ton/SQLLLM
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update

Browse Source
This commit is contained in:
tonaerospace
2025-03-19 11:29:31 +07:00
parent e6ce6f9954
commit 418c543d44
2 changed files with 160 additions and 30 deletions
Download Patch File Download Diff File Expand all files Collapse all files

146
src/interface.jl
View File

@@ -989,15 +989,22 @@ end
# Arguments
- `query<:AbstractString`
a query
A natural language query in English
- `executeSQL::Function`
a connection object to a database
A function that executes SQL queries against the database
- `text2textInstructLLM::Function`
A function that handles communication to text2text instruct LLM service.
A function that handles communication with a text-to-text instruction-based language model
# Return
- `resulttext::String`
The result of the query in English.
# Keyword Arguments
- `insertSQLVectorDB::Union{Function, Nothing}=nothing`
Optional function to insert SQL queries into a vector database for future reference
- `similarSQLVectorDB::Union{Function, Nothing}=nothing`
Optional function to find similar SQL queries from a vector database
# Returns
- `NamedTuple{(:text, :rawresponse), Tuple{Any, Any}}`
- `:text`: The query result in natural language
- `:rawresponse`: The raw database response
# Example
```jldoctest
@@ -1064,7 +1071,7 @@ julia> println(result)
function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
insertSQLVectorDB::Union{Function, Nothing}=nothing,
similarSQLVectorDB::Union{Function, Nothing}=nothing,
) where {T<:AbstractString}
)::NamedTuple{(:text, :rawresponse), Tuple{Any, Any}} where {T<:AbstractString}
# use similarSQLVectorDB to find similar SQL for the query
sql, distance = similarSQLVectorDB(query)
@@ -1095,9 +1102,128 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
:question=> query,
),
)
# context = Dict(
# :tablelist => listAllTable_str(executeSQL)[:result]
# )
#XXX find a way to recreate the schema from a existing database
context = Dict(
:tablelist => listAllTable_str(executeSQL)[:result]
:tablelist =>
"""
Here are SQL that used to create tables in the database:
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,
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,
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
);
"""
)
transitionargs = (
decisionMaker=decisionMaker,
evaluator=evaluator,
@@ -1115,7 +1241,7 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
LLMMCTS.runMCTS(initialstate, transition, transitionargs;
horizontalSampleExpansionPhase=5,
horizontalSampleSimulationPhase=2,
maxSimulationDepth=10,
maxSimulationDepth=5,
maxiterations=1,
explorationweight=1.0,
earlystop=earlystop,
@@ -1307,7 +1433,7 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
responsedict = GeneralUtils.textToDict(response, header;
dictKey=dictkey, symbolkey=true)
response = "Q1: " * responsedict[:q1]
println("\n~~~ SQLLLM generatequestion() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
# println("\n~~~ SQLLLM generatequestion() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
pprintln(Dict(responsedict))
return response
catch e

40
src/llmfunction.jl
View File

@@ -36,7 +36,7 @@ julia> result = response[:result]
# Signature
"""
function listAllTable_json(executeSQL::Function
)::NamedTuple{(:result, :success),Tuple{DataFrame,Bool}}
)::NamedTuple{(:result, :success),Tuple{DataFrame,Bool}}
sql = """
SELECT
@@ -788,32 +788,37 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
end
""" Assigns a scalar value to each new child node to be used for selec-
tion and backpropagation. This value effectively quantifies the agent's progress in task completion,
serving as a heuristic to steer the search algorithm towards the most promising regions of the tree.
""" Compare multiple solution attempts and select the most accurate one.
This function evaluates multiple solution attempts for a given question and determines which attempt
provides the most accurate and relevant response. It uses an LLM to analyze and compare the attempts,
considering their actions and observations.
# Arguments
- `state<:AbstractDict`
one of Yiem's agent
- `text2textInstructLLM::Function`
- `question::String`
The original question or task that was attempted to be solved
- `highValueStateList::Vector{Dict}`
List of states containing different solution attempts and their results
- `text2textInstructLLM::Function`
A function that handles communication to LLM service
# Return
- `score::Integer`
# Returns
- `Integer`
The index of the selected best response (1-based indexing)
# Example
```jldoctest
julia>
```
# TODO
- [] update docs
- [WORKING] implement
# Signature
# Notes
- The function makes up to 10 attempts to get a valid response from the LLM
- Each state in highValueStateList should contain a thoughtHistory with action_input and observation
- The LLM evaluates attempts based on accuracy and relevance to the original question
"""
function compareState(question::String, highValueStateList, text2textInstructLLM::Function)
println(typeof(highValueStateList))
function compareState(question::String, highValueStateList::Vector{Dict},
text2textInstructLLM::Function)::Integer
systemmsg =
"""
<Your profile>
@@ -947,7 +952,7 @@ function compareState(question::String, highValueStateList, text2textInstructLLM
return responsedict[:selected_response_number]
end
error("compareState failed to generate an evaluation, Response: \n$response\n<|End of error|>", @__FILE__, ":", @__LINE__, " $(Dates.now())")
error("compareState() failed to generate an evaluation, Response: \n$response\n<|End of error|>", @__FILE__, ":", @__LINE__, " $(Dates.now())")
end
@@ -988,7 +993,6 @@ end
end # module llmfunction