update

2025-04-04 15:04:02 +07:00 · 2025-04-01 21:17:15 +07:00 · 2025-03-31 21:30:14 +07:00 · 2025-03-22 15:34:00 +07:00 · 2025-03-22 09:42:51 +07:00 · 2025-03-21 10:13:53 +07:00
10 changed files with 1684 additions and 1138 deletions
--- a/Project.toml
+++ b/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"
--- a/src/interface.jl
+++ b/src/interface.jl
--- a/src/llmfunction.jl
+++ b/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:
-  - reasoning: state your understanding of the current situation
-  - wine_name: name of the wine
-  - winery: name of the winery
-  - vintage: the year of the wine
-  - 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"
-  - 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
-  - tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
-  - wine_price: price range of wine.
-  - 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
-  
+  You should then respond to the user with:
+    Thought: state your understanding of the current situation
+    Wine_name: name of the wine
+    Winery: name of the winery
+    Vintage: the year of the wine
+    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"
+    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
+    Tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
+    Wine_price: price range of wine.
+    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

-  You should only respond in the user's preference form (JSON) 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": ...}
+  You should only respond in format as described below:
+  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
-  {"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"}
+  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": "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 *=
-      """
-      <|start_header_id|>assistant<|end_header_id|>
-      """
-
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")
    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))
-
-    # convert 
-
-    delete!(responsedict, :reasoning)
+    delete!(responsedict, :thought)
    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
-            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."
-              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
-              checkFlag == true
-              break
-            end
-          end
+          # for x in content  #check whether price are mentioned in the input
+          #   if !occursin("NA", responsedict[j]) && !occursin(x, input)
+          #     errornote = "$x is not mentioned in the user query, you must only use the info from the query."
+          #     println("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+          #     checkFlag == true
+          #     break
+          #   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:
-  - 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
-  - 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
-  - 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
-  - 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
+  <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: ( 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: ( 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: ( 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: ( 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:
-  {
-    "sweetness_keyword": ...,
-    "sweetness": ...,
-    "acidity_keyword": ...,
-    "acidity": ...,
-    "tannin_keyword": ...,
-    "tannin": ...,
-    "intensity_keyword": ...,
-    "intensity": ...
-  }
+  <You should only respond in format as described below>
+    Sweetness_keyword: ...
+    Sweetness: ...
+    Acidity_keyword: ...
+    Acidity: ...
+    Tannin_keyword: ...
+    Tannin: ...
+    Intensity_keyword: ...
+    Intensity: ...
+  </You should only respond in format as described below>

-  Here are some examples:
-  User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
-  {
-    "sweetness_keyword": "NA",
-    "sweetness": "NA",
-    "acidity_keyword": "low acidity",
-    "acidity": "1-2",
-    "tannin_keyword": "medium tannin",
-    "tannin": "3-4",
-    "intensity_keyword": "medium-bodied",
-    "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"
-  }
+  <Here are some examples>
+    User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
+    Sweetness_keyword: NA
+    Sweetness: NA
+    Acidity_keyword: low acidity
+    Acidity: 1-2
+    Tannin_keyword: medium tannin
+    Tannin: 3-4
+    Intensity_keyword: medium-bodied
+    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
+  </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 *=
-      """
-      <|start_header_id|>assistant<|end_header_id|>
-      """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")

    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 *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
+    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="qwen")

    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
--- a/src/util.jl
+++ b/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
-  a string with the formatted dictionaries
+# Returns
+  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
-      for d in vecd
-          # Extract the 'name' and 'text' keys from the dictionary
-          name = d[:name]
-          _text = d[:text]
-          
-          # Append the formatted string to the text variable
-          text *= "$name> $_text \n"
+    # Loop through each dictionary in the input vector
+    for d in elements
+        # Extract the 'name' and 'text' keys from the dictionary
+        name = d[:name]
+        _text = d[:text]
+        
+        # Append the formatted string to the text variable
+        text *= "$name:> $_text \n"
      end
  else
-      # Loop through each dictionary in the input vector
-      for d in vecd
-          # Iterate over all key-value pairs in the dictionary
-          for (k, v) in d
-              # Append the formatted string to the text variable
-              text *= "$v \n"
-          end
-      end
+    # Loop through each dictionary in the input vector
+    for d in elements
+        # Iterate over all key-value pairs in the dictionary
+        for (k, v) in d
+            # Append the formatted string to the text variable
+            text *= "$v \n"
+        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}
-  events = memory[1:end-skiprecent]
+""" Create a formatted timeline string from a sequence of events.

+# 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
-      timeline *= "$i) $(event[:subject])> $(event[:actioninput])\n"
+  
+  # Determine which indices to use - either provided range or full length
+  ind = 
+    if eventindex !== nothing
+      [eventindex...]
    else
-      timeline *= "$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
--- a/test/Manifest.toml
+++ b/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"
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -0,0 +1,2 @@
+[deps]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
--- a/test/config.json
+++ b/test/config.json
@@ -27,30 +27,50 @@
      "description": "agent role"
  },
  "organization": {
-      "value": "yiem_hq",
+      "value": "yiem_branch_1",
      "description": "organization name"
  },
  "externalservice": {
-      "text2textinstruct": {
-          "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      },
-      "virtualWineCustomer_1": {
-          "mqtttopic": "/virtualenvironment/winecustomer",
-          "description": "text to text service with instruct LLM that act as wine customer",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      },
-      "text2textchat": {
-          "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      }
+    "loadbalancer": {
+      "mqtttopic": "/loadbalancer/requestingservice",
+      "description": "text to text service with instruct LLM"
+    },
+    "text2textinstruct": {
+        "mqtttopic": "/loadbalancer/requestingservice",
+        "description": "text to text service with instruct LLM",
+        "llminfo": {
+            "name": "llama3instruct"
+        }
+    },
+    "virtualWineCustomer_1": {
+        "mqtttopic": "/virtualenvironment/winecustomer",
+        "description": "text to text service with instruct LLM that act as wine customer",
+        "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"
+    }
  }
 }
--- a/test/etc.jl
+++ b/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)
-
-
--- a/test/runtests.jl
+++ b/test/runtests.jl
--- a/test/runtest.jl
+++ b/test/runtest.jl
@@ -1,272 +1,292 @@
-using Revise
-using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
-using YiemAgent, GeneralUtils
-using Base.Threads
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-
-# load config
-config = JSON3.read("./test/config.json")
-# config = copy(JSON3.read("../mountvolume/config.json"))
-
-
-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")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function executeSQLVectorDB(sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function text2textInstructLLM(prompt::String)
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose="inference",
-    senderName="yiemagent",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => prompt,
-      :kwargs => Dict(
-        :num_ctx => 16384,
-        :temperature => 0.2,
-      )
-    )
-  )
-
-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
-  response = _response[:response][:text]
-
-  return response
-end
-
-# get text embedding from a LLM service
-function getEmbedding(text::T) where {T<:AbstractString}
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose="embedding",
-    senderName="yiemagent",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => [text] # must be a vector of string
-    )
-  )
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
-  embedding = response[:response][:embeddings]
-  return embedding
-end
-
-function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
-   vectorDB::Function; limit::Integer=1
-   )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-
-  # get embedding from LLM service
-  embedding = getEmbedding(text)[1] 
-
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-  sql = """
-        SELECT *, $embeddingColumnName <-> '$embedding' as distance
-        FROM $tablename
-        ORDER BY distance LIMIT $limit;
-        """
-  response = vectorDB(sql)
-  df = DataFrame(response)
-  return df
-end
-
-
-function similarSQLVectorDB(query; maxdistance::Integer=100)
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  df = findSimilarTextFromVectorDB(query, 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 SQL, return it.
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    output_str = String(base64decode(output_b64))
-    rowid = df[1, :id]
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    return (dict=output_str, distance=distance)
-  else
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    return (dict=nothing, distance=nothing)
-  end
-end
-
-
-function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) where {T1<:AbstractString, T2<:AbstractString}
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  # query = state[:thoughtHistory][:question]
-  df = findSimilarTextFromVectorDB(query, 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
-    query_embedding = getEmbedding(query)[1]
-    query = replace(query, "'" => "")
-    sql_base64 = base64encode(SQL)
-    sql_ = replace(SQL, "'" => "")
-
-    sql = """
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-          """
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-    println(sql)
-    _ = executeSQLVectorDB(sql)
-  end
-end
-
-
-function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
-  )::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 = a.func[: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 $distance ", @__FILE__, " ", @__LINE__)
-  end
-end
-
-
-sessionId = "12345"
-
-externalFunction = (
-    getEmbedding=getEmbedding,
-    text2textInstructLLM=text2textInstructLLM,
-    executeSQL=executeSQL,
-    similarSQLVectorDB=similarSQLVectorDB,
-    insertSQLVectorDB=insertSQLVectorDB,
-    similarSommelierDecision=similarSommelierDecision,
-    insertSommelierDecision=insertSommelierDecision,
-  )
-
-
-
-a = YiemAgent.sommelier(
-  externalFunction;
-  name="Ton",
-  id=sessionId,  # agent instance id
-  retailername="Yiem",
-)
-
-while true
-  println("your respond: ")
-  user_answer = readline()
-  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
-  println("\n$response")
-end
-
-
-# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
+using Revise
+using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
+using YiemAgent, GeneralUtils
+using Base.Threads
+
+# ---------------------------------------------- 100 --------------------------------------------- #
+
+
+
+# load config
+config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")
+# config = copy(JSON3.read("../mountvolume/config.json"))
+
+
+function executeSQL(sql::T) where {T<:AbstractString}
+  host = config[:externalservice][:wineDB][:host]
+  port = config[:externalservice][:wineDB][:port]
+  dbname = config[:externalservice][:wineDB][:dbname]
+  user = config[:externalservice][:wineDB][:user]
+  password = config[:externalservice][:wineDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function executeSQLVectorDB(sql)
+  host = config[:externalservice][:SQLVectorDB][:host]
+  port = config[:externalservice][:SQLVectorDB][:port]
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+  user = config[:externalservice][:SQLVectorDB][:user]
+  password = config[:externalservice][:SQLVectorDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function text2textInstructLLM(prompt::String; maxattempt::Integer=2, modelsize::String="medium")
+  msgMeta = GeneralUtils.generate_msgMeta(
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="inference",
+    senderName="yiemagent",
+    senderId=sessionId,
+    receiverName="text2textinstruct_$modelsize",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => prompt,
+      :kwargs => Dict(
+        :num_ctx => 16384,
+        :temperature => 0.2,
+      )
+    )
+  )
+
+  response = nothing
+  for attempts in 1:maxattempt
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=180, maxattempt=maxattempt)
+    payload = _response[:response]
+    if _response[:success] && payload[:text] !== nothing
+      response = _response[:response][:text]
+      break
+    else
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(outgoingMsg)
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      sleep(3)
+    end
+  end
+
+  return response
+end
+
+# get text embedding from a LLM service
+function getEmbedding(text::T) where {T<:AbstractString}
+  msgMeta = GeneralUtils.generate_msgMeta(
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="embedding",
+    senderName="yiemagent",
+    senderId=sessionId,
+    receiverName="textembedding",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
+  )
+
+  outgoingMsg = Dict(
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => [text] # must be a vector of string
+    )
+  )
+
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
+  embedding = response[:response][:embeddings]
+  return embedding
+end
+
+function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
+  vectorDB::Function; limit::Integer=1
+  )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+  # get embedding from LLM service
+  embedding = getEmbedding(text)[1]
+  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
+  sql = """
+        SELECT *, $embeddingColumnName <-> '$embedding' as distance
+        FROM $tablename
+        ORDER BY distance LIMIT $limit;
+        """
+  response = vectorDB(sql)
+  df = DataFrame(response)
+  return df
+end
+
+function similarSQLVectorDB(query; maxdistance::Integer=100)
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  # println(df[1, [:id, :function_output]])
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]  
+  # distance = 100  # CHANGE this is for testing only
+  if row != 0 && distance < maxdistance
+    # if there is usable SQL, return it.
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    output_str = String(base64decode(output_b64))
+    rowid = df[1, :id]
+    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=output_str, distance=distance)
+  else
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=nothing, distance=nothing)
+  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
+  # query = state[:thoughtHistory][:question]
+  df = findSimilarTextFromVectorDB(query, 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
+    query_embedding = getEmbedding(query)[1]
+    query = replace(query, "'" => "")
+    sql_base64 = base64encode(SQL)
+    sql_ = replace(SQL, "'" => "")
+
+    sql = """
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+          """
+    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # println(sql)
+    _ = executeSQLVectorDB(sql)
+  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 = a.func[: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 $distance ", @__FILE__, " ", @__LINE__)
+  end
+end
+
+
+sessionId = "12345"
+
+externalFunction = (
+    getEmbedding=getEmbedding,
+    text2textInstructLLM=text2textInstructLLM,
+    executeSQL=executeSQL,
+    similarSQLVectorDB=similarSQLVectorDB,
+    insertSQLVectorDB=insertSQLVectorDB,
+    similarSommelierDecision=similarSommelierDecision,
+    insertSommelierDecision=insertSommelierDecision,
+  )
+
+
+
+a = YiemAgent.sommelier(
+  externalFunction;
+  name="Ton",
+  id=sessionId,  # agent instance id
+  retailername="Yiem",
+)
+
+while true
+  print("\nyour respond: ")
+  user_answer = readline()
+  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
+  println("\n$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.
+
+
+"""
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Author	SHA1	Message	Date
narawat lamaiin	c0edf7dadf	update	2025-04-04 15:04:02 +07:00
narawat lamaiin	c21f943b12	update	2025-04-01 21:17:15 +07:00
narawat lamaiin	b8fd772a28	update	2025-03-31 21:30:14 +07:00
narawat lamaiin	883f581b2a	update	2025-03-22 15:34:00 +07:00
narawat lamaiin	5a890860a6	update	2025-03-22 09:42:51 +07:00
tonaerospace	7d5bc14a09	mark new version	2025-03-21 10:13:53 +07:00
ton	37ba3a9d31	Merge pull request 'v0.1.3-dev' (#2 ) from v0.1.3-dev into main Reviewed-on: #2	2025-03-21 03:09:16 +00:00
tonaerospace	bfadd53033	update	2025-03-21 10:03:08 +07:00
tonaerospace	8fc3afe348	update	2025-03-20 16:15:38 +07:00
tonaerospace	c60037226a	update	2025-03-13 19:11:20 +07:00
narawat lamaiin	db6c9c5f2b	update	2025-03-07 13:34:15 +07:00
narawat lamaiin	6504099959	update	2025-01-31 09:50:44 +07:00