update

Reviewed-on: #2

Project.toml

@@ -1,7 +1,7 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.1.2"
+version = "0.1.4"
 
 [deps]
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"

src/llmfunction.jl

@@ -291,20 +291,20 @@ julia> result = checkinventory(agent, input)
 function checkinventory(a::T1, input::T2
   ) where {T1<:agent, T2<:AbstractString}
 
-  println("\n~~~ checkinventory order: $input ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory order: $input ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
   wineattributes_1 = extractWineAttributes_1(a, input)
   wineattributes_2 = extractWineAttributes_2(a, input)
 
   _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
   inventoryquery = "Retrieves winery, wine_name, vintage, region, country, wine_type, grape, serving_temperature, sweetness, intensity, tannin, acidity, tasting_notes, price and currency of wines that match the following criteria - {$_inventoryquery}"
-  println("~~~ checkinventory input: $inventoryquery ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
   # add suppport for similarSQLVectorDB
   textresult, rawresponse = SQLLLM.query(inventoryquery, a.func[:executeSQL], 
                         a.func[:text2textInstructLLM],
                         insertSQLVectorDB=a.func[:insertSQLVectorDB], 
                         similarSQLVectorDB=a.func[:similarSQLVectorDB])
 
-  println("\n~~~ checkinventory result ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory result ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
   println(textresult)
 
   return (result=textresult, rawresponse=rawresponse, success=true, errormsg=nothing)
@@ -326,7 +326,7 @@ julia>
 
 # TODO
   - [] update docstring
-  - [x] implement the function
+  - implement the function
 
 # Signature
 """
@@ -336,46 +336,63 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
   """
   As a helpful sommelier, your task is to extract the user information from the user's query as much as possible to fill out user's preference form.
 
-  At each round of conversation, the user will give you the current situation:
+  At each round of conversation, the user will give you the following:
   User's query: ...
 
   You must follow the following guidelines:
-    1) If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
+    - If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
       Additionally, words like 'any' or 'unlimited' mean no information is available.
-    2) Do not generate other comments.
+    - Do not generate other comments.
 
-  You should then respond to the user with the following points:
+  You should then respond to the user with:
-  - reasoning: state your understanding of the current situation
+    Thought: state your understanding of the current situation
-  - wine_name: name of the wine
+    Wine_name: name of the wine
-  - winery: name of the winery
+    Winery: name of the winery
-  - vintage: the year of the wine
+    Vintage: the year of the wine
-  - region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
+    Region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
-  - country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
+    Country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
-  - wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
+    Wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
-  - grape_varietal: the name of the primary grape used to make the wine
+    Grape_varietal: the name of the primary grape used to make the wine
-  - tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
+    Tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
-  - wine_price: price range of wine.
+    Wine_price: price range of wine.
-  - occasion: the occasion the user is having the wine for
+    Occasion: the occasion the user is having the wine for
-  - food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
+    Food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
-  
 
-  You should only respond in the user's preference form (JSON) as described below:
+  You should only respond in format as described below:
-  {"reasoning": ..., "winery": ..., "wine_name": ..., "vintage": ..., "region": ..., "country": ..., "wine_type": ..., "grape_varietal": ..., "tasting_notes": ..., "wine_price": ..., "occasion": ..., "food_to_be_paired_with_wine": ...}
+  Thought: ...
+  Wine_name: ...
+  Winery: ...
+  Vintage: ...
+  Region: ...
+  Country: ...
+  Wine_type:
+  Grape_varietal: ...
+  Tasting_notes: ...
+  Wine_price: ...
+  Occasion: ...
+  Food_to_be_paired_with_wine: ...
   
   Here are some example:
   User's query: red, Chenin Blanc, Riesling, 20 USD
   {"reasoning": ..., "winery": "NA", "wine_name": "NA", "vintage": "NA", "region": "NA", "country": "NA", "wine_type": "red, white", "grape_varietal": "Chenin Blanc, Riesling", "tasting_notes": "NA", "wine_price": "0-20", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
 
-  User's query: Domaine du Collier Saumur Blanc 2019, France, white, Chenin Blanc
+  User's query: Domaine du Collier Saumur Blanc 2019, France, white, Merlot
-  {"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_varietal": "Chenin Blanc", "tasting_notes": "NA", "wine_price": "NA", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
+  {"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_varietal": "Merlot", "tasting_notes": "NA", "wine_price": "NA", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
 
   Let's begin!
   """
 
-  attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
+  header = ["Thought:", "Wine_name:", "Winery:", "Vintage:", "Region:", "Country:", "Wine_type:", "Grape_varietal:", "Tasting_notes:", "Wine_price:", "Occasion:", "Food_to_be_paired_with_wine:"]
+  dictkey = ["thought", "wine_name", "winery", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
   errornote = ""
   
-  for attempt in 1:5
+  for attempt in 1:10
+    #[WORKING] I should add generatequestion()
+
+    if attempt > 1
+      println("\nYiemAgent extractWineAttributes_1() attempt $attempt/10 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    end
+
     usermsg =
     """
     User's query: $input
@@ -389,32 +406,35 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
-    prompt *=
-      """
-      <|start_header_id|>assistant<|end_header_id|>
-      """
-
     response = a.func[:text2textInstructLLM](prompt)
     response = GeneralUtils.remove_french_accents(response)
 
     # check wheter all attributes are in the response
     checkFlag = false
-    for word in attributes
+    for word in header
       if !occursin(word, response)
         errornote = "$word attribute is missing in previous attempts"
-        println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
         checkFlag = true
         break
       end
     end
     checkFlag == true ? continue : nothing
+    
+    # check whether response has all header
+    detected_kw = GeneralUtils.detect_keyword(header, response)
+    if 0 ∈ values(detected_kw)
+      errornote = "\nYiemAgent extractWineAttributes_1() response does not have all header"
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "\nYiemAgent extractWineAttributes_1() response has duplicated header"
+      continue
+    end
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
-    responsedict = copy(JSON3.read(response))
+    delete!(responsedict, :thought)
-
-    # convert 
-
-    delete!(responsedict, :reasoning)
     delete!(responsedict, :tasting_notes)
     delete!(responsedict, :occasion)
     delete!(responsedict, :food_to_be_paired_with_wine)
@@ -424,16 +444,16 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
 
     # check if winery, wine_name, region, country, wine_type, grape_varietal's value are in the query because sometime AI halucinates
     checkFlag = false
-    for i in attributes
+    for i in dictkey
       j = Symbol(i)
-      if j ∉ [:reasoning, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
+      if j ∉ [:thought, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
         # in case j is wine_price it needs to be checked differently because its value is ranged
         if j == :wine_price
           if responsedict[:wine_price] != "NA" 
             # check whether wine_price is in ranged number
             if !occursin('-', responsedict[:wine_price]) 
               errornote = "wine_price must be a range number"
-              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+              println("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
               checkFlag = true
               break
             end
@@ -448,7 +468,7 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
             # price range like 100-100 is not good
             if minprice == maxprice
               errornote = "wine_price with minimum equals to maximum is not valid"
-              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+              println("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
               checkFlag = true
               break
             end
@@ -464,14 +484,14 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
             content = [content]
           end
 
-          for x in content  #check whether price are mentioned in the input
+          # for x in content  #check whether price are mentioned in the input
-            if !occursin("NA", responsedict[j]) && !occursin(x, input)
+          #   if !occursin("NA", responsedict[j]) && !occursin(x, input)
-              errornote = "$x is not mentioned in the user query, you must only use the info from the query."
+          #     errornote = "$x is not mentioned in the user query, you must only use the info from the query."
-              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+          #     println("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-              checkFlag == true
+          #     checkFlag == true
-              break
+          #     break
-            end
+          #   end
-          end
+          # end
         end
       end
     end
@@ -509,7 +529,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
 
   conversiontable =
   """
-  Conversion Table:
+  <Conversion Table>
   Intensity level:
     1 to 2: May correspond to "light-bodied" or a similar description.
     2 to 3: May correspond to "med light bodied", "medium light" or a similar description.
@@ -534,6 +554,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
     3 to 4: May correspond to "medium acidity" or a similar description.
     4 to 5: May correspond to "semi high acidity" or a similar description.
     4 to 5: May correspond to "high acidity" or a similar description.
+  </Conversion Table>
   """
 
   systemmsg =
@@ -547,67 +568,64 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
   The preference form requires the following information:
   sweetness, acidity, tannin, intensity
 
-  You must follow the following guidelines:
+  <You must follow the following guidelines>
     1) If specific information required in the preference form is not available in the query or there isn't any, mark with 'NA' to indicate this.
       Additionally, words like 'any' or 'unlimited' mean no information is available.
     2) Use the conversion table to convert the descriptive word level of sweetness, intensity, tannin, and acidity into a corresponding integer.
     3) Do not generate other comments.
+  </You must follow the following guidelines>
 
-  You should then respond to the user with the following points:
+  <You should then respond to the user with>
-  - sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
+    Sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
-  - sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
+    Sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
-  - acidity_keyword: The exact keywords in the user's query describing the acidity level of the wine.
+    Acidity_keyword: The exact keywords in the user's query describing the acidity level of the wine.
-  - acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
+    Acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
-  - tannin_keyword: The exact keywords in the user's query describing the tannin level of the wine.
+    Tannin_keyword: The exact keywords in the user's query describing the tannin level of the wine.
-  - tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
+    Tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
-  - intensity_keyword: The exact keywords in the user's query describing the intensity level of the wine.
+    Intensity_keyword: The exact keywords in the user's query describing the intensity level of the wine.
-  - intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
+    Intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
+  </You should then respond to the user with>
 
-  You should only respond in the form (JSON) as described below:
+  <You should only respond in format as described below>
-  {
+    Sweetness_keyword: ...
-    "sweetness_keyword": ...,
+    Sweetness: ...
-    "sweetness": ...,
+    Acidity_keyword: ...
-    "acidity_keyword": ...,
+    Acidity: ...
-    "acidity": ...,
+    Tannin_keyword: ...
-    "tannin_keyword": ...,
+    Tannin: ...
-    "tannin": ...,
+    Intensity_keyword: ...
-    "intensity_keyword": ...,
+    Intensity: ...
-    "intensity": ...
+  </You should only respond in format as described below>
-  }
 
-  Here are some examples:
+  <Here are some examples>
-  User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
+    User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
-  {
+    Sweetness_keyword: NA
-    "sweetness_keyword": "NA",
+    Sweetness: NA
-    "sweetness": "NA",
+    Acidity_keyword: low acidity
-    "acidity_keyword": "low acidity",
+    Acidity: 1-2
-    "acidity": "1-2",
+    Tannin_keyword: medium tannin
-    "tannin_keyword": "medium tannin",
+    Tannin: 3-4
-    "tannin": "3-4",
+    Intensity_keyword: medium-bodied
-    "intensity_keyword": "medium-bodied",
+    Intensity: 3-4
-    "intensity": "3-4"
-  }
-
-
-  User's query: German red wine, under 100, pairs with spicy food
-  {
-    "sweetness_keyword": "NA",
-    "sweetness": "NA",
-    "acidity_keyword": "NA",
-    "acidity": "NA",
-    "tannin_keyword": "NA",
-    "tannin": "NA",
-    "intensity_keyword": "NA",
-    "intensity": "NA"
-  }
 
+    User's query: German red wine, under 100, pairs with spicy food
+    Sweetness_keyword: NA
+    Sweetness: NA
+    Acidity_keyword: NA
+    Acidity: NA
+    Tannin_keyword: NA
+    Tannin: NA
+    Intensity_keyword: NA
+    Intensity: NA
+  </Here are some examples>
 
   Let's begin!
   """
-
+  header = ["Sweetness_keyword:", "Sweetness:", "Acidity_keyword:", "Acidity:", "Tannin_keyword:", "Tannin:", "Intensity_keyword:", "Intensity:"]
+  dictkey = ["sweetness_keyword", "sweetness", "acidity_keyword", "acidity", "tannin_keyword", "tannin", "intensity_keyword", "intensity"]
   errornote = ""
 
-  for attempt in 1:5
+  for attempt in 1:10
     usermsg =
     """
     $conversiontable
@@ -622,14 +640,22 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
-    prompt *=
-      """
-      <|start_header_id|>assistant<|end_header_id|>
-      """
 
     response = a.func[:text2textInstructLLM](prompt)
-    responsedict = copy(JSON3.read(response))
+    
+    # check whether response has all header
+    detected_kw = GeneralUtils.detect_keyword(header, response)
+    if 0 ∈ values(detected_kw)
+      errornote = "\nYiemAgent extractWineAttributes_2() response does not have all header"
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "\nYiemAgent extractWineAttributes_2() response has duplicated header"
+      continue
+    end
+
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
     # check whether each describing keyword is in the input to prevent halucination
     for i in ["sweetness", "acidity", "tannin", "intensity"]
@@ -637,7 +663,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
       value = responsedict[keyword]
       if value != "NA" && !occursin(value, input)
         errornote = "WARNING. Keyword $keyword: $value does not appear in the input. You must use information from the input only"
-        println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
         continue
       end
 
@@ -653,7 +679,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
       if !occursin("keyword", string(k))
         if v !== "NA" && (!occursin('-', v) || length(v) > 5)
           errornote = "WARNING: The non-range value {$k: $v} is not allowed. It should be specified in a range format, i.e. min-max."
-          println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+          println("Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
           continue
         end
       end
@@ -711,7 +737,7 @@ function paraphrase(text2textInstructLLM::Function, text::String)
     - N/A
 
     You should then respond to the user with:
-    1) Paraphrase: Paraphrased text
+    Paraphrase: Paraphrased text
 
     You should only respond in format as described below:
     Paraphrase: ...
@@ -719,6 +745,9 @@ function paraphrase(text2textInstructLLM::Function, text::String)
     Let's begin!
   """
 
+  header = ["Paraphrase:"]
+  dictkey = ["paraphrase"]
+
   errornote = ""
   response = nothing # placeholder for show when error msg show up
 
@@ -736,29 +765,37 @@ function paraphrase(text2textInstructLLM::Function, text::String)
       ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
 
     try
       response = text2textInstructLLM(prompt)
       # sometime the model response like this "here's how I would respond: ..."
       if occursin("respond:", response)
         errornote = "You don't need to intro your response"
-        error("\n~~~ paraphrase() response contain : ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        error("\nparaphrase() response contain : ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
       end
       response = GeneralUtils.remove_french_accents(response)
       response = replace(response, '*'=>"")
       response = replace(response, '$' => "USD")
       response = replace(response, '`' => "")
       response = GeneralUtils.remove_french_accents(response)
-      responsedict = GeneralUtils.textToDict(response, ["Paraphrase"],
+
-        rightmarker=":", symbolkey=true, lowercasekey=true)
+      # check whether response has all header
+      detected_kw = GeneralUtils.detect_keyword(header, response)
+      if 0 ∈ values(detected_kw)
+        errornote = "\nYiemAgent paraphrase() response does not have all header"
+        continue
+      elseif sum(values(detected_kw)) > length(header)
+        errornote = "\nnYiemAgent paraphrase() response has duplicated header"
+        continue
+      end
+
+      responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
       for i ∈ [:paraphrase]
         if length(JSON3.write(responsedict[i])) == 0
-          error("$i is empty ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+          error("$i is empty ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
         end
       end
 
@@ -770,7 +807,7 @@ function paraphrase(text2textInstructLLM::Function, text::String)
         end
       end
 
-      println("\n~~~ paraphrase() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+      println("\nparaphrase() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
       pprintln(Dict(responsedict))
 
       result = responsedict[:paraphrase]
@@ -781,10 +818,10 @@ function paraphrase(text2textInstructLLM::Function, text::String)
       showerror(io, e)
       errorMsg = String(take!(io))
       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     end
   end
-  error("generatechat failed to generate a response")
+  error("paraphrase() failed to generate a response")
 end
 
 
@@ -947,7 +984,7 @@ end
 #   ]
 
 #   # put in model format
-#   prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+#   prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
 #   prompt *=
 #     """
 #     <|start_header_id|>assistant<|end_header_id|>
@@ -979,7 +1016,7 @@ end
 #         state[:isterminal] = true
 #         state[:reward] = 1
 #       end
-#       println("--> 5 Evaluator ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+#       println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
 #       pprintln(Dict(responsedict))
 #       return responsedict[:score]
 #     catch e

src/util.jl

@@ -122,47 +122,53 @@ This function takes in a vector of dictionaries and outputs a single string wher
 
 # Arguments
   - `vecd::Vector`
-    a vector of dictionaries
+    A vector of dictionaries containing chat messages
   - `withkey::Bool` 
-    whether to include the key in the output text. Default is true
+    Whether to include the name as a prefix in the output text. Default is true
+  - `range::Union{Nothing,UnitRange,Int}` 
+    Optional range of messages to include. If nothing, includes all messages
 
-# Return
+# Returns
-  a string with the formatted dictionaries
+  A formatted string where each line contains either:
+  - If withkey=true: "name> message\n"
+  - If withkey=false: "message\n"
 
 # Example
-```jldoctest
+
 julia> using Revise
 julia> using GeneralUtils
 julia> vecd = [Dict(:name => "John", :text => "Hello"), Dict(:name => "Jane", :text => "Goodbye")]
 julia> GeneralUtils.vectorOfDictToText(vecd, withkey=true)
 "John> Hello\nJane> Goodbye\n"
 ```
-# Signature
 """
-function chatHistoryToText(vecd::Vector; withkey=true)::String
+function chatHistoryToText(vecd::Vector; withkey=true, range=nothing)::String
   # Initialize an empty string to hold the final text
   text = ""
   
+  # Get the elements within the specified range, or all elements if no range provided
+  elements = isnothing(range) ? vecd : vecd[range]
+  
   # Determine whether to include the key in the output text or not
   if withkey
-      # Loop through each dictionary in the input vector
+    # Loop through each dictionary in the input vector
-      for d in vecd
+    for d in elements
-          # Extract the 'name' and 'text' keys from the dictionary
+        # Extract the 'name' and 'text' keys from the dictionary
-          name = d[:name]
+        name = d[:name]
-          _text = d[:text]
+        _text = d[:text]
-          
+        
-          # Append the formatted string to the text variable
+        # Append the formatted string to the text variable
-          text *= "$name> $_text \n"
+        text *= "$name:> $_text \n"
       end
   else
-      # Loop through each dictionary in the input vector
+    # Loop through each dictionary in the input vector
-      for d in vecd
+    for d in elements
-          # Iterate over all key-value pairs in the dictionary
+        # Iterate over all key-value pairs in the dictionary
-          for (k, v) in d
+        for (k, v) in d
-              # Append the formatted string to the text variable
+            # Append the formatted string to the text variable
-              text *= "$v \n"
+            text *= "$v \n"
-          end
+        end
-      end
+    end
   end
   
   # Return the final text
@@ -191,6 +197,35 @@ end
 
 
 
+""" Create a dictionary representing an event with optional details.
+
+# Arguments
+  - `event_description::Union{String, Nothing}`
+    A description of the event
+  - `timestamp::Union{DateTime, Nothing}`
+    The time when the event occurred
+  - `subject::Union{String, Nothing}`
+    The subject or entity associated with the event
+  - `thought::Union{AbstractDict, Nothing}`
+    Any associated thoughts or metadata
+  - `actionname::Union{String, Nothing}`
+    The name of the action performed (e.g., "CHAT", "CHECKINVENTORY")
+  - `actioninput::Union{String, Nothing}`
+    Input or parameters for the action
+  - `location::Union{String, Nothing}`
+    Where the event took place
+  - `equipment_used::Union{String, Nothing}`
+    Equipment involved in the event
+  - `material_used::Union{String, Nothing}`
+    Materials used during the event
+  - `outcome::Union{String, Nothing}`
+    The result or consequence of the event after action execution
+  - `note::Union{String, Nothing}`
+    Additional notes or comments
+
+# Returns
+  A dictionary with event details as symbol-keyed key-value pairs
+"""
 function eventdict(;
   event_description::Union{String, Nothing}=nothing,
   timestamp::Union{DateTime, Nothing}=nothing,
@@ -220,24 +255,62 @@ function eventdict(;
 end
 
 
-function createTimeline(memory::T1; skiprecent::Integer=0) where {T1<:AbstractVector}
+""" Create a formatted timeline string from a sequence of events.
-  events = memory[1:end-skiprecent]
 
+# Arguments
+  - `events::T1`
+    Vector of event dictionaries containing subject, actioninput and optional outcome fields
+    Each event dictionary should have the following keys:
+    - :subject - The subject or entity performing the action
+    - :actioninput - The action or input performed by the subject
+    - :outcome - (Optional) The result or outcome of the action
+
+# Returns
+  - `timeline::String`
+    A formatted string representing the events with their subjects, actions, and optional outcomes
+    Format: "{index}) {subject}> {actioninput} {outcome}\n" for each event
+
+# Example
+
+events = [
+  Dict(:subject => "User", :actioninput => "Hello", :outcome => nothing),
+  Dict(:subject => "Assistant", :actioninput => "Hi there!", :outcome => "with a smile")
+]
+timeline = createTimeline(events)
+# 1) User> Hello
+# 2) Assistant> Hi there! with a smile
+
+"""
+function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothing
+  ) where {T1<:AbstractVector}
+  # Initialize empty timeline string
   timeline = ""
-  for (i, event) in enumerate(events)
+  
-    if event[:outcome] === nothing
+  # Determine which indices to use - either provided range or full length
-      timeline *= "$i) $(event[:subject])> $(event[:actioninput])\n"
+  ind = 
+    if eventindex !== nothing
+      [eventindex...]
     else
-      timeline *= "$i) $(event[:subject])> $(event[:actioninput]) $(event[:outcome])\n"
+      1:length(events)
+    end
+
+  # Iterate through events and format each one
+  for (i, event) in zip(ind, events)
+    # If no outcome exists, format without outcome
+    if event[:outcome] === nothing
+      timeline *= "Event_$i $(event[:subject])> $(event[:actioninput])\n"
+    # If outcome exists, include it in formatting  
+    else
+      timeline *= "Event_$i $(event[:subject])> $(event[:actioninput]) $(event[:outcome])\n"
     end
   end
 
+  # Return formatted timeline string
   return timeline
 end
 
 
 
-
 # """ Convert a single chat dictionary into LLM model instruct format.
 
 # # Llama 3 instruct format example

test/Manifest.toml

@@ -0,0 +1,41 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.11.4"
+manifest_format = "2.0"
+project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
+
+[[deps.Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+version = "1.11.0"
+
+[[deps.InteractiveUtils]]
+deps = ["Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+version = "1.11.0"
+
+[[deps.Logging]]
+uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
+version = "1.11.0"
+
+[[deps.Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+version = "1.11.0"
+
+[[deps.Random]]
+deps = ["SHA"]
+uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+version = "1.11.0"
+
+[[deps.SHA]]
+uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+version = "0.7.0"
+
+[[deps.Serialization]]
+uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+version = "1.11.0"
+
+[[deps.Test]]
+deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
+uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+version = "1.11.0"

test/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/config.json

@@ -27,30 +27,50 @@
       "description": "agent role"
   },
   "organization": {
-      "value": "yiem_hq",
+      "value": "yiem_branch_1",
       "description": "organization name"
   },
   "externalservice": {
-      "text2textinstruct": {
+    "loadbalancer": {
-          "mqtttopic": "/loadbalancer/requestingservice",
+      "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
+      "description": "text to text service with instruct LLM"
-          "llminfo": {
+    },
-              "name": "llama3instruct"
+    "text2textinstruct": {
-          }
+        "mqtttopic": "/loadbalancer/requestingservice",
-      },
+        "description": "text to text service with instruct LLM",
-      "virtualWineCustomer_1": {
+        "llminfo": {
-          "mqtttopic": "/virtualenvironment/winecustomer",
+            "name": "llama3instruct"
-          "description": "text to text service with instruct LLM that act as wine customer",
+        }
-          "llminfo": {
+    },
-              "name": "llama3instruct"
+    "virtualWineCustomer_1": {
-          }
+        "mqtttopic": "/virtualenvironment/winecustomer",
-      },
+        "description": "text to text service with instruct LLM that act as wine customer",
-      "text2textchat": {
+        "llminfo": {
-          "mqtttopic": "/loadbalancer/requestingservice",
+            "name": "llama3instruct"
-          "description": "text to text service with instruct LLM",
+        }
-          "llminfo": {
+    },
-              "name": "llama3instruct"
+    "text2textchat": {
-          }
+        "mqtttopic": "/loadbalancer/requestingservice",
-      }
+        "description": "text to text service with instruct LLM",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "wineDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10201,
+      "dbname": "wineDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    },
+    "SQLVectorDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10203,
+      "dbname": "SQLVectorDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    }
   }
 }

test/etc.jl

@@ -1,9 +0,0 @@
-using GeneralUtils
-
-response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria."
-
-responsedict = GeneralUtils.textToDict(response,
-            ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], 
-            rightmarker=":", symbolkey=true)
-
-

test/test_1.jl

@@ -1,272 +1,292 @@
-using Revise
+using Revise
-using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
+using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
-using YiemAgent, GeneralUtils
+using YiemAgent, GeneralUtils
-using Base.Threads
+using Base.Threads
-
+
-# ---------------------------------------------- 100 --------------------------------------------- #
+# ---------------------------------------------- 100 --------------------------------------------- #
-
+
-
+
-
+
-# load config
+# load config
-config = JSON3.read("./test/config.json")
+config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")
-# config = copy(JSON3.read("../mountvolume/config.json"))
+# config = copy(JSON3.read("../mountvolume/config.json"))
-
+
-
+
-function executeSQL(sql::T) where {T<:AbstractString}
+function executeSQL(sql::T) where {T<:AbstractString}
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
+  host = config[:externalservice][:wineDB][:host]
-  result = LibPQ.execute(DBconnection, sql)
+  port = config[:externalservice][:wineDB][:port]
-  close(DBconnection)
+  dbname = config[:externalservice][:wineDB][:dbname]
-  return result
+  user = config[:externalservice][:wineDB][:user]
-end
+  password = config[:externalservice][:wineDB][:password]
-
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-function executeSQLVectorDB(sql)
+  result = LibPQ.execute(DBconnection, sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
+  close(DBconnection)
-  result = LibPQ.execute(DBconnection, sql)
+  return result
-  close(DBconnection)
+end
-  return result
+
-end
+function executeSQLVectorDB(sql)
-
+  host = config[:externalservice][:SQLVectorDB][:host]
-function text2textInstructLLM(prompt::String)
+  port = config[:externalservice][:SQLVectorDB][:port]
-  msgMeta = GeneralUtils.generate_msgMeta(
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+  user = config[:externalservice][:SQLVectorDB][:user]
-    msgPurpose="inference",
+  password = config[:externalservice][:SQLVectorDB][:password]
-    senderName="yiemagent",
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-    senderId=string(uuid4()),
+  result = LibPQ.execute(DBconnection, sql)
-    receiverName="text2textinstruct",
+  close(DBconnection)
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+  return result
-    mqttBrokerPort=config[:mqttServerInfo][:port],
+end
-  )
+
-
+function text2textInstructLLM(prompt::String; maxattempt::Integer=2, modelsize::String="medium")
-  outgoingMsg = Dict(
+  msgMeta = GeneralUtils.generate_msgMeta(
-    :msgMeta => msgMeta,
+    config[:externalservice][:loadbalancer][:mqtttopic];
-    :payload => Dict(
+    msgPurpose="inference",
-      :text => prompt,
+    senderName="yiemagent",
-      :kwargs => Dict(
+    senderId=sessionId,
-        :num_ctx => 16384,
+    receiverName="text2textinstruct_$modelsize",
-        :temperature => 0.2,
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-      )
+    mqttBrokerPort=config[:mqttServerInfo][:port],
-    )
+  )
-  )
+
-
+  outgoingMsg = Dict(
-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
+    :msgMeta => msgMeta,
-  response = _response[:response][:text]
+    :payload => Dict(
-
+      :text => prompt,
-  return response
+      :kwargs => Dict(
-end
+        :num_ctx => 16384,
-
+        :temperature => 0.2,
-# get text embedding from a LLM service
+      )
-function getEmbedding(text::T) where {T<:AbstractString}
+    )
-  msgMeta = GeneralUtils.generate_msgMeta(
+  )
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+
-    msgPurpose="embedding",
+  response = nothing
-    senderName="yiemagent",
+  for attempts in 1:maxattempt
-    senderId=string(uuid4()),
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=180, maxattempt=maxattempt)
-    receiverName="text2textinstruct",
+    payload = _response[:response]
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    if _response[:success] && payload[:text] !== nothing
-    mqttBrokerPort=config[:mqttServerInfo][:port],
+      response = _response[:response][:text]
-  )
+      break
-
+    else
-  outgoingMsg = Dict(
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    :msgMeta => msgMeta,
+      pprintln(outgoingMsg)
-    :payload => Dict(
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-      :text => [text] # must be a vector of string
+      sleep(3)
-    )
+    end
-  )
+  end
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
+
-  embedding = response[:response][:embeddings]
+  return response
-  return embedding
+end
-end
+
-
+# get text embedding from a LLM service
-function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
+function getEmbedding(text::T) where {T<:AbstractString}
-   vectorDB::Function; limit::Integer=1
+  msgMeta = GeneralUtils.generate_msgMeta(
-   )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+    config[:externalservice][:loadbalancer][:mqtttopic];
-
+    msgPurpose="embedding",
-  # get embedding from LLM service
+    senderName="yiemagent",
-  embedding = getEmbedding(text)[1] 
+    senderId=sessionId,
-
+    receiverName="textembedding",
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-  sql = """
+    mqttBrokerPort=config[:mqttServerInfo][:port],
-        SELECT *, $embeddingColumnName <-> '$embedding' as distance
+  )
-        FROM $tablename
+
-        ORDER BY distance LIMIT $limit;
+  outgoingMsg = Dict(
-        """
+    :msgMeta => msgMeta,
-  response = vectorDB(sql)
+    :payload => Dict(
-  df = DataFrame(response)
+      :text => [text] # must be a vector of string
-  return df
+    )
-end
+  )
-
+
-
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
-function similarSQLVectorDB(query; maxdistance::Integer=100)
+  embedding = response[:response][:embeddings]
-  tablename = "sqlllm_decision_repository"
+  return embedding
-  # get embedding of the query
+end
-  df = findSimilarTextFromVectorDB(query, tablename, 
+
-                                  "function_input_embedding", executeSQLVectorDB)
+function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
-  row, col = size(df)
+  vectorDB::Function; limit::Integer=1
-  distance = row == 0 ? Inf : df[1, :distance]  
+  )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-  if row != 0 && distance < maxdistance
+  # get embedding from LLM service
-    # if there is usable SQL, return it.
+  embedding = getEmbedding(text)[1]
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
+  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-    output_str = String(base64decode(output_b64))
+  sql = """
-    rowid = df[1, :id]
+        SELECT *, $embeddingColumnName <-> '$embedding' as distance
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+        FROM $tablename
-    return (dict=output_str, distance=distance)
+        ORDER BY distance LIMIT $limit;
-  else
+        """
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+  response = vectorDB(sql)
-    return (dict=nothing, distance=nothing)
+  df = DataFrame(response)
-  end
+  return df
-end
+end
-
+
-
+function similarSQLVectorDB(query; maxdistance::Integer=100)
-function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) where {T1<:AbstractString, T2<:AbstractString}
+  tablename = "sqlllm_decision_repository"
-  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
-  # get embedding of the query
+  df = findSimilarTextFromVectorDB(query, tablename, 
-  # query = state[:thoughtHistory][:question]
+                                  "function_input_embedding", executeSQLVectorDB)
-  df = findSimilarTextFromVectorDB(query, tablename, 
+  # println(df[1, [:id, :function_output]])
-                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
-  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]  
-  distance = row == 0 ? Inf : df[1, :distance]
+  # distance = 100  # CHANGE this is for testing only
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+  if row != 0 && distance < maxdistance
-    query_embedding = getEmbedding(query)[1]
+    # if there is usable SQL, return it.
-    query = replace(query, "'" => "")
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    sql_base64 = base64encode(SQL)
+    output_str = String(base64decode(output_b64))
-    sql_ = replace(SQL, "'" => "")
+    rowid = df[1, :id]
-
+    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    sql = """
+    return (dict=output_str, distance=distance)
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+  else
-          """
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+    return (dict=nothing, distance=nothing)
-    println(sql)
+  end
-    _ = executeSQLVectorDB(sql)
+end
-  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
-function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
+  # query = state[:thoughtHistory][:question]
-  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+  df = findSimilarTextFromVectorDB(query, tablename, 
-  tablename = "sommelier_decision_repository"
+                                  "function_input_embedding", executeSQLVectorDB)
-  # find similar
+  row, col = size(df)
-  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+  distance = row == 0 ? Inf : df[1, :distance]
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-                                  "function_input_embedding", executeSQLVectorDB)
+    query_embedding = getEmbedding(query)[1]
-  row, col = size(df)
+    query = replace(query, "'" => "")
-  distance = row == 0 ? Inf : df[1, :distance]
+    sql_base64 = base64encode(SQL)
-  if row != 0 && distance < maxdistance
+    sql_ = replace(SQL, "'" => "")
-    # if there is usable decision, return it.
+
-    rowid = df[1, :id]
+    sql = """
-    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
+          """
-    _output_str = String(base64decode(output_b64))
+    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    output = copy(JSON3.read(_output_str))
+    # println(sql)
-    return output
+    _ = executeSQLVectorDB(sql)
-  else
+  end
-    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+end
-    return nothing
+
-  end
+
-end
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
-
+  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-
+  tablename = "sommelier_decision_repository"
-function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+  # find similar
-  ) where {T1<:AbstractString, T2<:AbstractDict}
+  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
-  tablename = "sommelier_decision_repository"
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-  # find similar
+                                  "function_input_embedding", executeSQLVectorDB)
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+  row, col = size(df)
-                                  "function_input_embedding", executeSQLVectorDB)
+  distance = row == 0 ? Inf : df[1, :distance]
-  row, col = size(df)
+  if row != 0 && distance < maxdistance
-  distance = row == 0 ? Inf : df[1, :distance]
+    # if there is usable decision, return it.
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    rowid = df[1, :id]
-    recentevents_embedding = a.func[:getEmbedding](recentevents)[1]
+    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    recentevents = replace(recentevents, "'" => "")
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    decision_json = JSON3.write(decision)
+    _output_str = String(base64decode(output_b64))
-    decision_base64 = base64encode(decision_json)
+    output = copy(JSON3.read(_output_str))
-    decision = replace(decision_json, "'" => "")
+    return output
-    
+  else
-    sql = """
+    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+    return nothing
-          """
+  end
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+end
-    println(sql)
+
-    _ = executeSQLVectorDB(sql)
+
-  else
+function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+  ) where {T1<:AbstractString, T2<:AbstractDict}
-  end
+  tablename = "sommelier_decision_repository"
-end
+  # find similar
-
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-
+                                  "function_input_embedding", executeSQLVectorDB)
-sessionId = "12345"
+  row, col = size(df)
-
+  distance = row == 0 ? Inf : df[1, :distance]
-externalFunction = (
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    getEmbedding=getEmbedding,
+    recentevents_embedding = a.func[:getEmbedding](recentevents)[1]
-    text2textInstructLLM=text2textInstructLLM,
+    recentevents = replace(recentevents, "'" => "")
-    executeSQL=executeSQL,
+    decision_json = JSON3.write(decision)
-    similarSQLVectorDB=similarSQLVectorDB,
+    decision_base64 = base64encode(decision_json)
-    insertSQLVectorDB=insertSQLVectorDB,
+    decision = replace(decision_json, "'" => "")
-    similarSommelierDecision=similarSommelierDecision,
+    
-    insertSommelierDecision=insertSommelierDecision,
+    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)
-a = YiemAgent.sommelier(
+    _ = executeSQLVectorDB(sql)
-  externalFunction;
+  else
-  name="Ton",
+    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
-  id=sessionId,  # agent instance id
+  end
-  retailername="Yiem",
+end
-)
+
-
+
-while true
+sessionId = "12345"
-  println("your respond: ")
+
-  user_answer = readline()
+externalFunction = (
-  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
+    getEmbedding=getEmbedding,
-  println("\n$response")
+    text2textInstructLLM=text2textInstructLLM,
-end
+    executeSQL=executeSQL,
-
+    similarSQLVectorDB=similarSQLVectorDB,
-
+    insertSQLVectorDB=insertSQLVectorDB,
-# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
+    similarSommelierDecision=similarSommelierDecision,
-
+    insertSommelierDecision=insertSommelierDecision,
-
+  )
-
+
-
+
-
+
-
+a = YiemAgent.sommelier(
-
+  externalFunction;
-
+  name="Ton",
-
+  id=sessionId,  # agent instance id
-
+  retailername="Yiem",
-
+)
-
+
-
+while true
-
+  print("\nyour respond: ")
-
+  user_answer = readline()
-
+  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
-
+  println("\n$response")
-
+end
-
+
-
+
-
+# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
-
+
-
+
-
+"""
-
+hello I want to get a bottle of red wine for my boss. I have a budget around 50 dollars. Show me some options.
-
+
-
+I have no idea about his wine taste but he likes spicy food.
-
+
-
+
-
+"""
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