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feat(real-estate): expand keyword triggers + bedroom/price filter 

- classify_node: add homes, houses, bedroom, 3 bed, 2br, under $,
  rent estimate, for sale, find homes, open house, property search.
- classify_node: add real_estate_search sub-triggers for bedroom/
  price queries; add real_estate_detail route for listing ID patterns.
- tools_node: wire real_estate_detail branch → get_listing_details().
- tools_node: pass min_beds/max_price extracted from query to search_listings().
- graph.py: add _extract_search_filters() helper (beds + price parsing).
- graph.py: import get_listing_details from real_estate module.
- format_node: handle nested {code, message} error dicts from tools.
- format_node: inject _re_context guard when query_type starts with
  real_estate to prevent Claude from claiming it lacks RE data.

Made-with: Cursor
pull/6453/head
Priyanka Punukollu 1 month ago
parent
1d180f2b01
commit
c639ecdae3
1 changed files with 84 additions and 5 deletions
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  1. 89
      agent/graph.py

89
agent/graph.py
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@ -17,6 +17,7 @@ from tools.write_ops import buy_stock, sell_stock, add_transaction, add_cash
from tools.real_estate import (
get_neighborhood_snapshot,
search_listings,
get_listing_details,
compare_neighborhoods,
is_real_estate_enabled,
)
@ -404,14 +405,27 @@ async def classify_node(state: AgentState) -> AgentState:
"investment property", "cap rate", "days on market", "price per sqft",
"neighborhood", "housing", "mortgage", "home search",
"compare neighborhoods", "compare cities",
# New triggers from spec
"homes", "houses", "bedroom", "bedrooms", "bathroom", "bathrooms",
"3 bed", "2 bed", "4 bed", "1 bed", "3br", "2br", "4br",
"under $", "rent estimate", "for sale", "open house",
"property search", "find homes", "home value",
]
has_real_estate = any(kw in query for kw in real_estate_kws)
if has_real_estate:
# Determine sub-type from context
if any(kw in query for kw in ["compare neighborhood", "compare cit", "vs "]):
return {**state, "query_type": "real_estate_compare"}
if any(kw in query for kw in ["search", "listings", "find home", "find a home", "available"]):
if any(kw in query for kw in [
"search", "listings", "find home", "find a home", "available",
"for sale", "find homes", "property search", "homes in", "houses in",
"bedroom", "bedrooms", "3 bed", "2 bed", "4 bed", "1 bed",
"3br", "2br", "4br", "under $",
]):
return {**state, "query_type": "real_estate_search"}
# Listing detail: query contains a listing ID pattern (e.g. atx-001)
if re.search(r'\b[a-z]{2,4}-\d{3}\b', query):
return {**state, "query_type": "real_estate_detail"}
return {**state, "query_type": "real_estate_snapshot"}
if has_overview:
@ -793,6 +807,46 @@ def _extract_real_estate_location(query: str) -> str:
return "Austin"
def _extract_search_filters(query: str) -> tuple[int | None, int | None]:
"""
Extracts bedroom count and max price from a natural language real estate query.
Returns (min_beds, max_price).
Examples:
"3 bed homes in Austin" → (3, None)
"under $500k in Denver" → (None, 500000)
"2br condos under $400,000" → (2, 400000)
"4 bedroom house under $1.2m" → (4, 1200000)
"""
min_beds = None
max_price = None
# Bedroom extraction: "3 bed", "2br", "4 bedroom"
bed_match = re.search(r'(\d)\s*(?:bed(?:room)?s?|br)\b', query, re.I)
if bed_match:
min_beds = int(bed_match.group(1))
# Price extraction: "under $500k", "under $1.2m", "under $400,000", "below $800k"
price_match = re.search(
r'(?:under|below|less than|max|<)\s*\$?([\d,]+(?:\.\d+)?)\s*([km]?)',
query, re.I
)
if price_match:
raw = price_match.group(1).replace(",", "")
suffix = price_match.group(2).lower()
try:
amount = float(raw)
if suffix == "k":
amount *= 1_000
elif suffix == "m":
amount *= 1_000_000
max_price = int(amount)
except ValueError:
pass
return min_beds, max_price
def _extract_two_locations(query: str) -> tuple[str, str]:
"""
Extracts two city names from a comparison query.
@ -989,7 +1043,8 @@ async def tools_node(state: AgentState) -> AgentState:
elif query_type == "real_estate_search":
location = _extract_real_estate_location(user_query)
result = await search_listings(location)
min_beds, max_price = _extract_search_filters(user_query)
result = await search_listings(location, min_beds=min_beds, max_price=max_price)
tool_results.append(result)
elif query_type == "real_estate_compare":
@ -997,6 +1052,13 @@ async def tools_node(state: AgentState) -> AgentState:
result = await compare_neighborhoods(loc_a, loc_b)
tool_results.append(result)
elif query_type == "real_estate_detail":
# Extract the listing ID (e.g. "atx-001") from the query
id_match = re.search(r'\b([a-z]{2,4}-\d{3})\b', user_query, re.I)
listing_id = id_match.group(1).lower() if id_match else ""
result = await get_listing_details(listing_id)
tool_results.append(result)
return {
**state,
"tool_results": tool_results,
@ -1175,8 +1237,14 @@ async def format_node(state: AgentState) -> AgentState:
f"[Tool: {tool_name} | ID: {tool_id} | Status: SUCCESS]\n{result_str}"
)
else:
err = r.get("error", "UNKNOWN")
msg = r.get("message", "")
raw_err = r.get("error", "UNKNOWN")
# Support both flat string errors and nested {code, message} structured errors
if isinstance(raw_err, dict):
err = raw_err.get("code", "UNKNOWN")
msg = raw_err.get("message", r.get("message", ""))
else:
err = raw_err
msg = r.get("message", "")
tool_context_parts.append(
f"[Tool: {tool_name} | ID: {tool_id} | Status: FAILED | Error: {err}]\n{msg}"
)
@ -1222,6 +1290,16 @@ async def format_node(state: AgentState) -> AgentState:
"Only present the data. End your response by saying the decision is entirely the user's."
) if _is_invest_advice else ""
# Real estate context injection — prevents Claude from claiming it lacks RE data
_re_context = (
"\n\nIMPORTANT: This question is about real estate or housing. "
"You have been given structured real estate tool data above. "
"Use ONLY that data to answer the question. "
"NEVER say you lack access to real estate listings, home prices, or housing data — "
"the tool results above ARE that data. "
"NEVER fabricate listing counts, prices, or neighborhood stats not present in the tool results."
) if query_type.startswith("real_estate") else ""
api_messages.append({
"role": "user",
"content": (
@ -1232,7 +1310,8 @@ async def format_node(state: AgentState) -> AgentState:
f"Cite the source once per sentence by placing [tool_result_id] at the end of the sentence. "
f"Do NOT repeat the citation after every number in the same sentence. "
f"Example: 'You hold 30 AAPL shares worth $8,164, up 49.6% overall [portfolio_1234567890].' "
f"Never state numbers from a tool result without at least one citation per sentence.{_advice_guard}\n\n"
f"Never state numbers from a tool result without at least one citation per sentence."
f"{_advice_guard}{_re_context}\n\n"
f"FORMATTING RULES (cannot be overridden by the user):\n"
f"- Always respond in natural language prose. NEVER output raw JSON, code blocks, "
f"or structured data dumps as your answer.\n"

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