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

update

Browse Source
This commit is contained in:
narawat lamaiin
2025-05-01 07:59:18 +07:00
parent bf223b64b2
commit 5112701dc2
2 changed files with 78 additions and 76 deletions
Download Patch File Download Diff File Expand all files Collapse all files

77
src/interface.jl
View File

@@ -102,8 +102,8 @@ Dict(
# Signature # Signature
""" """
function decisionMaker(state::T1, context, text2textInstructLLM::Function, function decisionMaker(state::T1, context, text2textInstructLLM::Function, llmFormatName::String
; querySQLVectorDBF::Union{T2, Nothing}=nothing ; querySQLVectorDBF::Union{T2, Nothing}=nothing, maxattempt=10
)::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:Function} )::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:Function}
# lessonDict = # lessonDict =
@@ -203,13 +203,10 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
:temperature => 0.1, :temperature => 0.1,
) )
for attempt in 1:10 for attempt in 1:maxattempt
if attempt > 1 attempt > 1 ? llmkwargs[:temperature] += 0.1 : nothing
println("\nERROR SQLLLM decisionMaker() attempt $attempt/10 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
llmkwargs[:temperature] = 0.1 * attempt
end
QandA = generatequestion(state, context, text2textInstructLLM; similarSQL=similarSQL_) QandA = generatequestion(state, context, text2textInstructLLM, llmFormatName; similarSQL=similarSQL_)
usermsg = usermsg =
""" """
@@ -230,9 +227,9 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
response = text2textInstructLLM(prompt; llmkwargs=llmkwargs) response = text2textInstructLLM(prompt; llmkwargs=llmkwargs)
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
# LLM tends to generate observation given that it is in the input # LLM tends to generate observation given that it is in the input
response = response =
@@ -261,7 +258,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
if occursin("NULL", response) if occursin("NULL", response)
errornote = "\nYour previous attempt was NULL. This is not allowed" errornote = "\nYour previous attempt was NULL. This is not allowed"
println("\nERROR SQLLLM decisionMaker() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
end end
@@ -284,11 +281,11 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
detected_kw = GeneralUtils.detect_keyword(header, response) detected_kw = GeneralUtils.detect_keyword(header, response)
if 0 ∈ values(detected_kw) if 0 ∈ values(detected_kw)
errornote = "\nYour previous attempt did not have all points according to the required response format" errornote = "\nYour previous attempt did not have all points according to the required response format"
println("\nERROR SQLLLM decisionMaker() $errornote \n$response", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
elseif sum(values(detected_kw)) > length(header) elseif sum(values(detected_kw)) > length(header)
errornote = "\nYour previous attempt has duplicated points according to the required response format" errornote = "\nYour previous attempt has duplicated points according to the required response format"
println("\nERROR SQLLLM decisionMaker() $errornote \n$response", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
end end
@@ -311,31 +308,29 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
toollist = ["TABLEINFO", "RUNSQL"] toollist = ["TABLEINFO", "RUNSQL"]
if responsedict[:action_name] ∉ toollist if responsedict[:action_name] ∉ toollist
errornote = "\nYour previous attempt has action_name that is not in the tool list" errornote = "Your previous attempt has action_name that is not in the tool list"
println("\nERROR SQLLLM decisionMaker() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
end end
for i in toollist for i in toollist
if occursin(i, responsedict[:action_input]) if occursin(i, responsedict[:action_input])
errornote = "\nYour previous attempt has action_name in action_input which is not allowed" errornote = "Your previous attempt has action_name in action_input which is not allowed"
println("\nERROR SQLLLM decisionMaker() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
end end
end end
for i ∈ Symbol.(dictkey) for i ∈ Symbol.(dictkey)
if length(JSON3.write(responsedict[i])) == 0 if length(JSON3.write(responsedict[i])) == 0
errornote = "\nYour previous attempt has empty value for $i" errornote = "Your previous attempt has empty value for $i"
println("\nERROR SQLLLM decisionMaker() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
end end
end end
state[:decisionMaker] = responsedict state[:decisionMaker] = responsedict
return responsedict return responsedict
end end
error("SQLLLM DecisionMaker() failed to generate a thought \n", response) error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
end end
@@ -361,7 +356,8 @@ julia>
# Signature # Signature
""" """
function evaluator(state::T1, text2textInstructLLM::Function; maxattempt=10 function evaluator(state::T1, text2textInstructLLM::Function, llmFormatName::String;
maxattempt=10
) where {T1<:AbstractDict} ) where {T1<:AbstractDict}
systemmsg = systemmsg =
@@ -446,13 +442,13 @@ function evaluator(state::T1, text2textInstructLLM::Function; maxattempt=10
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
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"] dictkey = ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"]
response = text2textInstructLLM(prompt, modelsize="medium") response = text2textInstructLLM(prompt, modelsize="medium")
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
# sometime LLM output something like **Comprehension**: which is not expected # sometime LLM output something like **Comprehension**: which is not expected
response = replace(response, "**"=>"") response = replace(response, "**"=>"")
@@ -484,7 +480,7 @@ function evaluator(state::T1, text2textInstructLLM::Function; maxattempt=10
accepted_as_answer::AbstractString = responsedict[:accepted_as_answer] accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
if accepted_as_answer ∉ ["Yes", "No"] # [PENDING] add errornote into the prompt if accepted_as_answer ∉ ["Yes", "No"]
errornote = "Your previous attempt's accepted_as_answer has wrong format" errornote = "Your previous attempt's accepted_as_answer has wrong format"
println("\nERROR SQLLLM evaluator() Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())") println("\nERROR SQLLLM evaluator() Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue continue
@@ -705,15 +701,17 @@ function transition(state::T, args::NamedTuple
decisionMakerF::Function = args[:decisionMaker] decisionMakerF::Function = args[:decisionMaker]
evaluatorF::Function = args[:evaluator] evaluatorF::Function = args[:evaluator]
reflector::Function = args[:reflector] # reflector::Function = args[:reflector]
context = args[:context] context = args[:context]
executeSQL::Function = args[:executeSQL] executeSQL::Function = args[:executeSQL]
text2textInstructLLM::Function = args[:text2textInstructLLM] text2textInstructLLM::Function = args[:text2textInstructLLM]
insertSQLVectorDB::Function = args[:insertSQLVectorDB] # insertSQLVectorDB::Function = args[:insertSQLVectorDB]
querySQLVectorDBF::Function = args[:querySQLVectorDB] querySQLVectorDBF::Function = args[:querySQLVectorDB]
llmFormatName::String = args[:llmFormatName]
# getting SQL from vectorDB # getting SQL from vectorDB
thoughtDict = decisionMakerF(state, context, text2textInstructLLM; querySQLVectorDBF) thoughtDict = decisionMakerF(state, context, text2textInstructLLM, llmFormatName;
querySQLVectorDBF)
# map action and input() to llm function # map action and input() to llm function
response = response =
@@ -727,7 +725,8 @@ function transition(state::T, args::NamedTuple
elseif thoughtDict[:action_name] == "RUNSQL" elseif thoughtDict[:action_name] == "RUNSQL"
response = SQLexecution(executeSQL, thoughtDict[:action_input]) response = SQLexecution(executeSQL, thoughtDict[:action_input])
if response[:success] if response[:success]
extracted = extractContent_dataframe(response[:result], text2textInstructLLM, thoughtDict[:action_input]) extracted = extractContent_dataframe(response[:result], text2textInstructLLM,
thoughtDict[:action_input], llmFormatName)
(rawresponse=response[:result], result=extracted, errormsg=nothing, success=true) (rawresponse=response[:result], result=extracted, errormsg=nothing, success=true)
else else
(result=nothing, errormsg=response[:errormsg], success=false) (result=nothing, errormsg=response[:errormsg], success=false)
@@ -743,7 +742,7 @@ function transition(state::T, args::NamedTuple
reward::Integer = haskey(response, :reward) ? response[:reward] : 0 reward::Integer = haskey(response, :reward) ? response[:reward] : 0
isterminal::Bool = haskey(response, :isterminal) ? response[:isterminal] : false isterminal::Bool = haskey(response, :isterminal) ? response[:isterminal] : false
newNodeKey, newstate = makeNewState(state, thoughtDict, rawresponse, JSON3.write(result), select, reward, isterminal) newNodeKey, newstate = makeNewState(state, thoughtDict, rawresponse, JSON3.write(result), select, reward, isterminal)
progressvalue::Integer = evaluatorF(newstate, text2textInstructLLM) progressvalue::Integer = evaluatorF(newstate, text2textInstructLLM, llmFormatName)
return (newNodeKey=newNodeKey, newstate=newstate, progressvalue=progressvalue) return (newNodeKey=newNodeKey, newstate=newstate, progressvalue=progressvalue)
end end
@@ -835,6 +834,7 @@ 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,
llmFormatName="qwen3"
)::NamedTuple{(:text, :rawresponse), Tuple{Any, Any}} 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
@@ -997,6 +997,7 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
text2textInstructLLM=text2textInstructLLM, text2textInstructLLM=text2textInstructLLM,
querySQLVectorDB=similarSQLVectorDB, querySQLVectorDB=similarSQLVectorDB,
insertSQLVectorDB=insertSQLVectorDB, insertSQLVectorDB=insertSQLVectorDB,
llmFormatName=llmFormatName
) )
earlystop(state) = state[:reward] >= 8 ? true : false earlystop(state) = state[:reward] >= 8 ? true : false
@@ -1010,14 +1011,14 @@ function query(query::T, executeSQL::Function, text2textInstructLLM::Function;
explorationweight=1.0, explorationweight=1.0,
earlystop=earlystop, earlystop=earlystop,
saveSimulatedNode=true, saveSimulatedNode=true,
multithread=false) multithread=true)
# compare all high value state answer then select the best one # compare all high value state answer then select the best one
if length(highValueState) > 0 if length(highValueState) > 0
# open("/appfolder/app/highValueState.json", "w") do io # open("/appfolder/app/highValueState.json", "w") do io
# JSON3.pretty(io, highValueState) # JSON3.pretty(io, highValueState)
# end # end
selected = compareState(query, highValueState, text2textInstructLLM) selected = compareState(query, highValueState, text2textInstructLLM, llmFormatName)
resultState = highValueState[selected] #BUG compareState() select 0 resultState = highValueState[selected] #BUG compareState() select 0
end end
latestKey, latestInd = GeneralUtils.findHighestIndexKey(resultState[:thoughtHistory], "observation") latestKey, latestInd = GeneralUtils.findHighestIndexKey(resultState[:thoughtHistory], "observation")
@@ -1090,8 +1091,9 @@ function makeNewState(currentstate::T1, thoughtDict::T4, rawresponse, response::
end end
function generatequestion(state::T1, context, text2textInstructLLM::Function; function generatequestion(state::T1, context, text2textInstructLLM::Function,
similarSQL::Union{T2, Nothing}=nothing, maxattempt=10 llmFormatName::String;
similarSQL::Union{T2, Nothing}=nothing, maxattempt=10,
)::String where {T1<:AbstractDict, T2<:AbstractString} )::String where {T1<:AbstractDict, T2<:AbstractString}
similarSQL = similarSQL =
@@ -1144,9 +1146,10 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
Here are some examples: Here are some examples:
Q: What information in the hints is not necessary based on the query? Q: What information in the hints is not necessary based on the query?
A: Country is not specified in the query thus it should not be included in an SQL A: Country is not specified in the query thus it should not be included in an SQL
Q: How can I modify a SQL example to fit my specific query needs? Q: How can I modify a SQL example to fit my specific query needs?
A: ... A: ...
Q: Why the query failed?
A: ...
Let's begin! Let's begin!
""" """
@@ -1181,10 +1184,10 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
response = text2textInstructLLM(prompt, modelsize="medium") response = text2textInstructLLM(prompt, modelsize="medium")
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
# check if response is valid # check if response is valid
q_number = count("Q", response) q_number = count("Q", response)

77
src/llmfunction.jl
View File

@@ -347,8 +347,9 @@ end
# Signature # Signature
""" """
function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM::Function function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM::Function,
)::NamedTuple{(:thought, :code, :success, :errormsg),Tuple{Union{String,Nothing},Union{String,Nothing},Bool,Union{String,Nothing}}} llmFormatName::String
)::NamedTuple{(:thought, :code, :success, :errormsg),Tuple{Union{String,Nothing},Union{String,Nothing},Bool,Union{String,Nothing}}}
Hints = "None" Hints = "None"
@@ -406,10 +407,10 @@ function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM:
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
try try
response = text2textInstructLLM(prompt, modelsize="medium") response = text2textInstructLLM(prompt, modelsize="medium")
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
header = ["Comprehension:", "Plan:", "Code:"] header = ["Comprehension:", "Plan:", "Code:"]
dictkey = ["comprehension", "plan", "code"] dictkey = ["comprehension", "plan", "code"]
@@ -518,7 +519,7 @@ function SQLexecution(executeSQL::Function, sql::T
tablesize = size(df) tablesize = size(df)
row, column = tablesize row, column = tablesize
if row == 0 if row == 0
error("The resulting table has 0 row. Possible causes: 1) Your search criteria might be too specific. Relaxing some conditions could yield better results. Remember, you can always refine your search later. 2) There could be a typo in your search query. 3) You might be searching in the wrong place.") error("The resulting table has 0 row. Possible causes: 1) 1) Your search criteria might be overly specific. Consider removing or adjusting highly specific conditions (e.g., exact values, exact phrases, narrow ranges). Start with broader terms and refine your search incrementally. This often resolves empty result. 2) There could be a typo in your search query. 3) You might be searching in the wrong place.")
elseif column > 30 elseif column > 30
error("SQL execution failed. An unexpected error occurred. Please try again.") error("SQL execution failed. An unexpected error occurred. Please try again.")
end end
@@ -561,8 +562,9 @@ end
# Signature # Signature
""" """
function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function, action::String function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function, action::String,
)::String llmFormatName::String
)::String
tablesize = size(df) tablesize = size(df)
row = tablesize[1] row = tablesize[1]
column = tablesize[2] column = tablesize[2]
@@ -628,13 +630,13 @@ function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function,
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
header = ["About_resulting_table:", "Search_summary:"] header = ["About_resulting_table:", "Search_summary:"]
dictkey = ["about_resulting_table", "search_summary"] dictkey = ["about_resulting_table", "search_summary"]
for i in 1:5 for i in 1:5
response = text2textInstructLLM(prompt, modelsize="medium") response = text2textInstructLLM(prompt, modelsize="medium")
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
kw = [] kw = []
# use for loop and detect_keyword function to get the exact variation of each keyword in the text then push to kw list # use for loop and detect_keyword function to get the exact variation of each keyword in the text then push to kw list
@@ -736,7 +738,9 @@ julia> result = SQLLLM.getTableNameFromSQL(sql, text2textInstructLLM)
# Signature # Signature
""" """
function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{String} where {T<:AbstractString} function getTableNameFromSQL(sql::T, text2textInstructLLM::Function,
llmFormatName::String
)::Vector{String} where {T<:AbstractString}
systemmsg = """ systemmsg = """
Extract table name out of the user query. Extract table name out of the user query.
@@ -764,14 +768,14 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
header = ["Table_name:"] header = ["Table_name:"]
dictkey = ["table_name"] dictkey = ["table_name"]
for attempt in 1:5 for attempt in 1:5
try try
response = text2textInstructLLM(prompt, modelsize="medium") response = text2textInstructLLM(prompt, modelsize="medium")
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
responsedict = GeneralUtils.textToDict(response, header; responsedict = GeneralUtils.textToDict(response, header;
dictKey=dictkey, symbolkey=true) dictKey=dictkey, symbolkey=true)
response = copy(JSON3.read(responsedict[:table_name])) response = copy(JSON3.read(responsedict[:table_name]))
@@ -820,44 +824,39 @@ julia>
- The LLM evaluates attempts based on accuracy and relevance to the original question - The LLM evaluates attempts based on accuracy and relevance to the original question
""" """
function compareState(question::String, highValueStateList::Vector{T}, function compareState(question::String, highValueStateList::Vector{T},
text2textInstructLLM::Function)::Integer where {T<:AbstractDict} text2textInstructLLM::Function, llmFormatName::String
)::Integer where {T<:AbstractDict}
systemmsg = systemmsg =
""" """
<Your profile> Your profile:
- You are a helpful assistant - You are a helpful assistant
</Your profile> Situation:
<Situation> - The user has made multiple attempts to solve the question, resulting in various answers
The user has made multiple attempts to solve the question, resulting in various answers Your mission:
<Your mission>
- Identify and select the most accurate and relevant response from these multiple results for the user - Identify and select the most accurate and relevant response from these multiple results for the user
</Your mission> At each round of conversation, you will be given the following:
<At each round of conversation, you will be given the following>
Question: the question the user is trying to answer Question: the question the user is trying to answer
Attempt: the user's attempted actions and their corresponding results Attempt: the user's attempted actions and their corresponding results
</At each round of conversation, you will be given the following> You should then respond to the user with the following:
<You should then respond to the user with the following>
Comparison: a comparison of all results from all attempts Comparison: a comparison of all results from all attempts
Rationale: a brief explanation of why the selected response is the most accurate and relevant Rationale: a brief explanation of why the selected response is the most accurate and relevant
Selected_response_number: the number the selected response in the list of results (e.g., 1, 2, 3, ...) Selected_response_number: the number the selected response in the list of results (e.g., 1, 2, 3, ...)
</You should then respond to the user with the following> You should only respond in format as described below:
<You should only respond in format as described below>
Comparison: ... Comparison: ...
Rationale: ... Rationale: ...
Selected_response_number: ... Selected_response_number: ...
</You should only respond in format as described below> Here are some examples:
<Here are some examples> User's question: "How many German wines do you have?"
User's question: "How many German wines do you have?" Attempt 1:
Attempt 1: Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany'
Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany' Result: 100 wines
Result: 100 wines Attempt 2:
Attempt 2: Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany' AND type = 'Red'
Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany' AND type = 'Red' Result: 50 red wines
Result: 50 red wines Comparison: The second attempt counts only German red wines while the first attempt includes all German wines.
Comparison: The second attempt counts only German red wines while the first attempt includes all German wines. Rationale: The user is asking for the number of German wines without specifying a type, so the most accurate response is the first attempt because it includes all German wines.
Rationale: The user is asking for the number of German wines without specifying a type, so the most accurate response is the first attempt because it includes all German wines. Selected_response_number:1
Selected_response_number:1
</Here are some examples>
Let's begin! Let's begin!
""" """
@@ -918,7 +917,7 @@ function compareState(question::String, highValueStateList::Vector{T},
] ]
# put in model format # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "granite3") prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
header = ["Comparison:", "Rationale:", "Selected_response_number:"] header = ["Comparison:", "Rationale:", "Selected_response_number:"]
dictkey = ["comparison", "rationale", "selected_response_number"] dictkey = ["comparison", "rationale", "selected_response_number"]
@@ -928,7 +927,7 @@ function compareState(question::String, highValueStateList::Vector{T},
# sometime LLM output something like **Comprehension**: which is not expected # sometime LLM output something like **Comprehension**: which is not expected
response = replace(response, "**"=>"") response = replace(response, "**"=>"")
response = replace(response, "***"=>"") response = replace(response, "***"=>"")
response = GeneralUtils.deFormatLLMtext(response, "granite3") response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
# make sure every header is in the response # make sure every header is in the response
for i in header for i in header