mirror of
https://github.com/dataforcanada/d4c-service-geo-assistant.git
synced 2026-06-13 22:41:01 +02:00
remove old code
This commit is contained in:
Martha
@@ -86,242 +86,3 @@ def get_place(
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},
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)
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# @tool
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# def get_overture_locations(
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# area_of_interest: Feature,
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# place_name: Optional[str] = None,
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# place_type: Optional[str] = None,
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# overture_release: str = "2024-11-13.0",
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# similarity_threshold: float = 0.6,
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# tool_call_id: Annotated[str, InjectedToolCallId] = "",
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# ) -> Command:
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# """
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# Get locations from Overture Maps.
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# Parameters
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# ----------
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# area_of_interest : Feature
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# Area of interest to search for locations in
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# place_name : str, optional
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# Name of the place to search for
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# place_type : str, optional
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# Type of the place to search for
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# overture_release : str
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# Overture Maps release version
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# similarity_threshold : float
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# Minimum similarity score (0-1) for fuzzy name matching
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# tool_call_id : str
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# Tool call ID
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# Returns
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# -------
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# Command
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# Command that updates state with location features
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# """
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# con = duckdb.connect()
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# con.execute("INSTALL spatial;")
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# con.execute("LOAD spatial;")
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# con.execute("INSTALL httpfs;")
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# con.execute("LOAD httpfs;")
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# con.execute(
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# """
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# CREATE OR REPLACE TABLE aoi AS
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# SELECT ST_GeomFromGeoJSON(?) AS geom
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# """,
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# [area_of_interest.geometry.model_dump_json()],
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# )
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# base_url = f"s3://overturemaps-us-west-2/release/{overture_release}/theme=places/type=place/*"
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# where_conditions = ["ST_Within(ST_GeomFromWKB(geometry), (SELECT geom FROM aoi))"]
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# if place_type:
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# where_conditions.append(f"categories.primary = '{place_type}'")
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# if place_name:
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# where_conditions.append(
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# f"jaro_winkler_similarity(LOWER(names.primary), LOWER('{place_name}')) >= {similarity_threshold}"
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# )
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# where_clause = " AND ".join(where_conditions)
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# query = f"""
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# SELECT
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# id,
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# ST_AsText(ST_GeomFromWKB(geometry)) as geometry_wkt,
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# names.primary as name,
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# categories.primary as primary_category,
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# confidence,
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# websites,
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# phones,
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# addresses
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# FROM read_parquet('{base_url}', filename=true, hive_partitioning=1)
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# WHERE {where_clause}
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# """
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# result = con.execute(query).fetchall()
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# columns = [desc[0] for desc in con.description]
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# locations = [dict(zip(columns, row)) for row in result]
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# # Convert locations to GeoJSON Features
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# features = []
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# for loc in locations:
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# # Parse WKT geometry to GeoJSON
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# geom_wkt = loc.get("geometry_wkt")
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# if geom_wkt:
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# shapely_geom = wkt.loads(geom_wkt)
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# geom_dict = mapping(shapely_geom)
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# # Create properties from location data
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# properties = {
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# "id": loc.get("id"),
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# "name": loc.get("name"),
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# "primary_category": loc.get("primary_category"),
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# "confidence": loc.get("confidence"),
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# "websites": loc.get("websites"),
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# "phones": loc.get("phones"),
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# "addresses": loc.get("addresses"),
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# }
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# feature = Feature(geometry=geom_dict, properties=properties)
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# features.append(feature)
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# con.close()
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# tool_message = f"Found {len(features)} locations matching the criteria"
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# return Command(
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# update={
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# "features": features,
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# "messages": [ToolMessage(content=tool_message, tool_call_id=tool_call_id)],
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# },
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# )
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# @tool
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# def geocode_division(
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# query: str,
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# level: Optional[str] = None,
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# overture_release: str = "2024-11-13.0",
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# similarity_threshold: float = 0.6,
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# limit: int = 10,
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# tool_call_id: Annotated[str, InjectedToolCallId] = "",
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# ) -> Command:
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# """
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# Geocode a place name using Overture divisions data.
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# Parameters
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# ----------
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# query : str
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# Place name to search for (e.g., "San Francisco", "California", "United States")
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# level : str, optional
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# Division level to filter by. Options:
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# - 'country'
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# - 'region' (states, provinces)
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# - 'county' (counties, districts)
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# - 'locality' (cities, towns)
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# - 'localadmin' (local administrative areas)
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# - 'neighborhood'
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# overture_release : str
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# Overture Maps release version
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# similarity_threshold : float
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# Minimum similarity score (0-1) for fuzzy name matching
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# limit : int
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# Maximum number of results to return
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# Returns
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# -------
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# Command
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# Command that updates state with division features
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# """
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# con = duckdb.connect()
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# con.execute("INSTALL spatial;")
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# con.execute("LOAD spatial;")
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# con.execute("INSTALL httpfs;")
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# con.execute("LOAD httpfs;")
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# base_url = f"s3://overturemaps-us-west-2/release/{overture_release}/theme=divisions/type=division/*"
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# where_conditions = [
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# f"jaro_winkler_similarity(LOWER(names.primary), LOWER('{query}')) >= {similarity_threshold}"
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# ]
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# if level:
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# where_conditions.append(f"subtype = '{level}'")
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# where_clause = " AND ".join(where_conditions)
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# query_sql = f"""
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# SELECT
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# id,
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# ST_AsText(ST_GeomFromWKB(geometry)) as geometry_wkt,
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# names.primary as name,
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# names.common as common_names,
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# subtype as division_level,
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# country,
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# region,
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# hierarchies,
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# population,
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# capital,
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# wikidata,
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# sources,
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# jaro_winkler_similarity(LOWER(names.primary), LOWER('{query}')) as similarity_score
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# FROM read_parquet('{base_url}', filename=true, hive_partitioning=1)
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# WHERE {where_clause}
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# ORDER BY similarity_score DESC
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# LIMIT {limit}
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# """
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# result = con.execute(query_sql).fetchall()
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# columns = [desc[0] for desc in con.description]
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# divisions = [dict(zip(columns, row)) for row in result]
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# # Convert divisions to GeoJSON Features
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# features = []
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# for div in divisions:
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# # Parse WKT geometry to GeoJSON
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# geom_wkt = div.get("geometry_wkt")
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# if geom_wkt:
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# shapely_geom = wkt.loads(geom_wkt)
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# geom_dict = mapping(shapely_geom)
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# # Create properties from division data
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# properties = {
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# "id": div.get("id"),
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# "name": div.get("name"),
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# "common_names": div.get("common_names"),
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# "division_level": div.get("division_level"),
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# "country": div.get("country"),
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# "region": div.get("region"),
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# "hierarchies": div.get("hierarchies"),
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# "population": div.get("population"),
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# "capital": div.get("capital"),
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# "wikidata": div.get("wikidata"),
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# "sources": div.get("sources"),
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# "similarity_score": div.get("similarity_score"),
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# }
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# feature = Feature(geometry=geom_dict, properties=properties)
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# features.append(feature)
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# con.close()
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# tool_message = f"Found {len(features)} divisions matching '{query}'"
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# return Command(
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# update={
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# "features": features,
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# "messages": [ToolMessage(content=tool_message, tool_call_id=tool_call_id)],
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# },
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# )
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