""" Intent parser for Simu — MSKit's AI assistant. Parses natural language into structured simulation requests. Two-tier approach: 1. Rule-based fast parser (zero-latency, no model needed) 2. LLM-augmented extraction (SmolLM2, used for ambiguous inputs) Intent schema: { "sim_type": "random_walk" | "projectile" | "water_flow" | "agent" | "traffic" | "profile", "location": {"lat": float, "lon": float, "name": str}, "params": { ... sim-specific params ... }, "confidence": float, # 0.0–1.0 "raw": str, # original user text "explanation": str, # what Simu understood } """ from __future__ import annotations import re import math from typing import Any, Dict, List, Optional, Tuple # --------------------------------------------------------------------------- # Well-known city coordinates # --------------------------------------------------------------------------- CITIES: Dict[str, Tuple[float, float]] = { "tokyo": (35.6762, 139.6503), "london": (51.5074, -0.1278), "paris": (48.8566, 2.3522), "new york": (40.7128, -74.0060), "new york city": (40.7128, -74.0060), "nyc": (40.7128, -74.0060), "los angeles": (34.0522, -118.2437), "la": (34.0522, -118.2437), "sydney": (-33.8688, 151.2093), "melbourne": (-37.8136, 144.9631), "singapore": (1.3521, 103.8198), "hong kong": (22.3193, 114.1694), "dubai": (25.2048, 55.2708), "zurich": (47.3769, 8.5417), "berlin": (52.5200, 13.4050), "madrid": (40.4168, -3.7038), "munich": (48.1351, 11.5820), "hamburg": (53.5753, 9.9935), "toronto": (43.6532, -79.3832), "chicago": (41.8781, -87.6298), "seattle": (47.6062, -122.3321), "san francisco": (37.7749, -122.4194), "sf": (37.7749, -122.4194), "boston": (42.3601, -71.0589), "seoul": (37.5665, 127.0000), "beijing": (39.9042, 116.4074), "shanghai": (31.2304, 121.4737), "bangkok": (13.7563, 100.5018), "jakarta": (-6.2088, 106.8456), "mumbai": (19.0760, 72.8777), "delhi": (28.6139, 77.2090), "cairo": (30.0444, 31.2357), "nairobi": (-1.2921, 36.8219), "johannesburg": (-26.2041, 28.0473), "buenos aires": (-34.6037, -58.3816), "sao paulo": (-23.5505, -46.6333), "mexico city": (19.4326, -99.1332), "rome": (41.9028, 12.4964), "amsterdam": (52.3676, 4.9041), "stockholm": (59.3293, 18.0686), "oslo": (59.9139, 10.7522), "vienna": (48.2082, 16.3738), "prague": (50.0755, 14.4378), "warsaw": (52.2297, 21.0122), "istanbul": (41.0082, 28.9784), "moscow": (55.7558, 37.6173), "mount fuji": (35.3606, 138.7274), "fuji": (35.3606, 138.7274), "alps": (46.5000, 8.5000), "himalayas": (27.9881, 86.9250), "everest": (27.9881, 86.9250), "amazon": (-3.4653, -62.2159), "sahara": (23.4162, 25.6628), "grand canyon": (36.1069, -112.1129), "yellowstone": (44.4280, -110.5885), } # --------------------------------------------------------------------------- # Sim type keywords # --------------------------------------------------------------------------- SIM_PATTERNS = { "random_walk": [ r"\brandom\s*walk\b", r"\bwalk\b", r"\bwander\b", r"\bwandering\b", r"\bhiker\b", r"\bhike\b", r"\bstroll\b", r"\bwalk(?:ing)?\b", r"\bexplore\b", r"\bexploring\b", ], "projectile": [ r"\bprojectile\b", r"\bballist", r"\blaunch\b", r"\bthrow\b", r"\bshoot\b", r"\bfire\b", r"\bcannon\b", r"\bmissile\b", r"\btrajectory\b", r"\bdrop\b", r"\bcatapult\b", r"\brange\b", ], "water_flow": [ r"\bwater\b", r"\bflow\b", r"\briver\b", r"\bflood\b", r"\brunoff\b", r"\brain\b", r"\bstream\b", r"\bdrain(?:age)?\b", r"\bwaterfall\b", r"\bsimulate.*water\b", r"\bflood\b", ], "agent": [ r"\bagent\b", r"\bnavigate\b", r"\bnavigation\b", r"\bpath\b", r"\breinforcement\b", r"\brl\b", r"\bgo\s+from\b", r"\btravel\b", r"\broute.*agent\b", r"\bfind.*way\b", ], "traffic": [ r"\btraffic\b", r"\bcongestion\b", r"\bcommute\b", r"\bjam\b", r"\bspeed.*road\b", r"\broad.*speed\b", r"\bdriving\b", ], "profile": [ r"\bprofile\b", r"\bcross.?section\b", r"\belevation.*between\b", r"\bterrain.*between\b", r"\bheight.*profile\b", ], } # --------------------------------------------------------------------------- # Parameter patterns # --------------------------------------------------------------------------- _STEPS_RE = re.compile(r"(\d+)\s*steps?", re.I) _SPEED_RE = re.compile(r"(\d+(?:\.\d+)?)\s*(?:m/?s|meters?\s*per\s*second)", re.I) _KMH_RE = re.compile(r"(\d+(?:\.\d+)?)\s*km/?h", re.I) _ANGLE_RE = re.compile(r"(\d+(?:\.\d+)?)\s*(?:degree|°)\s*(?:angle|elevation)?", re.I) _AZIMUTH_RE = re.compile(r"(\d+(?:\.\d+)?)\s*(?:degree|°)?\s*(?:azimuth|bearing|heading)", re.I) _RADIUS_RE = re.compile(r"(\d+(?:\.\d+)?)\s*km\s*radius", re.I) _LAT_RE = re.compile(r"lat(?:itude)?\s*[=:]?\s*(-?\d+(?:\.\d+)?)", re.I) _LON_RE = re.compile(r"lon(?:gitude)?\s*[=:]?\s*(-?\d+(?:\.\d+)?)", re.I) _COORD_RE = re.compile(r"(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)") _TO_RE = re.compile(r"\bto\s+(-?\d+(?:\.\d+)?)\s*,\s*(-?\d+(?:\.\d+)?)", re.I) def _find_location(text: str) -> Optional[Dict[str, Any]]: """Extract a location from text. Returns dict with lat, lon, name.""" t = text.lower() # Explicit lat/lon lat_m = _LAT_RE.search(text) lon_m = _LON_RE.search(text) if lat_m and lon_m: return {"lat": float(lat_m.group(1)), "lon": float(lon_m.group(1)), "name": "custom"} # Bare coordinate pair "35.6, 139.7" coord_m = _COORD_RE.search(text) if coord_m: la, lo = float(coord_m.group(1)), float(coord_m.group(2)) if -90 <= la <= 90 and -180 <= lo <= 180: return {"lat": la, "lon": lo, "name": "custom"} # Named city — longest match wins matches = [(name, coords) for name, coords in CITIES.items() if name in t] if matches: best = max(matches, key=lambda x: len(x[0])) return {"lat": best[1][0], "lon": best[1][1], "name": best[0].title()} return None def _find_sim_type(text: str) -> Tuple[str, float]: """Return (sim_type, confidence).""" t = text.lower() scores: Dict[str, int] = {} for sim, patterns in SIM_PATTERNS.items(): score = sum(1 for p in patterns if re.search(p, t)) if score: scores[sim] = score if not scores: return "random_walk", 0.3 # default best = max(scores, key=scores.get) confidence = min(0.5 + scores[best] * 0.2, 1.0) return best, confidence def _extract_params(sim_type: str, text: str) -> Dict[str, Any]: """Extract simulation-specific parameters from text.""" params: Dict[str, Any] = {} steps_m = _STEPS_RE.search(text) if steps_m: params["steps"] = int(steps_m.group(1)) speed_m = _SPEED_RE.search(text) if speed_m: params["speed_ms"] = float(speed_m.group(1)) kmh_m = _KMH_RE.search(text) if kmh_m: params["speed_ms"] = float(kmh_m.group(1)) / 3.6 if sim_type == "projectile": angle_m = _ANGLE_RE.search(text) if angle_m: params["elevation_deg"] = float(angle_m.group(1)) az_m = _AZIMUTH_RE.search(text) if az_m: params["azimuth_deg"] = float(az_m.group(1)) # compass directions for direction, az in [("north", 0), ("northeast", 45), ("east", 90), ("southeast", 135), ("south", 180), ("southwest", 225), ("west", 270), ("northwest", 315)]: if direction in text.lower(): params.setdefault("azimuth_deg", az) if sim_type == "water_flow": r_m = _RADIUS_RE.search(text) if r_m: params["patch_km"] = float(r_m.group(1)) if sim_type == "random_walk": params.setdefault("steps", 500) if "steep" in text.lower() or "mountain" in text.lower(): params["slope_bias"] = 0.8 elif "flat" in text.lower() or "plain" in text.lower(): params["slope_bias"] = 0.1 # Two-point destination (for agent / profile) to_m = _TO_RE.search(text) if to_m: params["target_lat"] = float(to_m.group(1)) params["target_lon"] = float(to_m.group(2)) return params def parse_intent(text: str) -> Dict[str, Any]: """ Parse a natural language simulation request into a structured intent dict. Pure rule-based — instant, no model required. """ sim_type, confidence = _find_sim_type(text) location = _find_location(text) params = _extract_params(sim_type, text) # Default location if none found if location is None: location = {"lat": 35.6762, "lon": 139.6503, "name": "Tokyo (default)"} confidence *= 0.7 explanation = _explain(sim_type, location, params) return { "sim_type": sim_type, "location": location, "params": params, "confidence": round(confidence, 2), "raw": text, "explanation": explanation, } def _explain(sim_type: str, location: Dict, params: Dict) -> str: sim_labels = { "random_walk": "a slope-biased random walk", "projectile": "a ballistic trajectory", "water_flow": "a water runoff simulation", "agent": "a terrain-navigating AI agent episode", "traffic": "a traffic + congestion query", "profile": "an elevation profile transect", } label = sim_labels.get(sim_type, sim_type) loc = location["name"] parts = [f"Run {label} at {loc}"] if "steps" in params: parts.append(f"{params['steps']} steps") if "speed_ms" in params: parts.append(f"{params['speed_ms']*3.6:.0f} km/h") if "elevation_deg" in params: parts.append(f"{params['elevation_deg']}° launch angle") if "azimuth_deg" in params: parts.append(f"{params['azimuth_deg']}° bearing") return ", ".join(parts) + "."