update
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+6
-1
@@ -140,6 +140,11 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
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response = String(split(response, ", observation")[1]) # in case LLM generate observation key which it isn't supposed to
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response = strip(response)
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# dollar sign in Julia means string interpolation
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while occursin('$', response)
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response = replace(response, '$' => "USD")
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end
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responsedict = nothing
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if occursin(requiredKeys[2], response)
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try
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@@ -467,8 +472,8 @@ function think(a::T)::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict,
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# end
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# a.memory["shortmem"]["$(max_ind + 1)"] = thoughtdict
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@info "YiemAgent think() 7 " @__LINE__
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pprintln(thoughtdict)
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@info "YiemAgent think() 7 " @__LINE__
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return (thoughtdict=thoughtdict, result_raw=result_raw)
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end
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+3
-123
@@ -480,13 +480,13 @@ function generatesql(a::T, searchterm::String,
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errornote = ""
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# provide similar sql only for the first attempt
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sql, distance = a.context.similarSQLVectorDB(searchterm)
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similarSQL_ = sql !== nothing ? sql : "None"
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# if sql is really close, just use it
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if distance <= 0.1
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if sql !== nothing && distance <= 0.1
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return similarSQL_
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end
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similarSQL_ = sql !== nothing ? sql : "None"
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context =
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"""
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<internal_context_for_assistant>
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@@ -1207,126 +1207,6 @@ externalService = config["externalservice"]["text2textinstruct"]
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end
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# function isrecommend(state::T1, text2textInstructLLM::Function
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# ) where {T1<:AbstractDict}
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# systemmsg =
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# """
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# You are a helpful assistant that analyzes agent's trajectories to find solutions and observations (i.e., the results of actions) to answer the user's questions.
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# Definitions:
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# "question" is the user's question.
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# "thought" is step-by-step reasoning about the current situation.
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# "plan" is what to do to complete the task from the current situation.
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# “action_name” is the name of the action taken, which can be one of the following functions:
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# 1) CHAT_BOX[text], which you can use to talk with the user. "text" is in verbal English.
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# 2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English. The best query must includes "budget", "type of wine", "characteristics of wine" and "food pairing".
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# "action_input" is the input to the action
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# "observation" is result of the preceding immediate action.
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# At each round of conversation, the user will give you:
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# Context: ...
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# Trajectories: ...
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# You should then respond to the user with:
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# 1) trajectory_evaluation:
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# - Analyze the trajectories of a solution to answer the user's original question.
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# Then given a question and a trajectory, evaluate its correctness and provide your reasoning and
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# analysis in detail. Focus on the latest thought, action, and observation.
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# Incomplete trajectories can be correct if the thoughts and actions so far are correct,
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# even if the answer is not found yet. Do not generate additional thoughts or actions.
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# 2) answer_evaluation: Focus only on the matter mentioned in the question and analyze how the latest observation addresses the question.
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# 3) accepted_as_answer: Decide whether the latest observation's content answers the question. The possible responses are either 'Yes' or 'No.'
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# Bad example (The observation didn't answers the question):
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# question: Find cars with 4 wheels.
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# observation: There are 2 cars in the table.
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# Good example (The observation answers the question):
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# question: Find cars with a stereo.
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# observation: There are 1 cars in the table. 1) brand: Toyota, model: yaris, color: black.
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# 4) score: Correctness score s where s is a single integer between 0 to 9.
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# - 0 means the trajectories are incorrect.
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# - 9 means the trajectories are correct, and the observation's content directly answers the question.
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# 5) suggestion: if accepted_as_answer is "No", provide suggestion.
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# You should only respond in format as described below:
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# trajectory_evaluation: ...
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# answer_evaluation: ...
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# accepted_as_answer: ...
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# score: ...
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# suggestion: ...
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# Let's begin!
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# """
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# thoughthistory = ""
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# for (k, v) in state[:thoughtHistory]
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# thoughthistory *= "$k: $v\n"
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# end
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# usermsg =
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# """
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# Context: None
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# Trajectories: $thoughthistory
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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, "granite3")
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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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# for attempt in 1:5
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# try
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# response = text2textInstructLLM(prompt)
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# responsedict = GeneralUtils.textToDict(response,
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# ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"],
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# rightmarker=":", symbolkey=true)
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# # check if dict has all required value
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# trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
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# answerevaluation_text::AbstractString = responsedict[:answer_evaluation]
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# responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
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# score::Integer = responsedict[:score]
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# accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
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# suggestion::AbstractString = responsedict[:suggestion]
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# # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
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# state[:evaluation] = "$(responsedict[:trajectory_evaluation]) $(responsedict[:answer_evaluation])"
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# state[:evaluationscore] = responsedict[:score]
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# state[:accepted_as_answer] = responsedict[:accepted_as_answer]
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# state[:suggestion] = responsedict[:suggestion]
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# # mark as terminal state when the answer is achieved
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# if accepted_as_answer == "Yes"
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# state[:isterminal] = true
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# state[:reward] = 1
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# end
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# println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
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# pprintln(Dict(responsedict))
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# return responsedict[:score]
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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))
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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("")
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# end
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# end
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# error("evaluator failed to generate an evaluation")
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# end
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