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