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from dataclasses import dataclass, field
from typing import List, Optional
@dataclass
class ParsedIntent:
intent_type: str
crop: Optional[str] = None
pest: Optional[str] = None
disease: Optional[str] = None
climate_conditions: List[str] = field(default_factory=list)
soil_type: Optional[str] = None
pesticide: Optional[str] = None
language_hint: str = "en"
class IntentParser:
"""Simple rule-based parser for agricultural advisory questions."""
def __init__(self, kg_builder):
self.kg = kg_builder
self.intent_patterns = {
"crop_risk_assessment": [
r"risk.*(?:for|in)\s+(\w+)",
r"what.*(?:pest|disease).*(?:in|for)\s+(\w+)",
r"alerts?.*(?:for|in)\s+(\w+)",
],
"pest_management": [
r"how.*control\s+(.+)",
r"manage\s+(.+)",
r"treatment.*(?:for)?\s+(.+)",
r"spray.*(?:for)?\s+(.+)",
],
"disease_management": [
r"how.*treat\s+(.+)",
r"disease.*(?:in|for)\s+(\w+)",
r"fungicide.*(?:for)?\s+(.+)",
],
"climate_alert": [
r"(?:rain|humid|temperature|weather).*(?:risk|alert|issue)",
r"current weather.*(?:what|which).*(?:pest|disease)",
r"hot and humid",
r"high humidity",
],
"mix_safety": [
r"mix\s+(.+)\s+(?:and|with)\s+(.+)",
r"tank mix",
r"compatible\s+with",
],
"soil_compatibility": [
r"soil.*(?:safe|compatib|suitable)",
r"(?:alkaline|acidic|black|red|sandy|clay).*(?:soil)",
],
}
self.climate_keywords = {
"high_humidity": ["high humidity", "humid", "moist"],
"moderate_humidity": ["moderate humidity"],
"low_humidity": ["low humidity", "dry air"],
"high_temperature": ["high temp", "hot", "heat"],
"moderate_temperature": ["moderate temp", "warm"],
"low_temperature": ["low temp", "cold", "cool"],
"heavy_rain": ["heavy rain", "raining", "rainy", "monsoon", "downpour"],
"drizzle": ["drizzle", "light rain"],
"drought": ["drought", "water stress", "very dry"],
"windy": ["windy", "high wind", "strong wind"],
"cloudy": ["cloudy", "overcast"],
}
self.soil_keywords = {
"alkaline_soil": ["alkaline soil", "high ph soil", "basic soil"],
"acidic_soil": ["acidic soil", "low ph soil", "sour soil"],
"black_soil": ["black soil", "regur"],
"red_soil": ["red soil"],
"sandy_soil": ["sandy soil", "sand soil"],
"clay_soil": ["clay soil", "heavy soil"],
"loamy_soil": ["loamy soil", "loam"],
"waterlogged": ["waterlogged", "water logging"],
}
def parse(self, user_query: str) -> ParsedIntent:
q = (user_query or "").strip().lower()
language_hint = self._detect_language(q)
intent_type = self._classify_intent(q)
crop = self._extract_crop(q)
pest = self._extract_entity(q, node_type="pest")
disease = self._extract_entity(q, node_type="disease")
pesticides = self._extract_entities(q, node_type="pesticide")
pesticide = None
if pesticides:
if intent_type == "mix_safety" and len(pesticides) >= 2:
pesticide = ",".join(pesticides[:2])
else:
pesticide = pesticides[0]
climate_conditions = self._extract_climate_conditions(q)
soil_type = self._extract_soil_type(q)
return ParsedIntent(
intent_type=intent_type,
crop=crop,
pest=pest,
disease=disease,
climate_conditions=climate_conditions,
soil_type=soil_type,
pesticide=pesticide,
language_hint=language_hint,
)
def _detect_language(self, q: str) -> str:
devanagari_range = re.compile(r"[\u0900-\u097F]")
return "hi" if devanagari_range.search(q) else "en"
def _classify_intent(self, q: str) -> str:
scores = {k: 0 for k in self.intent_patterns.keys()}
for intent, patterns in self.intent_patterns.items():
for pat in patterns:
if re.search(pat, q):
scores[intent] += 2
if any(k in q for k in ["weather", "rain", "humidity", "humid", "temperature", "hot", "cold"]):
scores["climate_alert"] += 1
if any(k in q for k in ["mix", "compatible", "tank"]):
scores["mix_safety"] += 1
if any(k in q for k in ["soil", "ph", "alkaline", "acidic", "sandy", "clay"]):
scores["soil_compatibility"] += 1
if any(k in q for k in ["pest", "insect", "worm", "borer", "aphid"]):
scores["pest_management"] += 1
if any(k in q for k in ["disease", "fungus", "blight", "rot", "mildew", "rust"]):
scores["disease_management"] += 1
best = max(scores.items(), key=lambda x: x[1])
if best[1] == 0:
return "general_advisory"
if best[0] in ["pest_management", "disease_management"]:
if any(k in q for k in ["risk", "alert", "forecast", "current"]):
return "crop_risk_assessment"
return best[0]
def _extract_crop(self, q: str) -> Optional[str]:
for node_id, attrs in self.kg.G.nodes(data=True):
if attrs.get("node_type") != "crop":
continue
name_en = (attrs.get("name_en") or "").lower()
name_hi = (attrs.get("name_hi") or "").lower()
if name_en and name_en in q:
return node_id
if name_hi and name_hi in q:
return node_id
token_candidates = re.findall(r"\b[a-z][a-z_]{2,}\b", q)
for token in token_candidates:
node_id = self.kg.resolve_node(token)
if node_id and self.kg.G.nodes[node_id].get("node_type") == "crop":
return node_id
return None
def _extract_entity(self, q: str, node_type: str) -> Optional[str]:
best_match = None
best_len = 0
for node_id, attrs in self.kg.G.nodes(data=True):
if attrs.get("node_type") != node_type:
continue
names = [
(attrs.get("name_en") or "").lower(),
(attrs.get("name_hi") or "").lower(),
(attrs.get("scientific_name") or "").lower(),
]
for n in names:
if n and n in q and len(n) > best_len:
best_match = node_id
best_len = len(n)
if best_match:
return best_match
token_candidates = re.findall(r"\b[a-z][a-z_]{2,}\b", q)
for token in token_candidates:
node_id = self.kg.resolve_node(token)
if node_id and self.kg.G.nodes[node_id].get("node_type") == node_type:
return node_id
return None
def _extract_entities(self, q: str, node_type: str) -> List[str]:
matches = []
for node_id, attrs in self.kg.G.nodes(data=True):
if attrs.get("node_type") != node_type:
continue
names = [
(attrs.get("name_en") or "").lower(),
(attrs.get("name_hi") or "").lower(),
(attrs.get("scientific_name") or "").lower(),
]
if any(n and n in q for n in names):
matches.append(node_id)
# Keep insertion order while deduplicating.
deduped = []
for m in matches:
if m not in deduped:
deduped.append(m)
if deduped:
return deduped
# Fallback token-wise fuzzy resolution.
token_candidates = re.findall(r"\b[a-z][a-z_]{2,}\b", q)
for token in token_candidates:
node_id = self.kg.resolve_node(token)
if node_id and self.kg.G.nodes[node_id].get("node_type") == node_type and node_id not in deduped:
deduped.append(node_id)
return deduped
def _extract_climate_conditions(self, q: str) -> List[str]:
found = []
for climate_id, keywords in self.climate_keywords.items():
for kw in keywords:
if kw in q:
found.append(climate_id)
break
if "temperature" in q and not any(c.endswith("temperature") for c in found):
nums = re.findall(r"(\d{2})\s*\D*c", q)
if nums:
try:
t = int(nums[0])
if t >= 33:
found.append("high_temperature")
elif t <= 18:
found.append("low_temperature")
else:
found.append("moderate_temperature")
except Exception:
pass
if "humidity" in q and not any(c.endswith("humidity") for c in found):
nums = re.findall(r"(\d{2,3})\s*%", q)
if nums:
try:
h = int(nums[0])
if h >= 80:
found.append("high_humidity")
elif h <= 40:
found.append("low_humidity")
else:
found.append("moderate_humidity")
except Exception:
pass
uniq = []
for c in found:
if c not in uniq:
uniq.append(c)
return uniq
def _extract_soil_type(self, q: str) -> Optional[str]:
for soil_id, keywords in self.soil_keywords.items():
for kw in keywords:
if kw in q:
return soil_id
node_id = self.kg.resolve_node(q)
if node_id and self.kg.G.nodes[node_id].get("node_type") == "soil_type":
return node_id
return None
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