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This commit is contained in:
narawat
2026-07-24 08:31:36 +07:00
parent d7adfaffa8
commit 7ec3edfd77
4 changed files with 198 additions and 393 deletions
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etc.jl
+10 -200
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@@ -1,200 +1,10 @@
using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames, DataStructures # check if this column has vector embedding. if there is one, seach vector version instead
using GeneralUtils, SQLLLM, YiemAgent column_name_embedding = column_name * "_embedding"
if occursin(column_name_embedding, tables_schema[column_name_embedding])
config = JSON.parsefile("./appconfig.json") vector_column = Dict(
host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':') "table_name"=> table_name,
port = parse(Int, _port) "column_name"=> column_name_embedding,
dbname = "winedb" "operator"=> "vector_similarity",
user = config["externalservice"]["sommpanion_db"]["user"] "value"=> column_obj["value"]
password = config["externalservice"]["sommpanion_db"]["password"] )
pg_conn_str = "host=$host_url port=$port dbname=$dbname user=$user password=$password" end
function execute_sql_winedb(sql::T) where {T<:AbstractString}
host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
port = parse(Int, _port)
dbname = "winedb"
user = config["externalservice"]["sommpanion_db"]["user"]
password = config["externalservice"]["sommpanion_db"]["password"]
db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
result = nothing
try
result = LibPQ.execute(db_connection, sql)
catch e
LibPQ.close(db_connection)
end
LibPQ.close(db_connection)
return result
end
sql =
"""
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';
"""
textresult, sql_result_raw, _, _ = YiemAgent.SQLexecution(execute_sql_winedb, sql)
result_vec = GeneralUtils.dfToVectorDict(sql_result_raw)
for d in result_vec
wine_name = d["wine_name"]
image_url_json_str = d["image_url"]
image_url_json_obj = JSON.parse(image_url_json)
base_url = "http://192.168.88.106:8080/"
image_base64 =
if haskey(image_url_json_obj, "bottle")
url = base_url * image_url_json_obj["bottle"]
image_data = HTTP.get(url) # vector{int} data
image_base64_string = base64encode(image_data)
else
nothing
end
d["image"] = image_base64
end
using LibPQ
using Tables
"""
update_car_regions_one_by_one(conn::LibPQ.Connection, target_word::String)
Iterates through all rows in the 'car' table where the region is "German",
and updates them one-by-one to the `target_word`.
"""
function update_car_regions_one_by_one(pg_conn_str::String, replace_word::String , target_word::String)
conn = LibPQ.Connection(pg_conn_str)
# 1. Fetch the target rows. Assumes 'id' is the primary key.
# We select the ID to target rows individually during the update step.
select_query = "SELECT id FROM car WHERE region = '$replace_word';"
result = execute(conn, select_query)
rows = Tables.rows(result)
# 2. Prepare the update statement for execution reuse
# Using explicit types for parameter placeholders ($1, $2)
update_query = "UPDATE car SET region = \$1 WHERE id = \$2;"
println("Starting one-by-one update...")
updated_count = 0
# 3. Iterate through rows one-by-one
for row in rows
# LibPQ row values are accessed via properties or column names
row_id = row.id
# Execute the parameterized statement safely
execute(conn, update_query, [target_word, row_id])
updated_count += 1
end
println("Successfully updated \$updated_count rows.")
return updated_count
end
function generate_wine_retail_sql(conditions::Dict{String, Any})::String
# 1. Base SQL structure
base_query = """
SELECT
w.winery,
w.wine_name,
w.wine_id,
w.vintage,
w.region,
w.country,
w.wine_type,
w.grape,
w.serving_temperature,
w.sweetness,
w.intensity,
w.tannin,
w.acidity,
w.tasting_notes,
rw.price,
rw.currency,
w.image_url,
NULL AS retailer_name,
rw.retailer_id
FROM wine AS w
JOIN retailer_wine AS rw
ON w.wine_id = rw.wine_id
"""
# 2. Dynamic WHERE Clause Builder
where_clauses = String[]
# Iterate over each table condition provided
for (table_name, table_conditions) in conditions
# Determine table alias
alias = if table_name == "wine"
"w"
elseif table_name == "retailer_wine"
"rw"
else
continue # Skip unsupported tables
end
# Process condition dictionaries
if isa(table_conditions, Dict) && !isempty(table_conditions)
for (column_name, filter_details) in table_conditions
if isa(filter_details, Dict) && haskey(filter_details, "operator")
op = filter_details["operator"]
raw_val = filter_details["value"]
# --- Value Type Handling ---
# Use tryparse instead of try/catch for cleaner, faster parsing
final_val = raw_val
if op in ("=", "<", ">", "<=", ">=")
str_val = string(raw_val)
num_val = tryparse(Float64, str_val)
if !isnothing(num_val)
final_val = isinteger(num_val) ? round(Int, num_val) : num_val
end
end
# --- SQL Formatting ---
if isa(final_val, Number)
clause = "$(alias).$(column_name) $(op) $(final_val)"
else
# Escape single quotes within string values
escaped_val = replace(string(final_val), "'" => "''")
clause = "$(alias).$(column_name) $(op) '$(escaped_val)'"
end
push!(where_clauses, clause)
end
end
end
end
# 3. Assemble Final Query
where_sql = isempty(where_clauses) ? "" : "WHERE " * join(where_clauses, " AND ")
return string(base_query, where_sql, ";")
end
src/interface.jl
+2 -2
View File
@@ -163,7 +163,7 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
""" """
# strict output format # strict output format
json_schema = Dict( response_format = Dict(
"type"=> "json_schema", "type"=> "json_schema",
"json_schema"=> Dict( "json_schema"=> Dict(
"name"=> "user_profile", "name"=> "user_profile",
@@ -185,7 +185,7 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
"model"=> "gemma-4-E4B-it-UD-Q4_K_XL", "model"=> "gemma-4-E4B-it-UD-Q4_K_XL",
"messages"=> a.chathistory, "messages"=> a.chathistory,
"temperature"=> 0.7, "temperature"=> 0.7,
"response_format"=> json_schema, "response_format"=> response_format,
) )
for attempt in 1:maxattempt for attempt in 1:maxattempt
src/llmfunction.jl
+186 -190
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@@ -637,45 +637,47 @@ function predefined_wine_search_sql(a::T, searchterm::String,
# your responsibility includes # your responsibility includes
Fulfill the objective. Fulfill the objective.
# you should only respond in JSON format as described below # You must output your response as a JSON object containing a single key: "extracted_info".
{ The "extracted_info" key must contain an array of objects. Each object must contain:
table_name_1: 1) "table_name": The name of the table.
column_name_1: 2) "column_name": The specific column being filtered.
operator: "=" 3) "operator": The comparison operator (e.g., "=", ">", "LIKE").
value: "..." 4) "value": The value to compare against.
column_name_2:
operator: "=" If the user does not specify any filters, return an empty array for "extracted_info": {"extracted_info": []}.
value: "..."
...
table_name_2:
column_name_1:
operator: "="
value: "..."
column_name_2:
operator: "="
value: "..."
...
}
# here are some example # here are some example
<user> <user>
4-wheel drive car with red color that will give me fast and furious emotion. No more than 7000 USD 4-wheel drive car with red color that will give me fast and furious emotion. No more than 7000 USD
</user> </user>
<assistant> <assistant>
car_info: # table_name {
drive_type: # column_name "extracted_info": [
operator: "=" # operator is not "N/A" because drive_type column store quantitative value {
value: "4-wheel" # column_value "table_name": "car_info",
color: "column_name": "drive_type",
operator: "=" # operator is not "N/A" because color column store quantitative value "operator": "=",
value: "red" "value": "4-wheel"
drive_feeling: },
operator: "N/A" # operator is "N/A" because drive_feeling column store qualitative value {
value: "fast and furious" "table_name": "car_info",
price_list: "column_name": "color",
price: "operator": "=",
operator: "<" # operator is not "N/A" because drive_type column store quantitative value "value": "red"
value: "7000" },
{
"table_name": "car_info",
"column_name": "drive_feeling",
"operator": "ILIKE",
"value": "fast and furious"
},
{
"table_name": "price_list",
"column_name": "price",
"operator": "<",
"value": "7000"
}
}
</assistant> </assistant>
""" """
@@ -702,23 +704,47 @@ function predefined_wine_search_sql(a::T, searchterm::String,
""" """
input = context * searchterm input = context * searchterm
json_schema = Dict( response_format = Dict(
"type"=> "json_schema", "type" => "json_schema",
"json_schema"=> Dict( "json_schema" => Dict(
"name"=> "user_profile", "name" => "extracted_conditions",
"strict"=> true, "strict" => true,
"schema"=> Dict( "schema" => Dict(
"type"=> "object", "type" => "object",
"properties"=> Dict( "properties" => Dict(
"plan"=> Dict("type"=> "string"), "extracted_info" => Dict(
"action_name"=> Dict("type"=> "string"), "type" => "array",
"action_input"=> Dict("type"=> "string"), "items" => Dict(
), "type" => "object",
"required"=> ["plan", "action_name", "action_input"], "properties" => Dict(
"additionalProperties"=> false "table_name" => Dict(
"type" => "string",
"description" => "The name of the database table."
),
"column_name" => Dict(
"type" => "string",
"description" => "The name of the column to filter on."
),
"operator" => Dict(
"type" => "string",
"enum" => ["=", "!=", ">", "<", ">=", "<=", "LIKE", "IN", "IS NULL", "IS NOT NULL"],
"description" => "The SQL comparison operator."
),
"value" => Dict(
"type" => ["string", "null"],
"description" => "The value to compare against. Use null for IS NULL/IS NOT NULL."
)
),
"required" => ["table_name", "column_name", "operator", "value"],
"additionalProperties" => false
)
)
),
"required" => ["extracted_info"],
"additionalProperties" => false
) )
)
) )
)
msg = Dict( msg = Dict(
"model" => "gemma-4-E4B-it-UD-Q4_K_XL", "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
@@ -736,85 +762,63 @@ function predefined_wine_search_sql(a::T, searchterm::String,
] ]
), ),
], ],
"temperature" => 0.7 "temperature" => 0.7,
"response_format"=> response_format,
) )
for attempt in 1:maxattempt for attempt in 1:maxattempt
response = a.context.text2textInstructLLM("random_id", msg) response = a.context.text2textInstructLLM("random_id", msg)
responsedict = JSON.parse(response)
responsedict = nothing # responsedict = nothing
try # try
responsedict = Serde.parse_yaml(response) # responsedict = Serde.parse_yaml(response)
catch e # catch e
println("\nERROR YiemAgent predefined_wine_search_sql() Error: $e --(not qualify response)-> $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n") # println("\nERROR YiemAgent predefined_wine_search_sql() Error: $e --(not qualify response)-> $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
continue # continue
end # end
# println("\n ", table_schema) # println("\n ", table_schema)
println("\n ", responsedict) println("\n ", responsedict)
@info "before BM25 " @__LINE__ @info "before BM25 " @__LINE__
""" #WORKING to ensure user input is correct
responsedict = Dict( for entry in responsedict["extracted_info"]
"wine" => Dict( table_name = entry["table_name"]::String
"tasting_notes" => Dict( column_name = entry["column_name"]::String
"operator" => "N/A",
"value" => "casual dinner" bucket = classify_column(a.context.pg_conn_str, table_name, column_name)
),
"wine_type" => Dict( if bucket == "fuzzy_correction"
"operator" => "=", words_catalog = GeneralUtils.harvest_entity_catalog(a.context.pg_conn_str, table_name, column_name)
"value" => "red" resolved_word = GeneralUtils.resolve_entity(entry["value"], words_catalog; threshold=0.9)
) entry["value"] = resolved_word
),
"retailer_wine" => Dict(
"currency" => Dict(
"operator" => "=", "value" => "USD"
),
"price" => Dict(
"operator" => "<", "value" => "1000"
)
)
)
"""
for (table_name, table_info_dict) in responsedict
for (column_name, v) in table_info_dict
bucket = classify_column(a.context.pg_conn_str, table_name, column_name)
if bucket == "fuzzy_correction"
words_catalog = GeneralUtils.harvest_entity_catalog(a.context.pg_conn_str, table_name, column_name)
resolved_word = GeneralUtils.resolve_entity(v["value"], words_catalog; threshold=0.9)
table_info_dict[column_name]["value"] = resolved_word
end
end end
end end
# filter for column that will be used for hard condition (SQL where clause) # filter for column that will be used for hard condition (SQL where clause)
# column with "N/A" operator will be used in vector search # column with non-standard operator will be used in vector search
vector_search_words = "" vector_search_words = ""
for (table_name, table_dict) in responsedict hard_operators = ["=","<>","!=",">","<",">=","<=","!<","!>","<=>"]
for (column_name, column_dict) in table_dict
if column_dict["operator"] ∉ ["=","<>","!=",">","<",">=","<=","!<","!>","<=>"]
vector_search_words = vector_search_words * column_dict["value"] * ", "
delete!(table_dict, column_name)
# remove table from responsedict if there is no column to used # Build new list of hard condition entries
if length(responsedict[table_name]) == 0 hard_conditions = JSON.Object{String, Any}[]
delete!(responsedict, table_name) for entry in responsedict["extracted_info"]
end if entry["operator"] ∈ hard_operators
end push!(hard_conditions, entry)
else
vector_search_words = vector_search_words * entry["value"] * ", "
end end
end end
responsedict = hard_conditions
println("") println("")
pprintln(responsedict) @show responsedict
@info "predefined_wine_search_sql() " @__LINE__ @info "predefined_wine_search_sql() " @__LINE__
#WORKING do vector searched #WORKING do vector search
println("") println("")
@show vector_search_words @show vector_search_words
# error(9999)
sql = predefined_wine_search_sql(responsedict) sql = predefined_wine_search_sql(responsedict)
return sql return sql
@@ -822,6 +826,83 @@ function predefined_wine_search_sql(a::T, searchterm::String,
error("SQLLLM DecisionMaker() failed to generate a thought \n", response) error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
end end
function predefined_wine_search_sql(conditions::Vector{JSON.Object{String, Any}})::String
# 1. Base SQL structure
base_query =
"""
SELECT
w.winery,
w.wine_name,
w.wine_id,
w.vintage,
w.region,
w.country,
w.wine_type,
w.grape,
w.serving_temperature,
w.sweetness,
w.intensity,
w.tannin,
w.acidity,
w.tasting_notes,
rw.price,
rw.currency,
w.image_url,
r.retailer_name,
rw.retailer_id
FROM wine AS w
JOIN retailer_wine AS rw ON w.wine_id = rw.wine_id
JOIN retailer AS r ON rw.retailer_id = r.retailer_id
"""
# 2. Dynamic WHERE Clause Builder
where_clauses = String[]
# Iterate over each condition object in the array
for cond in conditions
table_name = String(cond["table_name"])
column_name = String(cond["column_name"])
op = String(cond["operator"])
raw_val = cond["value"]
# Determine table alias
alias = if table_name == "wine"
"w"
elseif table_name == "retailer_wine"
"rw"
else
continue
end
# --- Value Type Handling ---
final_val = raw_val
if op in ("=", "<", ">", "<=", ">=")
str_val = string(raw_val)
num_val = tryparse(Float64, str_val)
if !isnothing(num_val)
final_val = isinteger(num_val) ? round(Int, num_val) : num_val
end
end
# --- SQL Formatting ---
if isa(final_val, Number)
clause = "$(alias).$(column_name) $(op) $(final_val)"
else
escaped_val = replace(string(final_val), "'" => "''")
clause = "$(alias).$(column_name) $(op) '$(escaped_val)'"
end
push!(where_clauses, clause)
end
# 3. Assemble Final Query
where_sql = isempty(where_clauses) ? "" : "WHERE " * join(where_clauses, " AND ")
return string(base_query, where_sql, ";")
end
function SQLexecution(executeSQL::Function, sql::T function SQLexecution(executeSQL::Function, sql::T
)::NamedTuple where {T<:AbstractString} )::NamedTuple where {T<:AbstractString}
@@ -1306,92 +1387,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
error("extractWineAttributes_2() failed to get a response") error("extractWineAttributes_2() failed to get a response")
end end
function predefined_wine_search_sql(conditions::Dict{String, Any})::String
# 1. Base SQL structure
base_query =
"""
SELECT
w.winery,
w.wine_name,
w.wine_id,
w.vintage,
w.region,
w.country,
w.wine_type,
w.grape,
w.serving_temperature,
w.sweetness,
w.intensity,
w.tannin,
w.acidity,
w.tasting_notes,
rw.price,
rw.currency,
w.image_url,
r.retailer_name,
rw.retailer_id
FROM wine AS w
JOIN retailer_wine AS rw ON w.wine_id = rw.wine_id
JOIN retailer AS r ON rw.retailer_id = r.retailer_id
"""
# 2. Dynamic WHERE Clause Builder
where_clauses = String[]
# Iterate over each table condition provided
for (table_name, table_conditions) in conditions
# Determine table alias
alias = if table_name == "wine"
"w"
elseif table_name == "retailer_wine"
"rw"
else
continue # Skip unsupported tables
end
# Process condition dictionaries
if isa(table_conditions, Dict) && !isempty(table_conditions)
for (column_name, filter_details) in table_conditions
if isa(filter_details, Dict) && haskey(filter_details, "operator")
op = filter_details["operator"]
raw_val = filter_details["value"]
# --- Value Type Handling ---
# Use tryparse instead of try/catch for cleaner, faster parsing
final_val = raw_val
if op in ("=", "<", ">", "<=", ">=")
str_val = string(raw_val)
num_val = tryparse(Float64, str_val)
if !isnothing(num_val)
final_val = isinteger(num_val) ? round(Int, num_val) : num_val
end
end
# --- SQL Formatting ---
if isa(final_val, Number)
clause = "$(alias).$(column_name) $(op) $(final_val)"
else
# Escape single quotes within string values
escaped_val = replace(string(final_val), "'" => "''")
clause = "$(alias).$(column_name) $(op) '$(escaped_val)'"
end
push!(where_clauses, clause)
end
end
end
end
# 3. Assemble Final Query
where_sql = isempty(where_clauses) ? "" : "WHERE " * join(where_clauses, " AND ")
return string(base_query, where_sql, ";")
end
src/type.jl
-1
View File
@@ -164,7 +164,6 @@ function sommelier(
- You can only recommend wines that are currently in our inventory - You can only recommend wines that are currently in our inventory
- Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences. - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
- Ask the user one question at a time. - Ask the user one question at a time.
- Do not ask the user about wine's flavor e.g. floral, citrusy, nutty or some thing similar as these terms cannot be used to search the database.
- Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services. - Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
- Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future. - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
- Spicy foods should be paired only with light red wines. - Spicy foods should be paired only with light red wines.