update

2026-07-15 18:51:21 +07:00 · 2026-07-15 18:49:51 +07:00 · 2026-07-15 18:47:18 +07:00 · 2026-07-15 14:25:04 +07:00 · 2026-07-15 14:01:32 +07:00 · 2026-07-15 13:59:28 +07:00
11 changed files with 931 additions and 863 deletions
@@ -2,7 +2,7 @@

 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "09bd5c43d6ad954d8be233d27fc343ea1149c0b0"
+project_hash = "a5128932115e2d3aa9658af105ac712db061d523"

 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -38,6 +38,12 @@ version = "1.1.3"
 uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
 version = "1.1.2"

+[[deps.ArnoldiMethod]]
+deps = ["LinearAlgebra", "Random", "StaticArrays"]
+git-tree-sha1 = "d57bd3762d308bded22c3b82d033bff85f6195c6"
+uuid = "ec485272-7323-5ecc-a04f-4719b315124d"
+version = "0.4.0"
+
 [[deps.ArrowTypes]]
 deps = ["Sockets", "UUIDs"]
 git-tree-sha1 = "404265cd8128a2515a81d5eae16de90fdef05101"
@@ -181,6 +187,20 @@ git-tree-sha1 = "e98abef36d02a0ec385d68cd7dadbce9b28cbd88"
 uuid = "abce61dc-4473-55a0-ba07-351d65e31d42"
 version = "0.4.1"

+[[deps.Distances]]
+deps = ["LinearAlgebra", "Statistics", "StatsAPI"]
+git-tree-sha1 = "c7e3a542b999843086e2f29dac96a618c105be1d"
+uuid = "b4f34e82-e78d-54a5-968a-f98e89d6e8f7"
+version = "0.10.12"
+
+    [deps.Distances.extensions]
+    DistancesChainRulesCoreExt = "ChainRulesCore"
+    DistancesSparseArraysExt = "SparseArrays"
+
+    [deps.Distances.weakdeps]
+    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+    SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+
 [[deps.Distributed]]
 deps = ["Random", "Serialization", "Sockets"]
 uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
@@ -250,6 +270,7 @@ deps = ["LinearAlgebra"]
 git-tree-sha1 = "2f979084d1e13948a3352cf64a25df6bd3b4dca3"
 uuid = "1a297f60-69ca-5386-bcde-b61e274b549b"
 version = "1.16.0"
+weakdeps = ["PDMats", "SparseArrays", "StaticArrays", "Statistics"]

    [deps.FillArrays.extensions]
    FillArraysPDMatsExt = "PDMats"
@@ -257,12 +278,6 @@ version = "1.16.0"
    FillArraysStaticArraysExt = "StaticArrays"
    FillArraysStatisticsExt = "Statistics"

-    [deps.FillArrays.weakdeps]
-    PDMats = "90014a1f-27ba-587c-ab20-58faa44d9150"
-    SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
-    StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
-    Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
-
 [[deps.Future]]
 deps = ["Random"]
 uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
@@ -274,12 +289,25 @@ uuid = "a0844989-3bd2-4988-8bea-c9407ab0941b"
 version = "1.1.0"

 [[deps.GeneralUtils]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "HTTP", "JSON", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "UUIDs"]
-git-tree-sha1 = "7c0600c166a5deb2c607018a491c04eb25969c2e"
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "UUIDs"]
+git-tree-sha1 = "a75a088ee8e5faf10f554ca00748e0e6ca58d1ca"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.4.9"
+version = "0.5.1"
+
+[[deps.Graphs]]
+deps = ["ArnoldiMethod", "DataStructures", "Inflate", "LinearAlgebra", "Random", "SimpleTraits", "SparseArrays", "Statistics"]
+git-tree-sha1 = "7eb45fe833a5b7c51cf6d89c5a841d5967e44be3"
+uuid = "86223c79-3864-5bf0-83f7-82e725a168b6"
+version = "1.14.0"
+
+    [deps.Graphs.extensions]
+    GraphsSharedArraysExt = "SharedArrays"
+
+    [deps.Graphs.weakdeps]
+    Distributed = "8ba89e20-285c-5b6f-9357-94700520ee1b"
+    SharedArrays = "1a1011a3-84de-559e-8e89-a11a2f7dc383"

 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "Dates", "EnumX", "PrecompileTools", "Random", "Reseau", "SHA", "URIs", "UUIDs", "Zlib_jll"]
@@ -310,6 +338,11 @@ git-tree-sha1 = "cf8234411cbeb98676c173f930951ea29dca3b23"
 uuid = "a303e19e-6eb4-11e9-3b09-cd9505f79100"
 version = "0.2.4"

+[[deps.Inflate]]
+git-tree-sha1 = "d1b1b796e47d94588b3757fe84fbf65a5ec4a80d"
+uuid = "d25df0c9-e2be-5dd7-82c8-3ad0b3e990b9"
+version = "0.1.5"
+
 [[deps.InlineStrings]]
 git-tree-sha1 = "8f3d257792a522b4601c24a577954b0a8cd7334d"
 uuid = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
@@ -760,11 +793,11 @@ version = "0.7.0"

 [[deps.SQLLLM]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "FileIO", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "PrettyPrinting", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
-git-tree-sha1 = "93cc1ae6202279a2eb4e1dbfff706c5bc158609d"
+git-tree-sha1 = "bae2fd2e2b087753fbb3415896be41df1ae0eb90"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/SQLLLM"
 uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
-version = "0.2.5"
+version = "0.2.8"

 [[deps.SQLStrings]]
 git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
@@ -793,6 +826,12 @@ version = "1.4.10"
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 version = "1.11.0"

+[[deps.SimpleTraits]]
+deps = ["InteractiveUtils", "MacroTools"]
+git-tree-sha1 = "7ddb0b49c109481b046972c0e4ab02b2127d6a75"
+uuid = "699a6c99-e7fa-54fc-8d76-47d257e15c1d"
+version = "0.9.6"
+
 [[deps.Sockets]]
 uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
 version = "1.11.0"
@@ -826,6 +865,25 @@ version = "2.8.0"
    [deps.SpecialFunctions.weakdeps]
    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

+[[deps.StaticArrays]]
+deps = ["LinearAlgebra", "PrecompileTools", "Random", "StaticArraysCore"]
+git-tree-sha1 = "246a8bb2e6667f832eea063c3a56aef96429a3db"
+uuid = "90137ffa-7385-5640-81b9-e52037218182"
+version = "1.9.18"
+
+    [deps.StaticArrays.extensions]
+    StaticArraysChainRulesCoreExt = "ChainRulesCore"
+    StaticArraysStatisticsExt = "Statistics"
+
+    [deps.StaticArrays.weakdeps]
+    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
+    Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
+
+[[deps.StaticArraysCore]]
+git-tree-sha1 = "6ab403037779dae8c514bad259f32a447262455a"
+uuid = "1e83bf80-4336-4d27-bf5d-d5a4f845583c"
+version = "1.4.4"
+
 [[deps.Statistics]]
 deps = ["LinearAlgebra"]
 git-tree-sha1 = "ae3bb1eb3bba077cd276bc5cfc337cc65c3075c0"
@@ -862,6 +920,12 @@ version = "2.2.0"
    ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
    InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"

+[[deps.StringDistances]]
+deps = ["Distances", "StatsAPI"]
+git-tree-sha1 = "cd83a04baf746e3b43b83c61b7de77ab0409b80a"
+uuid = "88034a9c-02f8-509d-84a9-84ec65e18404"
+version = "1.0.0"
+
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
 git-tree-sha1 = "d05693d339e37d6ab134c5ab53c29fce5ee5d7d5"
@@ -983,10 +1047,10 @@ uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"

 [[deps.YiemAgent]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.6.4"

 [[deps.Zlib_jll]]
 deps = ["Libdl"]
@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.6.4"
 authors = ["narawat lamaiin <narawat@outlook.com>"]

 [deps]
@@ -25,9 +25,9 @@ UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 [compat]
 CSV = "0.10.15"
 DataFrames = "1.7.0"
-GeneralUtils = "0.4.9"
+GeneralUtils = "0.5.1"
 HTTP = "2.4.0"
 JSON = "1.6.1"
 LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-SQLLLM = "0.2.5"
+SQLLLM = "0.2.8"
@@ -54,7 +54,7 @@ Your name is $(newAgent.name). You are a helpful sommelier for website-based $(n
 # Available Actions

 - **CHAT_BOX** which you can use to talk with the user.
- **CHECK_WINE** allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
+- **SEARCH_WINE_DATABASE** allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
    - Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
    - Example query 2: "Red or white wine, medium tannin, price under 700 USD"
    - Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
@@ -6,6 +6,7 @@
  "testingOrProduction": "testing",
  "agentId": "2b74b87a-5413-4fe2-a4d3-405891051680",
  "agentCentralConfigSubject": "/yiem/hq/agent/sommelier/backend/config/api/v1.1",
+  "this_service_name": "agent_backend",
  "this_service_input_channel": {
    "mqtt": [
        "/yiem/hq/agent/sommpanion/backend/db/api_v1"
@@ -16,7 +17,7 @@
  },
  "agentRole": "sommelier",
  "organization": "yiem_hq",
-  "externalService": {
+  "externalservice": {
    "servicesloadbalancer": {
      "nats": "sommpanion.backend.servicesloadbalancer.v1.inbox" 
    },
@@ -35,15 +36,15 @@
      "description": "A database connection info for LibPQ client",
      "url": "192.168.88.106:5432",
      "dbname": "winedb",
-      "user": "yiemtechnologies@gmail.com",
-      "password": "yiemtechnologies@Postgres_0.0"
+      "user": "admin",
+      "password": "admin@Sommpanion_0.0"
    },
    "sommpanion_vectordb" : {
      "description": "A wine database connection info for LibPQ client",
      "url": "192.168.88.106:5433",
      "dbname": "vectordb",
-      "user": "yiemtechnologies@gmail.com",
-      "password": "yiemtechnologies@Postgres_0.0"
+      "user": "admin",
+      "password": "admin@Sommpanion_0.0"
    },
    "fileserver": {
    "description": "temporary file server",
@@ -1,13 +1,28 @@
-
-
-
-d = Dict(
-  "hello"=> 555,
-  "world"=> Dict(
-    "name"=> "ton"
-  )
-)
-
-x = 55
-
-@info "YiemAgent think() 1 " d x @__LINE__
+"""
+CREATE TABLE "public"."wine" (
+    "wine_id" uuid DEFAULT gen_random_uuid() NOT NULL,
+    "seo_name" character varying(128) NOT NULL,
+    "wine_name" character varying(128) NOT NULL,
+    "winery" character varying(128) NOT NULL,
+    "vintage" integer NOT NULL,
+    "region" character varying(128) NOT NULL,
+    "country" character varying(128) NOT NULL,
+    "wine_type" character varying(128) NOT NULL,
+    "grape" character varying(128) NOT NULL,
+    "serving_temperature" character varying(128) NOT NULL,
+    "intensity" integer NULL,
+    "sweetness" integer NULL,
+    "tannin" integer NULL,
+    "acidity" integer NULL,
+    "fizziness" integer NULL,
+    "tasting_notes" text NULL,
+    "image_url" jsonb NULL,
+    "manufacturer_sku" text NULL,
+    "note" text NULL,
+    "other_attributes" jsonb NULL,
+    "created_time" timestamp with time zone DEFAULT CURRENT_TIMESTAMP NULL,
+    "updated_time" timestamp with time zone DEFAULT CURRENT_TIMESTAMP NULL,
+    "description" text NULL,
+    PRIMARY KEY (wine_id)
+);
+"""
@@ -68,14 +68,14 @@ julia> result = decisionMaker(agent)

 OrderedDict{String, Any} with 4 entries:
  "plan"          => "The user provided an image of a sparkling white wine (Asolo Prosecco Bella Principessa from Italy) and requested a search for similar wines in the inventory. According to store guidelines, I must st…
-  "action_name"   => "CHECK_WINE"
+  "action_name"   => "SEARCH_WINE_DATABASE"
  "action_input"  => "Sparkling white wine from Italy"
  "action_result" => "1) winery: Terrazze dell Etna, wine_name: Rose Brut.
 ```
 """
 function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
  ) where {T<:agent}
-  println("\nExecuting YiemAgent decisionMaker()")
+
  # lessonDict = copy(JSON.parsefile("lesson.json"))

  # lesson =
@@ -108,9 +108,6 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
  context = 
    """
    <internal_context_for_assistant>
-      <thought_history>
-        $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-      </thought_history>
    </internal_context_for_assistant>
    """

@@ -142,6 +139,11 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
    think, response = GeneralUtils.extractthink(response)
    response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
    response = strip(response)
+    
+    # dollar sign in Julia means string interpolation
+    while occursin('$', response)
+      response = replace(response, '$' => "USD")
+    end

    responsedict = nothing
    if occursin(requiredKeys[2], response) 
@@ -152,25 +154,17 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
        println("\nERROR YiemAgent decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        continue
      end
-
-    # fall back to normal text because LLM default to natural chat when it didn't use action_call
-    else  
-      responsedict = OrderedDict(
-        "plan"=> "I will talk to the user",
-        "action_name"=> "CHAT_BOX",
-        "action_input"=> response[2:end-1]  # remove { } at the front and back that added by clean_json_response
-      )
    end

    # check whether all answer's key points are in responsedict
    ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
    if !ispass
      errornote = errormsg
-      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)-> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
      continue
    end

-    # if responsedict["action_name"] ∉ ["CHAT_BOX", "CHECK_WINE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
+    # if responsedict["action_name"] ∉ ["CHAT_BOX", "SEARCH_WINE_DATABASE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
    #   errornote = "Your previous attempt didn't use the given functions"
    #   println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    #   continue
@@ -269,9 +263,9 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
    context =
    """
    <context>
-      <trajectory>
+      <assistant_trajectories>
      $timeline
-      </trajectory>
+      </assistant_trajectories>
      <evaluatee_context>
      $evaluateecontext
      </evaluatee_context>
@@ -320,177 +314,10 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext

    println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    pprintln(Dict(responsedict))
-
-    # # read sessionId
-    # sessionid = a.id
-    # # save to filename ./log/decisionlog.txt
-    # println("saving SQLLLM evaluator() to disk")
-    # filename = "agent_evaluator_log_$(sessionid[:id]).json"
-    # filepath = "/appfolder/app/log/$filename"
-    # # check whether there is a file path exists before writing to it
-    # if !isfile(filepath)
-    #   decisionlist = [responsedict]
-    #   println("Creating file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # else
-    #   # read the file and append new data
-    #   decisionlist = copy(JSON.parsefile(filepath))
-    #   push!(decisionlist, responsedict)
-    #   println("Appending new data to file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # end
-
-      return responsedict
+    return responsedict
  end
  error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
 end
-# function evaluator(a::T1, timeline, decisiondict, evaluateecontext
-#   ) where {T1<:agent}
-
-#   systemmsg = 
-#   """
-#   <Your role>
-#     - You are a master sommelier of an online wine store.
-#   </Your role>
-#   <Situation>
-#     - Under your supervision, a trainee sommelier is engaging with a store customer. Each time the customer speaks, the trainee will assess the situation, determine the next course of action, and pause to await your guidance before proceeding.
-#   </Situation>
-#   <Your mission>
-#     - Improve a trainee sommelier decision based on the store policy and guidelines while ensuring seamless interactions between the trainee and customers.
-#   </Your mission>
-#   <At each round of conversation, you will be given the following information> 
-#     - trajectory: A conversation between your trainee and the customer that have occurred up until now
-#     - evaluatee_context: The context that evaluatee use to make a decision
-#     - evaluatee_decision: The decision made by the evaluatee, consists of the following elements:
-#       "plan" is the trainee's plan
-#       "action_name" is the name of the action taken, which can be one of the available tool name.
-#       "action_input" is the input to the action.
-#   </At each round of conversation, you will be given the following information>
-#   <You must follow the following policy>
-#     - Use only infomation provided by the store policy and guidelines as a bedrocks for your response.
-#   </You must follow the following policy>
-#   <You should follow the following guidelines>
-#     - The trainee's plan, action_name, and action_input must be logically consistent
-#     - The trainee's action_input should be in a proper format as specified by the tools.
-#     - The trainee's action name and action input should make sense. For example, if the trainee isn't finished talking, he shouldn't use the END_CONVER_GUIDELINE tool.
-#   </You should follow the following guidelines>
-#   <You should then respond to the user with>
-#     1) trajectory_evaluation: Analyze the trajectory of a solution to answer the user's original question.
-#       - Evaluate the correctness of each section and the overall trajectory based on the given question.
-#       - Provide detailed reasoning and analysis, focusing on the latest thought, action, and observation.
-#       - Incomplete trajectory are acceptable if the thoughts and actions up to that point are correct, even if the final answer isn't reached.
-#       - Do not generate additional thoughts or actions.
-#     2) decision_evaluation: 
-#       - Examine how the trainee's decisions align with the store's policies and guidelines before proceeding.
-#     3) suggestion: Based store policy and guidelines, provide a suggestion for the immediate decision step only.
-#     4) approval: Can be "yes" or "no". "no" if the suggestion contradict the trainee's decision; otherwise, it is "yes".
-
-#   </You should then respond to the user with>
-#   <You should only respond in JSON format as described below>
-#   {
-#     "trajectory_evaluation": "...",
-#     "decision_evaluation": "...",
-#     "suggestion": "...",
-#     "approval": "...",
-#   }
-#   </You should only respond in format as described below>
-
-#   Let's begin!
-#   """
-#   requiredKeys = [:trajectory_evaluation, :decision_evaluation, :approval, :suggestion]
-#   errornote = "N/A"
-
-#   for attempt in 1:10
-#     evaluateecontext = replace(evaluateecontext, "<context>" => "")
-#     evaluateecontext = replace(evaluateecontext, "</context>" => "")
-
-#     context =
-#     """
-#     <context>
-#       <trajectory>
-#       $timeline
-#       </trajectory>
-#       <evaluatee_context>
-#       $evaluateecontext
-#       </evaluatee_context>
-#       <evaluatee_decision>
-#       {plan: $(decisiondict["plan"]), action_name: $(decisiondict["action_name"]), action_input: $(decisiondict["action_input"])}
-#       </evaluatee_decision>
-#       P.S. $errornote
-#     </context>
-#     """
-
-#     unformatPrompt =
-#       [
-#         Dict("name" => "system", "text" => systemmsg),
-#       ]
-
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(unformatPrompt, a.llmFormatName)
-#     # add info
-#     prompt = prompt * context
-
-#     response = a.context.text2textInstructLLM(prompt; senderId=a.id)
-#     response = GeneralUtils.deFormatLLMtext(response, a.llmFormatName)
-#     response = GeneralUtils.remove_french_accents(response)
-#     # response = replace(response, '$'=>"USD")
-#     think, response = GeneralUtils.extractthink(response)
-    
-#     responsedict = nothing
-#     try
-#       responsedict = copy(JSON.parsefile(response))  
-#     catch
-#       println("\nERROR YiemAgent generatechat() failed to parse response: $response", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       continue
-#     end
-
-#     # check whether all answer's key points are in responsedict
-#     ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
-#     if !ispass
-#       errornote = errormsg
-#       println("\nERROR YiemAgent evaluator() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-#       continue
-#     end
-
-#     # if accepted_as_answer ∉ ["yes", "no"] # [PENDING] add errornote into the prompt
-#     #   error("generated accepted_as_answer has wrong format")
-#     # end
-
-#     println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     pprintln(Dict(responsedict))
-
-#     # # read sessionId
-#     # sessionid = a.id
-#     # # save to filename ./log/decisionlog.txt
-#     # println("saving SQLLLM evaluator() to disk")
-#     # filename = "agent_evaluator_log_$(sessionid[:id]).json"
-#     # filepath = "/appfolder/app/log/$filename"
-#     # # check whether there is a file path exists before writing to it
-#     # if !isfile(filepath)
-#     #   decisionlist = [responsedict]
-#     #   println("Creating file $filepath")
-#     #   open(filepath, "w") do io
-#     #     JSON.pretty(io, decisionlist)
-#     #   end
-#     # else
-#     #   # read the file and append new data
-#     #   decisionlist = copy(JSON.parsefile(filepath))
-#     #   push!(decisionlist, responsedict)
-#     #   println("Appending new data to file $filepath")
-#     #   open(filepath, "w") do io
-#     #     JSON.pretty(io, decisionlist)
-#     #   end
-#     # end
-
-#       return responsedict
-#   end
-#   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
-# end
-

 """ Chat with llm.

@@ -520,7 +347,7 @@ message => Dict(
 function conversation(a::sommelier; userinput::Union{Dict{String, Any}, JSON.Object{String, Any}},
                      maximumMsg=50, max_think_loop::Integer=3)
  
-  @info "YiemAgent conversation() 1" @__LINE__
+  @info "YiemAgent conversation() 1 " @__LINE__
  userinput = GeneralUtils.dictify(userinput; keytype=String, sort_order=["text"])

  # find text in usermsg
@@ -536,75 +363,71 @@ function conversation(a::sommelier; userinput::Union{Dict{String, Any}, JSON.Obj
    clearhistory(a)
    return "Okay. What shall we talk about?"
  else
-    @info "YiemAgent conversation() 2" @__LINE__
+    @info "YiemAgent conversation() 2 " @__LINE__
    # add usermsg to a.chathistory but how do I handle images?
    addNewMessage(a, "user", userinput; maximumMsg=maximumMsg)

    # thinking loop until AI wants to communicate with the user
    loopcount = 0
    while true
-      @info "YiemAgent conversation() 2-0 count $loopcount" @__LINE__
      loopcount += 1
-      thoughtdict, _ = think(a)
+      if loopcount > max_think_loop
+        @info "YiemAgent conversation() 2-1 think count $loopcount " @__LINE__
+        r = generatechat!(a)
+        @info "YiemAgent conversation() 2-2 think count $loopcount " @__LINE__
+        return r
+      end
+
+      @info "YiemAgent conversation() 2-3 think count $loopcount " @__LINE__
+      thoughtdict, result_raw = think(a)
      if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
-        @info "YiemAgent conversation() 2-1" @__LINE__
+        @info "YiemAgent conversation() 2-4 think count $loopcount " @__LINE__
        assistant_response = Dict{String, Any}(
          "role" => "assistant",
          "content" => [Dict("type" => "text", "text" => thoughtdict["action_input"]),]
        )
        addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)
+        @info "YiemAgent conversation() 2-5 think count $loopcount " @__LINE__
        return thoughtdict["action_input"]
-      end
+      # elseif thoughtdict["action_name"] ∈ ["CHAT_BOX"]
+      #   @info "YiemAgent conversation() 2-4 think count $loopcount " @__LINE__
+      #   assistant_response = Dict{String, Any}(
+      #     "role" => "assistant",
+      #     "content" => [
+      #         Dict("type" => "text", "text" => thoughtdict["action_input"]),
+      #         Dict( #WORKING put 1st image here
+      #           "type" => "image_url",
+      #           "image_url" => Dict("url" => image1_data_uri)
+      #         ),
+      #         Dict( #WORKING put 2nd image here
+      #           "type" => "image_url",
+      #           "image_url" => Dict("url" => image2_data_uri)
+      #         ),
+      #       ]
+      #   )
+      #   addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)

-      if loopcount > max_think_loop
-        @info "YiemAgent conversation() 2-2" @__LINE__
-        r = generatechat(a)
-        @info "YiemAgent conversation() 2-3" @__LINE__
-        return r
+      #   return thoughtdict["action_input"]  #XXX change output from string to dict
+      else
+        action_name = thoughtdict["action_name"]
+        action_input = thoughtdict["action_input"]
+        action_call = Dict{String, Any}(
+            "role" => "action_call",
+            "content" => [Dict("type" => "text", "text" => "{action_name: $action_name, action_input: $action_input}"),]
+          )
+        addNewMessage(a, "action_call", action_call; maximumMsg=maximumMsg)
+
+        action_result = thoughtdict["action_result"]
+        actionresult = Dict{String, Any}(
+            "role" => "action_result",
+            "content" => [Dict("type" => "text", "text" => "$action_result"),]
+          )
+        addNewMessage(a, "actionresult", actionresult; maximumMsg=maximumMsg)
      end
    end
  end
 end

-# function conversation(a::Union{companion, virtualcustomer}, userinput::Dict; 
-#                       converPartnerName::Union{String, Nothing}=nothing,
-#                       maximumMsg=50)
-
-#   chatresponse = nothing
-
-#   if userinput["text"] == "newtopic"
-#     clearhistory(a)
-#     return "Okay. What shall we talk about?"
-#   else
-#     # add usermsg to a.chathistory
-#     addNewMessage(a, "user", userinput["text"]; maximumMsg=maximumMsg)
-
-#     # add user activity to events memory
-#     push!(a.memory["events"],
-#       eventdict(;
-#         event_description="the user talks to the assistant.",
-#         timestamp=Dates.now(),
-#         subject="user",
-#         action_name="CHAT_BOX",
-#         action_input=userinput["text"],
-#       )
-#     )
-#     chatresponse = generatechat(a; converPartnerName=converPartnerName, recentEventNum=20)
-
-#     addNewMessage(a, "assistant", chatresponse; maximumMsg=maximumMsg)
-
-#     push!(a.memory["events"],
-#       eventdict(;
-#         event_description="the assistant talks to the user.",
-#         timestamp=Dates.now(),
-#         subject="assistant",
-#         action_name="CHAT_BOX",
-#         action_input=chatresponse,
-#       )
-#     )
-#     return chatresponse
-#   end
-# end

 """
 # Arguments
@@ -620,43 +443,33 @@ julia>
 function think(a::T)::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
  # a.memory[:recap] = generateSituationReport(a, a.context["text"2textInstructLLM]; skiprecent=0)
  thoughtdict = decisionMaker(a)
-  @info "YiemAgent think() 1" @__LINE__
+  @info "YiemAgent think() 1 " @__LINE__
  # pprintln(thoughtdict)

  result_raw = nothing
  if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
-    @info "YiemAgent think() 2" @__LINE__
-    thoughtdict, result_raw = chatbox!(a, thoughtdict)
+    @info "YiemAgent think() 2 " @__LINE__
+    thoughtdict, result_raw = generatechat!(a)
    
  elseif thoughtdict["action_name"] == "END_CONVER_GUIDELINE"
-    @info "YiemAgent think() 3" @__LINE__
+    @info "YiemAgent think() 3 " @__LINE__
    thoughtdict, result_raw = end_conversation_guideline!(a, thoughtdict)
    
  elseif thoughtdict["action_name"] ∈ ["WINE_PRESENTATION_GUIDELINE"]
-    @info "YiemAgent think() 4" @__LINE__
+    @info "YiemAgent think() 4 " @__LINE__
    thoughtdict, result_raw =  wine_presentation_guideline!(a, thoughtdict)
-    
-
-  elseif thoughtdict["action_name"] == "CHECK_WINE" 
-    @info "YiemAgent think() 5" @__LINE__
-    thoughtdict, result_raw = checkwine!(a, thoughtdict)

+  elseif thoughtdict["action_name"] == "SEARCH_WINE_DATABASE" 
+    @info "YiemAgent think() 5 " @__LINE__
+    thoughtdict, result_raw = search_wine_database!(a, thoughtdict; useSQLLLM=false)
+    #WORKING result_raw will be a df. i need to get images so i can send to frontend
  else
-    @info "YiemAgent think() 6" @__LINE__
+    @info "YiemAgent think() 6 " @__LINE__
    error("condition is not defined ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  end
-
-  max_ind =
-    if length(a.memory["shortmem"]) == 0
-      0
-    else
-      k = keys(a.memory["shortmem"])
-      maximum(parse.(Int, k))
-    end
-  a.memory["shortmem"]["$(max_ind + 1)"] = thoughtdict
-
-  @info "YiemAgent think() 7" @__LINE__
-  pprintln(thoughtdict)
+  println("\n")
+  @show thoughtdict
+  @info "YiemAgent think() 7 " @__LINE__
  return (thoughtdict=thoughtdict, result_raw=result_raw)
 end

@@ -731,8 +544,8 @@ end


 #PENDING
-function generatechat(a::T; recentevents::Integer=20, maxattempt=10
-  )::String where {T<:agent}
+function generatechat!(a::T; maxattempt::Integer=10
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}

  # lessonDict = copy(JSON.parsefile("lesson.json"))

@@ -806,9 +619,9 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
      - Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.

    # you should then respond to the user with interleaving plan, action_name, action_input
-      1) **plan**, Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
-      2) **action_name**, (Typically corresponds to the execution of the first step in your plan). Must be "CHAT_BOX
-      3) **action_input**, Dialogue you want to chat with the user according to your plan.
+      1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) "action_name", Must be "CHAT_BOX
+      3) "action_input", Dialogue you want to chat with the user according to your plan.
      After the action is executed you gets "action_result". It is the output from the action you selected.

    # you should only respond in JSON format as described below
@@ -824,27 +637,11 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
      ]
    )

-  chathistory = deepcopy(a.chathistory[2:end])
+  chathistory = deepcopy(a.chathistory[2:end])  # use deep copy because I want to replace system msg
  pushfirst!(chathistory, system_msg)

  requiredKeys = ["plan", "action_name", "action_input"]
-  context = 
-    """
-    <internal_context_for_assistant>
-      <thought_history>
-        $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-      </thought_history>
-    </internal_context_for_assistant>
-    """

-  # add context to text of the latest message (in the front). 
-  # use for loop because in openai format, each msg may contain both text and image. 
-  for d in chathistory[end]["content"]
-    if d["type"] == "text"
-      d["text"] = context * d["text"]
-      break
-    end
-  end
  errornote = "N/A"
  response = nothing # placeholder for show when error msg show up

@@ -855,7 +652,7 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
    
    msg = Dict(
      "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
-      "messages" => a.chathistory,
+      "messages" => chathistory,
      "temperature" => 0.7
    )

@@ -866,7 +663,6 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
    response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
    
    response = strip(response)
-    @show response

    responsedict = nothing
    if occursin(requiredKeys[2], response) 
@@ -877,14 +673,6 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
        println("\nERROR YiemAgent generatechat() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        continue
      end
-
-    # fall back to normal text because LLM default to natural chat when it didn't use action_call
-    else  
-      responsedict = OrderedDict(
-        "plan"=> "I will talk to the user",
-        "action_name"=> "CHAT_BOX",
-        "action_input"=> response[2:end-1]  # remove { } at the front and back that added by clean_json_response
-      )
    end

    # check whether all answer's key points are in responsedict
@@ -895,7 +683,7 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
      continue
    end

-    # if responsedict["action_name"] ∉ ["CHAT_BOX", "CHECK_WINE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
+    # if responsedict["action_name"] ∉ ["CHAT_BOX", "SEARCH_WINE_DATABASE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
    #   errornote = "Your previous attempt didn't use the given functions"
    #   println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    #   continue
@@ -904,187 +692,10 @@ function generatechat(a::T; recentevents::Integer=20, maxattempt=10
    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    # pprintln(responsedict)

-    return responsedict["action_input"]
+    return (thoughtdict=responsedict, result_raw=responsedict["action_input"])
  end
  error("YiemAgent generatechat() failed to generate a thought ", response)
 end
-# function generatechat(a::T; recentevents::Integer=20, maxattempt=10
-#   )::String where {T<:agent}
-
-#   # lessonDict = copy(JSON.parsefile("lesson.json"))
-
-#   # lesson =
-#   # if isempty(lessonDict)
-#   #   ""
-#   # else
-#   #   lessons = Dict{String, Any}()
-#   #   for (k, v) in lessonDict
-#   #     lessons[k] = lessonDict[k][:lesson]
-#   #   end
-
-#   #   """
-#   #   You have attempted to help the user before and failed, either because your reasoning for the 
-#   #   recommendation was incorrect or your response did not exactly match the user expectation. 
-#   #   The following lesson(s) give a plan to avoid failing to help the user in the same way you 
-#   #   did previously. Use them to improve your strategy to help the user.
-
-#   #   Here are some lessons in JSON format:
-#   #   $(JSON.json(lessons))
-
-#   #   When providing the thought and action for the current trial, that into account these failed 
-#   #   trajectories and make sure not to repeat the same mistakes and incorrect answers. 
-#   #   """
-#   # end
-
-#   # recentevents_ind = GeneralUtils.recentElementsIndex(
-#   #   length(a.memory["events"]), recentevents; includelatest=true)
-
-#   systemmsg = 
-#     """
-#     <store_policy>
-#       - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
-#       - If you found wines in the store's database, they are in stock.
-#       - You can only recommend wines that are currently in our inventory
-#       - Before searching the database for wine, ensure you have at least the following information: 1) budget, 2) wine type, and 3) occasion. Additional details are always helpful. If the user is unsure, provide relevant information and gather insights to make reasonable inferences.
-#       - Ask the user one question at a time.
-#       - Do not ask the user about wine's flavor e.g. floral, citrusy, nutty or some thing similar as these terms cannot be used to search the database.
-#       - Once the user has selected their wine, if you haven't already, ask the user whether they need any further assistance. Do not offer any additional services.
-#       - Only end the conversation when the user explicitly intends to do so. When ending, ensure a polite farewell and an invitation to return in the future.
-#       - Spicy foods should be paired only with light red wines.
-#       - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user immediately if they are looking for these types of wines. Do not sell our wines as such.
-#       - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
-#     </store_policy>
-#     <store_guidelines>
-#       - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
-#       - Customer may provide images for you to look up.
-#       - Encourage the customer to explore different options and try new things.
-#       - If you are unable to locate the desired item in the database after 2 attempts, it may not be available in your inventory. In such cases, inform the user that the item is unavailable and suggest an alternative instead.
-#       - Your store carries only wine.
-#       - Vintage 0 means non-vintage.
-#       - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
-#     </store_guidelines>
-#     <situation>
-#       You are continuing the conversation with the user.
-#     </situation>
-#     <your role>
-#       Your name is $(a.name). You are a helpful sommelier for website-based $(a.retailername)'s wine store. You are working under your mentor supervision.
-#     </your role>
-#     <objective>
-#       1) Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
-#       2) Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-#     </objective>
-#     <your responsibility includes>
-#       1) According to the store's policy and guidelines, make an informed decision about what you need to do to achieve the objective
-#       2) Keep the conversation with the customer going smoothly
-#     </your responsibility includes>
-#     <your responsibility does NOT includes>
-#       1) Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
-#       2) Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
-#       3) Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
-#     </your responsibility does NOT includes>
-#     <you should then respond to the user with>
-#       Dialogue you want to chat with the user
-#     </you should then respond to the user with interleaving plan, action_name, action_input>
-#     <you should only respond in JSON format as described below>
-#       "CHAT_BOX": "..."
-#     </you should only respond in JSON format as described below>
-#     """
-  
-#   system_msg = Dict(
-#       "role" => "system",
-#       "content" => [
-#         Dict("type" => "text", "text" => systemmsg),
-#       ]
-#     )
-
-#   chathistory = deepcopy(a.chathistory[2:end])
-#   pushfirst!(chathistory, system_msg)
-
-#   requiredKeys = ["CHAT_BOX"]
-#   context = 
-#     """
-#     <internal_context_for_assistant>
-#       <thought_history>
-#         $(GeneralUtils.dict_to_string_html(a.memory["shortmem"]))
-#       </thought_history>
-#     </internal_context_for_assistant>
-#     """
-
-#   # add context to text of the latest message (in the front). 
-#   # use for loop because in openai format, each msg may contain both text and image. 
-#   for d in chathistory[end]["content"]
-#     if d["type"] == "text"
-#       d["text"] = context * d["text"]
-#       break
-#     end
-#   end
-#   errornote = "N/A"
-#   response = nothing # placeholder for show when error msg show up
-
-#   for attempt in 1:maxattempt
-#     if attempt > 1
-#       println("\nYiemAgent generatechat() attempt $attempt/$maxattempt ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     end
-    
-#     msg = Dict(
-#       "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
-#       "messages" => chathistory,
-#       "temperature" => 0.7
-#     )
-#     @info "YiemAgent generatechat() 2-2 attempt $attempt " @__LINE__
-#     response = a.context.text2textInstructLLM(a.id, msg)
-#     @info "YiemAgent generatechat() 2-3 attempt $attempt " @__LINE__
-#     response = GeneralUtils.clean_json_response(response)
-
-#     response = GeneralUtils.remove_french_accents(response)
-#     think, response = GeneralUtils.extractthink(response)
-#     response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
-#     response = strip(response)
-#     @show response
-#     responsedict = nothing
-#     if occursin("CHAT_BOX", response) 
-#       @info "YiemAgent generatechat() 2-4 attempt $attempt " @__LINE__
-#       try
-#         @info "YiemAgent generatechat() 2-5 attempt $attempt " @__LINE__
-#         _responsedict = JSON.parse(response)
-#         responsedict = GeneralUtils.dictify(_responsedict; keytype=String, sort_order=requiredKeys)
-#       catch
-#         @info "YiemAgent generatechat() 2-6 attempt $attempt " @__LINE__
-#         println("\nERROR YiemAgent generatechat() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#         continue
-#       end
-#     # fall back to normal text because LLM default to natural chat when it didn't use action_call
-#     else  
-#       @info "YiemAgent generatechat() 2-7 attempt $attempt " @__LINE__
-#       responsedict = OrderedDict(
-#         "CHAT_BOX"=> response
-#       )
-#     end
-
-#     if length(keys(responsedict)) > length(requiredKeys)
-#       @info "YiemAgent generatechat() 2-7-1 attempt $attempt " @__LINE__
-#       continue
-#     end
-
-#     @info "YiemAgent generatechat() 2-8 attempt $attempt " @__LINE__
-#     # check whether all answer's key points are in responsedict
-#     ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
-#     if !ispass
-#       @info "YiemAgent generatechat() 2-9 attempt $attempt " @__LINE__
-#       errornote = errormsg
-#       println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-#       continue
-#     end
-
-#     @info "YiemAgent generatechat() 2-12 attempt $attempt " @__LINE__
-#     # println("\nYiemAgent generatechat() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     # pprintln(responsedict)
-
-#     return responsedict["CHAT_BOX"]
-#   end
-#   @info "YiemAgent generatechat() 2-13 attempt $attempt " @__LINE__
-#   error("YiemAgent generatechat() failed to generate a thought ", response)
-# end


 function generatequestion(a, text2textInstructLLM::Function, timeline)::String
@@ -1,6 +1,6 @@
 module llmfunction

-export  virtualWineUserChatbox, jsoncorrection, checkwine!, # recommendbox,
+export  virtualWineUserChatbox, jsoncorrection, search_wine_database!, # recommendbox,
        virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
        extractWineAttributes_2, paraphrase

@@ -282,30 +282,398 @@ julia> result = checkinventory(agent, input)
 "{"wine 1": {\"Winery\": \"Pichon Baron\", \"wine name\": \"Pauillac (Grand Cru Classé)\", \"grape variety\": \"Cabernet Sauvignon\", \"year\": 2010, \"price\": \"125 USD\", \"stock ID\": \"ar-17\"}, }"
 ```
 """
-function checkwine!(a::T, thoughtdict::AbstractDict
+function search_wine_database!(a::T, thoughtdict::AbstractDict; useSQLLLM::Bool=false
  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}

  println("\ncheckinventory order: $(thoughtdict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  wineattributes_1 = extractWineAttributes_1(a, thoughtdict["action_input"])
  wineattributes_2 = extractWineAttributes_2(a, thoughtdict["action_input"])

-  retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency"]
-  _inventoryquery = "$wineattributes_1, $wineattributes_2"
+  retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency", "image_url", "retailer_name", "retailer_id"]
+  _inventoryquery = "$wineattributes_1, $wineattributes_2, retailer_name: $(a.retailername), retailerid: $(a.retailerid)"
  inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
  println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  # add suppport for similarSQLVectorDB
-  textresult, result_raw = SQLLLM.query(
-                        inventoryquery, 
-                        a.context.executeSQL, 
-                        a.context.text2textInstructLLM;
-                        insertSQLVectorDB=a.context.insertSQLVectorDB, 
-                        similarSQLVectorDB=a.context.similarSQLVectorDB,
-                        llmFormatName="qwen3")
-  thoughtdict["action_result"] = textresult
+
+  if useSQLLLM
+    # add suppport for similarSQLVectorDB
+    textresult, result_raw = SQLLLM.query(
+                          inventoryquery, 
+                          a.context.executeSQL, 
+                          a.context.text2textInstructLLM;
+                          insertSQLVectorDB=a.context.insertSQLVectorDB, 
+                          similarSQLVectorDB=a.context.similarSQLVectorDB,
+                          llmFormatName="qwen3")
+    thoughtdict["action_result"] = textresult
+  else
+
+    # direct query with possible sql instead of SQLLLM. 
+    sql = generatesql(a, inventoryquery)
+    println("\nSQL: $sql ", @__FILE__, ":", @__LINE__, " $(Dates.now()) \n")
+    textresult, result_raw, _, _ = SQLexecution(a.context.executeSQL, sql)
+    #WORKING if result_raw != nothing, get image from image_url column of a df. 
+    # then store in a.memory["shortmem]["image"] = OrderedDict(
+    #   Dict(
+    #       "wine_id"=> "wine_id,
+    #       "name"=> "wine name",
+    #       "image_url" => Dict("url" => data1_uri)
+    #     )
+ 
+    # )
+    
+
+    # # 1. Read local file and encode to base64 string
+    # image2_path = "test/small_image.png"
+    # image2_bytes = read(image2_path)
+    # image2_base64_string = base64encode(image2_bytes)
+    # mime_type = "image/png" 
+    # data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
+
+    # # 3. Construct payload with the Data URI
+    # message = Dict(
+    #     "role" => "user",
+    #     "content" => [
+    #       Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
+    #       Dict(
+    #         "type" => "image_url",
+    #         "image_url" => Dict("url" => data1_uri)
+    #       )
+    #     ]
+    #   )
+
+
+
+
+
+
+
+    thoughtdict["action_result"] = textresult
+  end

  return (thoughtdict=thoughtdict, result_raw=result_raw)
 end

+
+function generatesql(a::T, searchterm::String,
+  ; maxattempt=10
+  )::String where {T<:agent}
+
+  systemmsg = 
+    """
+    # database_search_guidelines
+      - Keep SQL queries focused only on the provided information.
+      - Use wildcard character (%) to search more effectively.
+      - Do not create any table in the database.
+      - A junction table can be used to link tables together. Another use case is for filtering data.
+      - If you can't find a single table that can be used to answer the user's search term, try joining multiple tables to see if you can obtain the answer.
+      - Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
+      - If there is no search result from the database, remove the restrictive criteria until a search result is available, and proceed from there.
+
+    # situation
+      At each round of conversation, you will be given the following:
+      - user search term
+
+    # objective
+      Consult the database_search_guidelines. Then find the data from a database to satisfy the user's search term.
+
+    # your responsibility includes
+      Fulfill the objective.
+    
+    # you should then respond to the user with interleaving plan, action_name, action_input
+      1) "plan, Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) "action_name, Must be "RUNSQL"
+      3) "action_input, The input to the action you are about to perform according to your plan.
+      After the action is executed you gets "action_result". It is the output from the action you selected.
+
+    # you should only respond in JSON format as described below
+      "plan": "...",
+      "action_name": "...",
+      "action_input": "..."
+
+     # available_actions
+      "RUNSQL", which you can use to execute SQL against the database. 
+        The input must be a single SQL query to be executed against the database.
+        For more effective text search, it's necessary to use case-insensitivity and the ILIKE operator.
+        Do not wrap the SQL as it will be executed against the database directly and SQL must be ended with ';'.
+    """
+
+  # table_schema =
+  #   """
+  #   create table customer (
+  #     customer_id uuid primary key default gen_random_uuid (),
+  #     customer_firstname varchar(128),
+  #     customer_lastname varchar(128),
+  #     customer_displayname varchar(128) not null,
+  #     customer_username varchar(128),
+  #     customer_password varchar(128),
+  #     customer_gender varchar(128),
+  #     country	varchar(128),
+  #     telephone varchar(128),
+  #     email varchar(128) not null,
+  #     customer_birthdate varchar(128),
+  #     note text,
+      
+  #     other_attributes jsonb,
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp,
+  #     description	text
+  #   );
+
+  #   create table retailer (
+  #     retailer_id uuid primary key default gen_random_uuid (),
+  #     retailer_name varchar(128) not null,
+  #     retailer_username varchar(128) not null,
+  #     retailer_password varchar(128) not null,
+  #     retailer_address	text not null,
+  #     country	varchar(128) not null,
+  #     contact_person varchar(128) not null,
+  #     telephone	varchar(128) not null,
+  #     email	varchar(128) not null,
+  #     note text,
+      
+  #     other_attributes jsonb,
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp,
+  #     description	text
+  #   ); 
+
+  #   create table food (
+  #     food_id uuid primary key default gen_random_uuid (),
+  #     food_name varchar(128) not null,
+  #     country	varchar(128),
+  #     spiciness	integer,
+  #     sweetness	integer,
+  #     sourness	integer,
+  #     savoriness	integer, 
+  #     bitterness	integer,
+  #     serving_temperature	integer,
+  #     image_url jsonb,
+  #     note text,
+  #     other_attributes jsonb,
+
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp,
+  #     description	text
+  #   );
+
+  #   create table wine (
+  #     wine_id uuid primary key default gen_random_uuid (),
+  #     seo_name varchar(128) not null,
+  #     wine_name varchar(128) not null,
+  #     winery varchar(128) not null,
+  #     vintage integer not null,
+  #     region varchar(128) not null,
+  #     country	varchar(128) not null,
+  #     wine_type	varchar(128) not null,
+  #     grape	varchar(128) not null,
+  #     serving_temperature	varchar(128) not null,
+  #     intensity	integer,
+  #     sweetness	integer,
+  #     tannin integer,
+  #     acidity integer,
+  #     fizziness	integer,
+  #     tasting_notes text,
+  #     image_url jsonb,
+  #     manufacturer_sku text,
+  #     note text,
+  #     other_attributes jsonb,
+      
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp,
+  #     description	text
+  #   );
+
+  #   create table wine_food (
+  #     wine_id uuid references wine(wine_id),
+  #     food_id uuid references food(food_id),
+  #     constraint wine_food_id primary key (wine_id, food_id),
+
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp
+  #   );
+
+  #   CREATE TABLE retailer_wine (
+  #     retailer_id uuid references retailer(retailer_id),
+  #     wine_id uuid references wine(wine_id),
+  #     constraint retailer_wine_id primary key (retailer_id, wine_id),
+  #     price NUMERIC(10, 2),
+  #     currency varchar(3) not null,
+      
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp
+  #   );
+
+  #   CREATE TABLE retailer_food (
+  #     retailer_id uuid references retailer(retailer_id),
+  #     food_id uuid references food(food_id),
+  #     constraint retailer_food_id primary key (retailer_id, food_id),
+  #     price NUMERIC(10, 2),
+  #     currency varchar(3) not null,
+      
+  #     created_time timestamptz default current_timestamp,
+  #     updated_time timestamptz default current_timestamp
+  #   );
+  #   """
+
+  requiredKeys = ["plan", "action_name", "action_input"]
+  errornote = ""
+  # provide similar sql only for the first attempt
+  # sql, distance = a.context.similarSQLVectorDB(searchterm)
+
+  # similarSQL_ = sql !== nothing ? sql : "None"
+  # # if sql is really close, just use it
+  # if similarSQL_ != "None" && distance <= 0.1
+  #   return similarSQL_
+  # end
+
+  #CHANGE use find_related_tables_for_user_question and inject only related table schema instead
+  # of hard code table schema. CPU embedding is too slow. use embedding service on GPU. 
+  related_tables = a.context.find_related_tables_for_user_question(searchterm)
+  table_schema = ""
+  for table in related_tables
+    _table_schema_str = GeneralUtils.get_db_table_schema_simple(a.context.pg_conn_str, table)
+    table_schema_str = sprint(show, _table_schema_str) * "\n"
+    table_schema = table_schema * table_schema_str
+  end
+
+  context = 
+    """
+    <internal_context_for_assistant>
+      <database_table_schema>
+        $table_schema
+      </database_table_schema>
+    </internal_context_for_assistant>
+    """
+  input = context * searchterm
+
+  msg = Dict(
+    "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
+    "messages" => [
+      Dict(
+        "role" => "system",
+        "content" => [
+          Dict("type" => "text", "text" => systemmsg),
+        ]
+      ),
+      Dict(
+        "role" => "user",
+        "content" => [
+          Dict("type" => "text", "text" => input),
+        ]
+      ),
+    ],
+    "temperature" => 0.7
+  )
+
+  for attempt in 1:maxattempt
+    response = a.context.text2textInstructLLM("random_id", msg)
+
+    response = GeneralUtils.clean_json_response(response)
+
+    think, response = GeneralUtils.extractthink(response)
+    responsedict = nothing
+     try
+      _responsedict = JSON.parse(response)
+      responsedict = GeneralUtils.dictify(_responsedict, keytype=String, sort_order=requiredKeys)
+    catch
+      println("\nERROR decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    # check whether all answer's key points are in responsedict
+    ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
+    if !ispass
+      errornote = errormsg
+      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      continue
+    end
+
+    # remove backticks Error occurred: MethodError: no method matching occursin(::String, ::Vector{String})
+    if occursin("```", responsedict["action_input"])
+      sql = GeneralUtils.extract_triple_backtick_text(responsedict["action_input"])[1]
+      if sql[1:4] == "sql\n"
+        sql = sql[5:end]
+      end
+      sql = split(sql, ';')   # some time there are comments in the sql
+      sql = sql[1] * ';'
+
+      responsedict["action_input"] = sql
+    end
+
+    toollist = ["RUNSQL"]
+    if responsedict["action_name"] ∉ toollist
+      errornote = "Your previous attempt has action_name that is not in the tool list"
+      println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote --(not qualify response)--> $(responsedict["action_name"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    for i in toollist
+      if occursin(i, responsedict["action_input"])
+        errornote = "Your previous attempt has action_name in action_input which is not allowed"
+        println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote --(not qualify response)--> $(responsedict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        continue
+      end
+    end
+    
+    # println("\nSQLLLM decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # pprintln(responsedict)
+    # println("---")
+    
+    return responsedict["action_input"]
+  end
+  error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
+end
+
+function SQLexecution(executeSQL::Function, sql::T
+  )::NamedTuple where {T<:AbstractString}
+
+  try
+    # add LIMIT to the SQL to prevent loading large data
+    sql = strip(sql)
+
+    # remove DISTINCT keyword because it is incompatible with RANDOM()
+    sql = replace(sql, "DISTINCT" => "")  
+
+    if sql[end] == ';'
+      if !occursin("LIMIT", sql)
+        sql = sql[1:end-1] * " ORDER BY RANDOM() LIMIT 2;"
+      end
+    else
+      sql = sql * ";"
+    end
+    result = executeSQL(sql)
+    df = DataFrame(result)
+    tablesize = size(df)
+    row, column = tablesize
+    if row == 0
+      return (result_str="No records found. Try loosening your search criteria.", result_raw=nothing, success=true, errormsg=nothing)
+    elseif column > 30
+      return (result_str="There are more than 30 columns. Please be more specific.", result_raw=df, success=true, errormsg=nothing)
+    else
+      df1 =
+        if row > 2
+          # ramdom row to pick
+          df[sample(1:nrow(df), 2, replace=false), :] # random select 2 rows from df
+        else
+          df
+        end
+      result = GeneralUtils.dfToString(df1)
+      # println("\n~~~ SQLexecution() result: ", @__FILE__, " ", @__LINE__)
+      # println(sql)
+      # println(df1)
+      # println("\n")
+      return (result_str=result, result_raw=df1, success=true, errormsg=nothing)
+    end
+  catch e
+    io = IOBuffer()
+    showerror(io, e)
+    errorMsg = String(take!(io))
+    st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+    println(errorMsg)
+    return (result_str=nothing, result_raw=nothing, success=false, errormsg=errorMsg)
+  end
+end
+
+
 """

 # Arguments
@@ -352,6 +720,7 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
    wine_price_max: maximum price range of wine. Example: For wine price 20, wine_price_max will be 20. For wine price 10 to 100, wine_price_max will be 100.
    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
+    _keyword suffice is the related keyword that appears in user's query. each keyword can not be used twice.
  </you should then respond to the user with>
  <you should only respond in JSON format as described below>
    "wine_name": "...",
@@ -386,11 +755,11 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
      "wine_name": "Saumur Blanc",
      "winery": "Domaine du Collier",
      "vintage": "2019",
-      "region": "Saumur",
+      "region": "N/A",
      "country": "France",
      "wine_type": "white",
      "grape_varietal": "Merlot",
-      "tasting_notes": "plum",
+      "tasting_notes": "N/A",
      "wine_price_min": "N/A",
      "wine_price_max": "N/A",
      "occasion": "N/A",
@@ -430,9 +799,6 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
  for attempt in 1:maxattempt
    response = a.context.text2textInstructLLM(a.id, msg)
    response = GeneralUtils.clean_json_response(response)
-    println("\n--- extractWineAttributes_1-1()")
-    println(response)
-    println("--- \n")

    response = GeneralUtils.remove_french_accents(response)
    think, response = GeneralUtils.extractthink(response)
@@ -460,6 +826,20 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
      _v = replace(v, r"\(.*?\)" => "")
      responsedict[k] = _v
    end
+
+    # println("\n--- extractWineAttributes_1-1()")
+    # @show responsedict
+    # @info "---\n" @__LINE__
+
+    # check each attributes against each column in a database table with BM25
+    for (k, v) in responsedict
+      if k ∉ ["wine_price_min", "wine_price_max"]
+        words_catalog = GeneralUtils.harvest_entity_catalog(a.context.pg_conn_str, "wine", k)
+        resolved_word = GeneralUtils.resolve_entity(v, words_catalog;threshold=0.9)
+        responsedict[k] = resolved_word
+      end
+    end
+
    result = ""
    for (k, v) in responsedict
      # some time LLM generate text with "(some comment)". this line removes it
@@ -467,10 +847,12 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
        result *= "$k: $v, "
      end
    end
+
    result = result[1:end-2]  # remove the ending ", "
-    println("\n--- extractWineAttributes_1-2()")
-    println(result)
-    println("--- \n")
+    # println("\n--- extractWineAttributes_1-2()")
+    # @show responsedict
+    # @show result
+    # @info "---\n" @__LINE__
    return result
  end
  error("extractWineAttributes_1() failed to get a response")
@@ -845,7 +1227,7 @@ function jsoncorrection(config::T1, input::T2, correctJsonExample::T3;
      """

      # apply LLM specific instruct format
-externalService = config["externalservice"]["text2textinstruct"]
+      externalService = config["externalservice"]["text2textinstruct"]
      llminfo = externalService["llminfo"]
      prompt = 
      if llminfo["name"] == "llama3instruct"
@@ -881,126 +1263,6 @@ externalService = config["externalservice"]["text2textinstruct"]
 end


-# function isrecommend(state::T1, text2textInstructLLM::Function
-#   ) where {T1<:AbstractDict}
-
-#   systemmsg = 
-#   """
-#   You are a helpful assistant that analyzes agent's trajectories to find solutions and observations (i.e., the results of actions) to answer the user's questions.
-
-#   Definitions:
-#   "question" is the user's question.
-#   "thought" is step-by-step reasoning about the current situation.
-#   "plan" is what to do to complete the task from the current situation.
-#   “action_name” is the name of the action taken, which can be one of the following functions:
-#       1) CHAT_BOX[text], which you can use to talk with the user. "text" is in verbal English. 
-#       2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English. The best query must includes "budget", "type of wine", "characteristics of wine" and "food pairing".
-#   "action_input" is the input to the action
-#   "observation" is result of the preceding immediate action.
-
-#   At each round of conversation, the user will give you:
-#   Context: ...
-#   Trajectories: ...
-    
-#   You should then respond to the user with:
-#   1) trajectory_evaluation:
-#     - Analyze the trajectories of a solution to answer the user's original question. 
-#       Then given a question and a trajectory, evaluate its correctness and provide your reasoning and
-#       analysis in detail. Focus on the latest thought, action, and observation. 
-#       Incomplete trajectories can be correct if the thoughts and actions so far are correct, 
-#       even if the answer is not found yet. Do not generate additional thoughts or actions.
-#   2) answer_evaluation: Focus only on the matter mentioned in the question and analyze how the latest observation addresses the question.
-#   3) accepted_as_answer: Decide whether the latest observation's content answers the question. The possible responses are either 'Yes' or 'No.'
-#     Bad example (The observation didn't answers the question):
-#       question: Find cars with 4 wheels.
-#       observation: There are 2 cars in the table.
-#     Good example (The observation answers the question): 
-#       question: Find cars with a stereo.
-#       observation: There are 1 cars in the table. 1) brand: Toyota, model: yaris, color: black.
-#   4) score: Correctness score s where s is a single integer between 0 to 9. 
-#       - 0 means the trajectories are incorrect.
-#       - 9 means the trajectories are correct, and the observation's content directly answers the question.
-#   5) suggestion: if accepted_as_answer is "No", provide suggestion.
-
-#   You should only respond in format as described below:
-#   trajectory_evaluation: ...
-#   answer_evaluation: ...
-#   accepted_as_answer: ...
-#   score: ...
-#   suggestion: ...
-
-#   Let's begin!
-#   """
-
-#   thoughthistory = ""
-#   for (k, v) in state[:thoughtHistory]
-#     thoughthistory *= "$k: $v\n" 
-#   end
-
-#   usermsg =
-#   """
-#   Context: None
-#   Trajectories: $thoughthistory
-#   """
-
-#   _prompt = 
-#   [
-#     Dict(:name=> "system", :text=> systemmsg),
-#     Dict(:name=> "user", :text=> usermsg)
-#   ]
-
-#   # put in model format
-#   prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
-#   prompt *=
-#     """
-#     <|start_header_id|>assistant<|end_header_id|>
-#     """
-
-#   for attempt in 1:5
-#     try
-#       response = text2textInstructLLM(prompt)
-#       responsedict = GeneralUtils.textToDict(response,
-#             ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], 
-#             rightmarker=":", symbolkey=true)
-
-#       # check if dict has all required value
-#       trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
-#       answerevaluation_text::AbstractString = responsedict[:answer_evaluation]
-#       responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
-#       score::Integer = responsedict[:score]
-#       accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
-#       suggestion::AbstractString = responsedict[:suggestion]
-
-#       # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
-#       state[:evaluation] = "$(responsedict[:trajectory_evaluation]) $(responsedict[:answer_evaluation])" 
-#       state[:evaluationscore] = responsedict[:score] 
-#       state[:accepted_as_answer] = responsedict[:accepted_as_answer]
-#       state[:suggestion] = responsedict[:suggestion]
-
-#       # mark as terminal state when the answer is achieved
-#       if accepted_as_answer == "Yes"
-#         state[:isterminal] = true
-#         state[:reward] = 1
-#       end
-#       println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       pprintln(Dict(responsedict))
-#       return responsedict[:score]
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("")
-#       println("Attempt $attempt. Error occurred: $errorMsg\n$st")
-#       println("")
-#     end
-#   end
-#   error("evaluator failed to generate an evaluation")
-# end
-
-
-
-



@@ -16,6 +16,9 @@ mutable struct agentcontext
  insertSQLVectorDB::Function
  similarSommelierDecision::Function
  insertSommelierDecision::Function
+  find_related_tables_for_user_question::Function
+  pg_conn_str::String
+  agentconfig::AbstractDict
 end

 abstract type agent end
@@ -93,6 +96,7 @@ mutable struct sommelier <: agent
  name::String  # agent name
  id::String    # agent id
  retailername::String
+  retailerid::String
  tools::Dict   
  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
  chathistory::Vector{Dict{String, Any}}
@@ -140,11 +144,12 @@ julia> agent = sommelier(context, name="WineExpert", id="123", retailername="MyW
 ```
 """
 function sommelier(
-  context::agentcontext,  # app context
+  context::agentcontext,  # agent functions, db connect and other context
  ;
  name::String= "Assistant",
  id::String= string(uuid4()),
-  retailername::String= "retailer_name",
+  retailername::String= "not specified",
+  retailerid::String= "not specified",
  maxHistoryMsg::Integer= 20,
  chathistory::Vector{Dict{String, Any}} = Vector{Dict{String, Any}}(),
  llmFormatName::String= "granite3"
@@ -216,6 +221,7 @@ function sommelier(
    name,
    id,
    retailername,
+    retailerid,
    tools,
    maxHistoryMsg,
    chathistory,
@@ -267,25 +273,24 @@ function sommelier(
      - Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.

    # you should then respond to the user with interleaving plan, action_name, action_input
-      1) **plan**, Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
-      2) **action_name**, (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
-      3) **action_input**, The input to the action you are about to perform according to your plan.
+      1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) "action_name", (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
+      3) "action_input", The input to the action you are about to perform according to your plan.
      After the action is executed you gets "action_result". It is the output from the action you selected.

-    # you should only respond in JSON format as described below
+    # you should only respond in JSON format as described below (not Markdown format)
      "plan": "...",
      "action_name": "...",
      "action_input": "..."

    # available actions
-      **CHAT_BOX**, which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
-      **CHECK_WINE**, allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
+      "CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
+      "SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is supported search criteria including: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
        Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
        Example query 2: "Red or white wine, medium tannin, price under 700 USD"
        Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
-      **WINE_PRESENTATION_GUIDELINE**, which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-      **END_CONVER_GUIDELINE**, which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-
+      "WINE_PRESENTATION_GUIDELINE", which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
+      "END_CONVER_GUIDELINE", which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
    """
    
  system_msg = Dict(
@@ -95,15 +95,15 @@ end
 """
 function addNewMessage(a::T1, name::String, userinput::T2; 
                      maximumMsg::Integer=30) where {T1<:agent, T2<:AbstractDict}
-  if name ∉ ["system", "user", "assistant"]   # guard against typo
-    error("name is not in agent.availableRole $(@__LINE__)")
-  end
+  # if name ∉ ["system", "user", "assistant"]   # guard against typo
+  #   error("name is not in agent.availableRole $(@__LINE__)")
+  # end

  #TODO summarize the oldest 10 message
  if length(a.chathistory) > maximumMsg
    summarize(a.chathistory)
  else
-    userinput["timestamp"] = Dates.now()
+    # userinput["timestamp"] = Dates.now()
    push!(a.chathistory, userinput)
  end
 end
@@ -297,7 +297,7 @@ function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothin
    #   timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"])\n"
    # elseif event["action_name"] == "CHECKINVENTORY" && event["observation"] === nothing
    #   timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"]), observation: Not done yet.\n"
-    if event["action_name"] == "CHECK_WINE"
+    if event["action_name"] == "SEARCH_WINE_DATABASE"
      timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"]), observation: $(event["observation"])\\n"
    else
      timeline *= "Event_$i $(event["subject"])> action_name: $(event["action_name"]), action_input: $(event["action_input"])\\n"
@@ -128,7 +128,7 @@ systemmsg =

  # Available Actions
  - **CHAT_BOX** which you can use to talk with the user.
-  - **CHECK_WINE** allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
+  - **SEARCH_WINE_DATABASE** allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
      - Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
      - Example query 2: "Red or white wine, medium tannin, price under 700 USD"
      - Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
@@ -157,7 +157,7 @@ openai_msg = Dict(
      "content" => [
        Dict("type" => "text", "text" => 
        """
-        " <plan>I will greet the customer warmly as Janie, acknowledge their request to find a similar wine for their wedding party based on the image, identify the wine type and country (Italian Sparkling Wine), and then use the CHECK_WINE action to search the inventory for suitable options.</plan>\n <action_name>CHAT_BOX</action_name>\n <action_input>Hello! I'm Janie, and I'd be delighted to help you find the perfect wine for your wedding party. That beautiful wine in the image appears to be an Italian sparkling wine, which is wonderful for a celebration like a wedding! Since you have an unlimited budget, I can certainly look for some truly exceptional options. To start, I will check our inventory for similar Italian sparkling wines that are perfect for a wedding celebration.</action_input><action_result> User response in the next message </action_result>"
+        " <plan>I will greet the customer warmly as Janie, acknowledge their request to find a similar wine for their wedding party based on the image, identify the wine type and country (Italian Sparkling Wine), and then use the SEARCH_WINE_DATABASE action to search the inventory for suitable options.</plan>\n <action_name>CHAT_BOX</action_name>\n <action_input>Hello! I'm Janie, and I'd be delighted to help you find the perfect wine for your wedding party. That beautiful wine in the image appears to be an Italian sparkling wine, which is wonderful for a celebration like a wedding! Since you have an unlimited budget, I can certainly look for some truly exceptional options. To start, I will check our inventory for similar Italian sparkling wines that are perfect for a wedding celebration.</action_input><action_result> User response in the next message </action_result>"
        """
        ),
      ]
@@ -2,200 +2,236 @@ using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructu
      NATS, Base.Threads
 using YiemAgent, GeneralUtils, msghandler

+  function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
+    payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
+    _, msg_envelope_json_str = msghandler.smartpack(
+          config["externalservice"]["servicesloadbalancer"]["nats"], 
+          payloads;
+          sender_id=sender_id,
+          msg_purpose="text2text",  
+          broker_url=config["nats_server_info"]["url"],
+          fileserver_url=config["externalservice"]["fileserver"]["url"])

-function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
-  payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
-  _, msg_envelope_json_str = msghandler.smartpack(
-        config["externalService"]["servicesloadbalancer"]["nats"], 
-        payloads;
-        sender_id=sender_id,
-        msg_purpose="text2text",  
-        broker_url=config["nats_server_info"]["url"],
-        fileserver_url=config["externalService"]["fileserver"]["url"])
+    reply = NATS.request(agent_conn, 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
+                        msg_envelope_json_str, timeout=120)

-  reply = NATS.request(agent_conn, 
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
-                      msg_envelope_json_str, timeout=120)
-
-  incoming_env_json_str = String(reply.payload)
-  incoming_env = msghandler.smartunpack(incoming_env_json_str)
-  _llm_response = incoming_env["payloads"][1][2]
-  llm_response = _llm_response["choices"][1]["message"]["content"]
-  return llm_response
-end
-
-#TESTING get text embedding from a LLM service
-function get_embedding(text::AbstractArray{String})
-  documents_dict = Dict("documents" => text)
-  payloads = [("documents", documents_dict, "dictionary")]
-  _, msg_envelope_json_str = msghandler.smartpack(
-    config["externalService"]["servicesloadbalancer"]["nats"], 
-    payloads;   
-    msg_purpose="embedding",  
-    broker_url=config["nats_server_info"]["url"],
-    fileserver_url=config["externalService"]["fileserver"]["url"])
-
-  reply = NATS.request(agent_conn, 
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
-                      msg_envelope_json_str, timeout=120)
-  incoming_env_json_str = String(reply.payload)
-  incoming_env = msghandler.smartunpack(incoming_env_json_str)
-  embedding_response = incoming_env["payloads"][1][2]
-
-  return embedding_response
-end
-
-#TESTING
-function execute_sql_winedb(config::JSON.Object, sql::T) where {T<:AbstractString}
-  host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-  port = parse(Int, _port)
-  dbname = "winedb"
-  user = config["externalservice"]["sommpanion_db"]["user"]
-  password = config["externalservice"]["sommpanion_db"]["password"]
-  db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-  result = LibPQ.execute(db_connection, sql)
-  LibPQ.close(db_connection)
-  return result
-end
-
-#TESTING
-function similar_sql_vectordb(query; maxdistance::Integer=100)
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  df = find_similar_text_from_vectordb(query, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  # 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)
+    incoming_env_json_str = String(reply.payload)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
+    _llm_response = incoming_env["payloads"][1][2]
+    llm_response = _llm_response["choices"][1]["message"]["content"]
+    return llm_response
  end
-end

-#TESTING
-function insert_sql_vectordb(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 = find_similar_text_from_vectordb(query, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  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 = get_embedding([query])[1]
-    query_embedding = _query_embedding["data"][1]["embedding"]
-    query = replace(query, "'" => "")
-    sql_base64 = base64encode(SQL)
-    sql_ = replace(SQL, "'" => "")
+  """ get a single text embedding from a LLM service
+  Example
+  text = ["hello"]
+  embedding = get_embedding(text)
+  """
+  function get_embedding(text::AbstractArray{String})
+    documents_dict = Dict("documents" => text)
+    payloads = [("documents", documents_dict, "dictionary")]
+    _, msg_envelope_json_str = msghandler.smartpack(
+      config["externalservice"]["servicesloadbalancer"]["nats"], 
+      payloads;   
+      msg_purpose="embedding",  
+      broker_url=config["nats_server_info"]["url"],
+      fileserver_url=config["externalservice"]["fileserver"]["url"])

-    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)
-    _ = execute_sql_vectordb(sql)
+    reply = NATS.request(agent_conn, 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
+                        msg_envelope_json_str, timeout=120)
+    incoming_env_json_str = String(reply.payload)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
+    embedding_response = incoming_env["payloads"][1][2]
+
+    return embedding_response
  end
-end

-#TESTING
-function execute_sql_vectordb(sql::T) where {T<:AbstractString}
-  host_url, _port = split(config["SQLVectorDB"]["url"], ':')
-  port = parse(Int, _port)
-  dbname = config[:externalservice][:SQLVectorDB][:dbname]
-  user = config[:externalservice][:SQLVectorDB][:user]
-  password = config[:externalservice][:SQLVectorDB][:password]
-  DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-
-function similar_sommelier_decision(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 = find_similar_text_from_vectordb(recentevents, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  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(JSON.read(_output_str))
-    return output
-  else
-    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    return nothing
-  end
-end
-
-#TESTING
-function find_similar_text_from_vectordb(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 = get_embedding([text])[1]
-  embedding = _embedding["data"][1]["embedding"]
-  # 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 $limit;
-        """
-  response = vectorDB(sql)
-  df = DataFrame(response)
-  return df
-end
-
-
-function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
-  ) where {T1<:AbstractString, T2<:AbstractDict}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  df = find_similar_text_from_vectordb(recentevents, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    _embedding = get_embedding([recentevents])[1]
-    recentevents_embedding = _embedding["data"][1]["embedding"]
-    recentevents = replace(recentevents, "'" => "")
-    decision_json = JSON.json(decision)
-    decision_base64 = base64encode(decision_json)
-    decision = replace(decision_json, "'" => "")
+  """ sql = "SELECT * FROM wine;"
+  result = execute_sql_winedb(sql)
+  """
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
+    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
+    port = parse(Int, _port)
+    dbname = "winedb"
+    user = config["externalservice"]["sommpanion_db"]["user"]
+    password = config["externalservice"]["sommpanion_db"]["password"]
+    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = nothing
+    try
+      result = LibPQ.execute(db_connection, sql)  
+    catch e
+      LibPQ.close(db_connection)
+    end
    
-    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)
-    _ = execute_sql_vectordb(sql)
-  else
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+    LibPQ.close(db_connection)
+    return result
  end
-end

+  """ find similar sql from vector database
+  sql = "SELECT * FROM wine;"
+  result, distance = similar_sql_vectordb(sql)
+  """
+  function similar_sql_vectordb(sql::T; maxdistance::Number=0.2) where {T<:AbstractString}
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    df = find_similar_text_from_vectordb(sql, tablename, 
+          "function_input_embedding", execute_sql_vectordb)
+    # println(df[1, [:id, :function_output]])
+    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--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(output_str)
+      return (result=output_str, distance=distance)
+    else
+      println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      return (result=nothing, distance=nothing)
+    end
+  end
+
+  """ insert query and sql into vector database
+  query = "get all wines from wine table"
+  sql = "SELECT * FROM wine;"
+  insert_sql_vectordb(query, sql)
+  """
+  function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Number=3
+    ) where {T1<:AbstractString, T2<:AbstractString}
+    
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    # query = state[:thoughtHistory][:question]
+    df = find_similar_text_from_vectordb(query, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    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 = get_embedding([query])
+      _query_embedding = GeneralUtils.dictify(_query_embedding)
+      # println("\n--- _query_embedding() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      # println(_query_embedding)
+      # println("---\n")
+      query_embedding = _query_embedding["data"][1]["embedding"]
+      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__, " $(Dates.now())")
+      # println(sql)
+      _ = execute_sql_vectordb(sql)
+    end
+  end
+
+  """ execute sql against vectordb
+  sql = "SELECT * FROM wine;"
+  result = execute_sql_vectordb(sql)
+  """
+  function execute_sql_vectordb(sql::T) where {T<:AbstractString}
+    host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
+    port = parse(Int, _port)
+    dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
+    user = config["externalservice"]["sommpanion_vectordb"]["user"]
+    password = config["externalservice"]["sommpanion_vectordb"]["password"]
+    DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = LibPQ.execute(DBconnection, sql)
+    close(DBconnection)
+    return result
+  end
+
+  """ search similar decision llm made from vectordb
+  """
+  function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
+    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = find_similar_text_from_vectordb(recentevents, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    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(JSON.read(_output_str))
+      return output
+    else
+      println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+      return nothing
+    end
+  end
+
+  """ search similar text from vectordb
+  """
+  function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColumnName::T3,
+    vectorDB::Function; limit::Integer=1
+    )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+    # get embedding from LLM service
+    _embedding = get_embedding([text])
+    _embedding = _embedding["data"][1]["embedding"]
+    _embedding = "$_embedding"
+
+    embedding = _embedding[4:end]
+
+    # 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
+
+  """ insert decision llm made to vectordb
+  """
+  function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
+    ) where {T1<:AbstractString, T2<:AbstractDict}
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = find_similar_text_from_vectordb(recentevents, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      _embedding = get_embedding([recentevents])[1]
+      recentevents_embedding = _embedding["data"][1]["embedding"]
+      recentevents = replace(recentevents, "'" => "")
+      decision_json = JSON.json(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)
+      _ = execute_sql_vectordb(sql)
+    else
+      println("--| similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+    end
+  end
+
+config = JSON.parsefile("./appconfig.json")
 sessionId = "0"
-backend_session_topic = "sommpanion.backend.agentbackend.v1.inbox.$sessionId"
-
-config = JSON.parsefile("./dummy_config.json")
+backend_session_topic = "sommpanion.testsubject"
 agent_ch = Channel(8)
 agent_conn  = NATS.connect(config["nats_server_info"]["url"])
+
 sub2 = NATS.subscribe(agent_conn, backend_session_topic) do msg
  put!(agent_ch, msg)
 end
@@ -210,32 +246,34 @@ agent_context = YiemAgent.agentcontext(
    insert_sommelier_decision
  )

-# can't instantiate
-agent = YiemAgent.sommelier(
-  agent_context;
-  name="Janie",
-  id=sessionId,  # agent instance id
-  retailername="Yiem",
-  llmFormatName=""
-)
+  # can't instantiate
+  agent = YiemAgent.sommelier(
+    agent_context;
+    name="Janie",
+    id=sessionId,  # agent instance id
+    retailername="Yiem Wine Ltd.",
+    llmFormatName=""
+  )


-
-
-# 1. Read local file and encode to base64 string
 image1_path = "test/large_image.png"
 image1_bytes = read(image1_path)
 image1_base64_string = base64encode(image1_bytes)
-
-# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
 mime_type = "image/png" 
 data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"

+# 1. Read local file and encode to base64 string
+image2_path = "test/small_image.png"
+image2_bytes = read(image2_path)
+image2_base64_string = base64encode(image2_bytes)
+mime_type = "image/png" 
+data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
+
 # 3. Construct payload with the Data URI
-usermsg = Dict{String, Any}(
+message = Dict(
    "role" => "user",
    "content" => [
-      Dict("type" => "text", "text" => "รู้จักไวน์ที่อยู่ในรูปมั้ย"),
+      Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
      Dict(
        "type" => "image_url",
        "image_url" => Dict("url" => data1_uri)
@@ -243,8 +281,80 @@ usermsg = Dict{String, Any}(
    ]
  )

-result = YiemAgent.conversation(agent; userinput=usermsg)
-println(result)
+result = YiemAgent.conversation(agent; userinput=message)
+println("\n$result")
+
+
+
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => 
+#       "
+#       เป็นงานเลี้ยงทั่วไป
+#       "),
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => "no thanks. that's all"),
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => "What about this wine?"),
+#       Dict(
+#         "type" => "image_url",
+#         "image_url" => Dict("url" => data2_uri)
+#       )
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+
+
+
+
+
+
+
+
Author	SHA1	Message	Date
ton	18b2d54ba7	update	2026-07-15 18:51:21 +07:00
ton	d004193b19	update	2026-07-15 18:49:51 +07:00
ton	686b9b2e92	update	2026-07-15 18:47:18 +07:00
ton	3acf46964b	update	2026-07-15 14:25:04 +07:00
ton	ad917ea8d0	update	2026-07-15 14:01:32 +07:00
ton	31daa805f3	update	2026-07-15 13:59:28 +07:00
ton	c9937ab5d7	update	2026-07-15 13:59:01 +07:00
ton	9d7eed7cde	update	2026-07-15 12:20:44 +07:00
ton	286da3cf2c	update	2026-07-15 12:16:59 +07:00
ton	e5b19dd268	update	2026-07-15 12:14:32 +07:00
ton	45e8ded111	update	2026-07-15 12:10:36 +07:00
ton	a503d4d759	update	2026-07-15 11:56:44 +07:00
ton	a798cd119e	updatet	2026-07-14 18:08:36 +07:00
ton	fa338dd0f8	Merge pull request 'v0.5.0' (#10 ) from v0.5.0 into main Reviewed-on: #10	2026-07-12 08:03:41 +00:00
ton	8d4bf5f01f	Merge pull request 'v0.5.0-tool_role' (#9 ) from v0.5.0-tool_role into v0.5.0 Reviewed-on: #9	2026-07-12 08:03:26 +00:00
ton	cd6f6ef961	update	2026-07-12 11:14:30 +07:00
ton	fdec34832d	update	2026-07-12 10:54:15 +07:00
ton	3c72373b85	update	2026-07-12 05:54:47 +07:00
ton	688a8c4df2	update	2026-07-11 21:45:49 +07:00
ton	8bd4986be2	Merge pull request 'v0.4.3' (#8 ) from v0.4.3 into main Reviewed-on: #8	2026-07-10 10:47:34 +00:00
ton	6e5809fc9b	Merge pull request 'v0.4.3-fix_markdown_response' (#7 ) from v0.4.3-fix_markdown_response into v0.4.3 Reviewed-on: #7	2026-07-10 10:47:18 +00:00
ton	2b7c0041e5	up version	2026-07-10 17:46:55 +07:00
ton	9ff0b48eec	update	2026-07-10 17:45:58 +07:00
ton	afeb4c7aef	Merge pull request 'v0.4.2' (#6 ) from v0.4.2 into main Reviewed-on: #6	2026-07-09 13:20:36 +00:00
ton	2942a89730	Merge pull request 'v0.4.2-limit_consecutive_same_tool_use' (#5 ) from v0.4.2-limit_consecutive_same_tool_use into v0.4.2 Reviewed-on: #5	2026-07-09 13:20:21 +00:00
ton	6a66f58e63	update	2026-07-09 20:19:41 +07:00
ton	1d0353d793	update	2026-07-09 20:14:04 +07:00
ton	0f6aa7c79f	update	2026-07-09 19:45:04 +07:00
ton	0320fd321f	Merge pull request 'v0.4.1' (#4 ) from v0.4.1 into main Reviewed-on: #4	2026-07-09 01:01:53 +00:00
ton	c29dccf597	up version	2026-07-09 08:01:08 +07:00
ton	70cf04b0db	Merge pull request 'v0.4.1-fix_agent_not_respond' (#3 ) from v0.4.1-fix_agent_not_respond into v0.4.1 Reviewed-on: #3	2026-07-09 00:58:33 +00:00