update

src/interface.jl

@@ -46,11 +46,12 @@ macro executeStringFunction(functionStr, args...)
   function_to_call(args...)
 end
 
+
 """ Think and choose action
 
 # Arguments
-  - `a::T1`
+  - `config::T1`
-    one of Yiem's agent
+    config
   - `state::T2`
     a game state
   
@@ -59,6 +60,23 @@ end
 
 # Example
 ```jldoctest
+julia>  config = Dict(
+          :mqttServerInfo => Dict(
+            :description => "mqtt server info", 
+            :port => 1883, 
+            :broker => "mqtt.yiem.cc"
+          ),
+          :externalservice => Dict(
+              :text2textinstruct => Dict(
+                :mqtttopic => "/loadbalancer/requestingservice", 
+                :description => "text to text service with instruct LLM", 
+                :llminfo => Dict(
+                  :name => "llama3instruct"
+                )
+              ),
+          )
+        )
+
 julia> output_thoughtDict = Dict(
         :thought_1     => "The customer wants to buy a bottle of wine. This is a good start!",
         :action_1      => Dict{Symbol, Any}(
@@ -70,7 +88,7 @@ julia> output_thoughtDict = Dict(
 ```
 
 # TODO
-  - [x] update docstring
+  - [] update docstring
   - [x] implement the function
   - [] implement RAG to pull similar experience
   - [] use customerinfo
@@ -398,260 +416,6 @@ function evaluator(config::T1, state::T2
   end
   error("evaluator failed to generate an evaluation")
 end
-# function evaluator(a::T1, state::T2)::Tuple{String, Integer} where {T1<:agent, T2<:AbstractDict}
-  
-#   _prompt = 
-#   """
-#   Analyze the trajectories of a solution to a question answering task. The trajectories are 
-#   labeled by environmental observations about the situation, thoughts that can reason about 
-#   the current situation and actions that can be three types:
-#   1) winestock[query], which you can use to find wine in your inventory.
-#   2) chatbox[text], which you can use to interact with the user.
-#   3) recommendbox[answer], which returns your wine recommendation to the user.
-
-#   Given a question and a trajectory, evaluate its correctness and provide your reasoning and
-#   analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories 
-#   can be correct if the thoughts and actions so far are correct, even if the answer is not found 
-#   yet. Do not generate additional thoughts or actions. Then ending with the correctness score s 
-#   where s is an integer from 0 to 10.
-
-#   You should only respond in JSON format as describe below:
-#   {"evaluation": "your evaluation", "score": "your evaluation score"}
-
-#   Here are some examples:
-#   {
-#     "question": "I'm looking for a sedan with an automatic driving feature.",
-#     "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
-#     "thought_2": "But there is only 1 model that has the feature customer wanted.",
-#     "thought_3": "I should check our inventory first to see if we have it.", 
-#     "action_1": {"name": "inventory", "input": "Yiem model A"},
-#     "observation_1": "Yiem model A is in stock."
-#   }
-#   {"evaluation": "This trajectory is correct as it is reasonable to check an inventory for info provided in the question.
-#                                     It is also better to have simple searches corresponding to a single entity, making this the best action.",
-#     "score": 10
-#   }
-
-#   {
-#     "question": "Do you have an all-in-one pen with 4 colors and a pencil for sale?",
-#     "thought_1": "Let me check our inventory first to see if I have it.",
-#     "action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
-#     "observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
-#     "thought_2": "Ok, I have what the user is asking. Let's tell the user.", 
-#     "action_2": {"name": "chatbox", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
-#     "observation_1": "This is not what I wanted."
-#   }
-#   {"evaluation": "This trajectory is incorrect as my search term should be related to a 4-colors pen with a pencil in it,
-#                   not a pen and a pencil seperately. A better search term should have been a 4-colors pen with a pencil, all-in-one.",
-#     "score": 0
-#   }
-
-#   Let's begin!:
-#   $(JSON3.write(state[:thoughtHistory]))
-#   {"evaluation"
-#   """
-
-#   # apply LLM specific instruct format
-#   externalService = a.config[:externalservice][:text2textinstruct]
-#   llminfo = externalService[:llminfo]
-#   prompt = 
-#   if llminfo[:name] == "llama3instruct"
-#     formatLLMtext_llama3instruct("system", _prompt)
-#   else
-#     error("llm model name is not defied yet $(@__LINE__)")
-#   end
-
-#   msgMeta = GeneralUtils.generate_msgMeta(
-#     a.config[:externalservice][:text2textinstruct][:mqtttopic],
-#     senderName= "evaluator",
-#     senderId= a.id,
-#     receiverName= "text2textinstruct",
-#     mqttBroker= a.config[:mqttServerInfo][:broker],
-#     mqttBrokerPort= a.config[:mqttServerInfo][:port],
-#   )
-
-#   outgoingMsg = Dict(
-#     :msgMeta=> msgMeta,
-#     :payload=> Dict(
-#       :text=> prompt,
-#       :kwargs=> Dict(
-#         :max_tokens=> 512,
-#         :stop=> ["<|eot_id|>"],
-#         )
-#       )
-#     )
-
-#   for attempt in 1:5
-#     try
-#       response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-#       _responseJsonStr = response[:response][:text]
-#       expectedJsonExample =
-#       """
-#       Here is an expected JSON format:
-#       {"evaluation": "...", "score": "..."}
-#       """
-#       responseJsonStr = jsoncorrection(a, _responseJsonStr, expectedJsonExample)
-#       evaluationDict = copy(JSON3.read(responseJsonStr)) 
-
-#       # check if dict has all required value
-#       dummya::AbstractString = evaluationDict[:evaluation]
-#       dummyb::Integer = evaluationDict[:score]
-
-#       return (evaluationDict[:evaluation], evaluationDict[:score])
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("")
-#       @warn "Attempt $attempt. Error occurred: $errorMsg\n$st"
-#       println("")
-#     end
-#   end
-#   error("evaluator failed to generate an evaluation")
-# end
-
-
-
-
-
-
-
-# """ 
-
-# # Arguments
-#   - `a::T1`
-#     one of Yiem's agent
-#   - `state::T2`
-#     a game state  
-
-# # Return
-#   - `evaluation::Tuple{String, Integer}`
-#     evaluation and score
-
-# # Example
-# ```jldoctest
-# julia> 
-# ```
-
-# # TODO
-#  - [] update docs
-#  - [] implement the function
-
-# # Signature
-# """
-# function comparer(a::T1, state::T2)::Tuple{String, Integer} where {T1<:agent, T2<:AbstractDict}
-  
-#   _prompt = 
-#   """
-#   Analyze the trajectories of a solution to a question answering task. The trajectories are 
-#   labeled by environmental observations about the situation, thoughts that can reason about 
-#   the current situation and actions that can be three types:
-#   1) winestock[query], which you can use to find wine in your inventory.
-#   2) chatbox[text], which you can use to interact with the user.
-#   3) recommendbox[answer], which returns your wine recommendation to the user.
-
-#   Given a question and a trajectory, evaluate its correctness and provide your reasoning and
-#   analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories 
-#   can be correct if the thoughts and actions so far are correct, even if the answer is not found 
-#   yet. Do not generate additional thoughts or actions. Then ending with the correctness score s 
-#   where s is an integer from 0 to 10.
-
-#   You should only respond in JSON format as describe below:
-#   {"evaluation": "your evaluation", "score": "your evaluation score"}
-
-#   Here are some examples:
-#   {
-#     "question": "I'm looking for a sedan with an automatic driving feature.",
-#     "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
-#     "thought_2": "But there is only 1 model that has the feature customer wanted.",
-#     "thought_3": "I should check our inventory first to see if we have it.", 
-#     "action_1": {"name": "inventory", "input": "Yiem model A"},
-#     "observation_1": "Yiem model A is in stock."
-#   }
-#   {"evaluation": "This trajectory is correct as it is reasonable to check an inventory for info provided in the question.
-#                                     It is also better to have simple searches corresponding to a single entity, making this the best action.",
-#     "score": 10
-#   }
-
-#   {
-#     "question": "Do you have an all-in-one pen with 4 colors and a pencil for sale?",
-#     "thought_1": "Let me check our inventory first to see if I have it.",
-#     "action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
-#     "observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
-#     "thought_2": "Ok, I have what the user is asking. Let's tell the user.", 
-#     "action_2": {"name": "chatbox", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
-#     "observation_1": "This is not what I wanted."
-#   }
-#   {"evaluation": "This trajectory is incorrect as my search term should be related to a 4-colors pen with a pencil in it,
-#                   not a pen and a pencil seperately. A better search term should have been a 4-colors pen with a pencil, all-in-one.",
-#     "score": 0
-#   }
-
-#   Let's begin!:
-#   $(JSON3.write(state[:thoughtHistory]))
-#   {"evaluation"
-#   """
-
-#   # apply LLM specific instruct format
-#   externalService = a.config[:externalservice][:text2textinstruct]
-#   llminfo = externalService[:llminfo]
-#   prompt = 
-#   if llminfo[:name] == "llama3instruct"
-#     formatLLMtext_llama3instruct("system", _prompt)
-#   else
-#     error("llm model name is not defied yet $(@__LINE__)")
-#   end
-
-#   msgMeta = GeneralUtils.generate_msgMeta(
-#     a.config[:externalservice][:text2textinstruct][:mqtttopic],
-#     senderName= "evaluator",
-#     senderId= a.id,
-#     receiverName= "text2textinstruct",
-#     mqttBroker= a.config[:mqttServerInfo][:broker],
-#     mqttBrokerPort= a.config[:mqttServerInfo][:port],
-#   )
-
-#   outgoingMsg = Dict(
-#     :msgMeta=> msgMeta,
-#     :payload=> Dict(
-#       :text=> prompt,
-#       :kwargs=> Dict(
-#         :max_tokens=> 512,
-#         :stop=> ["<|eot_id|>"],
-#         )
-#       )
-#     )
-
-#   for attempt in 1:5
-#     try
-#       response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-#       _responseJsonStr = response[:response][:text]
-#       expectedJsonExample =
-#       """
-#       Here is an expected JSON format:
-#       {"evaluation": "...", "score": "..."}
-#       """
-#       responseJsonStr = jsoncorrection(a, _responseJsonStr, expectedJsonExample)
-#       evaluationDict = copy(JSON3.read(responseJsonStr)) 
-
-#       # check if dict has all required value
-#       dummya::AbstractString = evaluationDict[:evaluation]
-#       dummyb::Integer = evaluationDict[:score]
-
-#       return (evaluationDict[:evaluation], evaluationDict[:score])
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("")
-#       @warn "Attempt $attempt. Error occurred: $errorMsg\n$st"
-#       println("")
-#     end
-#   end
-#   error("evaluator failed to generate an evaluation")
-# end
 
 
 """
@@ -946,6 +710,7 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
 
     if isempty(a.plan[:currenttrajectory])
 
+      # initial state
       a.plan[:currenttrajectory] = Dict{Symbol, Any}(
         # deepcopy the info to prevent modifying the info unintentionally during MCTS planning
         :customerinfo=> deepcopy(a.keywordinfo[:customerinfo]),
@@ -963,6 +728,9 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
           #[] :recap=>,
           :question=> userinput[:text],
           ),
+
+        # store conversation for virtual customer because the virtual customer agent is just 
+        # a function and stateless.
         :virtualCustomerChatHistory=> Vector{Dict{Symbol, Any}}(
           [Dict(:name=> "user", :text=> userinput[:text])]
           ),  
@@ -1002,6 +770,141 @@ end
 
 
 
+# """ 
+
+# # Arguments
+#   - `a::T1`
+#     one of Yiem's agent
+#   - `state::T2`
+#     a game state  
+
+# # Return
+#   - `evaluation::Tuple{String, Integer}`
+#     evaluation and score
+
+# # Example
+# ```jldoctest
+# julia> 
+# ```
+
+# # TODO
+#  - [] update docs
+#  - [] implement the function
+
+# # Signature
+# """
+# function comparer(a::T1, state::T2)::Tuple{String, Integer} where {T1<:agent, T2<:AbstractDict}
+  
+#   _prompt = 
+#   """
+#   Analyze the trajectories of a solution to a question answering task. The trajectories are 
+#   labeled by environmental observations about the situation, thoughts that can reason about 
+#   the current situation and actions that can be three types:
+#   1) winestock[query], which you can use to find wine in your inventory.
+#   2) chatbox[text], which you can use to interact with the user.
+#   3) recommendbox[answer], which returns your wine recommendation to the user.
+
+#   Given a question and a trajectory, evaluate its correctness and provide your reasoning and
+#   analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories 
+#   can be correct if the thoughts and actions so far are correct, even if the answer is not found 
+#   yet. Do not generate additional thoughts or actions. Then ending with the correctness score s 
+#   where s is an integer from 0 to 10.
+
+#   You should only respond in JSON format as describe below:
+#   {"evaluation": "your evaluation", "score": "your evaluation score"}
+
+#   Here are some examples:
+#   {
+#     "question": "I'm looking for a sedan with an automatic driving feature.",
+#     "thought_1": "I have many types of sedans in my inventory, each with diverse features.",
+#     "thought_2": "But there is only 1 model that has the feature customer wanted.",
+#     "thought_3": "I should check our inventory first to see if we have it.", 
+#     "action_1": {"name": "inventory", "input": "Yiem model A"},
+#     "observation_1": "Yiem model A is in stock."
+#   }
+#   {"evaluation": "This trajectory is correct as it is reasonable to check an inventory for info provided in the question.
+#                                     It is also better to have simple searches corresponding to a single entity, making this the best action.",
+#     "score": 10
+#   }
+
+#   {
+#     "question": "Do you have an all-in-one pen with 4 colors and a pencil for sale?",
+#     "thought_1": "Let me check our inventory first to see if I have it.",
+#     "action_1": {"name": "inventory", "input": "pen with 4 color and a pencil."},
+#     "observation_1": "I found {1: "Pilot Dr. grip 4-in-1 pen", 2: "Rotting pencil"}",
+#     "thought_2": "Ok, I have what the user is asking. Let's tell the user.", 
+#     "action_2": {"name": "chatbox", "input": "Yes, we do have a Pilot Dr. grip 4-in-1 pen and a Rotting pencil"},
+#     "observation_1": "This is not what I wanted."
+#   }
+#   {"evaluation": "This trajectory is incorrect as my search term should be related to a 4-colors pen with a pencil in it,
+#                   not a pen and a pencil seperately. A better search term should have been a 4-colors pen with a pencil, all-in-one.",
+#     "score": 0
+#   }
+
+#   Let's begin!:
+#   $(JSON3.write(state[:thoughtHistory]))
+#   {"evaluation"
+#   """
+
+#   # apply LLM specific instruct format
+#   externalService = a.config[:externalservice][:text2textinstruct]
+#   llminfo = externalService[:llminfo]
+#   prompt = 
+#   if llminfo[:name] == "llama3instruct"
+#     formatLLMtext_llama3instruct("system", _prompt)
+#   else
+#     error("llm model name is not defied yet $(@__LINE__)")
+#   end
+
+#   msgMeta = GeneralUtils.generate_msgMeta(
+#     a.config[:externalservice][:text2textinstruct][:mqtttopic],
+#     senderName= "evaluator",
+#     senderId= a.id,
+#     receiverName= "text2textinstruct",
+#     mqttBroker= a.config[:mqttServerInfo][:broker],
+#     mqttBrokerPort= a.config[:mqttServerInfo][:port],
+#   )
+
+#   outgoingMsg = Dict(
+#     :msgMeta=> msgMeta,
+#     :payload=> Dict(
+#       :text=> prompt,
+#       :kwargs=> Dict(
+#         :max_tokens=> 512,
+#         :stop=> ["<|eot_id|>"],
+#         )
+#       )
+#     )
+
+#   for attempt in 1:5
+#     try
+#       response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+#       _responseJsonStr = response[:response][:text]
+#       expectedJsonExample =
+#       """
+#       Here is an expected JSON format:
+#       {"evaluation": "...", "score": "..."}
+#       """
+#       responseJsonStr = jsoncorrection(a, _responseJsonStr, expectedJsonExample)
+#       evaluationDict = copy(JSON3.read(responseJsonStr)) 
+
+#       # check if dict has all required value
+#       dummya::AbstractString = evaluationDict[:evaluation]
+#       dummyb::Integer = evaluationDict[:score]
+
+#       return (evaluationDict[:evaluation], evaluationDict[:score])
+#     catch e
+#       io = IOBuffer()
+#       showerror(io, e)
+#       errorMsg = String(take!(io))
+#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+#       println("")
+#       @warn "Attempt $attempt. Error occurred: $errorMsg\n$st"
+#       println("")
+#     end
+#   end
+#   error("evaluator failed to generate an evaluation")
+# end
 
 
 

test/prompttest_2.jl

@@ -0,0 +1,119 @@
+using Revise
+using YiemAgent, GeneralUtils, JSON3, DataStructures, LibPQ
+using SQLLLM
+
+
+# _prompt = 
+# """
+# You are a helpful assistant.
+# answer the following question:
+# From the following CSV text:
+# "{\"tabledescription\":[\"The customer table stores information about customers. It includes details such as first name, last name, display name, username, password, gender, country, telephone number, email, birthdate, additional_search_term, other attributes (in JSON format) and a description.\",\"The wine table stores information about different wines. It includes details namely id, name, brand, manufacturer, region, country, wine_type, grape_variety, serving_temperature, intensity, sweetness, tannin, acidity, fizziness, additional_search_term, other attributes (in JSON format) and a description.\",\"The wine_food table represents the association between wines and food items. It estab" ⋯ 477 bytes ⋯ "ed to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description.\",\"The retailer_wine table represents the relationship between retailers and wines. It stores information about the wines available from which retailers, including vintage, their price, and the currency.\",\"The retailer_food table represents the relationship between retailers and food items. It stores information about the food items available from which retailers, including their price and the currency.\"],\"tablename\":[\"customer\",\"wine\",\"wine_food\",\"food\",\"retailer\",\"retailer_wine\",\"retailer_food\"]}"
+# What is the description of table wine?
+# """
+
+# prompt =  YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
+# @show prompt
+# msgMeta = Dict(:requestResponse => nothing,
+#   :msgPurpose => nothing,
+#   :receiverId => nothing,
+#   :getPost => nothing,
+#   :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
+#   :acknowledgestatus => nothing,
+#   :replyToMsgId => nothing,
+#   :msgFormatVersion => nothing,
+#   :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
+#   :sendTopic => "/loadbalancer/requestingservice",
+#   :receiverName => "text2textinstruct",
+#   :replyTopic => nothing,
+#   :senderName => "decisionMaker",
+#   :senderSelfnote => nothing,
+#   :senderId => "testingSessionID",
+#   :timeStamp => "2024-05-04T08:06:23.561"
+#   )
+
+# outgoingMsg = Dict(
+#   :msgMeta=> msgMeta,
+#   :payload=> Dict(
+#     :text=> prompt,
+#   )
+# )
+
+
+
+# _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+# result = _response[:response][:text]
+
+
+
+
+
+
+
+DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
+
+tableinfo, df1, df2, df3 = SQLLLM.tableinfo(DBconnection, "wine")
+
+
+_prompt = 
+"""
+You are a helpful assistant helping to answer user question from a database table.
+
+$tableinfo
+
+Are there any chardonnay?
+"""
+
+prompt =  YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
+@show prompt
+msgMeta = Dict(:requestResponse => nothing,
+  :msgPurpose => nothing,
+  :receiverId => nothing,
+  :getPost => nothing,
+  :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
+  :acknowledgestatus => nothing,
+  :replyToMsgId => nothing,
+  :msgFormatVersion => nothing,
+  :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
+  :sendTopic => "/loadbalancer/requestingservice",
+  :receiverName => "text2textinstruct",
+  :replyTopic => nothing,
+  :senderName => "decisionMaker",
+  :senderSelfnote => nothing,
+  :senderId => "testingSessionID",
+  :timeStamp => "2024-05-04T08:06:23.561"
+  )
+
+outgoingMsg = Dict(
+  :msgMeta=> msgMeta,
+  :payload=> Dict(
+    :text=> prompt,
+  )
+)
+
+
+
+_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
+result2 = _response[:response][:text]
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

test/runtest.jl

@@ -86,14 +86,6 @@ response = YiemAgent.winestock(a, dummyinput)
 
 
 
-"""
-Write me SQL command to search the database for wines that fits: {"wine_type": "red","sweetness": 2,"acidity": 3,"tannin": 1,"intensity": 5}
-"""
-
-
-mctsparam = Dict{Symbol, Any}()
-
-