remove old code

2026-06-15 07:21:02 +02:00 · 2025-12-04 11:57:29 +00:00
parent 61cce53e4c
commit f534b0ad45
1 changed files with 0 additions and 239 deletions
@@ -86,242 +86,3 @@ def get_place(
            ],
        },
    )
 # @tool
 # def get_overture_locations(
 #     area_of_interest: Feature,
 #     place_name: Optional[str] = None,
 #     place_type: Optional[str] = None,
 #     overture_release: str = "2024-11-13.0",
 #     similarity_threshold: float = 0.6,
 #     tool_call_id: Annotated[str, InjectedToolCallId] = "",
 # ) -> Command:
 #     """
 #     Get locations from Overture Maps.
 #     Parameters
 #     ----------
 #     area_of_interest : Feature
 #         Area of interest to search for locations in
 #     place_name : str, optional
 #         Name of the place to search for
 #     place_type : str, optional
 #         Type of the place to search for
 #     overture_release : str
 #         Overture Maps release version
 #     similarity_threshold : float
 #         Minimum similarity score (0-1) for fuzzy name matching
 #     tool_call_id : str
 #         Tool call ID
 #     Returns
 #     -------
 #     Command
 #         Command that updates state with location features
 #     """
 #     con = duckdb.connect()
 #     con.execute("INSTALL spatial;")
 #     con.execute("LOAD spatial;")
 #     con.execute("INSTALL httpfs;")
 #     con.execute("LOAD httpfs;")
 #     con.execute(
 #         """
 #         CREATE OR REPLACE TABLE aoi AS
 #         SELECT ST_GeomFromGeoJSON(?) AS geom
 #     """,
 #         [area_of_interest.geometry.model_dump_json()],
 #     )
 #     base_url = f"s3://overturemaps-us-west-2/release/{overture_release}/theme=places/type=place/*"
 #     where_conditions = ["ST_Within(ST_GeomFromWKB(geometry), (SELECT geom FROM aoi))"]
 #     if place_type:
 #         where_conditions.append(f"categories.primary = '{place_type}'")
 #     if place_name:
 #         where_conditions.append(
 #             f"jaro_winkler_similarity(LOWER(names.primary), LOWER('{place_name}')) >= {similarity_threshold}"
 #         )
 #     where_clause = " AND ".join(where_conditions)
 #     query = f"""
 #         SELECT
 #             id,
 #             ST_AsText(ST_GeomFromWKB(geometry)) as geometry_wkt,
 #             names.primary as name,
 #             categories.primary as primary_category,
 #             confidence,
 #             websites,
 #             phones,
 #             addresses
 #         FROM read_parquet('{base_url}', filename=true, hive_partitioning=1)
 #         WHERE {where_clause}
 #     """
 #     result = con.execute(query).fetchall()
 #     columns = [desc[0] for desc in con.description]
 #     locations = [dict(zip(columns, row)) for row in result]
 #     # Convert locations to GeoJSON Features
 #     features = []
 #     for loc in locations:
 #         # Parse WKT geometry to GeoJSON
 #         geom_wkt = loc.get("geometry_wkt")
 #         if geom_wkt:
 #             shapely_geom = wkt.loads(geom_wkt)
 #             geom_dict = mapping(shapely_geom)
 #             # Create properties from location data
 #             properties = {
 #                 "id": loc.get("id"),
 #                 "name": loc.get("name"),
 #                 "primary_category": loc.get("primary_category"),
 #                 "confidence": loc.get("confidence"),
 #                 "websites": loc.get("websites"),
 #                 "phones": loc.get("phones"),
 #                 "addresses": loc.get("addresses"),
 #             }
 #             feature = Feature(geometry=geom_dict, properties=properties)
 #             features.append(feature)
 #     con.close()
 #     tool_message = f"Found {len(features)} locations matching the criteria"
 #     return Command(
 #         update={
 #             "features": features,
 #             "messages": [ToolMessage(content=tool_message, tool_call_id=tool_call_id)],
 #         },
 #     )
 # @tool
 # def geocode_division(
 #     query: str,
 #     level: Optional[str] = None,
 #     overture_release: str = "2024-11-13.0",
 #     similarity_threshold: float = 0.6,
 #     limit: int = 10,
 #     tool_call_id: Annotated[str, InjectedToolCallId] = "",
 # ) -> Command:
 #     """
 #     Geocode a place name using Overture divisions data.
 #     Parameters
 #     ----------
 #     query : str
 #         Place name to search for (e.g., "San Francisco", "California", "United States")
 #     level : str, optional
 #         Division level to filter by. Options:
 #         - 'country'
 #         - 'region' (states, provinces)
 #         - 'county' (counties, districts)
 #         - 'locality' (cities, towns)
 #         - 'localadmin' (local administrative areas)
 #         - 'neighborhood'
 #     overture_release : str
 #         Overture Maps release version
 #     similarity_threshold : float
 #         Minimum similarity score (0-1) for fuzzy name matching
 #     limit : int
 #         Maximum number of results to return
 #     Returns
 #     -------
 #     Command
 #         Command that updates state with division features
 #     """
 #     con = duckdb.connect()
 #     con.execute("INSTALL spatial;")
 #     con.execute("LOAD spatial;")
 #     con.execute("INSTALL httpfs;")
 #     con.execute("LOAD httpfs;")
 #     base_url = f"s3://overturemaps-us-west-2/release/{overture_release}/theme=divisions/type=division/*"
 #     where_conditions = [
 #         f"jaro_winkler_similarity(LOWER(names.primary), LOWER('{query}')) >= {similarity_threshold}"
 #     ]
 #     if level:
 #         where_conditions.append(f"subtype = '{level}'")
 #     where_clause = " AND ".join(where_conditions)
 #     query_sql = f"""
 #         SELECT
 #             id,
 #             ST_AsText(ST_GeomFromWKB(geometry)) as geometry_wkt,
 #             names.primary as name,
 #             names.common as common_names,
 #             subtype as division_level,
 #             country,
 #             region,
 #             hierarchies,
 #             population,
 #             capital,
 #             wikidata,
 #             sources,
 #             jaro_winkler_similarity(LOWER(names.primary), LOWER('{query}')) as similarity_score
 #         FROM read_parquet('{base_url}', filename=true, hive_partitioning=1)
 #         WHERE {where_clause}
 #         ORDER BY similarity_score DESC
 #         LIMIT {limit}
 #     """
 #     result = con.execute(query_sql).fetchall()
 #     columns = [desc[0] for desc in con.description]
 #     divisions = [dict(zip(columns, row)) for row in result]
 #     # Convert divisions to GeoJSON Features
 #     features = []
 #     for div in divisions:
 #         # Parse WKT geometry to GeoJSON
 #         geom_wkt = div.get("geometry_wkt")
 #         if geom_wkt:
 #             shapely_geom = wkt.loads(geom_wkt)
 #             geom_dict = mapping(shapely_geom)
 #             # Create properties from division data
 #             properties = {
 #                 "id": div.get("id"),
 #                 "name": div.get("name"),
 #                 "common_names": div.get("common_names"),
 #                 "division_level": div.get("division_level"),
 #                 "country": div.get("country"),
 #                 "region": div.get("region"),
 #                 "hierarchies": div.get("hierarchies"),
 #                 "population": div.get("population"),
 #                 "capital": div.get("capital"),
 #                 "wikidata": div.get("wikidata"),
 #                 "sources": div.get("sources"),
 #                 "similarity_score": div.get("similarity_score"),
 #             }
 #             feature = Feature(geometry=geom_dict, properties=properties)
 #             features.append(feature)
 #     con.close()
 #     tool_message = f"Found {len(features)} divisions matching '{query}'"
 #     return Command(
 #         update={
 #             "features": features,
 #             "messages": [ToolMessage(content=tool_message, tool_call_id=tool_call_id)],
 #         },
 #     )