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narawat
2026-07-09 06:53:14 +07:00
parent 35f1482228
commit 42b8f5bdb1
2 changed files with 2 additions and 288 deletions
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src/interface.jl
+1 -287
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@@ -979,292 +979,6 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
return (result_str=latest_action["action_result"], result_raw=resultState["result_raw"])
end
# function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
# insertSQLVectorDB::Union{Function, Nothing}=nothing,
# similarSQLVectorDB::Union{Function, Nothing}=nothing,
# llmFormatName="qwen3"
# ) where {T<:AbstractString}
# # use similarSQLVectorDB to find similar SQL for the query
# sql, distance = similarSQLVectorDB(query)
# # if sql is really match, immediately check database then return
# if sql !== nothing && distance <= 1
# # query vector db to get wine
# response = SQLexecution(executeSQL, sql)
# if response[:success]
# return (result_str=response[:result_str], result_raw=response[:result_raw])
# else
# error(response[:errormsg])
# end
# end
# """
# chathistory= [
# Dict(
# "role" => "system",
# "content" => [
# Dict("type" => "text", "text" => "You are a helpful assistant"),
# ]
# ),
# ]
# """
# systemmsg =
# """
# <available_actions>
# - RUNSQL, which you can use to execute SQL against the database.
# action_input for this function must be a single SQL query to be executed against the database.
# For more effective text search, it's necessary to use case-insensitivity and the ILIKE operator.
# Do not wrap the SQL as it will be executed against the database directly and SQL must be ended with ';'.
# </available_actions>
# <situation>
# At each round of conversation, you will be given the following:
# - user question
# You are working under your mentor supervision and you are also eager to improve your helpfulness.
# </situation>
# <objective>
# Consult the database search guidelines. Then find the data from a database to satisfy the user's question.
# </objective>
# <your responsibility includes>
# Fulfill the objective.
# </your responsibility includes>
# <database search guidelines>
# - Keep SQL queries focused only on the provided information.
# - Do not create any table in the database
# - A junction table can be used to link tables together. Another use case is for filtering data.
# - If you can't find a single table that can be used to answer the user's query, try joining multiple tables to see if you can obtain the answer.
# - Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
# - If there is no search result from the database, remove the restrictive criteria until a search result is available, and proceed from there.
# </database search guidelines>
# <you should then respond to the user with interleaving plan, action_name, action_input>
# 1) plan: Based on the current situation, state a complete action plan to complete the task. Be specific.
# 2) action_name: (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
# 3) action_input: The input to the action you are about to perform according to your plan.
# After the action is executed you gets "action_result". It is the output from the action you selected.
# </you should then respond to the user with interleaving plan, action_name, action_input>
# <you should only respond in JSON format as described below>
# "plan": "...",
# "action_name": "...",
# "action_input": "..."
# </you should only respond in JSON format as described below>
# """
# # do MCTS if no data in the database
# # add extra context for Evaluator so that it knows the observation is from seaching a database
# initialstate = Dict{String, Any}(
# "reward"=> 0,
# "isterminal"=> false,
# "evaluation"=> "None",
# "evaluationscore"=> 0,
# "suggestion"=> "None",
# "accepted_as_answer"=> "No",
# "chathistory"=> Vector{Dict{String, Any}}(), # store system, user and assistant msg
# "question"=> query,
# "context"=> Dict{String, Any}(),
# "action_history"=> OrderedDict{String, Any}(
# # "1"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
# # "2"=> Dict("plan"=> "...", "action_name"=> "...", "action_input"=> "...", "action_result"=> "..."),
# # ...
# ),
# )
# systemmsg_dict = Dict(
# "role" => "system",
# "content" => [
# Dict("type" => "text", "text" => systemmsg),
# ]
# )
# usermsg = Dict(
# "role" => "user",
# "content" => [
# Dict("type" => "text", "text" => query),
# ]
# )
# push!(initialstate["chathistory"], systemmsg_dict)
# push!(initialstate["chathistory"], usermsg)
# #XXX find a way to recreate the schema from a existing database
# table_schema =
# """
# create table customer (
# customer_id uuid primary key default gen_random_uuid (),
# customer_firstname varchar(128),
# customer_lastname varchar(128),
# customer_displayname varchar(128) not null,
# customer_username varchar(128),
# customer_password varchar(128),
# customer_gender varchar(128),
# country varchar(128),
# telephone varchar(128),
# email varchar(128) not null,
# customer_birthdate varchar(128),
# note text,
# other_attributes jsonb,
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp,
# description text
# );
# create table retailer (
# retailer_id uuid primary key default gen_random_uuid (),
# retailer_name varchar(128) not null,
# retailer_username varchar(128) not null,
# retailer_password varchar(128) not null,
# retailer_address text not null,
# country varchar(128) not null,
# contact_person varchar(128) not null,
# telephone varchar(128) not null,
# email varchar(128) not null,
# note text,
# other_attributes jsonb,
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp,
# description text
# );
# create table food (
# food_id uuid primary key default gen_random_uuid (),
# food_name varchar(128) not null,
# country varchar(128),
# spiciness integer,
# sweetness integer,
# sourness integer,
# savoriness integer,
# bitterness integer,
# serving_temperature integer,
# image_url jsonb,
# note text,
# other_attributes jsonb,
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp,
# description text
# );
# create table wine (
# wine_id uuid primary key default gen_random_uuid (),
# seo_name varchar(128) not null,
# wine_name varchar(128) not null,
# winery varchar(128) not null,
# vintage integer not null,
# region varchar(128) not null,
# country varchar(128) not null,
# wine_type varchar(128) not null,
# grape varchar(128) not null,
# serving_temperature varchar(128) not null,
# intensity integer,
# sweetness integer,
# tannin integer,
# acidity integer,
# fizziness integer,
# tasting_notes text,
# image_url jsonb,
# manufacturer_sku text,
# note text,
# other_attributes jsonb,
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp,
# description text
# );
# create table wine_food (
# wine_id uuid references wine(wine_id),
# food_id uuid references food(food_id),
# constraint wine_food_id primary key (wine_id, food_id),
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp
# );
# CREATE TABLE retailer_wine (
# retailer_id uuid references retailer(retailer_id),
# wine_id uuid references wine(wine_id),
# constraint retailer_wine_id primary key (retailer_id, wine_id),
# price NUMERIC(10, 2),
# currency varchar(3) not null,
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp
# );
# CREATE TABLE retailer_food (
# retailer_id uuid references retailer(retailer_id),
# food_id uuid references food(food_id),
# constraint retailer_food_id primary key (retailer_id, food_id),
# price NUMERIC(10, 2),
# currency varchar(3) not null,
# created_time timestamptz default current_timestamp,
# updated_time timestamptz default current_timestamp
# );
# """
# # println("\n--- SQLLLM query() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
# # println("---")
# # error("SQLLLM query() end")
# initialstate["context"]["table_schema"] = table_schema
# transitionargs = (
# executeSQL=executeSQL,
# decisionMaker=decisionMaker,
# evaluator=evaluator,
# reflector=reflector,
# text2textInstructLLM=text2textInstructLLM,
# querySQLVectorDB=similarSQLVectorDB,
# insertSQLVectorDB=insertSQLVectorDB,
# llmFormatName=llmFormatName
# )
# earlystop(state) = state["reward"] >= 8 ? true : false
# root, _, resultState, highValueState =
# LLMMCTS.runMCTS(initialstate, transition, transitionargs;
# horizontalSampleExpansionPhase=1,
# horizontalSampleSimulationPhase=1,
# maxSimulationDepth=1,
# maxiterations=1,
# explorationweight=1.0,
# earlystop=earlystop,
# saveSimulatedNode=true,
# multithread=false)
# # error("SQLLLM query() end")
# # compare all high value state answer then select the best one
# if length(highValueState) > 1
# selected = compareState(query, highValueState, text2textInstructLLM, llmFormatName)
# resultState = highValueState[selected]
# end
# max_ind =
# if length(resultState["action_history"]) == 0
# 0
# else
# k = keys(resultState["action_history"])
# maximum(parse.(Int, k))
# end
# latest_action = resultState["action_history"]["$max_ind"]
# #CHANGE add to vectorDB only if the answer is achieved and the state is terminal
# sql = latest_action["action_input"]
# if insertSQLVectorDB !== nothing && resultState["isterminal"] == true &&
# resultState["accepted_as_answer"] == "yes"
# insertSQLVectorDB(resultState["question"], sql)
# end
# println("\n--- SQLLLM query() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
# # pprintln(resultState)
# println("---\n")
# return (result_str=latest_action["action_result"], result_raw=resultState["result_raw"])
# end
""" Make a new state.
@@ -1286,7 +1000,7 @@ function makeNewState(currentstate::T1, thoughtDict::T2, response::NamedTuple,
if response[:success]
thoughtDict["action_result"] = response[:result_str]
else
error(response[:errormsg])
thoughtDict["action_result"] = response[:errormsg]
end
newstate = deepcopy(currentstate)
src/llmfunction.jl
+1 -1
View File
@@ -497,7 +497,7 @@ function SQLexecution(executeSQL::Function, sql::T
else
sql = sql * ";"
end
result = executeSQL(sql) #BUG sometime return table, sometime error
result = executeSQL(sql)
df = DataFrame(result)
tablesize = size(df)
row, column = tablesize