update

src/interface.jl

@@ -141,7 +141,7 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function, llmFo
     For your information:
     - Observation: Result of the immediately preceding action
 
-    At each round of conversation, the user will give you the following:
+    At each round of conversation, you will be given the following information:
     User Query: ...
     Example: ...
     Your Q&A: ...
@@ -205,30 +205,37 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function, llmFo
 
     QandA = generatequestion(state, context, text2textInstructLLM, llmFormatName; similarSQL=similarSQL_)
 
-    usermsg =
+    assistantinfo =
     """
+    <information>
     $(context[:tablelist])
     User query: $(state[:thoughtHistory][:question])
-    Example: $similarSQL_
+    Similar SQL: $similarSQL_
     Your Q&A: $QandA
     Your work progress: $workprogress
     Evaluation: $(state[:evaluation])
     Suggestion: $(state[:suggestion])
+    Data specific guidelines:
+     - tasting_notes should not be used as search criteria.
     P.S. $errornote
+    </information>
     """
 
-    _prompt = 
+    unformatPrompt =
         [
           Dict(:name => "system", :text => systemmsg),
-      Dict(:name=> "user", :text=> usermsg)
         ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
+    prompt = GeneralUtils.formatLLMtext(unformatPrompt, llmFormatName)
+    # add info
+    prompt = prompt * assistantinfo
     response = text2textInstructLLM(prompt; llmkwargs=llmkwargs)
     response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
     think, response = GeneralUtils.extractthink(response)
 
+    #[WORKING] check for tasting_notes occurs AFTER where in the sql
+
     # LLM tends to generate observation given that it is in the input
     response = 
       if occursin("observation:", response)
@@ -328,11 +335,246 @@ function decisionMaker(state::T1, context, text2textInstructLLM::Function, llmFo
     end
 
     state[:decisionMaker] = responsedict
+
+    println("\nSQLLLM decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    pprintln(Dict(responsedict))
+
     return responsedict
   end
   error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
 end
 
+# function decisionMaker(state::T1, context, text2textInstructLLM::Function, llmFormatName::String
+#   ; querySQLVectorDBF::Union{T2, Nothing}=nothing, maxattempt=10
+#   )::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:Function}
+
+#   # lessonDict =
+#   # if isfile("lesson.json")
+#   #   lessonDict = copy(JSON3.read("lesson.json"))
+#   # else
+#   #   lessonDict = nothing
+#   # end
+
+#   # lessonDict = nothing
+
+#   # lesson =
+#   # if lessonDict === nothing
+#   #   ""
+#   # else
+#   #   """
+#   #   You have attempted to help the user before and failed, either because your reasoning for the 
+#   #   recommendation was incorrect or your response did not exactly match the user expectation. 
+#   #   The following lesson(s) give a plan to avoid failing to help the user in the same way you 
+#   #   did previously. Use them to improve your strategy to help the user.
+
+#   #   Here are some lessons in JSON format:
+#   #   $(JSON3.write(lessonDict))
+
+#   #   When providing the thought and action for the current trial, that into account these failed 
+#   #   trajectories and make sure not to repeat the same mistakes and incorrect answers. 
+#   #   """
+#   # end
+
+#   systemmsg = 
+#   """
+#     You are a helpful assistant that find the data from a database to satisfy the user's query. 
+#     You are also eager to improve your helpfulness.
+
+#     For your information:
+#     - Observation: Result of the immediately preceding action
+
+#     At each round of conversation, the user will give you the following:
+#     User Query: ...
+#     Example: ...
+#     Your Q&A: ...
+#     Your work progress: ...
+#     Evaluation: Evaluation of the immediately preceding action and observation
+#     Suggestion: Suggestion for the immediately preceding action and observation
+    
+#     You must follow the following guidelines:
+#     - Keep SQL queries focused only on the provided information.
+
+#     You should follow the following guidelines:
+#     - Do not create any table in the database
+#     - A junction table can be used to link tables together. Another use case is for filtering data.
+#     - If you can't find a single table that can be used to answer the user's query, try joining multiple tables to see if you can obtain the answer.
+#     - If you are unable to find the requested information, kindly inform the user, "The current data in our database does not provide the specific answer to your query".
+#     - Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
+
+#     You should then respond to the user with interleaving Comprehension, Plan, Action_name, Action_input:
+#       Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
+#       Action_name: (Typically corresponds to the execution of the first step in your plan)
+#         Can be one of the following function names: 
+#         - RUNSQL, which you can use to execute SQL against the database. Action_input for this function must be a single SQL query to be executed against the database.
+#           For more effective text search, it's necessary to use case-insensitivity and the ILIKE operator.
+#           Do not wrap the SQL as it will be executed against the database directly and SQL must be ended with ';'.
+#     4) Action_input: Input to the action
+
+#     You should only respond in format as described below:
+#     Plan: ...
+#     Action_name: ...
+#     Action_input: ...
+
+#     Let's begin!
+#   """
+  
+#   workprogress = ""
+#   for (k, v) in state[:thoughtHistory]
+#     if k ∉ [:question]
+#       workprogress *= "$k: $v\n"
+#     end
+#   end
+  
+#   response = nothing # store for show when error msg show up
+#   errornote = "N/A"
+
+#   # provide similar sql only for the first attempt
+#   similarSQL_ = "None"
+#   if length(state[:thoughtHistory]) == 1
+#     sql, distance = querySQLVectorDBF(state[:thoughtHistory][:question])
+#     similarSQL_ = sql !== nothing ? sql : "None"
+#   end
+
+#   header = ["Plan:", "Action_name:", "Action_input:"]
+#   dictkey = ["plan", "action_name", "action_input"]
+
+#   llmkwargs=Dict(
+#       :num_ctx => 32768,
+#       :temperature => 0.5,
+#     )
+
+#   for attempt in 1:maxattempt
+
+#     QandA = generatequestion(state, context, text2textInstructLLM, llmFormatName; similarSQL=similarSQL_)
+
+#     usermsg =
+#     """
+#     $(context[:tablelist])
+#     User query: $(state[:thoughtHistory][:question])
+#     Example: $similarSQL_
+#     Your Q&A: $QandA
+#     Your work progress: $workprogress
+#     Evaluation: $(state[:evaluation])
+#     Suggestion: $(state[:suggestion])
+#     P.S. $errornote
+#     """
+
+#     _prompt = 
+#     [
+#       Dict(:name=> "system", :text=> systemmsg),
+#       Dict(:name=> "user", :text=> usermsg)
+#     ]
+
+#     # put in model format
+#     prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
+#     response = text2textInstructLLM(prompt; llmkwargs=llmkwargs)
+#     response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
+#     think, response = GeneralUtils.extractthink(response)
+
+#     # LLM tends to generate observation given that it is in the input
+#     response = 
+#       if occursin("observation:", response)
+#         string(split(response, "observation:")[1])
+#       elseif occursin("Observation:", response)
+#         string(split(response, "Observation:")[1])
+#       elseif occursin("observation_", response)
+#         string(split(response, "observation_")[1])
+#       elseif occursin("Observation_", response)
+#         string(split(response, "Observation_")[1])
+#       else
+#         response
+#       end
+
+#     # sometime LLM output something like **Comprehension**: which is not expected
+#     response = replace(response, "**"=>"")
+#     response = replace(response, "***"=>"")
+
+#     # some time LLM output Plan_1: so we need to detect and replace topic numbering
+#     regex = r"_[0-1000]+:"
+#     matches = collect(eachmatch(regex, response))
+#     for m in matches
+#       response = replace(response, string(m.match)=>":") 
+#     end
+
+#     if occursin("NULL", response)
+#       errornote = "\nYour previous attempt was NULL. This is not allowed"
+#       println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#       continue
+#     end
+
+#     # # detect if there are more than 1 key per categories
+#     # wordcount = GeneralUtils.countGivenWords(response, header)
+#     # duplicateKeywordFlag = false
+#     # for (i, v) in enumerate(wordcount)
+#     #   keyword = header[i]
+#     #   keywordNumber = v
+#     #   if keywordNumber > 1
+#     #     errornote = "\nSQL query has duplicated keyword, $keyword"
+#     #     println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#     #     duplicateKeywordFlag = true
+#     #     break
+#     #   end
+#     # end
+#     # duplicateKeywordFlag == true ? continue : nothing
+
+#     # check whether response has all header
+#     detected_kw = GeneralUtils.detect_keyword(header, response)
+#     if 0 ∈ values(detected_kw)
+#       errornote = "\nYour previous attempt did not have all points according to the required response format"
+#       println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#       continue
+#     elseif sum(values(detected_kw)) > length(header)
+#       errornote = "\nYour previous attempt has duplicated points according to the required response format"
+#       println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#       continue
+#     end
+
+#     responsedict = GeneralUtils.textToDict(response, header; 
+#                                             dictKey=dictkey, symbolkey=true)
+
+#     delete!(responsedict, :observation)
+
+#     # remove backticks Error occurred: MethodError: no method matching occursin(::String, ::Vector{String})
+#     if occursin("```", responsedict[:action_input])
+#       sql = GeneralUtils.extract_triple_backtick_text(responsedict[:action_input])[1]
+#       if sql[1:4] == "sql\n"
+#         sql = sql[5:end]
+#       end
+#       sql = split(sql, ';')   # some time there are comments in the sql
+#       sql = sql[1] * ';'
+
+#       responsedict[:action_input] = sql
+#     end
+
+#     toollist = ["TABLEINFO", "RUNSQL"]
+#     if responsedict[:action_name] ∉ toollist
+#       errornote = "Your previous attempt has action_name that is not in the tool list"
+#       println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#       continue
+#     end
+
+#     for i in toollist
+#       if occursin(i, responsedict[:action_input])
+#         errornote = "Your previous attempt has action_name in action_input which is not allowed"
+#         println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#         continue
+#       end
+#     end
+
+#     for i ∈ Symbol.(dictkey)
+#       if length(JSON3.write(responsedict[i])) == 0
+#         errornote = "Your previous attempt has empty value for $i"
+#         println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+#         continue
+#       end
+#     end
+
+#     state[:decisionMaker] = responsedict
+#     return responsedict
+#   end
+#   error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
+# end
+
 
 """ Assigns a scalar value to each new child node to be used for selec-
 tion and backpropagation. This value effectively quantifies the agent's progress in task completion,

test/runtests.jl

@@ -3,7 +3,7 @@ using LibPQ, Dates, JSON3, PrettyPrinting, UUIDs, DataFrames, DataStructures, Ba
 using GeneralUtils, SQLLLM
 
 
-config = copy(JSON3.read("/appfolder/mountvolume/appdata/config.json"))
+config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")
 
 function executeSQL(sql::T) where {T<:AbstractString}
   host = config[:externalservice][:wineDB][:host]
@@ -29,13 +29,19 @@ function executeSQLVectorDB(sql)
   return result
 end
 
-function text2textInstructLLM(prompt::String; maxattempt=3)
+function text2textInstructLLM(prompt::String; maxattempt::Integer=3, modelsize::String="medium",
+    senderId=GeneralUtils.uuid4snakecase(), timeout=180,
+    llmkwargs=Dict(
+      :num_ctx => 32768,
+      :temperature => 0.5,
+    )
+  )
   msgMeta = GeneralUtils.generate_msgMeta(
     config[:externalservice][:loadbalancer][:mqtttopic];
     msgPurpose="inference",
     senderName="yiemagent",
-    senderId=sessionId,
+    senderId=senderId,
-    receiverName="text2textinstruct_small",
+    receiverName="text2textinstruct_$modelsize",
     mqttBrokerAddress=config[:mqttServerInfo][:broker],
     mqttBrokerPort=config[:mqttServerInfo][:port],
   )
@@ -44,16 +50,13 @@ function text2textInstructLLM(prompt::String; maxattempt=3)
     :msgMeta => msgMeta,
     :payload => Dict(
       :text => prompt,
-      :kwargs => Dict(
+      :kwargs => llmkwargs
-        :num_ctx => 16384,
-        :temperature => 0.2,
-      )
     )
   )
 
   response = nothing
   for attempts in 1:maxattempt
-    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=300, maxattempt=2)
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=timeout, maxattempt=maxattempt)
     payload = _response[:response]
     if _response[:success] && payload[:text] !== nothing
       response = _response[:response][:text]
@@ -76,7 +79,7 @@ function getEmbedding(text::T) where {T<:AbstractString}
     msgPurpose="embedding",
     senderName="yiemagent",
     senderId=sessionId,
-    receiverName="text2textinstruct_small",
+    receiverName="textembedding",
     mqttBrokerAddress=config[:mqttServerInfo][:broker],
     mqttBrokerPort=config[:mqttServerInfo][:port],
   )
@@ -87,7 +90,8 @@ function getEmbedding(text::T) where {T<:AbstractString}
       :text => [text] # must be a vector of string
     )
   )
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
+
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
   embedding = response[:response][:embeddings]
   return embedding
 end
@@ -108,7 +112,6 @@ function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnNam
   return df
 end
 
-
 function similarSQLVectorDB(query; maxdistance::Integer=100)
   tablename = "sqlllm_decision_repository"
   # get embedding of the query
@@ -131,7 +134,6 @@ function similarSQLVectorDB(query; maxdistance::Integer=100)
   end
 end
 
-
 function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
   tablename = "sqlllm_decision_repository"
   # get embedding of the query
@@ -155,6 +157,57 @@ function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1
   end
 end
 
+
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
+  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row != 0 && distance < maxdistance
+    # if there is usable decision, return it.
+    rowid = df[1, :id]
+    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    _output_str = String(base64decode(output_b64))
+    output = copy(JSON3.read(_output_str))
+    return output
+  else
+    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+    return nothing
+  end
+end
+
+
+function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+  ) where {T1<:AbstractString, T2<:AbstractDict}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    recentevents_embedding = getEmbedding(recentevents)[1]
+    recentevents = replace(recentevents, "'" => "")
+    decision_json = JSON3.write(decision)
+    decision_base64 = base64encode(decision_json)
+    decision = replace(decision_json, "'" => "")
+    
+    sql = """
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+          """
+    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+    println(sql)
+    _ = executeSQLVectorDB(sql)
+  else
+    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+  end
+end
+
 sessionId = "555"