coral springs": (26.271, -80.271),
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"parkland": (26.310, -80.237),
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"pompano beach": (26.238, -80.125),
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"deerfield beach": (26.318, -80.100),
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"lighthouse point": (26.276, -80.087),
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"oakland park": (26.172, -80.132),
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"wilton manors": (26.160, -80.139),
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"fort lauderdale": (26.122, -80.137),
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"lauderdale lakes": (26.166, -80.208),
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"hollywood": (26.011, -80.150),
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"dania beach": (26.052, -80.144),
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"hallandale beach": (25.981, -80.148),
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"pembroke pines": (26.008, -80.297),
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"pembroke park": (25.988, -80.178),
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"west park": (25.984, -80.199),
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"miramar": (25.977, -80.303),
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# Miami-Dade
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"miami": (25.775, -80.194),
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"miami beach": (25.790, -80.130),
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"brickell": (25.758, -80.193),
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"little havana": (25.773, -80.215),
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"north miami": (25.890, -80.187),
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"north miami beach": (25.933, -80.163),
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"aventura": (25.956, -80.139),
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"sunny isles beach": (25.943, -80.122),
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"miami gardens": (25.942, -80.246),
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"opa-locka": (25.902, -80.250),
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"opa locka": (25.902, -80.250),
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"miami lakes": (25.909, -80.309),
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"hialeah": (25.858, -80.278),
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"hialeah gardens": (25.865, -80.324),
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"medley": (25.838, -80.332),
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"doral": (25.819, -80.355),
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"sweetwater": (25.763, -80.373),
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"fontainebleau": (25.772, -80.346),
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"westchester": (25.745, -80.327),
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"west miami": (25.757, -80.296),
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"coral gables": (25.721, -80.268),
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"south miami": (25.707, -80.293),
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"pinecrest": (25.667, -80.308),
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"kendall": (25.679, -80.355),
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"west kendall": (25.706, -80.439),
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"kendale lakes": (25.708, -80.407),
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"the hammocks": (25.670, -80.445),
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"tamiami": (25.758, -80.402),
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"country walk": (25.633, -80.432),
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"richmond west": (25.611, -80.430),
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"palmetto bay": (25.622, -80.320),
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"cutler bay": (25.575, -80.336),
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"homestead": (25.469, -80.478),
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"florida city": (25.448, -80.479),
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"key biscayne": (25.691, -80.163),
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# Palm Beach (southern)
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"boca raton": (26.359, -80.130),
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"west boca": (26.351, -80.210),
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"delray beach": (26.461, -80.073),
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"boynton beach": (26.525, -80.087),
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"lake worth": (26.617, -80.072),
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"wellington": (26.659, -80.241),
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"west palm beach": (26.715, -80.054),
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}
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_PLACE_ALIASES = {
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"westin": "weston", # frequent STT spelling of Weston (hotel-chain bias)
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"ft lauderdale": "fort lauderdale",
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"ft. lauderdale": "fort lauderdale",
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"kendall west": "west kendall",
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"hallandale": "hallandale beach",
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"sunny isles": "sunny isles beach",
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"the pines": "pembroke pines",
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}
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# Gazetteer keys that ARE one of our office cities (used to prefer the caller's own
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# area over office names they merely asked about).
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OFFICE_PLACE_KEYS = frozenset({
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"hollywood", "fort lauderdale", "tamarac", "pembroke pines", "lauderdale lakes",
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"hialeah", "kendall", "miami gardens", "boca raton",
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})
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# Which office a gazetteer key belongs to, for the "right in <city>" special case.
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_OFFICE_FOR_KEY = {
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"hollywood": "Hollywood / Fort Lauderdale",
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"fort lauderdale": "Hollywood / Fort Lauderdale",
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"tamarac": "Tamarac",
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"pembroke pines": "Pembroke Pines",
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"lauderdale lakes": "Lauderdale Lakes",
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"hialeah": "Hialeah",
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"kendall": "Kendall",
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"miami gardens": "Miami Gardens",
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"boca raton": "Boca Raton",
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}
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# Longest names first so "north miami beach" wins over "north miami" over "miami".
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_PLACE_RE = re.compile(
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r"\b(" + "|".join(
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re.escape(k) for k in sorted(list(GAZETTEER) + list(_PLACE_ALIASES), key=len, reverse=True)
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) + r")\b"
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)
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# Straight-line → driving miles fudge factor for the South Florida grid.
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_ROAD_FACTOR = 1.3
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def _haversine_miles(a, b) -> float:
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import math
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lat1, lon1, lat2, lon2 = map(math.radians, (*a, *b))
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h = (math.sin((lat2 - lat1) / 2) ** 2
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+ math.cos(lat1) * math.cos(lat2) * math.sin((lon2 - lon1) / 2) ** 2)
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return 3958.8 * 2 * math.asin(math.sqrt(h))
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def _road_miles(a, b) -> int:
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return max(1, round(_haversine_miles(a, b) * _ROAD_FACTOR))
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def match_place(text: str):
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"""Canonical gazetteer key for a free-text place mention, or None."""
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if not text:
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return None
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m = _PLACE_RE.search(text.lower())
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if not m:
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return None
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k = m.group(1)
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return _PLACE_ALIASES.get(k, k)
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def places_in_text(text: str):
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"""All gazetteer places mentioned in the text, canonical, in order of appearance."""
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if not text:
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return []
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return [_PLACE_ALIASES.get(k, k) for k in _PLACE_RE.findall(text.lower())]
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def geography_block(place_key: str) -> str:
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"""Precomputed distance table from the caller's area to every office, closest
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first — injected into the system prompt so distance / nearest-office answers are
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read, never guessed. Returns "" for an unknown place."""
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coords = GAZETTEER.get(place_key)
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if not coords:
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return ""
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pretty = place_key.title()
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ranked = sorted(
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((l["city"], _road_miles(coords, l["latlon"])) for l in LOCATIONS),
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key=lambda x: x[1],
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)
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table = "; ".join(
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f"{city} about {mi} miles" + (" (CLOSEST)" if i == 0 else "")
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for i, (city, mi) in enumerate(ranked)
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)
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home = _OFFICE_FOR_KEY.get(place_key)
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if home:
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situated = f"We have an office right in {pretty} (the {home} office)."
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else:
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situated = (f"We do NOT have an office in {pretty}; the closest is "
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f"{ranked[0][0]}, about {ranked[0][1]} miles away.")
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return (
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f"GEOGRAPHY (precomputed, approximate driving distances from {pretty} — the ONLY "
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f"source you may use for any distance or closest-office answer; NEVER estimate a "
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f"distance yourself and NEVER confirm an office at any place not in PRACTICE FACTS):\n"
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f"- {situated}\n"
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f"- From {pretty}: {table}."
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)
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# ─── Tools (used when ENABLE_TOOLS=true and the model supports tool-calling) ──
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def persist_appointment(record: dict) -> str:
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"""Write an appointment request to Odoo (a crm.lead) if configured, else to the
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JSONL fallback so a request is never lost. Returns where it landed. Used by both
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the post-call extraction and the (optional) in-call tool."""
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record.setdefault("ts", datetime.now(timezone.utc).isoformat())
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kind = record.get("kind", "appointment")
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if os.environ.get("ODOO_USER") and os.environ.get("ODOO_API_KEY"):
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try:
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from odoo_client import create_appointment_request
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if kind == "callback":
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reason = record.get("reason") or "callback request"
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else:
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reason = f"[{record.get('location') or 'location TBD'}] {record.get('reason') or ''}".strip()
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model, rec_id = create_appointment_request(
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patient_name=record.get("patient_name"),
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callback_number=record.get("callback_number"),
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reason=reason,
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preferred_time=record.get("preferred_time"),
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insurance=record.get("insurance"),
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call_sid=record.get("call_sid"),
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kind=kind,
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)
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logger.info(f"{kind.capitalize()} -> Odoo {model} id={rec_id}: {record.get('patient_name')}")
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return f"odoo:{model}:{rec_id}"
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except Exception as e:
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logger.warning(f"Odoo write failed ({e!r}); falling back to local log")
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record["odoo_error"] = repr(e)
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with open(REQUESTS_LOG, "a") as fh:
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fh.write(json.dumps(record) + "\n")
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logger.info(f"{kind.capitalize()} -> JSONL: {record.get('patient_name')}")
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return "jsonl"
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async def record_appointment_request(params):
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"""In-call tool path (only used when ENABLE_TOOLS=true). Wraps persist_appointment."""
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args = params.arguments or {}
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persist_appointment({
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"call_sid": getattr(params, "call_sid", None),
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"patient_name": args.get("patient_name"),
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"callback_number": args.get("callback_number"),
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"location": args.get("location"),
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"reason": args.get("reason"),
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"preferred_time": args.get("preferred_time"),
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"source": "in_call_tool",
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})
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await params.result_callback(
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{"status": "captured", "message": "Got it — our staff will call you back to confirm the time."}
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)
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async def get_practice_info(params):
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"""Return practice facts (optionally narrowed to one location) for accurate answers."""
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args = params.arguments or {}
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loc = _find_location(args.get("location", ""))
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result = {
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"name": PRACTICE_FACTS["name"],
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"insurance": PRACTICE_FACTS["insurance"],
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"services": PRACTICE_FACTS["services"],
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"hours": "not published — offer to have the office confirm",
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}
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result["location"] = loc if loc else PRACTICE_FACTS["locations"]
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await params.result_callback(result)
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