ton/ChatAgent_v2
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity
Files
7d0e45d0677651afa0a3141d0bb22e9af2003015
ChatAgent_v2/src/interface.jl
tonaerospace 7d0e45d067 update
2023-12-16 17:44:18 +00:00

1615 lines
44 KiB
Julia
Executable File
Raw Blame History

module interface
export agentReact, agentReflex,
addNewMessage, clearMessage, removeLatestMsg, conversation, writeEvaluationGuideline,
grading, analyze, selfReflext, actor_mistral_openorca2, formulateUserresponse,
extractinfo, updateEnvState, chat_mistral_openorca
using JSON3, DataStructures, Dates, UUIDs, HTTP
using CommUtils, GeneralUtils
using ..type, ..utils
# ---------------------------------------------------------------------------- #
# pythoncall setting #
# ---------------------------------------------------------------------------- #
# Ref: https://github.com/JuliaPy/PythonCall.jl/issues/252
# by setting the following variables, PythonCall will use system python or conda python and
# packages installed by system or conda
# if these setting are not set (comment out), PythonCall will use its own python and package that
# installed by CondaPkg (from env_preparation.jl)
# ENV["JULIA_CONDAPKG_BACKEND"] = "Null"
# systemPython = split(read(`which python`, String), "\n")[1]
# ENV["JULIA_PYTHONCALL_EXE"] = systemPython # find python location with $> which python ex. raw"/root/conda/bin/python"
# using PythonCall
# const py_agents = PythonCall.pynew()
# const py_llms = PythonCall.pynew()
# function __init__()
# # PythonCall.pycopy!(py_cv2, pyimport("cv2"))
# # equivalent to from urllib.request import urlopen in python
# PythonCall.pycopy!(py_agents, pyimport("langchain.agents"))
# PythonCall.pycopy!(py_llms, pyimport("langchain.llms"))
# end
#------------------------------------------------------------------------------------------------100
""" Add new message to agent.
Arguments:
Return:
```jldoctest
julia> addNewMessage(agent1, "user", "Where should I go to buy snacks")
```
"""
function addNewMessage(a::T1, role::String, content::T2) where {T1<:agent, T2<:AbstractString}
if role ∉ a.availableRole # guard against typo
error("role is not in agent.availableRole $(@__LINE__)")
end
# check whether user messages exceed limit
userMsg = 0
for i in a.messages
if i[:role] == "user"
userMsg += 1
end
end
messageleft = 0
if userMsg > a.maxUserMsg # delete all conversation
clearMessage(a)
messageleft = a.maxUserMsg
else
userMsg += 1
d = Dict(:role=> role, :content=> content, :timestamp=> Dates.now())
push!(a.messages, d)
messageleft = a.maxUserMsg - userMsg
end
return messageleft
end
function clearMessage(a::T) where {T<:agent}
for i in eachindex(a.messages)
if length(a.messages) > 0
pop!(a.messages)
else
break
end
end
a.memory[:shortterm] = OrderedDict{String, Any}()
a.memory[:log] = OrderedDict{String, Any}()
@show a.messages
end
function removeLatestMsg(a::T) where {T<:agent}
if length(a.messages) > 1
pop!(a.messages)
end
end
# function generatePrompt_mistral_openorca(a::T, usermsg::String, role::Symbol) where {T<:agent}
# prompt =
# """
# <|im_start|>system
# {systemMsg}
# <|im_end|>
# Here are the context for the question:
# {context}
# """
# prompt = replace(prompt, "{systemMsg}" => a.roles[role])
# toolnames = ""
# toollines = ""
# for (toolname, v) in a.tools
# toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
# toollines *= toolline
# toolnames *= "$toolname,"
# end
# prompt = replace(prompt, "{toolnames}" => toolnames)
# prompt = replace(prompt, "{tools}" => toollines)
# prompt = replace(prompt, "{context}" => a.context)
# prompt *= "<|im_start|>user\n" * usermsg * "\n<|im_end|>\n"
# prompt *= "<|im_start|>assistant\n"
# return prompt
# end
# function generatePrompt_mistral_openorca(a::T, usermsg::String,
# thinkingMode::Symbol=:nothinking) where {T<:agent}
# prompt =
# """
# <|im_start|>system
# {systemMsg}
# You have access to the following tools:
# {tools}
# {thinkingMode}
# <|im_end|>
# Here are the context for the question:
# {context}
# """
# prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
# prompt = replace(prompt, "{thinkingMode}" => a.thinkingMode[thinkingMode])
# toolnames = ""
# toollines = ""
# for (toolname, v) in a.tools
# toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
# toollines *= toolline
# toolnames *= "$toolname,"
# end
# prompt = replace(prompt, "{toolnames}" => toolnames)
# prompt = replace(prompt, "{tools}" => toollines)
# prompt = replace(prompt, "{context}" => a.context)
# prompt *= "<|im_start|>user\nQuestion: " * usermsg * "\n<|im_end|>\n"
# prompt *= "<|im_start|>assistant\n"
# return prompt
# end
function generatePrompt_mistral_openorca(a::T, usermsg::String,
thinkingMode::Symbol=:nothinking) where {T<:agent}
prompt =
"""
<|im_start|>system
{systemMsg}
{tools}
{thinkingMode}
<|im_end|>
Here are the context for the stimulus:
{context}
"""
prompt = replace(prompt, "{systemMsg}" => a.roles[a.role])
prompt = replace(prompt, "{thinkingMode}" => a.thinkingMode[thinkingMode])
toolnames = ""
toollines = ""
for (toolname, v) in a.tools
toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
toollines *= toolline
toolnames *= "$toolname,"
end
prompt = replace(prompt, "{toolnames}" => toolnames)
prompt = replace(prompt, "{context}" => a.context)
prompt *= "<|im_start|>user\nStimulus: " * usermsg * "\n<|im_end|>\n"
prompt *= "<|im_start|>assistant\n"
return prompt
end
function chat_mistral_openorca(a::agentReflex)
"""
general prompt format:
"
<|im_start|>system
{role}
{tools}
{thinkingFormat}
{context}
<|im_end|>
<|im_start|>user
{usermsg}
<|im_end|>
<|im_start|>assistant
"
Note:
{context} =
"
{earlierConversation}
{env state}
{shortterm memory}
{longterm memory}
"
"""
prompt =
"""
<|im_start|>system
$(a.roles[a.role])
Your earlier talk with the user:
$(a.earlierConversation)
<|im_end|>
$(messagesToString(a.messages))
<|im_start|>assistant
"""
response = sendReceivePrompt(a, prompt)
response = split(response, "<|im_end|>")[1]
return response
end
function planner_mistral_openorca(a::agentReflex)
"""
general prompt format:
"
<|im_start|>system
{role}
{tools}
{thinkingFormat}
<|im_end|>
{context}
<|im_start|>user
{usermsg}
<|im_end|>
<|im_start|>assistant
"
Note:
{context} =
"
{earlierConversation}
{env state}
{shortterm memory}
{longterm memory}
"
"""
conversation = messagesToString(a.messages)
toollines = ""
for (toolname, v) in a.tools
if toolname ∉ ["chatbox"]
toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
toollines *= toolline
end
end
assistant_plan_prompt =
"""
<|im_start|>system
$(a.roles[a.role])
The required info you need for wine recommendation:
- type of food: ask the user
- occasion: ask the user
- user's personal taste of wine: ask the user
- wine price range: ask the user
- ambient temperature at the serving location: ask the user
- wines we have in stock
You provide a personalized recommendation of up to two wines based on the user's info above, and you describe the benefits of each wine in detail.
You have access to the following tools:
$toollines
Use the following format:
Objective: the objective you intend to do
Aware: ask yourself what info you don't have?
Plan: first you should always think about the objective, the info you have, the info you don't have and thoroughly then extract and devise a complete, step by step plan (pay attention to correct numeral calculation and commonsense).
p.s.1 each step of the plan should be a single action.
p.s.2 ask the user all you need to know and then search your inventory.
<|im_end|>
$conversation
<|im_start|>assistant
Objective:
"""
# p.s.1 each step of the plan should be a single action.
# p.s.2 the last step should be about responding.
result = sendReceivePrompt(a, assistant_plan_prompt, max_tokens=1024, temperature=0.1)
@show raw_plan = result
x = split(result, "<|im_end|>")[1]
x = split(x, "Step")[1]
x = split(x, "Plan:")
objective = x[1]
plan = x[2]
return objective, plan
end
# function planner_mistral_openorca(a::agentReflex)
# """
# general prompt format:
# "
# <|im_start|>system
# {role}
# {tools}
# {thinkingFormat}
# <|im_end|>
# {context}
# <|im_start|>user
# {usermsg}
# <|im_end|>
# <|im_start|>assistant
# "
# Note:
# {context} =
# "
# {earlierConversation}
# {env state}
# {shortterm memory}
# {longterm memory}
# "
# """
# prompt =
# """
# <|im_start|>system
# {role}
# {roleSpecificKnowledge}
# {tools}
# {thinkingFormat}
# {context}
# <|im_end|>
# <|im_start|>user
# {usermsg}
# <|im_end|>
# <|im_start|>assistant
# Plan:
# """
# prompt = replace(prompt, "{role}" => a.roles[a.role])
# prompt = replace(prompt, "{thinkingFormat}" => a.thinkingFormat[:planner])
# roleSpecificKnowledge =
# """
# Info you need from the user to be able to help them selecting their best wine:
# - type of food
# - occasion
# - user's personal taste of wine
# - wine price range
# - ambient temperature at the serving location
# - wines we have in stock
# You job is to provide a personalized recommendation of up to two wines based on the user's info above, and you describe the benefits of each wine in detail.
# """
# prompt = replace(prompt, "{roleSpecificKnowledge}" => roleSpecificKnowledge)
# toolnames = ""
# toollines = ""
# for (toolname, v) in a.tools
# toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
# toollines *= toolline
# toolnames *= "$toolname,"
# end
# prompt = replace(prompt, "{toolnames}" => toolnames)
# prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
# # prepare contex
# context =
# """
# My earlier talk with the user:
# $(a.earlierConversation)
# My earlier experience
# $(experience(a.memory[:longterm]))
# """
# prompt = replace(prompt, "{context}" => context)
# # initialize short term memory
# prompt = replace(prompt, "{usermsg}" => "Stimulus: $(a.memory[:shortterm]["user:"])")
# return prompt
# end
function actor_mistral_openorca(a::agentReflex)
"""
general prompt format:
"
<|im_start|>system
{role}
{tools}
{thinkingFormat}
<|im_end|>
{context}
<|im_start|>user
{usermsg}
<|im_end|>
<|im_start|>assistant
"
Note:
{context} =
"
{earlierConversation}
{env state}
{shortterm memory}
{longterm memory}
"
"""
prompt =
"""
<|im_start|>system
$(a.roles[a.role])
{tools}
$(a.thinkingFormat[:actor])
{context}
<|im_end|>
{shorttermMemory}
Thought $(a.step):
"""
s = dictToString(a.memory[:shortterm], skiplist=["user:", "Plan 1:"])
prompt = replace(prompt, "{shorttermMemory}" => s)
toolnames = ""
toollines = ""
for (toolname, v) in a.tools
toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
toollines *= toolline
toolnames *= "$toolname, "
end
prompt = replace(prompt, "{toolnames}" => toolnames)
prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
conversation = messagesToString_nomark(a.messages, addressAIas="I")
context =
"""
Your talk with the user:
$conversation
{env state}
{longterm memory}
{plan}
"""
# context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
context = replace(context, "{env state}" => "")
context = replace(context, "{longterm memory}" => "")
context = replace(context, "{plan}" => "My plan:\n$(a.memory[:shortterm]["Plan $(a.attempt):"])")
prompt = replace(prompt, "{context}" => context)
return prompt
end
# function actor_mistral_openorca(a::agentReflex)
# """
# general prompt format:
# "
# <|im_start|>system
# {role}
# {tools}
# {thinkingFormat}
# <|im_end|>
# {context}
# <|im_start|>user
# {usermsg}
# <|im_end|>
# <|im_start|>assistant
# "
# Note:
# {context} =
# "
# {earlierConversation}
# {env state}
# {shortterm memory}
# {longterm memory}
# "
# """
# mark = "$(a.step)"
# prompt =
# """
# <|im_start|>system
# {role}
# {tools}
# {thinkingFormat}
# {context}
# <|im_end|>
# {shorttermMemory}
# Thought $(a.step):
# """
# prompt = replace(prompt, "{role}" => a.roles[a.role])
# prompt = replace(prompt, "{thinkingFormat}" => a.thinkingFormat[:actor])
# prompt = replace(prompt, "{step}" => a.step)
# s = dictToString(a.memory[:shortterm], skiplist=["user:", "Plan 1:"])
# prompt = replace(prompt, "{shorttermMemory}" => s)
# toolnames = ""
# toollines = ""
# for (toolname, v) in a.tools
# toolline = "$toolname: $(v[:description]) $(v[:input]) $(v[:output])\n"
# toollines *= toolline
# toolnames *= "$toolname, "
# end
# prompt = replace(prompt, "{toolnames}" => toolnames)
# prompt = replace(prompt, "{tools}" => "You have access to the following tools:\n$toollines")
# context =
# """
# {env state}
# {longterm memory}
# {plan}
# """
# # context = replace(context, "{earlierConversation}" => "My earlier talk with the user:\n$(a.earlierConversation)")
# context = replace(context, "{env state}" => "")
# context = replace(context, "{longterm memory}" => "")
# context = replace(context, "{plan}" => "My plan:\n$(a.memory[:shortterm]["Plan $(a.attempt):"])")
# prompt = replace(prompt, "{context}" => context)
# return prompt
# end
"""
Chat with llm.
```jldoctest
julia> using JSON3, UUIDs, Dates, FileIO, CommUtils, ChatAgent
julia> mqttClientSpec = (
clientName= "someclient", # name of this client
clientID= "$(uuid4())",
broker= "mqtt.yiem.cc",
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/response",
txtAI="agent/api/v0.1.0/txt/response"),
keepalive= 30,
)
julia> msgMeta = Dict(
:msgPurpose=> "updateStatus",
:from=> "agent",
:to=> "llmAI",
:requestresponse=> "request",
:sendto=> "", # destination topic
:replyTo=> "agent/api/v0.1.0/txt/response", # requester ask responseer to send reply to this topic
:repondToMsgId=> "", # responseer is responseing 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
)
julia> response = ChatAgent.conversation(newAgent, "Hi! how are you?")
```
"""
function conversation(a::agentReflex, usermsg::String; attemptlimit::Int=3)
a.attemptlimit = attemptlimit
workstate = nothing
response = nothing
# a.earlierConversation = conversationSummary(a)
_ = addNewMessage(a, "user", usermsg)
isusetools = isUseTools(a)
# newinfo = extractinfo(a, usermsg)
# a.env = newinfo !== nothing ? updateEnvState(a, newinfo) : a.env
@show isusetools
if isusetools # use tools before responseing
workstate, response = work(a)
end
# if LLM using chatbox, use returning msg form chatbox as conversation response
if workstate == "chatbox"
#TODO paraphrase msg so that it is human friendlier word.
else
response = chat_mistral_openorca(a)
end
response = removeTrailingCharacters(response)
_ = addNewMessage(a, "assistant", response)
return response
end
"""
Continuously run llm functions except when llm is getting Answer: or chatbox.
There are many work() depend on thinking mode.
"""
function work(a::agentReflex)
workstate = nothing
response = nothing
while true # Work loop
a.memory[:shortterm] = OrderedDict{String, Any}()
a.memory[:log] = OrderedDict{String, Any}()
# plan
if a.attempt <= a.attemptlimit
toolname = nothing
toolinput = nothing
a.step = 0
objective, plan = planner_mistral_openorca(a)
println("")
@show objective
@show plan
# sometimes LLM add not-need word I don't want
# plan = splittext(response, ["Step 1", "<|im_end|>", "Response", "Execution",
# "Result", "Recommendation", "My response"])
# plan = replace(plan, "Plan:"=>"")
# println("")
# @show plan
a.memory[:shortterm]["Plan $(a.attempt):"] = plan
a.memory[:log]["Plan $(a.attempt):"] = plan
println("")
@show a.attempt
# enter actor loop
actorstate, msgToUser = actor(a) #WORKING
if actorstate == "chatbox"
response = msgToUser
workstate = actorstate
break
elseif actorstate == "all steps done"
println("all steps done")
response = formulateUserresponse(a)
println("")
formulatedresponse = response
@show formulatedresponse
a.memory[:shortterm]["response $(a.attempt):"] = response
a.memory[:log]["response $(a.attempt):"] = response
# evaluate. if score > 6/10 good enough.
guideline = writeEvaluationGuideline(a, objective)
println("")
@show guideline
score = grading(a, guideline, response)
@show score
if score > 5 # good enough answer
a.memory[:shortterm] = OrderedDict{String, Any}()
a.memory[:log] = OrderedDict{String, Any}()
break
else # self evaluate and reflect then try again
analysis = analyze(a)
println("")
@show analysis
lessonwithcontext = selfReflext(a, analysis)
println("")
@show lessonwithcontext
newdict = OrderedDict()
a.memory[:shortterm] = keepOnlyKeys(a.memory[:shortterm], ["user:"])
headerToDetect = ["Lesson:", "Context:", ]
headers = detectCharacters(lessonwithcontext, headerToDetect)
chunkedtext = chunktext(lessonwithcontext, headers)
a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
a.attempt += 1
println("")
println("RETRY $(a.attempt +1)")
println("")
end
else
error("undefied condition, actorstate $actorstate $(@__LINE__)")
break
end
else
error("attempt limit reach")
break
end
end
# good enough answer
return workstate, response
end
# function work(a::agentReflex, usermsg::String)
# response = nothing
# a.memory[:shortterm] = OrderedDict{String, Any}()
# a.memory[:log] = OrderedDict{String, Any}()
# a.memory[:shortterm]["user:"] = usermsg
# a.memory[:log]["user:"] = usermsg
# a.newplan = true
# while true # Work loop
# # plan
# if a.attempt <= a.attemptlimit
# toolname = nothing
# toolinput = nothing
# if a.newplan == true
# a.attempt += 1
# a.step = 0
# prompt_plan = planner_mistral_openorca(a)
# println("")
# @show prompt_plan
# response = sendReceivePrompt(a, prompt_plan, max_tokens=1024)
# # sometimes LLM add not-need word I don't want
# plan = splittext(response, ["Step 1", "<|im_end|>", "Response", "Execution",
# "Result", "Recommendation", "My response"])
# # plan = replace(plan, "Plan:"=>"")
# println("")
# @show plan
# a.newplan = false
# a.memory[:shortterm]["Plan $(a.attempt):"] = plan
# a.memory[:log]["Plan $(a.attempt):"] = plan
# end
# println("")
# @show a.attempt
# # enter actor loop
# actorstate, msgToUser = actor(a)
# if actorstate == "chatbox"
# response = msgToUser
# break
# elseif actorstate == "all steps done" || actorstate == "formulateUserresponse"
# println("all steps done")
# response = formulateUserresponse(a)
# println("")
# formulatedresponse = response
# @show formulatedresponse
# a.memory[:shortterm]["response $(a.attempt):"] = response
# a.memory[:log]["response $(a.attempt):"] = response
# # evaluate. if score < 8/10 try again.
# guideline = writeEvaluationGuideline(a, a.memory[:shortterm]["user:"])
# println("")
# @show guideline
# score = grading(a, guideline, response)
# @show score
# if score >= 6 # good enough answer
# break
# else # self evaluate and reflect then try again
# analysis = analyze(a)
# println("")
# @show analysis
# lessonwithcontext = selfReflext(a, analysis)
# println("")
# @show lessonwithcontext
# newdict = OrderedDict()
# a.memory[:shortterm] = keepOnlyKeys(a.memory[:shortterm], ["user:"])
# headerToDetect = ["Lesson:", "Context:", ]
# headers = detectCharacters(lessonwithcontext, headerToDetect)
# chunkedtext = chunktext(lessonwithcontext, headers)
# a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
# a.newplan = true
# println("")
# println("RETRY $(a.attempt +1)")
# println("")
# end
# else
# error("undefied condition, actorstate $actorstate $(@__LINE__)")
# break
# end
# else
# error("attempt limit reach")
# break
# end
# end
# # good enough answer
# # communicates with user
# _ = addNewMessage(a, "assistant", response)
# return response
# end
# function work(a::agentReflex, usermsg::String)
# response = nothing
# if a.thinkingmode == :new_thinking
# _ = addNewMessage(a, "user", usermsg)
# a.memory[:shortterm] = OrderedDict{String, Any}()
# a.memory[:log] = OrderedDict{String, Any}()
# a.memory[:shortterm]["user:"] = usermsg
# a.memory[:log]["user:"] = usermsg
# a.newplan = true
# elseif a.thinkingmode == :continue_thinking
# println("continue_thinking!!")
# _ = addNewMessage(a, "user", usermsg)
# a.memory[:shortterm]["Obs $(a.step):"] = usermsg
# a.memory[:log]["Obs $(a.step):"] = usermsg
# else
# error("undefined condition thinkingmode = $thinkingmode $(@__LINE__)")
# end
# while true # Work loop
# # plan
# if a.attempt <= a.attemptlimit
# toolname = nothing
# toolinput = nothing
# if a.newplan == true
# a.attempt += 1
# a.step = 0
# prompt_plan = planner_mistral_openorca(a)
# println("")
# @show prompt_plan
# response = sendReceivePrompt(a, prompt_plan, max_tokens=1024)
# # sometimes LLM add not-need word I don't want
# plan = splittext(response, ["Step 1", "<|im_end|>", "Response", "Execution",
# "Result", "Recommendation", "My response"])
# # plan = replace(plan, "Plan:"=>"")
# println("")
# @show plan
# a.newplan = false
# a.memory[:shortterm]["Plan $(a.attempt):"] = plan
# a.memory[:log]["Plan $(a.attempt):"] = plan
# end
# println("")
# @show a.attempt
# # enter actor loop
# actorstate, msgToUser = actor(a)
# if actorstate == "chatbox"
# response = msgToUser
# break
# elseif actorstate == "all steps done" || actorstate == "formulateUserresponse"
# println("all steps done")
# response = formulateUserresponse(a)
# println("")
# formulatedresponse = response
# @show formulatedresponse
# a.memory[:shortterm]["response $(a.attempt):"] = response
# a.memory[:log]["response $(a.attempt):"] = response
# # evaluate. if score < 8/10 try again.
# guideline = writeEvaluationGuideline(a, a.memory[:shortterm]["user:"])
# println("")
# @show guideline
# score = grading(a, guideline, response)
# @show score
# if score >= 6 # good enough answer
# break
# else # self evaluate and reflect then try again
# analysis = analyze(a)
# println("")
# @show analysis
# lessonwithcontext = selfReflext(a, analysis)
# println("")
# @show lessonwithcontext
# newdict = OrderedDict()
# a.memory[:shortterm] = keepOnlyKeys(a.memory[:shortterm], ["user:"])
# headerToDetect = ["Lesson:", "Context:", ]
# headers = detectCharacters(lessonwithcontext, headerToDetect)
# chunkedtext = chunktext(lessonwithcontext, headers)
# a.memory[:longterm][chunkedtext["Context:"]] = chunkedtext["Lesson:"]
# a.newplan = true
# println("")
# println("RETRY $(a.attempt +1)")
# println("")
# end
# else
# error("undefied condition, actorstate $actorstate $(@__LINE__)")
# break
# end
# else
# error("attempt limit reach")
# break
# end
# end
# # good enough answer
# # communicates with user
# _ = addNewMessage(a, "assistant", response)
# return response
# end
# function evaluate()
# end
"""
Actor function.
Arguments:
a, one of ChatAgent's agent.
plan, a step by step plan to response
Return:
case 1) if actor complete the plan successfully.
actorState = "all steps done" inidicates that all step in plan were done.
msgToUser = nothing.
case 2) if actor needs to talk to user for more context
actorState = "chatbox"
msgToUser = "message from assistant to user"
"""
function actor(a::agentReflex)
actorState = nothing
msgToUser = nothing
totalsteps = checkTotalStepInPlan(a)
while true # Actor loop
# decide whether to repeat step or do the next step
if a.step == 0
a.step = 1
else
decision, reason = goNogo(a)
println("")
@show decision
@show reason
# a.memory[:shortterm]["Check $(a.step):"] = reason
if decision == "Yes" # in case there is a cancel, go straight to evaluation
a.step += 1
elseif decision == "No"
# repeat the latest step
a.memory[:shortterm] = removeHeaders(a.memory[:shortterm], a.step, ["Plan"])
a.memory[:log] = removeHeaders(a.memory[:log], a.step, ["Plan"])
println("repeating step $(a.step)")
else
error("undefined condition decision = $decision $(@__LINE__)")
end
end
@show a.step
if a.step < totalsteps -1 # the last step of the plan is responding, let work() do this part
#WORKING check whether LLM already complete the current step
iscomplete = checkStepCompletion(a)
if iscomplete == false
prompt_actor = actor_mistral_openorca(a)
println("")
@show prompt_actor
response = sendReceivePrompt(a, prompt_actor)
response = splittext(response, ["Obs", "<|im_end|>"])
if !occursin("Thought", response)
response = "Thought: " * response
end
headerToDetect = ["Question:", "Plan:", "Thought:",
"Act:", "Actinput:", "Obs:", "...",
"Answer:", "Conclusion:", "Summary:"]
# replace headers with headers with correct attempt and step number
response = replaceHeaders(response, headerToDetect, a.step)
headers = detectCharacters(response, headerToDetect)
println("")
response_actor = response
@show response_actor
headerToDetect = ["Plan $(a.attempt):",
"Thought $(a.step):",
"Act $(a.step):",
"Actinput $(a.step):",
"Obs $(a.step):",
"Check $(a.step):",]
headers = detectCharacters(response, headerToDetect)
chunkedtext = chunktext(response, headers)
# add to memory
a.memory[:shortterm] = addShortMem!(a.memory[:shortterm], chunkedtext)
a.memory[:log] = addShortMem!(a.memory[:log], chunkedtext)
toolname = toolNameBeingCalled(chunkedtext["Act $(a.step):"], a.tools)
toolinput = chunkedtext["Actinput $(a.step):"]
@show toolname
@show toolinput
if toolname == "chatbox" # chat with user
msgToUser = toolinput
actorState = toolname
break
elseif toolname == "skipstep"
a.step += 1
else # function call
f = a.tools[toolname][:func]
toolresult = f(a, toolinput)
@show toolresult
a.memory[:shortterm]["Obs $(a.step):"] = toolresult
a.memory[:log]["Obs $(a.step):"] = toolresult
end
else
# already complete this step, go to the next step
end
else
actorState = "all steps done"
msgToUser = nothing
break
end
end
return actorState, msgToUser
end
""" Write evaluation guideline.
Arguments:
a, one of ChatAgent's agent.
usermsg, stimulus e.g. question, task and etc.
Return:
An evaluation guideline used to guage AI's work.
# Example
```jldoctest
julia> using ChatAgent, CommUtils
julia> agent = ChatAgent.agentReflex("Jene")
julia> usermsg = "What's AMD latest product?"
"
julia> evaluationGuideLine = writeEvaluationGuideline(agent, usermsg)
```
"""
function writeEvaluationGuideline(a::agentReflex, usermsg::T) where {T<:AbstractString}
prompt =
"""
<|im_start|>system
You have access to the following tools:
chatbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
Your work:
$usermsg
Your job are:
1. Write an evaluation guideline for your work in order to be able to evaluate your response.
2. An example of what the response should be.
<|im_end|>
"""
response = sendReceivePrompt(a, prompt)
return response
end
""" Determine a score out of 10 according to evaluation guideline.
Arguments:
a, one of ChatAgent's agent.
guidelines, an evaluation guideline.
shorttermMemory, a short term memory that logs what happened.
Return:
A score out of 10 based on guideline.
# Example
```jldoctest
julia> using ChatAgent, CommUtils
julia> agent = ChatAgent.agentReflex("Jene")
julia> shorttermMemory = OrderedDict{String, Any}(
"user" => "What's the latest AMD GPU?",
"Plan 1:" => " To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
"Act 1:" => " wikisearch\n",
"Actinput 1:" => " amd gpu latest\n",
"Obs 1:" => "No info available for your search query.",
"Act 2:" => " wikisearch\n",
"Actinput 2:" => " amd graphics card latest\n",
"Obs 2:" => "No info available for your search query.")
julia> guideline = "\nEvaluation Guideline:\n1. Check if the user's question has been understood correctly.\n2. Evaluate the steps taken to provide the information requested by the user.\n3. Assess whether the correct tools were used for the task.\n4. Determine if the user's request was successfully fulfilled.\n5. Identify any potential improvements or alternative approaches that could be used in the future.\n\nThe response should include:\n1. A clear understanding of the user's question.\n2. The steps taken to provide the information requested by the user.\n3. An evaluation of whether the correct tools were used for the task.\n4. A confirmation or explanation if the user's request was successfully fulfilled.\n5. Any potential improvements or alternative approaches that could be used in the future."
julia> score = grading(agent, guideline, shorttermMemory)
2
```
"""
function grading(a, guideline::T, text::T) where {T<:AbstractString}
prompt =
"""
<|im_start|>system
You have access to the following tools:
chatbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
$guideline
Your response: $text
You job are:
1. Evaluate your response using the evaluation guideline and an example response.
2. Give yourself a score out of 10 for your response.
Use the following format to answer:
{Evaluation} Score {}/10.
<|im_end|>
"""
println("")
prompt_grading = prompt
@show prompt_grading
response = sendReceivePrompt(a, prompt)
println("")
response_grading = response
@show response_grading
_score = split(response[end-5:end], "/")[1]
_score = split(_score, " ")[end]
score = parse(Int, _score)
return score
end
""" Analize work.
Arguments:
a, one of ChatAgent's agent.
Return:
A report of analized work.
# Example
```jldoctest
julia> using ChatAgent, CommUtils
julia> agent = ChatAgent.agentReflex("Jene")
julia> shorttermMemory = OrderedDict{String, Any}(
"user:" => "What's the latest AMD GPU?",
"Plan 1:" => " To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
"Act 1:" => " wikisearch\n",
"Actinput 1:" => " amd gpu latest\n",
"Obs 1:" => "No info available for your search query.",
"Act 2:" => " wikisearch\n",
"Actinput 2:" => " amd graphics card latest\n",
"Obs 2:" => "No info available for your search query.")
julia> report = analyze(agent, shorttermMemory)
```
"""
function analyze(a)
shorttermMemory = dictToString(a.memory[:shortterm])
prompt =
"""
<|im_start|>system
You have access to the following tools:
chatbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
Your work:
$shorttermMemory
You job is to do each of the following steps in detail to analize your work.
1. What happened?
2. List all relationships, each with cause and effect.
3. Look at each relationship, figure out why it behaved that way.
4. What could you do to improve the response?
<|im_end|>
<|im_start|>assistant
"""
response = sendReceivePrompt(a, prompt, max_tokens=1024, timeout=180)
return response
end
""" Write a lesson drawn from evaluation.
Arguments:
a, one of ChatAgent's agent.
report, a report resulted from analyzing shorttermMemory
Return:
A lesson.
# Example
```jldoctest
julia> using ChatAgent, CommUtils
julia> agent = ChatAgent.agentReflex("Jene")
julia> report =
"What happened: I tried to search for AMD's latest product using the wikisearch tool,
but no information was available in the search results.
Cause and effect relationships:
1. Searching \"AMD latest product\" -> No info available.
2. Searching \"most recent product release\" -> No info available.
3. Searching \"latest product\" -> No info available.
Analysis of each relationship:
1. The search for \"AMD latest product\" did not provide any information because the wikisearch tool could not find relevant results for that query.
2. The search for \"most recent product release\" also did not yield any results, indicating that there might be no recent product releases available or that the information is not accessible through the wikisearch tool.
3. The search for \"latest product\" similarly resulted in no information being found, suggesting that either the latest product is not listed on the encyclopedia or it is not easily identifiable using the wikisearch tool.
Improvements: To improve the response, I could try searching for AMD's products on a different
source or search engine to find the most recent product release. Additionally, I could ask
the user for more context or clarify their question to better understand what they are
looking for."
julia> lesson = selfReflext(agent, report)
```
"""
function selfReflext(a, analysis::T) where {T<:AbstractString}
prompt =
"""
<|im_start|>system
You have access to the following tools:
chatbox: Useful for when you need to ask a customer for more context. Input should be a conversation to customer.
wikisearch: Useful for when you need to search an encyclopedia Input is keywords and not a question.
Your report:
$analysis
Your job are:
1. Lesson: what lesson could you learn from your report?.
2. Context: what is the context this lesson could apply to?
<|im_end|>
"""
response = sendReceivePrompt(a, prompt, max_tokens=2048)
return response
end
""" Formulate a response from work for user's stimulus.
Arguments:
a, one of ChatAgent's agent.
Return:
A response for user's stimulus.
# Example
```jldoctest
julia> using ChatAgent, CommUtils
julia> agent = ChatAgent.agentReflex("Jene")
julia> shorttermMemory = OrderedDict{String, Any}(
"user:" => "What's the latest AMD GPU?",
"Plan 1:" => " To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
"Act 1:" => " wikisearch\n",
"Actinput 1:" => " amd gpu latest\n",
"Obs 1:" => "No info available for your search query.",
"Act 2:" => " wikisearch\n",
"Actinput 2:" => " amd graphics card latest\n",
"Obs 2:" => "No info available for your search query.")
julia> report = formulateUserresponse(agent, shorttermMemory)
```
"""
function formulateUserresponse(a)
conversation = messagesToString_nomark(a.messages, addressAIas="I")
work = dictToString(a.memory[:shortterm])
prompt =
"""
<|im_start|>system
Symbol:
Plan: a plan
Thought: your thought
Act: the action you took
Actinput: the input to the action
Obs: the result of the action
Your talk with the user:
$conversation
Your work:
$work
From your talk with the user and your work, formulate a response for the user.
<|im_end|>
response:
"""
response = sendReceivePrompt(a, prompt)
return response
end
""" Extract important info from text into key-value pair text.
Arguments:
a, one of ChatAgent's agent.
text, a text you want to extract info
Return:
key-value pair text.
# Example
```jldoctest
julia> using ChatAgent
julia> agent = ChatAgent.agentReflex("Jene")
julia> text = "We are holding a wedding party at the beach."
julia> extract(agent, text)
"location=beach, event=wedding party"
```
"""
function extractinfo(a, text::T) where {T<:AbstractString}
# determine whether there are any important info in an input text
prompt =
"""
<|im_start|>system
User's message:
$text
Your job is determine whether there are important info in the user's message. Answer: {Yes/No/Not sure}
<|im_end|>
Answer:
"""
response = sendReceivePrompt(a, prompt, temperature=0.0)
if occursin("Yes", response)
prompt =
"""
<|im_start|>system
User's message:
$text
Your job is to extract important info from the user's message into keys and values using this format: key=value,.
p.s.1 you can extract many key-value pairs.
<|im_end|>
"""
response = sendReceivePrompt(a, prompt, temperature=0.0)
return response
else
return nothing
end
end
""" Update important info from key-value pair text into another key-value pair text.
Arguments:
a, one of ChatAgent's agent
text, a key-value pair text
Return:
updated key-value pair text
# Example
```jldoctest
julia> using ChatAgent
julia> agent = ChatAgent.agentReflex("Jene")
julia> currentinfo = "location=beach, event=wedding party"
julia> newinfo = "wine_type=full body, dry and medium tannin\nprice_range=50 dollars"
julia> updateEnvState(agent, currentinfo, newinfo)
" location=beach, event=wedding party, wine_type=full body, dry and medium tannin, price_range=50 dollars"
```
"""
function updateEnvState(a, newinfo)
prompt =
"""
<|im_start|>system
Current state:
$(a.env)
New info:
$newinfo
Your job is to update or add information from new info into the current state which use key-value format.
<|im_end|>
Updated Current State:\n
"""
response = sendReceivePrompt(a, prompt, temperature=0.0)
return response
end
""" Determine whether LLM should go to next step.
Arguments:
a, one of ChatAgent's agent.
Return:
"Yes" or "no" decision to go next step.
# Example
```jldoctest
julia> using ChatAgent, CommUtils
julia> agent = ChatAgent.agentReflex("Jene")
julia> shorttermMemory = OrderedDict{String, Any}(
"user:" => "What's the latest AMD GPU?",
"Plan 1:" => " To answer this question, I will need to search for the latest AMD GPU using the wikisearch tool.\n",
"Act 1:" => " wikisearch\n",
"Actinput 1:" => " amd gpu latest\n",
"Obs 1:" => "No info available for your search query.",
"Act 2:" => " wikisearch\n",
"Actinput 2:" => " amd graphics card latest\n",
"Obs 2:" => "No info available for your search query.")
julia> decision = goNogo(agent)
"Yes"
```
"""
function goNogo(a)
# stimulus = a.memory[:shortterm]["user:"]
work = dictToString(a.memory[:shortterm])
prompt =
"""
<|im_start|>system
Symbol meaning:
Plan: a plan
Thought: your thought
Act: the action you took
Actinput: the input to the action
Obs: the result of the action
Your work:
$work
Your job is to check whether step $(a.step) of your work is completed according to the plan and choose only one of the following choices.
choice 1: If you get what you intend to do and you are ready to do the next step of the plan say, "{Yes}". And what is the rationale behind the decision to do the next step?
choice 2: If you didn't get what you intend to do and you need to repeat the latest step say, "{No}". And what is the rationale behind the decision to repeat the latest step?
<|im_end|>
<|im_start|>assistant
"""
response = sendReceivePrompt(a, prompt)
decision = nothing
reason = nothing
if occursin("Yes", response)
decision = "Yes"
elseif occursin("No", response)
decision = "No"
else
error("undefied condition, decision $decision $(@__LINE__)")
end
startInd = findfirst(decision, response)[end] +2
if occursin(":", response[startInd:end]) # check for ":" after decision cha
startInd2 = findnext(":", response, startInd)[end]+1
reason = response[startInd2:end]
else
reason = response[startInd:end]
end
return decision, reason
end
function checkStepCompletion(a::agentReflex)
result = false
#WORKING I need current step of the plan
plan = "Plan $(a.attempt):"
plan = a.memory[:shortterm][plan]
prompt =
"""
<|im_start|>system
Symbol meaning:
Plan: a plan
Thought: your thought
Act: the action you took
Actinput: the input to the action
Obs: the result of the action
Your plan:
$plan
What is step $(a.step) of the plan?
<|im_end|>
<|im_start|>assistant
"""
response = sendReceivePrompt(a, prompt)
response = split(response, "<|im_end|>")[1]
# mistral 7B already know info example: 2. Determine the occasion (wedding party).
if occursin("(", response) && occursin(")", response)
result = true
end
return result
end
end # module
Reference in New Issue View Git Blame Copy Permalink