112 lines
3.6 KiB
Julia
112 lines
3.6 KiB
Julia
using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames, DataStructures
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using GeneralUtils, SQLLLM, YiemAgent
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config = JSON.parsefile("./appconfig.json")
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host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
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port = parse(Int, _port)
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dbname = "winedb"
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user = config["externalservice"]["sommpanion_db"]["user"]
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password = config["externalservice"]["sommpanion_db"]["password"]
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pg_conn_str = "host=$host_url port=$port dbname=$dbname user=$user password=$password"
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function execute_sql_winedb(sql::T) where {T<:AbstractString}
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host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
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port = parse(Int, _port)
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dbname = "winedb"
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user = config["externalservice"]["sommpanion_db"]["user"]
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password = config["externalservice"]["sommpanion_db"]["password"]
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db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
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result = nothing
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try
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result = LibPQ.execute(db_connection, sql)
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catch e
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LibPQ.close(db_connection)
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end
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LibPQ.close(db_connection)
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return result
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end
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sql =
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"""
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SELECT T1.winery, T1.wine_name, T1.wine_id, T1.vintage, T1.region, T1.country, T1.wine_type, T1.grape, T1.serving_temperature, T1.sweetness, T1.intensity, T1.tannin, T1.acidity, T1.tasting_notes, T2.price, T2.currency, T1.image_url, T3.retailer_name, T3.retailer_id FROM "wine" AS T1 JOIN "retailer_wine" AS T2 ON T1.wine_id = T2.wine_id JOIN "retailer" AS T3 ON T2.retailer_id = T3.retailer_id WHERE T1.wine_name = 'Montrachet Grand Cru' AND T1.winery = 'Domaine Jacques Prieur' AND T3.retailer_name = 'Yiem Wines Ltd' AND T3.retailer_id = 'f54eab6b-7650-4448-b009-c53f3efbcc3b';
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"""
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textresult, sql_result_raw, _, _ = YiemAgent.SQLexecution(execute_sql_winedb, sql)
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result_vec = GeneralUtils.dfToVectorDict(sql_result_raw)
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for d in result_vec
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wine_name = d["wine_name"]
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image_url_json_str = d["image_url"]
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image_url_json_obj = JSON.parse(image_url_json)
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base_url = "http://192.168.88.106:8080/"
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image_base64 =
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if haskey(image_url_json_obj, "bottle")
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url = base_url * image_url_json_obj["bottle"]
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image_data = HTTP.get(url) # vector{int} data
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image_base64_string = base64encode(image_data)
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else
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nothing
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end
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d["image"] = image_base64
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end
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using LibPQ
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using Tables
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"""
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update_car_regions_one_by_one(conn::LibPQ.Connection, target_word::String)
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Iterates through all rows in the 'car' table where the region is "German",
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and updates them one-by-one to the `target_word`.
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"""
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function update_car_regions_one_by_one(pg_conn_str::String, replace_word::String , target_word::String)
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conn = LibPQ.Connection(pg_conn_str)
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# 1. Fetch the target rows. Assumes 'id' is the primary key.
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# We select the ID to target rows individually during the update step.
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select_query = "SELECT id FROM car WHERE region = '$replace_word';"
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result = execute(conn, select_query)
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rows = Tables.rows(result)
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# 2. Prepare the update statement for execution reuse
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# Using explicit types for parameter placeholders ($1, $2)
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update_query = "UPDATE car SET region = \$1 WHERE id = \$2;"
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println("Starting one-by-one update...")
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updated_count = 0
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# 3. Iterate through rows one-by-one
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for row in rows
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# LibPQ row values are accessed via properties or column names
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row_id = row.id
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# Execute the parameterized statement safely
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execute(conn, update_query, [target_word, row_id])
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updated_count += 1
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end
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println("Successfully updated \$updated_count rows.")
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return updated_count
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end
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