update
This commit is contained in:
narawat lamaiin
377
src/interface.jl
377
src/interface.jl
@@ -158,6 +158,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
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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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- If you are unable to find the requested information, kindly inform the user, "The current data in our database does not provide the specific answer to your query".
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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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- Do not use backticks (`). Use double quotes instead.
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You should then respond to the user with interleaving Understanding, Reasoning, Plan, Action:
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1) Understanding:
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@@ -183,7 +184,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
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Let's begin!
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"""
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# [WORKING] add no backtick
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workprogress = ""
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for (k, v) in state[:thoughtHistory]
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if k ∉ [:query]
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@@ -191,31 +192,37 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
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end
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end
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usermsg =
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"""
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$(context[:tablelist])
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User query: $(state[:thoughtHistory][:question])
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Hints: $similarSQL
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Your Q&A: $QandA
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Your work progress: $workprogress
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Evaluation: $(state[:evaluation])
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Suggestion: $(state[:suggestion])
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"""
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_prompt =
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[
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Dict(:name=> "system", :text=> systemmsg),
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Dict(:name=> "user", :text=> usermsg)
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]
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# put in model format
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prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
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prompt *=
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"""
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<|start_header_id|>assistant<|end_header_id|>
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"""
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response = nothing # store for show when error msg show up
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errornote = ""
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noise = ""
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for attempt in 1:10
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usermsg =
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"""
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$(context[:tablelist])
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User query: $(state[:thoughtHistory][:question])
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Hints: $similarSQL
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Your Q&A: $QandA
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Your work progress: $workprogress
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Evaluation: $(state[:evaluation])
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Suggestion: $(state[:suggestion])
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$errornote
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$noise
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"""
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_prompt =
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[
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Dict(:name=> "system", :text=> systemmsg),
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Dict(:name=> "user", :text=> usermsg)
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]
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# put in model format
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prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
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prompt *=
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"""
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<|start_header_id|>assistant<|end_header_id|>
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"""
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try
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response = text2textInstructLLM(prompt)
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@@ -226,6 +233,10 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
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delete!(responsedict, :observation)
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if occursin('`', response)
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response = replace(response, '`'=>"")
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end
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toollist = ["TABLEINFO", "GETDATA"]
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if responsedict[:action_name] ∉ toollist
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error("SQL decisionMaker() didn't use the given functions ", @__FILE__, " ", @__LINE__)
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@@ -258,220 +269,175 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function,
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errorMsg = String(take!(io))
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st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
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println("")
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println("Attempt $attempt. Error occurred: $errorMsg\n$st")
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println("\n~~~ SQLLLM decisionMaker() Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, " ", @__LINE__)
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println("")
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noise = GeneralUtils.randstrings(3, 5)
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end
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end
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error("DecisionMaker failed to generate a thought ", response)
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end
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# function decisionMaker(state::T1, context, text2textInstructLLM::Function,
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# QandA::T2; similarSQL::Union{T3, Nothing}=nothing
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# )::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:AbstractString, T3<:AbstractString}
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# function decisionMaker(state::T2, config::T1
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# )::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:AbstractDict}
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# similarSQL =
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# if similarSQL === nothing
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# "None"
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# else
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# "This is the closest matching SQL statement for a similar query: $similarSQL"
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# end
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# if isfile("lesson.json")
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# lessonDict = copy(JSON3.read("lesson.json"))
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# else
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# lessonDict = nothing
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# end
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# # lessonDict =
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# # if isfile("lesson.json")
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# # lessonDict = copy(JSON3.read("lesson.json"))
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# # else
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# # lessonDict = nothing
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# # end
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# lesson =
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# if lessonDict === nothing
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# ""
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# else
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# """
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# You have attempted to help the user before and failed, either because your reasoning for the
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# recommendation was incorrect or your response did not exactly match the user expectation.
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# The following lesson(s) give a plan to avoid failing to help the user in the same way you
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# did previously. Use them to improve your strategy to help the user.
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# # lessonDict = nothing
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# Here are some lessons in JSON format:
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# $(JSON3.write(lessonDict))
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# # lesson =
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# # if lessonDict === nothing
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# # ""
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# # else
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# # """
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# # You have attempted to help the user before and failed, either because your reasoning for the
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# # recommendation was incorrect or your response did not exactly match the user expectation.
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# # The following lesson(s) give a plan to avoid failing to help the user in the same way you
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# # did previously. Use them to improve your strategy to help the user.
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# When providing the thought and action for the current trial, that into account these failed
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# trajectories and make sure not to repeat the same mistakes and incorrect answers.
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# """
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# end
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# # Here are some lessons in JSON format:
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# # $(JSON3.write(lessonDict))
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# _prompt =
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# # When providing the thought and action for the current trial, that into account these failed
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# # trajectories and make sure not to repeat the same mistakes and incorrect answers.
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# # """
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# # end
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# systemmsg =
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# """
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# You are a helpful data engineer.
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# Your goal is to help the user to get what the user wants.
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# You are also keen to improve your helpfulness with lesson(s).
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# You are a helpful assistant that get the data from a database to satisfy the user's query.
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# You are also eager to improve your helpfulness.
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# You must follow the following criteria:
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# 1) Get to know what table are available in the database.
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# 2) Get to know what the data in the table looks like.
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# 3) If you can't find a single table that can be used to answer the user's question, try joining multiple tables to see if you can obtain the answer.
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# 4) If you are unable to find the requested information, kindly inform the user, "The current data in our database does not provide the specific answer to your question".
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# At each round of conversation, the user will give you the current situation:
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# User Query: ...
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# Hints: ...
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# Your Q&A: ...
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# Your work progress: ...
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# Evaluation: Evaluation of the latest action and observation
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# Suggestion: ...
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# You should only respond with interleaving Thought, Action, Observation steps.
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# Thought can reason about the current situation, and Action can be one of the following functions:
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# 1) listalltables[NA], which you can use to list all tables in the database and see their descriptions. "NA" word is the function input.
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# 2) tableinfo[table_name], which you can use to see the table and its column description. "table_name" is name of the table you want to get info.
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# 3) getdata[instructions], which you can use to ask other people to get the data from tables for you. "instructions" should clearly describe how you want others to extract the data.
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# For example,
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# a. Query the "Engine" table to identify the engine types that have 3 cylinders. This can be done using a SELECT statement in SQL, filtering the results where the number of cylinders equals 3.
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# b. Once you have identified the engine types with 3 cylinders, use this information to query the "Car" table. You're looking for car models that are associated with these engine types. This can be achieved by performing a JOIN operation between the "Car" and "Engine" tables based on the engine type.
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# 4) finalanswerbox[answer], which returns your answer to the user. "answer" is your answer to the user question.
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# After each observation, provide the next Thought and next Action.
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# You should consider the following guidelines:
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# - Do not create any table in the database
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# - Column name can be the same in different tables. Refer to column comments to get more details by using TABLEINFO function
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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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# - If you are unable to find the requested information, kindly inform the user, "The current data in our database does not provide the specific answer to your query".
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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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# You should only respond in JSON format as describe below:
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# {
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# "thought": "your reasoning",
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# "action": {"name": "action to take", "input": "Action input"},
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# "observation": "result of the action"
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# }
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# You should then respond to the user with interleaving Understanding, Reasoning, Plan, Action:
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# 1) Understanding:
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# - State your understanding about the current situation.
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# 2) Reasoning:
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# - State your step by step reasoning about the current situation.
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# 3) Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
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# 4) Action_name (Must be aligned with your plan): Can be one of the following functions:
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# - TABLEINFO[list_of_table_name], which you can use to get the data type of a table column. "list_of_table_name" is a list of table name you want to get info. e.g. TABLEINFO["table name 1", "table name 2"]
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# - GETDATA[SQL], which you can use to get the data from the database. "SQL" is the single SQL command 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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# 5) Action_input: Input to the action
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# 6) Observation: Result of the immediately preceding action
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# Here are some examples:
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# {
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# "question": "I would like to buy a sedan with 8 seats.",
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# "thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
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# "action_1": {"name": "inventory", "input": "sedan with 8 seats."},
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# "observation_1": "Several model has 8 seats. Available color are black, red green"
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# }
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# {
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# "thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
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# "action": {"name": "chatbox", "input": "Which color do you like?"}
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# "observation": "I'll take black."
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# }
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# $lesson
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# You should only respond in format as described below:
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# Understanding: ...
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# Reasoning: ...
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# Plan: ...
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# Action_name: ...
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# Action_input: ...
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# Observation: ...
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# Let's begin!
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# $(JSON3.write(state[:thoughtHistory]))
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# {"thought"
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# """
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# # _prompt =
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# # """
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# # You are a helpful data engineer.
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# # Your goal is to help the user to get what the user wants.
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# # You are also keen to improve your helpfulness with lesson(s).
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# # You must follow the following criteria:
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# # 1) Get to know what table are available in the database.
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# # 2) Get to know what the data in the table looks like.
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# # 3) If you can't find a single table that can be used to answer the user's question, try joining multiple tables to see if you can obtain the answer.
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# # 4) Keep trying even if you get SQL execution error.
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# # You should only respond with interleaving Thought, Action, Observation steps.
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# # Thought can reason about the current situation, and Action can be one of the following functions:
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# # 1) listalltables[NA], which you can use to list all tables in the database and see their descriptions. "NA" word is the function input.
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# # 2) tableinfo[table_name], which you can use to see the table and its column description. "table_name" is name of the table you want to get info.
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# # 3) getdata[SQL], which you can use to ask other people to get the data from tables for you. "SQL" is the command you will use to extract the data.
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# # 4) finalanswerbox[answer], which returns your answer to the user. "answer" is your answer to the user question.
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# # After each observation, provide the next Thought and next Action.
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# # You should only respond in JSON format as describe below:
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# # {
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# # "thought": "your reasoning",
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# # "action": {"name": "action to take", "input": "Action input"},
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# # "observation": "result of the action"
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# # }
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# # Here are some examples:
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# # {
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# # "question": "I would like to buy a sedan with 8 seats.",
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# # "thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
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# # "action_1": {"name": "inventory", "input": "sedan with 8 seats."},
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# # "observation_1": "Several model has 8 seats. Available color are black, red green"
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# # }
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# # {
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# # "thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
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# # "action": {"name": "chatbox", "input": "Which color do you like?"}
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# # "observation": "I'll take black."
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# # }
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# # $lesson
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# # Let's begin!
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# # $(JSON3.write(state[:thoughtHistory]))
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# # {"thought"
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# # """
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# # apply LLM specific instruct format
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# externalService = config[:externalservice][:text2textinstruct]
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# llminfo = externalService[:llminfo]
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# prompt =
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# if llminfo[:name] == "llama3instruct"
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# GeneralUtils.formatLLMtext_llama3instruct("system", _prompt)
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# else
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# error("llm model name is not defied yet $(@__LINE__)")
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# workprogress = ""
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# for (k, v) in state[:thoughtHistory]
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# if k ∉ [:query]
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# workprogress *= "$k: $v\n"
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# end
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# end
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# msgMeta = GeneralUtils.generate_msgMeta(
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# externalService[:mqtttopic],
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# senderName= "decisionMaker",
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# senderId= string(uuid4()),
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# receiverName= "text2textinstruct",
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# mqttBroker= config[:mqttServerInfo][:broker],
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# mqttBrokerPort= config[:mqttServerInfo][:port],
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# )
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# usermsg =
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# """
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# $(context[:tablelist])
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# User query: $(state[:thoughtHistory][:question])
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# Hints: $similarSQL
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# Your Q&A: $QandA
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# Your work progress: $workprogress
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# Evaluation: $(state[:evaluation])
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# Suggestion: $(state[:suggestion])
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# """
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# outgoingMsg = Dict(
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# :msgMeta=> msgMeta,
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# :payload=> Dict(
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# :text=> prompt,
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# :kwargs=> Dict(
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# :max_tokens=> 512,
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# :stop=> ["<|eot_id|>"],
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# )
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# )
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# )
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# @show outgoingMsg
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# _prompt =
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# [
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# Dict(:name=> "system", :text=> systemmsg),
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# Dict(:name=> "user", :text=> usermsg)
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# ]
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# for attempt in 1:5
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# # put in model format
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# prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
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# prompt *=
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# """
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# <|start_header_id|>assistant<|end_header_id|>
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# """
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# response = nothing # store for show when error msg show up
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# for attempt in 1:10
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# try
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# response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
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# _responseJsonStr = response[:response][:text]
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# expectedJsonExample =
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# """
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# Here is an expected JSON format:
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# {
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# "thought": "...",
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# "action": {"name": "...", "input": "..."},
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# "observation": "..."
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# }
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# """
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# responseJsonStr = FormatCorrector.jsoncorrection(config, _responseJsonStr, expectedJsonExample)
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# thoughtDict = copy(JSON3.read(responseJsonStr))
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# response = text2textInstructLLM(prompt)
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# # check if dict has all required value
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# thought::AbstractString = thoughtDict[:thought]
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# actionname::AbstractString = thoughtDict[:action][:name]
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# actioninput::AbstractString = thoughtDict[:action][:input]
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# if actionname ∈ ["listalltables", "tableinfo", "getdata", "finalanswerbox"]
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# # LLM use available function
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# elseif thought == ""
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# error("DecisionMaker has no thought")
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# elseif length(actioninput) == 0
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# error("DecisionMaker has no actioninput")
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# else
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# error("DecisionMaker use wrong function")
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# # textToDict() search for action_input
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# responsedict = GeneralUtils.textToDict(response,
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# ["Understanding", "Reasoning", "Plan", "Action_name", "Action_input", "Observation"],
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# rightmarker=":", symbolkey=true, lowercasekey=true)
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# delete!(responsedict, :observation)
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# toollist = ["TABLEINFO", "GETDATA"]
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# if responsedict[:action_name] ∉ toollist
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# error("SQL decisionMaker() didn't use the given functions ", @__FILE__, " ", @__LINE__)
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# end
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# for i in toollist
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# if occursin(i, responsedict[:action_input])
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# error("Action_name is in action_input which is not allowed.")
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# end
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# end
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# for i ∈ [:understanding, :reasoning, :plan, :action_name, :action_input]
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# if length(JSON3.write(responsedict[i])) == 0
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# error("$i is empty ", @__FILE__, " ", @__LINE__)
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# end
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# end
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# # check if there are more than 1 key per categories
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# for i ∈ ["thought", "action", "observation"]
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# matchkeys = GeneralUtils.findMatchingDictKey(thoughtDict, i)
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# for i ∈ [:understanding, :reasoning, :plan, :action_name, :action_input]
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# matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
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# if length(matchkeys) > 1
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# error("DecisionMaker has more than one key per categories")
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# end
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# end
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# return thoughtDict
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# return responsedict
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# catch e
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# io = IOBuffer()
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# showerror(io, e)
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||||
# errorMsg = String(take!(io))
|
||||
# st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
|
||||
# println("")
|
||||
# @warn "Attempt $attempt. Error occurred: $errorMsg\n$st"
|
||||
# println("Attempt $attempt. Error occurred: $errorMsg\n$st")
|
||||
# println("")
|
||||
# end
|
||||
# end
|
||||
# error("DecisionMaker failed to generate a thought")
|
||||
# error("DecisionMaker failed to generate a thought ", response)
|
||||
# end
|
||||
|
||||
|
||||
@@ -1221,6 +1187,7 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
|
||||
- State your understanding about the current situation.
|
||||
2) Q: Given the situation, "ask yourself" about the situation at least five, but no more than ten, questions.
|
||||
3) A: Given the situation, "answer to yourself" the best you can.
|
||||
- Do not generate any text after the last answer.
|
||||
|
||||
You must only respond in format as described below:
|
||||
Understanding: ...
|
||||
@@ -1244,9 +1211,9 @@ 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 =
|
||||
"""
|
||||
$(context[:tablelist])
|
||||
@@ -1254,6 +1221,7 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
|
||||
Hints: $similarSQL
|
||||
Your work progress: $workprogress
|
||||
$errornote
|
||||
$noise
|
||||
"""
|
||||
|
||||
_prompt =
|
||||
@@ -1271,10 +1239,18 @@ function generatequestion(state::T1, context, text2textInstructLLM::Function;
|
||||
|
||||
try
|
||||
response = text2textInstructLLM(prompt)
|
||||
|
||||
# check if response is valid
|
||||
q_number = count("Q", response)
|
||||
if q_number < 1
|
||||
error("too few questions only $q_number questions are generated ", @__FILE__, " ", @__LINE__)
|
||||
errornote = "too few question"
|
||||
error("too few questions only $q_number questions are generated")
|
||||
end
|
||||
if occursin('`', response)
|
||||
response = replace(response, '`'=>"")
|
||||
end
|
||||
|
||||
|
||||
# response = string(split(response, "Please")[1]) # LLM usually add comments which is no need.
|
||||
responsedict = GeneralUtils.textToDict(response,
|
||||
["Understanding", "Q1"],
|
||||
@@ -1288,9 +1264,8 @@ 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("")
|
||||
println("Attempt $attempt. Error occurred: $errorMsg\n$st")
|
||||
println("")
|
||||
println("\n~~~ SQLLLM generatequestion() Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, " ", @__LINE__)
|
||||
noise = GeneralUtils.randstrings(3, 5)
|
||||
end
|
||||
end
|
||||
error("generatequestion failed to generate a thought ", response)
|
||||
|
||||
@@ -653,7 +653,7 @@ function SQLexecution(executeSQL::Function, sql::T
|
||||
tablesize = size(df)
|
||||
row, column = tablesize
|
||||
if row == 0 # if 0 row
|
||||
error("The resulting table has 0 row. Possible causes: 1) You might be searching in the wrong place 2) There could be a typo in your search query 3) No data matches your search criteria.")
|
||||
error("The resulting table has 0 row. Possible causes: 1) You might be searching in the wrong place 2) There could be a typo in your search query.")
|
||||
elseif column > 30
|
||||
error("SQL execution failed. An unexpected error occurred. Please try again.")
|
||||
end
|
||||
|
||||
Reference in New Issue
Block a user