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 # Arguments
- `query<:AbstractString` - `query<:AbstractString`
a query A natural language query in English
- `executeSQL::Function` - `executeSQL::Function`
a connection object to a database A function that executes SQL queries against the database
- `text2textInstructLLM::Function` - `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 # Keyword Arguments
- `resulttext::String` - `insertSQLVectorDB::Union{Function, Nothing}=nothing`
The result of the query in English. 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 # Example
```jldoctest ```jldoctest
@@ -1064,7 +1071,7 @@ julia> println(result)
function query(query::T, executeSQL::Function, text2textInstructLLM::Function; function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
insertSQLVectorDB::Union{Function, Nothing}=nothing, insertSQLVectorDB::Union{Function, Nothing}=nothing,
similarSQLVectorDB::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 # use similarSQLVectorDB to find similar SQL for the query
sql, distance = similarSQLVectorDB(query) sql, distance = similarSQLVectorDB(query)
@@ -1095,9 +1102,128 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
:question=> query, :question=> query,
), ),
) )
# context = Dict(
# :tablelist => listAllTable_str(executeSQL)[:result]
# )
#XXX find a way to recreate the schema from a existing database
context = Dict( 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 = ( transitionargs = (
decisionMaker=decisionMaker, decisionMaker=decisionMaker,
evaluator=evaluator, evaluator=evaluator,
@@ -1115,7 +1241,7 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
LLMMCTS.runMCTS(initialstate, transition, transitionargs; LLMMCTS.runMCTS(initialstate, transition, transitionargs;
horizontalSampleExpansionPhase=5, horizontalSampleExpansionPhase=5,
horizontalSampleSimulationPhase=2, horizontalSampleSimulationPhase=2,
maxSimulationDepth=10, maxSimulationDepth=5,
maxiterations=1, maxiterations=1,
explorationweight=1.0, explorationweight=1.0,
earlystop=earlystop, earlystop=earlystop,
@@ -1307,7 +1433,7 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
responsedict = GeneralUtils.textToDict(response, header; responsedict = GeneralUtils.textToDict(response, header;
dictKey=dictkey, symbolkey=true) dictKey=dictkey, symbolkey=true)
response = "Q1: " * responsedict[:q1] response = "Q1: " * responsedict[:q1]
println("\n~~~ SQLLLM generatequestion() ", @__FILE__, ":", @__LINE__, " $(Dates.now())") # println("\n~~~ SQLLLM generatequestion() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
pprintln(Dict(responsedict)) pprintln(Dict(responsedict))
return response return response
catch e catch e

36
src/llmfunction.jl
View File

@@ -788,32 +788,37 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
end end
""" Assigns a scalar value to each new child node to be used for selec- """ Compare multiple solution attempts and select the most accurate one.
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. 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 # Arguments
- `state<:AbstractDict` - `question::String`
one of Yiem's agent 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` - `text2textInstructLLM::Function`
A function that handles communication to LLM service A function that handles communication to LLM service
# Return # Returns
- `score::Integer` - `Integer`
The index of the selected best response (1-based indexing)
# Example # Example
```jldoctest ```jldoctest
julia> julia>
``` ```
# TODO # Notes
- [] update docs - The function makes up to 10 attempts to get a valid response from the LLM
- [WORKING] implement - 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
# Signature
""" """
function compareState(question::String, highValueStateList, text2textInstructLLM::Function) function compareState(question::String, highValueStateList::Vector{Dict},
println(typeof(highValueStateList)) text2textInstructLLM::Function)::Integer
systemmsg = systemmsg =
""" """
<Your profile> <Your profile>
@@ -947,7 +952,7 @@ function compareState(question::String, highValueStateList, text2textInstructLLM
return responsedict[:selected_response_number] return responsedict[:selected_response_number]
end 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 end
@@ -988,7 +993,6 @@ end
end # module llmfunction end # module llmfunction