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tonaerospace
2025-03-11 00:13:50 +07:00
parent 4f1917e01b
commit d09e9c1071
4 changed files with 95 additions and 100 deletions
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146
src/interface.jl
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@@ -2,7 +2,7 @@ module interface
export decisionMaker, evaluator, reflector, transition, query
using LibPQ, DataStructures, JSON3, UUIDs, PrettyPrinting
using LibPQ, DataStructures, JSON3, UUIDs, PrettyPrinting, Dates
using GeneralUtils, LLMMCTS
using ..util, ..llmfunction
@@ -136,13 +136,16 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
You are a helpful assistant that find the data from a database to satisfy the user's query.
You are also eager to improve your helpfulness.
For your information:
- Observation: Result of the immediately preceding action
At each round of conversation, the user will give you the current situation:
User Query: ...
Example: ...
Your Q&A: ...
Your work progress: ...
Evaluation: Evaluation of the latest action and observation
Suggestion: ...
Evaluation: Evaluation of the immediately preceding action and observation
Suggestion: Suggestion for the immediately preceding action and observation
You must follow the following guidelines:
- Keep SQL queries focused only on the provided information.
@@ -155,21 +158,17 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
- Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
You should then respond to the user with interleaving Understanding, Reasoning, Plan, Action:
1) Understanding:
- State your understanding about the current situation.
2) Reasoning:
- State your step by step reasoning about the current situation.
3) Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
4) Action_name (Must be aligned with your plan): Can be one of the following functions:
1) Comprehension:
- State your comprehension about the current situation.
2) Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
3) Action_name (Must be aligned with your plan): Can be one of the following functions:
- GETDATA, which you can use to get the data from 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 ';'.
5) Action_input: Input to the action
6) Observation: Result of the immediately preceding action
4) Action_input: Input to the action
You should only respond in format as described below:
Understanding: ...
Reasoning: ...
Comprehension: ...
Plan: ...
Action_name: ...
Action_input: ...
@@ -217,21 +216,38 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
]
# put in model format
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
prompt *=
"""
<|start_header_id|>assistant<|end_header_id|>
"""
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
response = text2textInstructLLM(prompt)
println("\nSQL decisionMaker() rawresponse: \n", response)
# LLM tends to generate observation given that it is in the input
response =
if occursin("observation:", response)
string(split(response, "observation:")[1])
elseif occursin("Observation:", response)
string(split(response, "Observation:")[1])
elseif occursin("observation_", response)
string(split(response, "observation_")[1])
elseif occursin("Observation_", response)
string(split(response, "Observation_")[1])
else
response
end
# some time LLM output Plan_1: so we need to detect and replace topic numbering
regex = r"_[0-1000]+:"
matches = collect(eachmatch(regex, response))
for m in matches
response = replace(response, string(m.match)=>":")
end
if occursin("NULL", response)
errornote = "\nSQL decisionMaker() NULL response is not allowed"
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__)
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue
end
header = ["Understanding", "Reasoning", "Plan", "Action_name", "Action_input"]
header = ["Comprehension:", "Plan:", "Action_name:", "Action_input:"]
dictkey = ["comprehension", "plan", "action_name", "action_input"]
# detect if there are more than 1 key per categories
count = GeneralUtils.countGivenWords(response, header)
@@ -241,7 +257,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
keywordNumber = v
if keywordNumber > 1
errornote = "\nSQL query has duplicated keyword, $keyword"
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__)
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
duplicateKeywordFlag = true
break
end
@@ -256,13 +272,13 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
push!(kw, detected)
end
if nothing ∈ kw
println("Some keywords are missing, Required keywords=$header, Response keywords=$kw ", @__FILE__, ":", @__LINE__)
println("Some keywords are missing, Required keywords=$header, Response keywords=$kw ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue # try again next loop
end
# textToDict() search for action_input
responsedict = GeneralUtils.textToDict(response, header,
rightmarker=":", symbolkey=true, lowercasekey=true)
responsedict = GeneralUtils.textToDict(response, header;
dictKey=dictkey, symbolkey=true)
delete!(responsedict, :observation)
@@ -281,32 +297,32 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
toollist = ["TABLEINFO", "GETDATA"]
if responsedict[:action_name] ∉ toollist
errornote = "\nYou must only use the given functions"
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__)
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue
end
for i in toollist
if occursin(i, responsedict[:action_input])
errornote = "\n action_name is in action_input which is not allowed."
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__)
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue
end
end
for i ∈ [:understanding, :reasoning, :plan, :action_name, :action_input]
for i ∈ [:comprehension, :plan, :action_name, :action_input]
if length(JSON3.write(responsedict[i])) == 0
errornote = "\n $i is empty"
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__)
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue
end
end
# check if there are more than 1 key per categories
for i ∈ [:understanding, :reasoning, :plan, :action_name, :action_input]
for i ∈ [:comprehension, :plan, :action_name, :action_input]
matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
if length(matchkeys) > 1
errornote = "\n $i has more than one key"
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__)
println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue
end
end
@@ -316,7 +332,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
return responsedict
end
error("DecisionMaker failed to generate a thought ", response)
error("DecisionMaker failed to generate a thought \n", response)
end
""" Assigns a scalar value to each new child node to be used for selec-
@@ -552,7 +568,7 @@ julia>
# ]
# # put in model format
# prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
# prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
# prompt *=
# """
# <|start_header_id|>assistant<|end_header_id|>
@@ -603,7 +619,7 @@ julia>
# # evaluation score as reward because different answers hold different value for the user.
# state[:reward] = responsedict[:score]
# end
# println("\n~~~ Evaluator() ", @__FILE__, ":", @__LINE__)
# println("\n~~~ Evaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
# pprintln(Dict(responsedict))
# return responsedict[:score]
@@ -772,7 +788,7 @@ function evaluator(state::T1, text2textInstructLLM::Function;
<At each round of conversation, the user will give you>
Trajectory: ...
Error note: error note from your previous attempt
Error_note: error note from your previous attempt
</At each round of conversation, the user will give you>
<You must follow the following guidelines>
@@ -828,7 +844,7 @@ function evaluator(state::T1, text2textInstructLLM::Function;
usermsg =
"""
Trajectory: $thoughthistory
Error note: $errornote
Error_note: $errornote
"""
_prompt =
@@ -838,20 +854,18 @@ function evaluator(state::T1, text2textInstructLLM::Function;
]
# put in model format
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
prompt *=
"""
<|start_header_id|>assistant<|end_header_id|>
"""
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
header = ["Trajectory_evaluation", "Answer_evaluation", "Accepted_as_answer", "Score", "Suggestion"]
header = ["Trajectory_evaluation:", "Answer_evaluation:", "Accepted_as_answer:", "Score:", "Suggestion:"]
dictkey = ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"]
response = text2textInstructLLM(prompt)
# make sure every header is in the response
for i in header
detected = GeneralUtils.detect_keyword(i, response)
if detected === nothing
errornote = "Keyword $i not found in response"
errornote = "Your previous response didn't provide $i"
errorFlag = true
end
end
@@ -859,9 +873,8 @@ function evaluator(state::T1, text2textInstructLLM::Function;
continue # skip to the next iteration
end
responsedict = GeneralUtils.textToDict(response,
header;
rightmarker=":", symbolkey=true, lowercasekey=true)
responsedict = GeneralUtils.textToDict(response, header;
dictKey=dictkey, symbolkey=true)
# check if dict has all required value
trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
@@ -892,12 +905,12 @@ function evaluator(state::T1, text2textInstructLLM::Function;
# evaluation score as reward because different answers hold different value for the user.
state[:reward] = responsedict[:score]
end
println("\n~~~ Evaluator() ", @__FILE__, ":", @__LINE__)
println("\n~~~ Evaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
pprintln(Dict(responsedict))
return responsedict[:score]
end
error("evaluator failed to generate an evaluation")
error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
end
"""
@@ -995,15 +1008,9 @@ function reflector(config::T1, state::T2)::String where {T1<:AbstractDict, T2<:A
]
# put in model format
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
prompt *=
"""
<|start_header_id|>assistant<|end_header_id|>
"""
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
externalService = config[:externalservice][:text2textinstruct]
# apply LLM specific instruct format
externalService = config[:externalservice][:text2textinstruct]
@@ -1271,8 +1278,8 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
earlystop(state) = state[:reward] >= 8 ? true : false
_, _, resultState = LLMMCTS.runMCTS(initialstate, transition, transitionargs;
horizontalSampleExpansionPhase=5,
horizontalSampleSimulationPhase=2,
horizontalSampleExpansionPhase=1,
horizontalSampleSimulationPhase=1,
maxSimulationDepth=10, maxiterations=2,
explorationweight=1.0,
earlystop=earlystop,
@@ -1314,7 +1321,7 @@ function makeNewState(currentstate::T1, thoughtDict::T4, rawresponse, response::
reward::T3, isterminal::Bool
)::NamedTuple{(:newNodeKey, :newstate), Tuple{String, Dict{Symbol, <:Any}}} where {T1<:AbstractDict, T2<:AbstractString, T3<:Number, T4<:AbstractDict}
keys = [:understanding, :reasoning, :action_name, :action_input, :observation]
keys = [:comprehension, :action_name, :action_input, :observation]
# latestKeys = []
currentstate_latestKey, currentstate_latestIndice =
@@ -1418,7 +1425,6 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
response = nothing # store for show when error msg show up
errornote = ""
noise = ""
for attempt in 1:10
usermsg =
@@ -1428,7 +1434,6 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
Example: $similarSQL
Your work progress: $workprogress
$errornote
$noise
"""
_prompt =
@@ -1438,11 +1443,7 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
]
# put in model format
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
prompt *=
"""
<|start_header_id|>assistant<|end_header_id|>
"""
prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
try
response = text2textInstructLLM(prompt)
@@ -1457,13 +1458,13 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
response = replace(response, '`'=>"")
end
# response = string(split(response, "Please")[1]) # LLM usually add comments which is no need.
responsedict = GeneralUtils.textToDict(response,
["Understanding", "Q1"],
rightmarker=":", symbolkey=true; lowercasekey=true)
header = ["Understanding:", "Q1:"]
dictkey = ["understanding", "q1"]
responsedict = GeneralUtils.textToDict(response, header;
dictKey=dictkey, symbolkey=true)
response = "Q1: " * responsedict[:q1]
println("\n~~~ SQLLLM generatequestion() ", @__FILE__, ":", @__LINE__)
println("\n~~~ SQLLLM generatequestion() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
pprintln(Dict(responsedict))
return response
catch e
@@ -1471,8 +1472,7 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
showerror(io, e)
errorMsg = String(take!(io))
st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
println("\n~~~ SQLLLM generatequestion() Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, ":", @__LINE__)
noise = GeneralUtils.randstrings(3, 5)
println("\n~~~ SQLLLM generatequestion() Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
end
end
error("generatequestion failed to generate a thought ", response)