980 lines
32 KiB
Julia
Executable File
980 lines
32 KiB
Julia
Executable File
module interface
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export agentReact, addNewMessage, clearMessage, removeLatestMsg, generatePrompt_tokenPrefix,
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generatePrompt_tokenSuffix, conversation, work, detectCharacters, chunktext,
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findDetectedCharacter
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using JSON3, DataStructures, Dates, UUIDs
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using CommUtils, GeneralUtils
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# ---------------------------------------------------------------------------- #
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# pythoncall setting #
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# ---------------------------------------------------------------------------- #
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# Ref: https://github.com/JuliaPy/PythonCall.jl/issues/252
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# by setting the following variables, PythonCall will use system python or conda python and
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# packages installed by system or conda
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# if these setting are not set (comment out), PythonCall will use its own python and package that
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# installed by CondaPkg (from env_preparation.jl)
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# ENV["JULIA_CONDAPKG_BACKEND"] = "Null"
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# systemPython = split(read(`which python`, String), "\n")[1]
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# ENV["JULIA_PYTHONCALL_EXE"] = systemPython # find python location with $> which python ex. raw"/root/conda/bin/python"
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# using PythonCall
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# const py_agents = PythonCall.pynew()
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# const py_llms = PythonCall.pynew()
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# function __init__()
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# # PythonCall.pycopy!(py_cv2, pyimport("cv2"))
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# # equivalent to from urllib.request import urlopen in python
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# PythonCall.pycopy!(py_agents, pyimport("langchain.agents"))
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# PythonCall.pycopy!(py_llms, pyimport("langchain.llms"))
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# end
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#------------------------------------------------------------------------------------------------100
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abstract type agent end
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@kwdef mutable struct agentReact <: agent
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availableRole::AbstractVector = ["system", "user", "assistant"]
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agentName::String = "assistant"
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maxUserMsg::Int = 10
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earlierConversation::String = "" # summary of earlier conversation
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mqttClient::Union{mqttClient, Nothing} = nothing
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msgMeta::Union{Dict, Nothing} = nothing
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""" Dict(Role=> Content) ; Role can be system, user, assistant
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Example:
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messages=[
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Dict(:role=>"system", :content=> "You are a helpful assistant."),
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Dict(:role=>"assistant", :content=> "How may I help you"),
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Dict(:role=>"user", :content=> "Hello, how are you"),
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]
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"""
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role::Symbol = :assistant
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roles::Dict = Dict(:assistant => "You are a helpful assistant.",)
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# Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
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# messages= [Dict(:role=>"system", :content=> "", :timestamp=> Dates.now()),]
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messages = Vector{Dict{Symbol, Any}}()
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context::String = "nothing" # internal thinking area
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tools::Union{Dict, Nothing} = nothing
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thought::String = "nothing" # contain unfinished thoughts
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end
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function agentReact(
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agentName::String,
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mqttClientSpec::NamedTuple;
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role::Symbol=:assistant,
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roles::Dict=Dict(
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:assistant =>
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"""
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You are a helpful assistant.
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""",
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:assistant_react =>
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"""
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You are a helpful assistant. You don't know other people personal info previously.
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Use the following format:
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QTS: the input question your user is asking and you must answer
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Plan: first you should always think about the question and the info you have thoroughly then extract and devise a complete plan to find the answer (pay attention to variables and their corresponding numerals).
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Thought: you should always think about the info you need and what to do (pay attention to correct numeral calculation and commonsense).
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Act: the action tool related to what you intend to do, should be one of {toolnames}
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ActInput: the input to the action (pay attention to the tool's input)
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Obs: the result of the action
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..... (this Plan/Thought/Act/ActInput/Obs loop can repeat N times.)
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Thought: I think I know the answer
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ANS: Answer of the original question and the rationale behind your answer
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""",
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:sommelier =>
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"""
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You are a sommelier at an online wine reseller who always ask user for wine relevant info before you could help them choosing wine.
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You usually recommend atmost 2 wines for customers.
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You don't know other people personal info previously.
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Use the following format:
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QTS: the input question your user is asking and you must answer
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Plan: first you should always think about the question and the info you have thoroughly then extract and devise a complete plan to find the answer (pay attention to variables and their corresponding numerals).
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Thought: ask yourself do you have all the info you need? And what to do (pay attention to correct numeral calculation and commonsense).
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Act: the tool that match your thought, should be one of {toolnames}
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ActInput: the input to the action (pay attention to the tool's input)
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Obs: the result of the action
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..... (this Plan/Thought/Act/ActInput/Obs loop can repeat N times until you know the answer.)
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ANS: Answer of the original question. You describe detailed benefits of each answer to user's preference.
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Info used to select wine:
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- type of food
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- occasion
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- user's personal taste of wine
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- wine price range
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- temperature at the serving location
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- wine we have in stock
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""",
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),
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tools::Dict=Dict(
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:internetsearch=>Dict(
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:name => "internetsearch",
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:description => "Useful for when you need to search the Internet",
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:input => "Input should be a search query.",
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:output => "",
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:func => nothing # put function here
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),
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:chatbox=>Dict(
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:name => "chatbox",
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:description => "Useful for when you need to ask a customer what you need to know or to talk with them.",
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:input => "Input should be a conversation to customer.",
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:output => "" ,
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:func => nothing,
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),
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:wineStock=>Dict(
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:name => "wineStock",
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:description => "useful for when you need to search for wine by your description, price, name or ID.",
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:input => "Input should be a search query with as much details as possible.",
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:output => "" ,
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:func => nothing,
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),
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:nothing=>Dict(
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:name => "nothing",
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:description => "useful for when you don't need to use tools or actions",
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:input => "No input is needed",
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:output => "" ,
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:func => nothing,
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),
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),
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msgMeta::Dict=Dict(
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:msgPurpose=> "updateStatus",
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:from=> "chatbothub",
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:to=> "llmAI",
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:requestrespond=> "request",
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:sendto=> "", # destination topic
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:replyTo=> "chatbothub/llm/respond", # requester ask responder to send reply to this topic
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:repondToMsgId=> "", # responder is responding to this msg id
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:taskstatus=> "", # "complete", "fail", "waiting" or other status
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:timestamp=> Dates.now(),
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:msgId=> "$(uuid4())",
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),
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availableRole::AbstractArray=["system", "user", "assistant"],
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maxUserMsg::Int=10,)
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newAgent = agentReact()
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newAgent.availableRole = availableRole
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newAgent.maxUserMsg = maxUserMsg
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newAgent.mqttClient = CommUtils.mqttClient(mqttClientSpec)
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newAgent.msgMeta = msgMeta
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newAgent.tools = tools
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newAgent.role = role
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newAgent.roles = roles
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return newAgent
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end
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"""
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add new message to agent
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# Example
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```jldoctest
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julia> addNewMessage(agent1, "user", "Where should I go to buy snacks")
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````
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"""
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function addNewMessage(a::T1, role::String, content::T2) where {T1<:agent, T2<:AbstractString}
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if role ∉ a.availableRole # guard against typo
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error("role is not in agent.availableRole")
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end
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# check whether user messages exceed limit
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userMsg = 0
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for i in a.messages
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if i[:role] == "user"
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userMsg += 1
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end
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end
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messageleft = 0
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if userMsg > a.maxUserMsg # delete all conversation
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clearMessage(a)
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messageleft = a.maxUserMsg
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else
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userMsg += 1
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d = Dict(:role=> role, :content=> content, :timestamp=> Dates.now())
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push!(a.messages, d)
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messageleft = a.maxUserMsg - userMsg
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end
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return messageleft
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end
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function clearMessage(a::T) where {T<:agent}
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for i in eachindex(a.messages)
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if length(a.messages) > 1 # system instruction will NOT be deleted
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pop!(a.messages)
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else
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break
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end
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end
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end
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function removeLatestMsg(a::T) where {T<:agent}
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if length(a.messages) > 1
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pop!(a.messages)
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end
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end
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# function generatePrompt_tokenSuffix(a::agentReact;
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# userToken::String="[/INST]", assistantToken="[INST]",
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# systemToken="[INST]<<SYS>> content <</SYS>>")
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# prompt = nothing
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# for msg in a.messages
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# role = msg[:role]
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# content = msg[:content]
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# if role == "system"
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# prompt = replace(systemToken, "content" => content) * " "
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# elseif role == "user"
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# prompt *= " " * content * " " * userToken
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# elseif role == "assistant"
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# prompt *= " " * content * " " * assistantToken
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# else
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# error("undefied condition role = $role")
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# end
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# end
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# return prompt
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# end
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# function generatePrompt_tokenPrefix(a::agentReact;
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# userToken::String="Q:", assistantToken="A:",
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# systemToken="[INST]<<SYS>> content <</SYS>>")
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# prompt = nothing
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# for msg in a.messages
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# role = msg[:role]
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# content = msg[:content]
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# if role == "system"
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# prompt = replace(systemToken, "content" => content) * " "
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# elseif role == "user"
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# prompt *= userToken * " " * content * " "
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# elseif role == "assistant"
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# prompt *= assistantToken * " " * content * " "
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# else
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# error("undefied condition role = $role")
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# end
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# end
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# return prompt
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# end
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function generatePrompt_react_mistral_openorca(messages::Dict, systemMsg::String, context::String="",
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tools::Union{Dict, Nothing}=nothing)
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promptTemplate =
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"""
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<|im_start|>system
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{systemMsg}
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You have access to the following tools:
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{tools}
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Begin!
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<|im_end|>
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Here are the context for the question:
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{context}
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"""
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for msg in messages
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role = msg[:role]
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content = msg[:content]
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if role == "system"
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prompt = replace(promptTemplate, "{systemMsg}" => systemMsg)
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toollines = ""
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for tool in tools
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toolline = "$(tool[:name]): $(tool[:description]) $(tool[:input]) $(tool[:output])\n"
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toollines *= toolline
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end
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prompt = replace(promptTemplate, "{tools}" => toollines)
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prompt = replace(promptTemplate, "{context}" => context)
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elseif role == "user"
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prompt *= "<|im_start|>user\n" * content * "\n<|im_end|>\n"
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elseif role == "assistant"
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prompt *= "<|im_start|>assistant\n" * content * "\n<|im_end|>\n"
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else
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error("undefied condition role = $role")
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end
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end
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return prompt
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end
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function generatePrompt_mistral_openorca(a::T, usermsg::String) where {T<:agent}
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prompt =
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"""
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<|im_start|>system
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{systemMsg}
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<|im_end|>
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Here are the context for the question:
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{context}
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"""
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prompt = replace(prompt, "{systemMsg}" => a.roles[:assistant])
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toolnames = ""
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toollines = ""
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for (toolname, v) in a.tools
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toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
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toollines *= toolline
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toolnames *= "$toolname,"
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end
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prompt = replace(prompt, "{toolnames}" => toolnames)
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prompt = replace(prompt, "{tools}" => toollines)
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prompt = replace(prompt, "{context}" => a.context)
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prompt *= "<|im_start|>user\n" * usermsg * "\n<|im_end|>\n"
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prompt *= "<|im_start|>assistant\n"
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return prompt
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end
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function generatePrompt_react_mistral_openorca(a::T, usermsg::String,
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continuethought::Bool=false) where {T<:agent}
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prompt =
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"""
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<|im_start|>system
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{systemMsg}
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You have access to the following tools:
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{tools}
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Begin!
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<|im_end|>
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Here are the context for the question:
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{context}
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"""
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prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
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toolnames = ""
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toollines = ""
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for (toolname, v) in a.tools
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toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
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toollines *= toolline
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toolnames *= "$toolname,"
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end
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prompt = replace(prompt, "{toolnames}" => toolnames)
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prompt = replace(prompt, "{tools}" => toollines)
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prompt = replace(prompt, "{context}" => a.context)
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if continuethought == false
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prompt *= "<|im_start|>user\nQTS: " * usermsg * "\n<|im_end|>\n"
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prompt *= "<|im_start|>assistant\n"
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else
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prompt *= "Obs: $usermsg\n"
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end
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return prompt
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end
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"""
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Chat with llm.
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```jldoctest
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julia> using JSON3, UUIDs, Dates, FileIO, CommUtils, ChatAgent
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julia> mqttClientSpec = (
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clientName= "someclient", # name of this client
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clientID= "$(uuid4())",
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broker= "mqtt.yiem.ai",
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pubtopic= (imgAI="img/api/v0.0.1/gpu/request",
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txtAI="txt/api/v0.1.0/gpu/request"),
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subtopic= (imgAI="agent/api/v0.1.0/img/respond",
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txtAI="agent/api/v0.1.0/txt/respond"),
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keepalive= 30,
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)
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julia> msgMeta = Dict(
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:msgPurpose=> "updateStatus",
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:from=> "agent",
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:to=> "llmAI",
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:requestrespond=> "request",
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:sendto=> "", # destination topic
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:replyTo=> "agent/api/v0.1.0/txt/respond", # requester ask responder to send reply to this topic
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:repondToMsgId=> "", # responder is responding to this msg id
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:taskstatus=> "", # "complete", "fail", "waiting" or other status
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:timestamp=> Dates.now(),
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:msgId=> "$(uuid4())",
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)
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julia> newAgent = ChatAgent.agentReact(
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"Jene",
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mqttClientSpec,
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role=:assistant_react,
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msgMeta=msgMeta
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)
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julia> respond = ChatAgent.conversation(newAgent, "Hi! how are you?")
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```
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"""
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function conversation(a::T, usermsg::String) where {T<:agent}
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userintend = identifyUserIntention(a, usermsg)
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@show userintend
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respond = nothing
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# AI thinking mode
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if userintend == "chat"
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a.context = conversationSummary(a) #TODO should be long conversation before use summary because it leaves out details
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_ = addNewMessage(a, "user", usermsg)
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prompt = generatePrompt_mistral_openorca(a, usermsg)
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@show prompt
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respond = sendReceivePrompt(a, prompt)
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respond = split(respond, "<|im_end|>")[1]
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respond = replace(respond, "\n" => "")
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_ = addNewMessage(a, "assistant", respond)
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@show respond
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elseif userintend == "wine" #WORKING
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if a.thought == "nothing" # new thought
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a.context = conversationSummary(a)
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_ = addNewMessage(a, "user", usermsg)
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prompt = generatePrompt_react_mistral_openorca(a, usermsg)
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respond = work(a, prompt)
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else # continue thought
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error("wine done")
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end
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elseif userintend == "thought"
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else
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error("undefined condition userintend = $userintend")
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end
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# error("conversation done")
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return respond
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end
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"""
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Continuously run llm functions except when llm is getting ANS: or chatbox.
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"""
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function work(a::T, prompt::String) where {T<:agent}
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respond = nothing
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while true
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@show prompt
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toolname = nothing
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toolinput = nothing
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respond = sendReceivePrompt(a, prompt)
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@show respond
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try
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respond = split(respond, "Obs:")[1]
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catch
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end
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headers = detectCharacters(respond,
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["QTS:", "Plan:", "Thought:", "Act:", "ActInput:", "Obs:", ".....", "ANS:"])
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@show headers
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chunkedtext = chunktext(respond, headers)
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@show chunkedtext
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if headers[1][:char] == "ANS:"
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a.thought = "nothing" # question finished, no more thought
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respond = chunkedtext[1][:body]
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_ = addNewMessage(a, "assistant", respond)
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break
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else
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# check for tool being called
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ActInd = findDetectedCharacter(headers, "Act:")[1]
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toolname = toolNameBeingCalled(chunkedtext[ActInd][:body], a.tools)
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toolinput = chunkedtext[ActInd+1][:body]
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if toolname == "chatbox" # chat with user
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a.thought *= toolinput
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respond = toolinput
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_ = addNewMessage(a, "assistant", respond)
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break
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else # function call
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error("function call")
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f = a.tools[Symbol(toolname)][:func]
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_result = f(toolinput)
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result = "Obs: $_result\n"
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a.thought *= result
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prompt = a.thought
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end
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end
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end
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return respond
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end
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"""
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make a conversation summary.
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```jldoctest
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julia> conversation = [
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Dict(:role=> "user", :content=> "I would like to get a bottle of wine", :timestamp=> Dates.now()),
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Dict(:role=> "assistant", :content=> "What kind of Thai dishes are you having?", :timestamp=> Dates.now()),
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Dict(:role=> "user", :content=> "It a pad thai.", :timestamp=> Dates.now()),
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Dict(:role=> "assistant", :content=> "Is there any special occasion for this event?", :timestamp=> Dates.now()),
|
|
Dict(:role=> "user", :content=> "We'll hold a wedding party at the beach.", :timestamp=> Dates.now()),
|
|
Dict(:role=> "assistant", :content=> "What is your preferred type of wine?", :timestamp=> Dates.now()),
|
|
Dict(:role=> "user", :content=> "I like dry white wine with medium tanins.", :timestamp=> Dates.now()),
|
|
Dict(:role=> "assistant", :content=> "What is your preferred price range for this bottle of wine?", :timestamp=> Dates.now()),
|
|
Dict(:role=> "user", :content=> "lower than 50 dollars.", :timestamp=> Dates.now()),
|
|
Dict(:role=> "assistant", :content=> "Based on your preferences and our stock, I recommend the following two wines for you:
|
|
1. Pierre Girardin \"Murgers des Dents de Chien\" - Saint-Aubin 1er Cru (17 USD)
|
|
2. Etienne Sauzet'Les Perrieres' - Puligny Montrachet Premier Cru (22 USD)
|
|
The first wine, Pierre Girardin \"Murgers des Dents de Chien\" - Saint-Aubin 1er Cru, is a great choice for its affordable price and refreshing taste.
|
|
It pairs well with Thai dishes and will be perfect for your beach wedding party.
|
|
The second wine, Etienne Sauzet'Les Perrieres' - Puligny Montrachet Premier Cru, offers a more complex flavor profile and slightly higher price point, but still remains within your budget.
|
|
Both wines are suitable for serving at 22 C temperature.", :timestamp=> Dates.now()),
|
|
]
|
|
|
|
julia> summary = conversationSummary(conversation)
|
|
```
|
|
"""
|
|
function conversationSummary(a::T) where {T<:agent}
|
|
prompt =
|
|
"""
|
|
<|im_start|>system
|
|
You talked with a user earlier.
|
|
Now you make a detailed bullet summary of the conversation from your perspective.
|
|
|
|
Here are the context:
|
|
{context}
|
|
Here are the conversation:
|
|
{conversation}
|
|
<|im_end|>
|
|
|
|
"""
|
|
conversation = ""
|
|
summary = "nothing"
|
|
if length(a.messages)!= 0
|
|
for msg in a.messages
|
|
role = msg[:role]
|
|
content = msg[:content]
|
|
|
|
if role == "user"
|
|
conversation *= "$role: $content\n"
|
|
elseif role == "assistant"
|
|
conversation *= "I: $content\n"
|
|
else
|
|
error("undefied condition role = $role")
|
|
end
|
|
end
|
|
prompt = replace(prompt, "{conversation}" => conversation)
|
|
prompt = replace(prompt, "{context}" => a.context)
|
|
println("<<<<<")
|
|
@show prompt
|
|
result = sendReceivePrompt(a, prompt)
|
|
summary = result === nothing ? "nothing" : result
|
|
summary = replace(summary, "<|im_end|>" => "")
|
|
if summary[1:1] == "\n"
|
|
summary = summary[2:end]
|
|
end
|
|
@show summary
|
|
println(">>>>>")
|
|
end
|
|
|
|
return summary
|
|
end
|
|
|
|
# function work2(a::agentReact, usermsg::String)
|
|
# addNewMessage(a, "user", usermsg)
|
|
# userIntent = identifyUserIntention(a, usermsg)
|
|
# @show userIntent
|
|
|
|
# # checkReasonableness()
|
|
|
|
# if userIntent == "chat"
|
|
# prompt = generatePrompt_tokenPrefix(a, userToken="Q:", assistantToken="A:")
|
|
# result = sendReceivePrompt(a, prompt)
|
|
# addNewMessage(a, "assistant", result)
|
|
|
|
# return result
|
|
# elseif userIntent == "task"
|
|
# while true
|
|
# if thought == "nothing" # no unfinished thought
|
|
# prompt = generatePrompt_react_mistral_openorca(
|
|
# a.messages, a.roles[a.role], a.context, a.tools)
|
|
# output = sendReceivePrompt(a, prompt)
|
|
# obscount = count(output["text"], "Obs:")
|
|
# a.thought = prompt * out
|
|
# if contains(output["text"], "ANS:") # know the answer
|
|
# a.thought = "nothing"
|
|
# return output["text"]
|
|
# else
|
|
# out = split(output["text"], "Obs:")[1] # LLM may generate long respond with multiple Obs: but I do only 1 Obs: at a time(1st).
|
|
# act = react_act(out, "first")
|
|
# actinput = react_actinput(out, "first")
|
|
# toolname = toolNameBeingCalled(act, a.tools)
|
|
# if toolname == "chatbox"
|
|
# return actinput
|
|
# else # function call
|
|
# toolresult = a.tools[toolname][:func](actinput)
|
|
# Obs = "Obs: $toolresult\n" # observe in ReAct agent
|
|
# work(a, Obs)
|
|
# end
|
|
# end
|
|
# else # continue thought
|
|
# usermsg = "Obs: $usermsg"
|
|
# prompt = a.thought * usermsg
|
|
# output = sendReceivePrompt(a, prompt)
|
|
# obs = count(output["text"], "Obs:")
|
|
# out = split(output["text"], "Obs:")[1]
|
|
# a.thought = prompt * out
|
|
# if obs == 0 # llm config has too short characters generation
|
|
# error("No Obs: detected. Probably LLM config has too short max_tokens generation")
|
|
# elseif obs == 1 # first conversation
|
|
# act = react_act(out, "first")
|
|
# actinput = react_actinput(out, "first")
|
|
# toolname = toolNameBeingCalled(act, a.tools)
|
|
# if toolname == "chatbox"
|
|
# return actinput
|
|
# else # function call
|
|
# toolresult = a.tools[toolname][:func](actinput)
|
|
# Obs = "Obs: $toolresult\n" # observe in ReAct agent
|
|
# work(a, Obs)
|
|
# end
|
|
# else # later conversation
|
|
# act = react_act(out, "last")
|
|
# actinput = react_actinput(out, "last")
|
|
# toolname = toolNameBeingCalled(act, a.tools)
|
|
# if toolname == "chatbox"
|
|
# return actinput
|
|
# else # function call
|
|
# toolresult = a.tools[toolname][:func](actinput)
|
|
# Obs = "Obs: $toolresult\n" # observe in ReAct agent
|
|
# work(a, Obs)
|
|
# end
|
|
# end
|
|
# end
|
|
# else
|
|
# error("user intent $userIntent not define $(@__LINE__)")
|
|
# end
|
|
# end
|
|
|
|
function workContinue(a::agent)
|
|
|
|
end
|
|
|
|
|
|
function identifyUserIntention(a::T, usermsg::String) where {T<:agent}
|
|
prompt =
|
|
"""
|
|
<|im_start|>system
|
|
You are a helpful assistant. Your job is to determine intention of the question.
|
|
|
|
You have the following choices:
|
|
If the user question is about general conversation say, "{chat}".
|
|
If the user question is about getting wine say, "{wine}".
|
|
<|im_end|>
|
|
|
|
Here are the context for the question:
|
|
{context}
|
|
|
|
<|im_start|>user
|
|
{input}
|
|
<|im_end|>
|
|
<|im_start|>assistant
|
|
|
|
"""
|
|
prompt = replace(prompt, "{context}" => "")
|
|
prompt = replace(prompt, "{input}" => usermsg)
|
|
|
|
|
|
result = sendReceivePrompt(a, prompt)
|
|
answer = result === nothing ? nothing : GeneralUtils.getStringBetweenCharacters(result, "{", "}")
|
|
|
|
return answer
|
|
end
|
|
|
|
"""
|
|
Send a msg to registered mqtt topic within mqttClient.
|
|
|
|
```jldoctest
|
|
julia> using JSON3, UUIDs, Dates, FileIO, CommUtils, ChatAgent
|
|
julia> mqttClientSpec = (
|
|
clientName= "someclient", # name of this client
|
|
clientID= "$(uuid4())",
|
|
broker= "mqtt.yiem.ai",
|
|
pubtopic= (imgAI="img/api/v0.0.1/gpu/request",
|
|
txtAI="txt/api/v0.1.0/gpu/request"),
|
|
subtopic= (imgAI="agent/api/v0.1.0/img/respond",
|
|
txtAI="agent/api/v0.1.0/txt/respond"),
|
|
keepalive= 30,
|
|
)
|
|
julia> msgMeta = Dict(
|
|
:msgPurpose=> "updateStatus",
|
|
:from=> "agent",
|
|
:to=> "llmAI",
|
|
:requestrespond=> "request",
|
|
:sendto=> "", # destination topic
|
|
:replyTo=> "agent/api/v0.1.0/txt/respond", # requester ask responder to send reply to this topic
|
|
:repondToMsgId=> "", # responder is responding to this msg id
|
|
:taskstatus=> "", # "complete", "fail", "waiting" or other status
|
|
:timestamp=> Dates.now(),
|
|
:msgId=> "$(uuid4())",
|
|
)
|
|
julia> newAgent = ChatAgent.agentReact(
|
|
"Jene",
|
|
mqttClientSpec,
|
|
role=:assistant_react,
|
|
msgMeta=msgMeta
|
|
)
|
|
```
|
|
"""
|
|
function sendReceivePrompt(a::T, prompt::String; timeout::Int=30) where {T<:agent}
|
|
a.msgMeta[:msgId] = "$(uuid4())" # new msg id for each msg
|
|
msg = Dict(
|
|
:msgMeta=> a.msgMeta,
|
|
:txt=> prompt,
|
|
)
|
|
payloadChannel = Channel(1)
|
|
|
|
# send prompt
|
|
CommUtils.request(a.mqttClient, msg)
|
|
starttime = Dates.now()
|
|
result = nothing
|
|
|
|
while true
|
|
timepass = (Dates.now() - starttime).value / 1000.0
|
|
CommUtils.mqttRun(a.mqttClient, payloadChannel)
|
|
if isready(payloadChannel)
|
|
topic, payload = take!(payloadChannel)
|
|
if payload[:msgMeta][:repondToMsgId] == msg[:msgMeta][:msgId]
|
|
result = haskey(payload, :txt) ? payload[:txt] : nothing
|
|
break
|
|
end
|
|
elseif timepass <= timeout
|
|
# skip, within waiting period
|
|
elseif timepass > timeout
|
|
result = nothing
|
|
break
|
|
else
|
|
error("undefined condition $(@__LINE__)")
|
|
end
|
|
end
|
|
|
|
return result
|
|
end
|
|
|
|
"""
|
|
Extract toolname from text.
|
|
```jldoctest
|
|
julia> text = " internetsearch\n"
|
|
julia> tools = Dict(
|
|
:internetsearch=>Dict(
|
|
:name => "internetsearch",
|
|
:description => "Useful for when you need to search the Internet",
|
|
:input => "Input should be a search query.",
|
|
:output => "",
|
|
# :func => internetsearch # function
|
|
),
|
|
:chatbox=>Dict(
|
|
:name => "chatbox",
|
|
:description => "Useful for when you need to ask a customer what you need to know or to talk with them.",
|
|
:input => "Input should be a conversation to customer.",
|
|
:output => "" ,
|
|
),
|
|
)
|
|
julia> toolname = toolNameBeingCalled(text, tools)
|
|
```
|
|
"""
|
|
function toolNameBeingCalled(text::T, tools::Dict) where {T<:AbstractString}
|
|
toolNameBeingCalled = nothing
|
|
for (k, v) in tools
|
|
toolname = String(k)
|
|
if contains(text, toolname)
|
|
toolNameBeingCalled = toolname
|
|
break
|
|
end
|
|
end
|
|
return toolNameBeingCalled
|
|
end
|
|
|
|
|
|
|
|
|
|
|
|
#TODO
|
|
function checkReasonableness(userMsg::String, context::String, tools)
|
|
# Ref: https://www.youtube.com/watch?v=XV4IBaZqbps
|
|
|
|
prompt =
|
|
"""
|
|
<|im_start|>system
|
|
You are a helpful assistant. Your job is to check the reasonableness of user questions.
|
|
If the user question can be answered given the tools available say, "This is a reasonable question".
|
|
If the user question cannot be answered then provide some feedback to the user that may improve
|
|
their question.
|
|
|
|
Here is the context for the question:
|
|
{context}
|
|
|
|
<|im_end|>
|
|
|
|
<|im_start|>user
|
|
{question}
|
|
<|im_end|>
|
|
<|im_start|>assistant
|
|
|
|
"""
|
|
|
|
context = "You have access to the following tools:
|
|
WineStock: useful for when you need to find info about wine by matching your description, price, name or ID. Input should be a search query with as much details as possible."
|
|
prompt = replace(prompt, "{question}" => userMsg)
|
|
prompt = replace(prompt, "{context}" => context)
|
|
|
|
output_py = llm(
|
|
prompt,
|
|
max_tokens=512,
|
|
temperature=0.1,
|
|
# top_p=top_p,
|
|
echo=false,
|
|
stop=["</response>", "<<END>>", ],
|
|
)
|
|
_output_jl = pyconvert(Dict, output_py);
|
|
output = pyconvert(Dict, _output_jl["choices"][1]);
|
|
output["text"]
|
|
|
|
end
|
|
|
|
|
|
function react_plan(text::String, firstlast="first")
|
|
"Plan" * GeneralUtils.getStringBetweenCharacters(text, "Plan", "Thought", firstlast=firstlast)
|
|
end
|
|
|
|
function react_thought(text::String, firstlast="first")
|
|
"Thought" * GeneralUtils.getStringBetweenCharacters(text, "Thought", "Act", firstlast=firstlast)
|
|
end
|
|
|
|
function react_act(text::String, firstlast="first")
|
|
"Act" * GeneralUtils.getStringBetweenCharacters(text, "Act", "ActInput", firstlast=firstlast)
|
|
end
|
|
|
|
function react_actinput(text::String, firstlast="first")
|
|
"ActInput" * GeneralUtils.getStringBetweenCharacters(text, "ActInput", "Obs", firstlast=firstlast)
|
|
end
|
|
|
|
"""
|
|
Detect given characters. Output is a list of named tuple of detected char.
|
|
|
|
```jldoctest
|
|
julia> text = "I like to eat apples and use utensils."
|
|
julia> characters = ["eat", "use", "i"]
|
|
julia> result = detectCharacters(text, characters)
|
|
4-element Vector{Any}:
|
|
(char = "i", start = 4, stop = 4)
|
|
(char = "eat", start = 11, stop = 13)
|
|
(char = "use", start = 26, stop = 28)
|
|
(char = "i", start = 35, stop = 35)
|
|
```
|
|
"""
|
|
function detectCharacters(text::T1, characters::Vector{T2}) where {T1<:AbstractString, T2<:AbstractString}
|
|
result = []
|
|
for i in eachindex(text)
|
|
for char in characters
|
|
l = length(char)
|
|
char_startInd = i
|
|
char_endInd = i+l-1 # -1 because Julia use inclusive index
|
|
|
|
if char_endInd > length(text)
|
|
# skip
|
|
else
|
|
try # some time StringIndexError: invalid index [535], valid nearby indices [534]=>'é', [536]=>' '
|
|
if text[char_startInd: char_endInd] == char
|
|
push!(result, (char=char, start=char_startInd, stop=char_endInd))
|
|
end
|
|
catch
|
|
end
|
|
end
|
|
end
|
|
end
|
|
|
|
return result
|
|
end
|
|
|
|
"""
|
|
Find a given character from a vector of named tuple.
|
|
Output is character location index inside detectedCharacters
|
|
|
|
```jldoctest
|
|
julia a = [ (char = "i", start = 4, stop = 4)
|
|
(char = "eat", start = 11, stop = 13)
|
|
(char = "use", start = 26, stop = 28)
|
|
(char = "i", start = 35, stop = 35) ]
|
|
julia> findDetectedCharacter(a, "i")
|
|
[1, 4]
|
|
```
|
|
"""
|
|
function findDetectedCharacter(detectedCharacters, character)
|
|
allchar = [i[1] for i in detectedCharacters]
|
|
return findall(isequal.(allchar, character))
|
|
end
|
|
|
|
"""
|
|
Chunk a text into smaller pieces by header.
|
|
```jldoctest
|
|
julia> text = "Plan: First, we need to find out what kind of wine the user wants."
|
|
julia> headers = detectCharacters(text, ["Nope", "sick", "First", "user", "Then", ])
|
|
3-element Vector{Any}:
|
|
(char = "First", start = 7, stop = 11)
|
|
(char = "user", start = 56, stop = 59)
|
|
(char = "Then", start = 102, stop = 105)
|
|
julia> chunkedtext = chunktext(text, headers)
|
|
|
|
```
|
|
"""
|
|
function chunktext(text::T, headers) where {T<:AbstractString}
|
|
result = []
|
|
|
|
for (i, v) in enumerate(headers)
|
|
if i < length(headers)
|
|
nextheader = headers[i+1]
|
|
body = text[v[:stop]+1: nextheader[:start]-1]
|
|
push!(result, (header=v[:char], body=body))
|
|
else
|
|
body = text[v[:stop]+1: end]
|
|
push!(result, (header=v[:char], body=body))
|
|
end
|
|
end
|
|
|
|
return result
|
|
end
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
end # module |