update

Merge pull request 'v0.7.0' (#25 ) from v0.7.0 into main
2026-07-17 10:14:13 +07:00 · 2026-07-17 10:06:32 +07:00 · 2026-07-17 00:03:01 +00:00 · 2026-07-17 00:02:47 +00:00 · 2026-07-17 07:01:55 +07:00 · 2026-07-16 23:31:36 +07:00
12 changed files with 1346 additions and 1013 deletions
@@ -2,7 +2,7 @@
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "09bd5c43d6ad954d8be233d27fc343ea1149c0b0"
+project_hash = "1e317787f914f6d857feb7c23bb910d1185caed9"
 [[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"
@@ -91,9 +97,9 @@ uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
 version = "0.7.8"
 [[deps.CommonSolve]]
-git-tree-sha1 = "99ee296f88c12485402e37c2fd025f95ae097637"
+git-tree-sha1 = "eeaad7cef88554c2fa56b5a3f71cfd5cb708c662"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
-version = "0.2.9"
+version = "0.2.11"
 [[deps.Compat]]
 deps = ["TOML", "UUIDs"]
@@ -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"
@@ -226,9 +246,13 @@ version = "0.1.10"
 [[deps.FileIO]]
 deps = ["Pkg", "Requires", "UUIDs"]
-git-tree-sha1 = "91e0e5c68d02bcdaae76d3c8ceb4361e8f28d2e9"
+git-tree-sha1 = "6621fef488e496356c9c9625d0562c12a6070819"
 uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
-version = "1.16.5"
+version = "1.20.0"
 weakdeps = ["HTTP"]
    [deps.FileIO.extensions]
    HTTPExt = "HTTP"
 [[deps.FilePathsBase]]
 deps = ["Compat", "Dates"]
@@ -250,6 +274,7 @@ 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 +282,6 @@ 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"
@@ -274,18 +293,31 @@ 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"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "Graphs", "HTTP", "JSON", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SHA", "StringDistances", "UUIDs"]
-git-tree-sha1 = "7c0600c166a5deb2c607018a491c04eb25969c2e"
+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"]
-git-tree-sha1 = "eda1d37cb55d90a17d0957c75841138c88b361a1"
+git-tree-sha1 = "c2c808326222b6dc4bec295a83b55f79aeec98e0"
 uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-version = "2.5.4"
+version = "2.5.5"
 [[deps.HashArrayMappedTries]]
 git-tree-sha1 = "2eaa69a7cab70a52b9687c8bf950a5a93ec895ae"
@@ -310,6 +342,11 @@ 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"
@@ -406,7 +443,7 @@ version = "1.21.3+0"
 [[deps.LLMMCTS]]
 deps = ["GeneralUtils", "JSON", "PrettyPrinting"]
-git-tree-sha1 = "6b4f123b03c0fcce5b21c0dbcb947e8dd23f333a"
+git-tree-sha1 = "3dff98131dfa79be8c9bd84fc51cb0ba1832c472"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/LLMMCTS"
 uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
@@ -645,15 +682,17 @@ version = "0.4.2"
 [[deps.PrettyTables]]
 deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "REPL", "Reexport", "StringManipulation", "Tables"]
-git-tree-sha1 = "624de6279ab7d94fc9f672f0068107eb6619732c"
+git-tree-sha1 = "ebf455bb866ee6737030e3d3816bb6a0683c4325"
 uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
-version = "3.3.2"
+version = "3.4.0"
    [deps.PrettyTables.extensions]
    PrettyTablesExcelExt = "XLSX"
    PrettyTablesTypstryExt = "Typstry"
    [deps.PrettyTables.weakdeps]
    Typstry = "f0ed7684-a786-439e-b1e3-3b82803b501e"
    XLSX = "fdbf4ff8-1666-58a4-91e7-1b58723a45e0"
 [[deps.Printf]]
 deps = ["Unicode"]
@@ -734,9 +773,9 @@ version = "0.5.1+0"
 [[deps.Roots]]
 deps = ["Accessors", "CommonSolve", "Printf"]
-git-tree-sha1 = "ed45bcc7cf3c8887595b973f2b1efbe91dcc50ec"
+git-tree-sha1 = "a7caaf7ba8cf307112ca443784d1b56b4a591455"
 uuid = "f2b01f46-fcfa-551c-844a-d8ac1e96c665"
-version = "3.0.1"
+version = "3.0.5"
    [deps.Roots.extensions]
    RootsChainRulesCoreExt = "ChainRulesCore"
@@ -760,11 +799,11 @@ 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 = "c18ef75ef5d43b256be9624d6e9c1b10a91d5b64"
+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 +832,12 @@ 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 +871,25 @@ 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 +926,12 @@ 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 +1053,10 @@ uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 [[deps.YiemAgent]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.6.5"
 [[deps.Zlib_jll]]
 deps = ["Libdl"]
@@ -1,9 +1,10 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.7.0"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 [deps]
 Base64 = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
@@ -23,11 +24,12 @@ URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 [compat]
 Base64 = "1.11.0"
 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",
+      "user": "admin",
-      "password": "yiemtechnologies@Postgres_0.0"
+      "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",
+      "user": "admin",
-      "password": "yiemtechnologies@Postgres_0.0"
+      "password": "admin@Sommpanion_0.0"
    },
    "fileserver": {
    "description": "temporary file server",
@@ -1,89 +1,56 @@
-using DataStructures
+using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames, DataStructures
 using GeneralUtils, SQLLLM, YiemAgent
-function dictify2(x; keytype::Type=Any, sort_order::Union{Nothing, Vector}=nothing)
+config = JSON.parsefile("./appconfig.json")
-    # Dict-like objects
+host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-    if x isa AbstractDict
+port = parse(Int, _port)
-        out = OrderedDict{keytype, Any}()
+dbname = "winedb"
-        
+user = config["externalservice"]["sommpanion_db"]["user"]
-        # 1. Process and normalize all keys from the input dictionary
+password = config["externalservice"]["sommpanion_db"]["password"]
-        processed_dict = OrderedDict{keytype, Any}()
+pg_conn_str = "host=$host_url port=$port dbname=$dbname user=$user password=$password"
        for (k, v) in x
            if keytype === String
                newk = string(k)
            elseif keytype === Symbol
                newk = Symbol(string(k))
            else
                newk = k
            end
            processed_dict[newk] = dictify(v; keytype=keytype, sort_order=sort_order)
        end
-        # 2. If a sort order is specified, apply it
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
-        if !isnothing(sort_order)
+    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-            # Normalize the sort_order elements to match the requested keytype
+    port = parse(Int, _port)
-            normalized_order = map(sort_order) do tk
+    dbname = "winedb"
-                if keytype === String
+    user = config["externalservice"]["sommpanion_db"]["user"]
-                    return string(tk)
+    password = config["externalservice"]["sommpanion_db"]["password"]
-                elseif keytype === Symbol
+    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-                    return Symbol(string(tk))
+    result = nothing
-                else
+    try
-                    return tk
+      result = LibPQ.execute(db_connection, sql)  
-                end
+    catch e
-            end
+      LibPQ.close(db_connection)
            # First, insert keys that match the requested order
            for target_key in normalized_order
                if haskey(processed_dict, target_key)
                    out[target_key] = processed_dict[target_key]
                end
            end
            # Then, append any remaining keys that weren't in the sort_order
            for (k, v) in processed_dict
                if !haskey(out, k)
                    out[k] = v
                end
            end
        else
            # If no sort order is given, just use the processed dict
            out = processed_dict
        end
        return out
    # Arrays / vectors: map elements recursively
    elseif x isa AbstractArray
        return [dictify(element; keytype=keytype, sort_order=sort_order) for element in x]
    # Everything else: return as-is
    else
        return x
    end
-end
+    
-
+    LibPQ.close(db_connection)
-
+    return result
 function dict_to_string_html2(d::AbstractDict; indent_level=1, indent_str=" ")
  lines = String[]
  padding = indent_str ^ indent_level
  # Sort keys for predictable, clean output
  for k in keys(d)
      v = d[k]
      if v isa AbstractDict
          # Open tag, recurse for children, then close tag
          push!(lines, "$padding<$k>")
          ind_level = indent_level + 1
          push!(lines, dict_to_string_html(v; indent_level=ind_level, indent_str=indent_str))
          push!(lines, "$padding</$k>")
      else
          # Leaf node: put key and value on a single line
          push!(lines, "$padding<$k>$v</$k>")
      end
  end
-  return join(lines, "\n")
+
 sql =
 """
 SELECT T1.winery, T1.wine_name, T1.wine_id, T1.vintage, T1.region, T1.country, T1.wine_type, T1.grape, T1.serving_temperature, T1.sweetness, T1.intensity, T1.tannin, T1.acidity, T1.tasting_notes, T2.price, T2.currency, T1.image_url, T3.retailer_name, T3.retailer_id FROM "wine" AS T1 JOIN "retailer_wine" AS T2 ON T1.wine_id = T2.wine_id JOIN "retailer" AS T3 ON T2.retailer_id = T3.retailer_id WHERE T1.wine_name = 'Montrachet Grand Cru' AND T1.winery = 'Domaine Jacques Prieur' AND T3.retailer_name = 'Yiem Wines Ltd' AND T3.retailer_id = 'f54eab6b-7650-4448-b009-c53f3efbcc3b';
 """
 textresult, sql_result_raw, _, _ = YiemAgent.SQLexecution(execute_sql_winedb, sql)
 result_vec = GeneralUtils.dfToVectorDict(sql_result_raw)
 for d in result_vec
  wine_name = d["wine_name"]
  image_url_json_str = d["image_url"]
  image_url_json_obj = JSON.parse(image_url_json)
  base_url = "http://192.168.88.106:8080/"
  image_base64 =
    if haskey(image_url_json_obj, "bottle")
      url = base_url * image_url_json_obj["bottle"]
      image_data = HTTP.get(url)  # vector{int} data
      image_base64_string = base64encode(image_data)
    else
      nothing
    end
  d["image"] = image_base64
 end
@@ -91,3 +58,54 @@ end
 using LibPQ
 using Tables
 """
    update_car_regions_one_by_one(conn::LibPQ.Connection, target_word::String)
 Iterates through all rows in the 'car' table where the region is "German", 
 and updates them one-by-one to the `target_word`.
 """
 function update_car_regions_one_by_one(pg_conn_str::String, replace_word::String , target_word::String)
  conn = LibPQ.Connection(pg_conn_str)
    # 1. Fetch the target rows. Assumes 'id' is the primary key.
    # We select the ID to target rows individually during the update step.
    select_query = "SELECT id FROM car WHERE region = '$replace_word';"
    result = execute(conn, select_query)
    rows = Tables.rows(result)
    # 2. Prepare the update statement for execution reuse
    # Using explicit types for parameter placeholders ($1, $2)
    update_query = "UPDATE car SET region = \$1 WHERE id = \$2;"
    println("Starting one-by-one update...")
    updated_count = 0
    # 3. Iterate through rows one-by-one
    for row in rows
        # LibPQ row values are accessed via properties or column names
        row_id = row.id 
        # Execute the parameterized statement safely
        execute(conn, update_query, [target_word, row_id])
        updated_count += 1
    end
    println("Successfully updated \$updated_count rows.")
    return updated_count
 end
@@ -60,44 +60,22 @@ end
 # Keyword Arguments
 # Return        
-  - `thoughtDict::Dict`
+  - `thoughtdict::Dict`
 # Example
 ```jldoctest
-julia>  config = Dict(
+julia> result = decisionMaker(agent)
           "mqttServerInfo" => Dict(
             "description" => "mqtt server info", 
             "port" => 1883, 
             "broker" => "mqtt.yiem.cc"
           ),
           "externalservice" => Dict(
               "text2textinstruct" => Dict(
                 "mqtttopic" => "/loadbalancer/requestingservice", 
                 "description" => "text to text service with instruct LLM", 
                 "llminfo" => Dict(
                   "name" => "llama3instruct"
                 )
               ),
           )
         )
-julia> output_thoughtDict = Dict(
+OrderedDict{String, Any} with 4 entries:
-        "thought_1"     => "The customer wants to buy a bottle of wine. This is a good start!",
+  "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_1"      => Dict{String, Any}(
+  "action_name"   => "SEARCH_WINE_DATABASE"
-                            "action"=>"CHAT_BOX", 
+  "action_input"  => "Sparkling white wine from Italy"
-                            "input"=>"What occasion are you buying the wine for?"
+  "action_result" => "1) winery: Terrazze dell Etna, wine_name: Rose Brut.
                            ),
        "observation_1" => ""
        )
 ```
  - [] update docstring
  - [] use customerinfo
  - [] user storeinfo
 """
 function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
  ) where {T<:agent}
-  println("\nExecuting YiemAgent decisionMaker()")
+  @info "YiemAgent decisionMaker() start " @__LINE__
  # lessonDict = copy(JSON.parsefile("lesson.json"))
  # lesson =
@@ -130,9 +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>
    """
@@ -148,9 +123,7 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
  response = nothing # placeholder for show when error msg show up
  for attempt in 1:maxattempt
-    if attempt > 1
+
      println("\nYiemAgent decisionMaker() attempt $attempt/$maxattempt ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    end
    msg = Dict(
      "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
@@ -159,12 +132,18 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
    )
    response = a.context.text2textInstructLLM(a.id, msg)
    response = GeneralUtils.clean_json_response(response)
    response = GeneralUtils.clean_json_response(response)
    response = GeneralUtils.remove_french_accents(response)
    think, response = GeneralUtils.extractthink(response)
    response = String(split(response, ", observation")[1])  # in case LLM generate observation key which it isn't supposed to
    response = strip(response)
    # dollar sign in Julia means string interpolation
    while occursin('$', response)
      response = replace(response, '$' => "USD")
    end
    responsedict = nothing
    if occursin(requiredKeys[2], response) 
      try
@@ -174,33 +153,38 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
        println("\nERROR YiemAgent decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        continue
      end
-
+    else
-    # fall back to normal text because LLM default to natural chat when it didn't use action_call
+      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)-> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-    else  
+      continue
      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
    println("\n---")
    println(responsedict)
    println("---\n")
    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_input"] == "CHAT_BOX" && 
-      errornote = "Your previous attempt didn't use the given functions"
+      occursin("similar", responsedict["action_input"])
-      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+
      continue
    end
    # 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
    # end
    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    # pprintln(responsedict)
-
+    @info "YiemAgent decisionMaker() end " @__LINE__
    return responsedict
  end
  error("DecisionMaker failed to generate a thought ", response)
@@ -291,9 +275,9 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
    context =
    """
    <context>
-      <trajectory>
+      <assistant_trajectories>
      $timeline
-      </trajectory>
+      </assistant_trajectories>
      <evaluatee_context>
      $evaluateecontext
      </evaluatee_context>
@@ -332,7 +316,7 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
    ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
    if !ispass
      errornote = errormsg
-      println("\nERROR YiemAgent evaluator() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      println("\nERROR YiemAgent evaluator() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
      continue
    end
@@ -342,36 +326,11 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
    println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    pprintln(Dict(responsedict))
-
+    return responsedict
    # # read sessionId
    # sessionid = a.id
    # # save to filename ./log/decisionlog.txt
    # println("saving SQLLLM evaluator() to disk")
    # filename = "agent_evaluator_log_$(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.
 # Example userinput
@@ -396,92 +355,167 @@ message => Dict(
    ]
  )
 # ---------------------------------------------- 100 --------------------------------------------- #
 """
 function conversation(a::sommelier; userinput::Union{Dict{String, Any}, JSON.Object{String, Any}},
-                      maximumMsg=50)
+                      maximumMsg=50, max_think_loop::Integer=3)
  @info "YiemAgent conversation() start " @__LINE__
  userinput = GeneralUtils.dictify(userinput; keytype=String, sort_order=["text"])
  # find text in usermsg
  usertext = nothing
  text_position = nothing
  for (i, d) in enumerate(userinput["content"])
    if d["type"] == "text"
      d["text"] = GeneralUtils.remove_french_accents(d["text"])
      usertext = d["text"]
      text_position = i
    end
  end
  # place holder
  action_name = nothing
  result = nothing
  chatresponse = nothing
  if usertext == "newtopic"
    clearhistory(a)
    return "Okay. What shall we talk about?"
  else
-    userinput["content"][text_position]["text"] = GeneralUtils.remove_french_accents(usertext)
+
    # 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
-    chatresponse = nothing
+    loopcount = 0
-    while chatresponse === nothing
+    while true
-      action_name, result = think(a)
+      loopcount += 1
-      if action_name ∈ ["CHAT_BOX"]
+      if loopcount > max_think_loop
-        chatresponse = result
+
        thoughtdict, result_raw = generatechat!(a)
        assistant_response = Dict{String, Any}(
          "role" => "assistant",
          "content" => [Dict("type" => "text", "text" => thoughtdict["action_input"]),]
        )
        addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)
        items_info = []
        send_item_ind = []  # index of the item being send to frontend
        if haskey(a.memory["shortmem"], "items_info")
          for (i, item) in enumerate(a.memory["shortmem"]["items_info"])
            if haskey(item, "wine_name") && occursin(item["wine_name"], thoughtdict["action_input"])
              push!(items_info, deepcopy(item))
              push!(send_item_ind, i)
            end
          end
          # remove sent items
          deleteat!(a.memory["shortmem"]["items_info"], send_item_ind)
        end
        response_to_frontend = Dict{String, Any}(
          "role" => "assistant",
          "content" => [
              Dict("type" => "text", "text" => thoughtdict["action_input"]),
              Dict(
                "type" => "items_info",
                "items_info" => items_info
              ),
            ]
        )
        return response_to_frontend
      end
      thoughtdict, result_raw = think(a)
      if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
        assistant_response = Dict{String, Any}(
          "role" => "assistant",
          "content" => [Dict("type" => "text", "text" => thoughtdict["action_input"]),]
        )
        addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)
        items_info = []
        send_item_ind = []  # index of the item being send to frontend
        if haskey(a.memory["shortmem"], "items_info")
          for (i, item) in enumerate(a.memory["shortmem"]["items_info"])
            if haskey(item, "wine_name") && occursin(item["wine_name"], thoughtdict["action_input"])
              push!(items_info, deepcopy(item))
              push!(send_item_ind, i)
            end
          end
          # remove sent items
          deleteat!(a.memory["shortmem"]["items_info"], send_item_ind)
        end
        response_to_frontend = Dict{String, Any}(
          "role" => "assistant",
          "content" => [
              Dict("type" => "text", "text" => thoughtdict["action_input"]),
              Dict(
                "type" => "items_info",
                "items_info" => items_info
              ),
            ]
        )
        """ intended message to send to frontend should have the following format. 
        response_to_frontend = Dict{String, Any}(
          "role" => "assistant",
          "content" => [
            Dict("type" => "text", "text" => "assistant_text_response"),
            Dict(
              "type" => "items_info",
              "items_info" => [
                          Dict(
                            "wine_name"=> "wine name 1",
                            "wine_id"=> "...",
                            "image"=> base64 encoded image,
                            ...
                          ),
                          Dict(
                            "wine_name"=> "wine name 2",
                            "wine_id"=> "...",
                            "image"=> base64 encoded image,
                            ...
                          ),
                        ]
            ),
          ]
        )
        """
        return response_to_frontend
      else  # still in action
        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)
        @info "YiemAgent conversation() end think count $loopcount " @__LINE__
      end
    end
    assistant_response = Dict{String, Any}(
      "role" => "assistant",
      "content" => [Dict("type" => "text", "text" => chatresponse),]
    )
    addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)
    return chatresponse
  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
@@ -494,42 +528,66 @@ julia>
 ```
 """
-function think(a::T) where {T<:agent}
+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() start " @__LINE__
  thoughtdict = decisionMaker(a)
  @info "YiemAgent think() 1 " @__LINE__
  @show thoughtdict
  println("---\n")
-  println("\n--- YiemAgent think() 1 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  result_raw = nothing
-  pprintln(thoughtDict)
+  if thoughtdict["action_name"] ∈ ["CHAT_BOX"]
  println("---")
-  # # map action and input() to llm function
+    # sometime CHAT_BOX input is too short. 
-  # response =
+    # if thoughtdict["action_input] < 20 character, use generatechat!()
-  #   if thoughtDict["action_name"] == "CHAT_BOX" || thoughtDict["action_name"] == "END_CONVER_GUIDELINE"
+    if length(thoughtdict["action_input"]) < 20 
-  #     (result=thoughtDict["plan"], errormsg=nothing, success=true)
+      thoughtdict, result_raw = generatechat!(a)   
-  #   elseif thoughtDict["action_name"] == "CHECK_WINE"
+    else
-  #     checkwine(a, thoughtDict["action_input"])
+      thoughtdict["action_result"] = "Action result is the next user dialogue."
-  #   elseif thoughtDict["action_name"] == "PRESENT_WINE_GUIDELINE"
+      result_raw = thoughtdict["action_input"]
-  #     (result=thoughtDict["action_input"], errormsg=nothing, success=true)
+    end 
  #   else
  #     error("undefined LLM function. Requesting $(thoughtDict["action_name"])")
  #   end
-  # # this section allow LLM functions above to have different return values.
+  elseif thoughtdict["action_name"] == "END_CONVER_GUIDELINE"
  # result = haskey(response, "result") ? response["result"] : nothing
  # rawresponse = haskey(response, "rawresponse") ? response["rawresponse"] : nothing
  # select = haskey(response, "select") ? response["select"] : nothing
  # reward::Integer = haskey(response, "reward") ? response["reward"] : 0
  # isterminal::Bool = haskey(response, "isterminal") ? response["isterminal"] : false
  # errormsg::Union{AbstractString,Nothing} = haskey(response, "errormsg") ? response["errormsg"] : nothing
  # success::Bool = haskey(response, "success") ? response["success"] : false
-  result = nothing
+    thoughtdict, result_raw = end_conversation_guideline!(a, thoughtdict)
  if thoughtDict["action_name"] ∈ ["CHAT_BOX"]
    result = thoughtDict["action_input"]
  elseif thoughtDict["action_name"] == "END_CONVER_GUIDELINE"
-    # add guideline in to context
+  elseif thoughtdict["action_name"] ∈ ["WINE_PRESENTATION_GUIDELINE"]
-    guideline =
+
    thoughtdict, result_raw =  wine_presentation_guideline!(a, thoughtdict)
  elseif thoughtdict["action_name"] == "SEARCH_WINE_DATABASE" 
    thoughtdict, result_raw = search_wine_database!(a, thoughtdict; useSQLLLM=false)
    if result_raw !== nothing && result_raw isa Vector
      if haskey(a.memory["shortmem"], "items_info")
        append!(a.memory["shortmem"]["items_info"], result_raw)
      else
        a.memory["shortmem"]["items_info"] = result_raw
      end
    end
  else
    error("condition is not defined ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  end
  @info "YiemAgent think() end " @__LINE__
  @show thoughtdict
  println("---\n")
  return (thoughtdict=thoughtdict, result_raw=result_raw)
 end
 function chatbox!(a::T, thoughtdict::AbstractDict
  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
  thoughtdict["action_result"] = "Action result is the next user dialogue."
  return (thoughtdict=thoughtdict, result_raw=nothing)
 end
 function end_conversation_guideline!(a::T, thoughtdict::AbstractDict
  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
  guideline =
    """
    <end_conversation_guideline>
      - Provide customer with store contact info and business hours
@@ -540,20 +598,15 @@ function think(a::T) where {T<:agent}
      </store_info>
    </end_conversation_guideline>
    """
-    thoughtDict["action_result"] = guideline
+  thoughtdict["action_result"] = guideline
    max_ind =
      if length(a.memory["shortmem"]) == 0
        0
      else
        k = keys(a.memory["shortmem"])
        maximum(parse.(Int, k))
      end
    a.memory["shortmem"]["$(max_ind + 1)"] = thoughtDict
-  elseif thoughtDict["action_name"] ∈ ["PRESENT_WINE_GUIDELINE"]    #WORKING
+  return (thoughtdict=thoughtdict, result_raw=nothing)
-    
+end
-    # add guideline in to context
+
-    guideline =
+function wine_presentation_guideline!(a::T, thoughtdict::AbstractDict
  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
  guideline =
    """
    <wine_presentation_guideline>
    - Provide detailed introductions of the wines you've found to the user.
@@ -589,297 +642,170 @@ function think(a::T) where {T<:agent}
      </conversion_table>
    </wine_presentation_guideline>
    """
-    thoughtDict["action_result"] = guideline
+  thoughtdict["action_result"] = guideline
    max_ind =
      if length(a.memory["shortmem"]) == 0
        0
      else
        k = keys(a.memory["shortmem"])
        maximum(parse.(Int, k))
      end
    a.memory["shortmem"]["$(max_ind + 1)"] = thoughtDict
-  elseif thoughtDict["action_name"] == "CHECK_WINE"  
+  return (thoughtdict=thoughtdict, result_raw=nothing)
    result = checkwine(a, thoughtDict["action_input"])
    thoughtDict["action_result"] = result[:result_str]
    max_ind =
      if length(a.memory["shortmem"]) == 0
        0
      else
        k = keys(a.memory["shortmem"])
        maximum(parse.(Int, k))
      end
    a.memory["shortmem"]["$(max_ind + 1)"] = thoughtDict
  else
    error("condition is not defined ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  end
    println("\n--- YiemAgent think() 2 ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    pprintln(thoughtDict)
    println("---")
  return (action_name=thoughtDict["action_name"], result=result)
 end
-function presentbox(a::sommelier, thoughtDict; maxtattempt::Integer=10, recentevents::Integer=10)
+#PENDING
-  recentchat_ind = GeneralUtils.recentElementsIndex(length(a.chathistory), recentevents; 
+function generatechat!(a::T; maxattempt::Integer=10
-                                                    includelatest=true)
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
  @info "YiemAgent generatechat!() start " @__LINE__
  # 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 = 
-  """
+    """
-  <situation>
+    # store_policy
-    You have checked the inventory and found wines that may match what the user wants.
+      - 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.
-  </situation>
+      - If you found wines in the store's database, they are in stock.
-  <Your role>
+      - You can only recommend wines that are currently in our inventory
-    Your name is $(a.name). You are a helpful English-speaking assistant, acting as a polite, website-based sommelier for $(a.retailername)'s wine store.
+      - 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.
-  </Your role>
+      - Ask the user one question at a time.
-  <objective>
+      - 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.
-    Present the wines to the user in a way that keep the conversation smooth and engaging.
+      - 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.
-  </objective>
+      - 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_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.
    # situation
      You are continuing the conversation with the user.
    # 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.
    # objective
      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-  
+    # your responsibility includes
-  
+      - According to the store's policy and guidelines, continuing conversation with the customer using CHAT_BOX action.
      - Keep the conversation with the customer going smoothly
-  <At each round of conversation, you will be given the following information>
+    # your responsibility does NOT includes
-    Name of the wines that needs to be introduced: name of wines you are going to introduce to the user
+      - 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.
-    Database search result: the result of a database search using SQL commands you have found so far
+      - Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
-  </At each round of conversation, you will be given the following information>
+      - 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 follow the following guidelines>
  </You should follow the following guidelines>
  <You should then respond to the user with>
    dialogue: Your presentation to the user
  </You should then respond to the user with>
  <You should only respond in format as described below>
  {
    "dialogue": "..."
  }
  </You should only respond in format as described below>
-  Let's begin!
+    # 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.
-  requiredKeys = [:dialogue]
+      2) "action_name", Must be "CHAT_BOX
-  database_search_result = 
+      3) "action_input", Dialogue you want to chat with the user according to your plan.
-      if length(a.memory["shortmem"][:db_search_result]) != 0
+      After the action is executed you gets "action_result". It is the output from the action you selected.
-        availableWineToText(a.memory["shortmem"][:db_search_result])
+
-      else
+    # you should only respond in JSON format as described below
-        "N/A"
+      "plan": "...",
-      end
+      "action_name": "...",
      "action_input": "..."
    """
  system_msg = Dict(
      "role" => "system",
      "content" => [
        Dict("type" => "text", "text" => systemmsg),
      ]
    )
  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"]
  # chathistory = chatHistoryToText(a.chathistory)
  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__, " $(Dates.now())")
    end
    msg = Dict(
      "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
      "messages" => chathistory,
      "temperature" => 0.7
    )
-  # yourthought = "$(thoughtDict[:thought]) $(thoughtDict["plan"])"
+    response = a.context.text2textInstructLLM(a.id, msg)
-  # yourthought1 = nothing
+    response = GeneralUtils.clean_json_response(response)
  for attempt in 1:maxtattempt
    context = 
    """
    <context>
    Name of the wines that needs to be introduced: $(thoughtDict["action_input"])
    $(a.memory["shortmem"]["scratchpad"])
    P.S. $errornote
    </context>
    """
    unformatPrompt =
      [
        Dict("name" => "system", "text" => systemmsg),
      ]
    unformatPrompt = vcat(unformatPrompt, recentchat)
    # put in model format
    prompt = GeneralUtils.formatLLMtext(unformatPrompt, a.llmFormatName)
    # add info
    prompt = prompt * context
    response = a.context.text2textInstructLLM(prompt; senderId=a.id)
    response = GeneralUtils.deFormatLLMtext(response, a.llmFormatName)
    response = GeneralUtils.remove_french_accents(response)
    # response = replace(response, '$'=>"USD")
    think, response = GeneralUtils.extractthink(response)
-    response = replace(response, '*'=>"")
+    response = strip(response)
-    response = replace(response, '$' => "USD")
+
    response = replace(response, '`' => "")
    response = replace(response, "<|eot_id|>"=>"")
    responsedict = nothing
-    try
+    if occursin(requiredKeys[2], response) 
-      responsedict = copy(JSON.parsefile(response))  
+      try
-    catch
+        _responsedict = JSON.parse(response)
-      println("\nERROR YiemAgent presentbox() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        responsedict = GeneralUtils.dictify(_responsedict; keytype=String, sort_order=requiredKeys)
-      continue
+      catch
-    end
+        println("\nERROR YiemAgent generatechat() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    # check whether all answer's key points are in responsedict
    ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
    if !ispass
      errornote = errormsg
      println("\nERROR YiemAgent presentbox() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
      continue
    end
    # check if Context: is in dialogue
    if occursin("Context:", responsedict["dialogue"])
      errornote = "Your previous response contains 'Context:' which is not allowed"
      println("\nERROR YiemAgent presentbox() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
      continue
    end
    println("\nYiemAgent presentbox() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    pprintln(Dict(responsedict))
    # check whether an agent recommend wines before checking inventory or recommend wines 
    # outside its inventory
    # ask LLM whether there are any winery mentioned in the response
    mentioned_winery = detectWineryName(a, responsedict["dialogue"])
    if mentioned_winery != "None"
      mentioned_winery = String.(strip.(split(mentioned_winery, ",")))
      # check whether the wine is in event
      isWineInEvent = false
      for winename in mentioned_winery
        for event in a.memory["events"]
          if event["observation"] !== nothing && occursin(winename, event["observation"])
            isWineInEvent = true
            break
          end
        end
      end
      # if wine is mentioned but not in timeline or shortmem,
      # then the agent is not supposed to recommend the wine
      if isWineInEvent == false
        errornote = "Your previous response recommended wines that is not in your inventory which is not allowed"
        println("\nERROR YiemAgent presentbox() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        s = """
        Perfect choice! The Colgin Tychson Hill Vineyard Cabernet Sauvignon (2014) is an excellent match for your criteria. Here's why: 
        Boldness & Flavor: This wine delivers intense blackberry, black cherry, and dark fruit notes, layered with vanilla, oak, and earthy undertones. Its high intensity (rated 5/5) ensures a rich, full-bodied experience that's both powerful and balanced. 
        Family-Owned Legacy: Produced by Colgin Cellars, a renowned Napa Valley family winery, this vintage reflects their commitment to quality and tradition. While not a limited-edition release, it's a highly regarded, consistently excellent Cabernet Sauvignon. 
        Gift-Ready & Affordable: Priced at USD144 (well under your USD250 budget), it comes in a sleek, gift-ready box—perfect for impressing friends or loved ones. 
        Why I Recommend It: It perfectly balances your desire for bold fruit, oak, and a presentable format without sacrificing quality. If you're curious about alternatives, the 2017 Hunter Glenn Cabernet (also USD159) shares similar intensity but lacks specific tasting notes. However, the 2014 Tychson Hill is a more complete match for your criteria. Enjoy your selection!"""
        continue
      end
    else
      println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
      continue
    end
-    result = responsedict["dialogue"]
+
-    return result
+    # check whether all answer's key points are in responsedict
    ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
    if !ispass
      errornote = errormsg
      println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
      continue
    end
    # 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
    # end
    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
    # pprintln(responsedict)
    responsedict["action_result"] = "Action result is the next user dialogue."
    @info "YiemAgent generatechat!() end " @__LINE__
    return (thoughtdict=responsedict, result_raw=responsedict["action_input"])
  end
-  error("presentbox() failed to generate a response")
+  error("YiemAgent generatechat() failed to generate a thought ", response)
 end
 # function endconversation(a::sommelier, thoughtDict; maxattempt::Integer=10)
 #   text = 
 #   """
 #   ---
 #   """
 #   requiredKeys = ["dialogue"]
 #   system_msg = Dict(
 #     "role" => "system",
 #     "content" => [
 #       Dict("type" => "text", "text" => systemmsg),
 #     ]
 #   )
 #   for attempt in 1:maxattempt
 #     unformatPrompt =
 #       [
 #         Dict("name" => "system", "text" => systemmsg),
 #       ]
 #     # put in model format
 #     prompt = GeneralUtils.formatLLMtext(unformatPrompt, a.llmFormatName)
 #     # add info
 #     prompt = prompt * context
 #     response = a.context.text2textInstructLLM(prompt; senderId=a.id)
 #     response = GeneralUtils.deFormatLLMtext(response, a.llmFormatName)
 #     response = GeneralUtils.remove_french_accents(response)
 #     # response = replace(response, '$'=>"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 generatechat() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
 #       continue
 #     end
 #     # sometime the model response like this "here's how I would respond: ..."
 #     if occursin("respond:", response)
 #       errornote = "Your previous response contains 'response:' which is not allowed"
 #       println("\nERROR YiemAgent generatechat() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
 #       continue
 #     elseif occursin("Your thoughts:", response) || occursin("your thoughts:", response)
 #       errornote = "You don't need to put 'Your thoughts:' in your response"
 #       println("\nERROR YiemAgent generatechat() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
 #       continue
 #     end
 #     response = GeneralUtils.remove_french_accents(response)
 #     response = replace(response, '*'=>"")
 #     response = replace(response, '$' => "USD")
 #     response = replace(response, '`' => "")
 #     response = replace(response, "<|eot_id|>"=>"")
 #     # check whether an agent recommend wines before checking inventory or recommend wines 
 #     # outside its inventory
 #     # ask LLM whether there are any winery mentioned in the response
 #     mentioned_winery = detectWineryName(a, response)
 #     if mentioned_winery != "None"
 #       mentioned_winery = String.(strip.(split(mentioned_winery, ",")))
 #       # check whether the wine is in event
 #       isWineInEvent = false
 #       for winename in mentioned_winery
 #         for event in a.memory["events"]
 #           if event["observation"] !== nothing && occursin(winename, event["observation"])
 #             isWineInEvent = true
 #             break
 #           end
 #         end
 #       end
 #       # then the agent is not supposed to recommend the wine
 #       if isWineInEvent == false
 #         errornote = "You recommended wines that are not in your inventory before. Please only recommend wines that you have previously found in your inventory."        
 #         println("\nERROR YiemAgent generatechat() $errornote $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
 #         continue
 #       end
 #     end
 #     result = responsedict["dialogue"]
 #     return result
 #   end
 #   error("generatechat failed to generate a response")
 # end
 function generatequestion(a, text2textInstructLLM::Function, timeline)::String
  systemmsg = 
@@ -1,10 +1,11 @@
 module llmfunction
-export  virtualWineUserChatbox, jsoncorrection, checkwine, # recommendbox,
+export  virtualWineUserChatbox, jsoncorrection, search_wine_database!, # recommendbox,
        virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
-        extractWineAttributes_2, paraphrase
+        extractWineAttributes_2, paraphrase, SQLexecution
-using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames
+using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames, DataStructures,
      Base64
 using GeneralUtils, SQLLLM
 using ..type, ..util
@@ -211,7 +212,7 @@ pushfirst!(virtualCustomerChatHistory, Dict("name"=> "system", "text"=> systemms
    receiverName= "text2textinstruct",
    mqttBroker= config["mqttServerInfo"]["broker"],
    mqttBrokerPort= config["mqttServerInfo"]["port"],
-    msgId = string(uuid4()) #CHANGE remove after testing finished
+    msgId = string(uuid4()) # remove after testing finished
  )
  outgoingMsg = Dict(
@@ -269,7 +270,7 @@ end
 # Arguments
  - `a::T1` 
    one of ChatAgent's agent.
-  - `input::T2`
+  - `thoughtdict::AbstractDict`
 # Return
  A JSON string of available wine
@@ -281,71 +282,382 @@ julia> input = "{\"food\": \"pizza\", \"occasion\": \"anniversary\"}"
 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::T1, input::T2; maxattempt::Int=3
+function search_wine_database!(a::T, thoughtdict::AbstractDict; useSQLLLM::Bool=false
-  ) where {T1<:agent, T2<:AbstractString}
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
-  println("\ncheckinventory order: $input ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  println("\ncheckinventory order: $(thoughtdict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  wineattributes_1 = extractWineAttributes_1(a, input)
+  wineattributes_1 = extractWineAttributes_1(a, thoughtdict["action_input"])
-  wineattributes_2 = extractWineAttributes_2(a, input)
+  wineattributes_2 = extractWineAttributes_2(a, thoughtdict["action_input"])
-  # placeholder
+  retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency", "image_url", "retailer_name", "retailer_id"]
-  # textresult = nothing
+  _inventoryquery = "$(thoughtdict["action_input"]), $wineattributes_1, $wineattributes_2, retailer_name: $(a.retailername), retailerid: $(a.retailerid)"
  # rawresponse = nothing
  # for i in 1:maxattempt
  #   #CHANGE if you want to add retailer name
  #   # _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
  #   _inventoryquery = "$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"]
  #   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")
  #   # check if all of retrieve_attributes appears in textresult
  #   isin = [occursin(x, textresult) for x in retrieve_attributes]
  #   # check if rawresponse type is DataFrame so that I can check for column
  #   if typeof(result_raw) == DataFrame && 
  #     !occursin("The resulting table has 0 row", textresult) &&
  #     !all(isin)
  #     errornote = "Not all of $retrieve_attributes appear in search result"
  #     println("\nERROR YiemAgent checkwine() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  #     continue
  #   else
  #     break
  #   end
  # end
  #CHANGE if you want to add retailer name
    # _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
  retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency"]
  _inventoryquery = "$wineattributes_1, $wineattributes_2"
  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")
  # println("\n--- YiemAgent checkwine() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  # println(textresult)
  # println(result_raw)
  # println("---")
-  return (result_str=textresult, result_raw=result_raw, success=true, errormsg=nothing)
+  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, sql_result_df, success, _ = SQLexecution(a.context.executeSQL, sql)
    items = nothing
    if sql_result_df !== nothing
      result_vec = GeneralUtils.dfToVectorDict(sql_result_df)
      # get image
      for d in result_vec
        image_url_json_str = d["image_url"]
        image_url_json_obj = JSON.parse(image_url_json_str)
        base_url = "http://192.168.88.106:8080/"
        if haskey(image_url_json_obj, "bottle")
          url = base_url * image_url_json_obj["bottle"]
          image_data = HTTP.get(url)  # vector{int} data
          image_base64_string = base64encode(image_data.body)
          d["image"] = image_base64_string
        else
          d["image"] = nothing
        end
      end
      items = result_vec  # image is added to each item
    end
    thoughtdict["action_result"] = textresult
  end
  return (thoughtdict=thoughtdict, result_raw=items)
 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.
      - Overly strict condition usually yields empth result
    # 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
@@ -386,7 +698,6 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
    wine_name: name of the wine
    winery: name of the winery
    vintage: the year of the wine
    region: a region, such as Burgundy, Bordeaux, Champagne, Napa Valley, Tuscany, California, Oregon, etc. Use "or" if there are multiple regions.
    country: a country where wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States". Use "or" if there are multiple countries.
    wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
    grape_varietal: the name of the primary grape used to make the wine
@@ -395,12 +706,12 @@ 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.
  </you should then respond to the user with>
  <you should only respond in JSON format as described below>
    "wine_name": "...",
    "winery": "...",
    "vintage": "...",
    "region": "...",
    "country": "...",
    "wine_type": "...",
    "grape_varietal": "...",
@@ -415,7 +726,6 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
      "wine_name": "N/A",
      "winery": "N/A",
      "vintage": "N/A",
      "region": "Tuscany or Napa Valley",
      "country": "Italy or United States",
      "wine_type": "red or white",
      "grape_varietal": "Chenin Blanc or Riesling",
@@ -429,18 +739,17 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
      "wine_name": "Saumur Blanc",
      "winery": "Domaine du Collier",
      "vintage": "2019",
      "region": "Saumur",
      "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",
      "food_to_be_paired_with_wine": "N/A"
  </here are some examples>
  """
-  requiredKeys = ["wine_name", "winery", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price_min", "wine_price_max", "occasion", "food_to_be_paired_with_wine"]
+  requiredKeys = ["wine_name", "winery", "vintage", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price_min", "wine_price_max", "occasion", "food_to_be_paired_with_wine"]
  errornote = ""
  context = 
  """
@@ -473,9 +782,6 @@ 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)
@@ -503,6 +809,20 @@ function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
      _v = replace(v, r"\(.*?\)" => "")
      responsedict[k] = _v
    end
    @info "YiemAgent extractWineAttributes_1() " @__LINE__
    @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
@@ -510,10 +830,13 @@ 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)
+    @info "YiemAgent extractWineAttributes_1() " @__LINE__
-    println("--- \n")
+    @show result
    @info "---\n" @__LINE__
    return result
  end
  error("extractWineAttributes_1() failed to get a response")
@@ -666,9 +989,6 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
  for attempt in 1:10
    response = a.context.text2textInstructLLM(a.id, msg)
    response = GeneralUtils.clean_json_response(response)
    println("\n--- extractWineAttributes_2-1()")
    println(response)
    println("--- \n")
    response = GeneralUtils.remove_french_accents(response)
    think, response = GeneralUtils.extractthink(response)
@@ -706,9 +1026,11 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
      end
    end
    result = result[1:end-2]  # remove the ending ", "
-    println("\n--- extractWineAttributes_2-2()")
+
-    println(result)
+    @info "YiemAgent extractWineAttributes_2() " @__LINE__
-    println("--- \n")
+    @show result
    @info "---\n" @__LINE__
    return result
  end
  error("extractWineAttributes_2() failed to get a response")
@@ -888,7 +1210,7 @@ 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"
@@ -924,126 +1246,6 @@ 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,
@@ -223,10 +229,9 @@ function sommelier(
    context,
    llmFormatName
    )
  systemmsg = 
    """
-    <store_policy>
+    # 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
@@ -238,8 +243,8 @@ function sommelier(
      - 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 imediately 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>
+    # 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.
@@ -247,47 +252,46 @@ function sommelier(
      - 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>
+      - User usually ask for something similar. This means you should use the search term based on the profile they like.
-    <situation>
+
-      Your customer is coming into the store
+    # situation
-    </situation>
+      You are having conversation with a customer.
-    <your role>
+
-      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store. You are working under your mentor supervision.
+    # your role
-    </your role>
+      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
-    <objective>
+
-      1) Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
+    # objective
-      2) Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
+      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
-    </objective>
+      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-    <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
+    # your responsibility includes
-      2) Keep the conversation with the customer going smoothly
+      - According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
-      2) Obey your mentor's suggestions.
+      - Keep the conversation with the customer going smoothly
-    </your responsibility includes>
+
-    <your responsibility does NOT 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.
+      - 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.
+      - 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.
+      - 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 interleaving plan, action_name, action_input>
+    # 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.
+      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
+      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.
+      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 then respond to the user with interleaving plan, action_name, action_input>
+
-    <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": "..."
-    </you should only respond in JSON format as described below>
+
-    <available_actions>
+    # available actions
-      - CHAT_BOX which you can use to talk with the user.
+      "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.
+      "SEARCH_WINE_DATABASE", allows you to search information about wines you want in your inventory's database. The input is strictly supported search term including: retailer_name, wine price, winery, name, vintage, region, country, type of wine, 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 1: "Dry, full-bodied red wine from Burgundy, France. Grape varietal could be Merlot or Syrah. price 100 to 1000 USD."
-          Example query 2: "Red or white wine, medium tannin, price under 700 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
+        Example query 3: "white wine from Tuscany, Italy or Bordeaux, France
-      - PRESENT_WINE_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.
+      "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.
+      "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.
    </available_actions>
    """
  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
+  # if name ∉ ["system", "user", "assistant"]   # guard against typo
-    error("name is not in agent.availableRole $(@__LINE__)")
+  #   error("name is not in agent.availableRole $(@__LINE__)")
-  end
+  # end
  #TODO summarize the oldest 10 message
  if length(a.chathistory) > maximumMsg
    summarize(a.chathistory)
  else
-    userinput["timestamp"] = Dates.now()
+    # userinput["timestamp"] = Dates.now()
    push!(a.chathistory, userinput)
  end
 end
@@ -297,7 +297,7 @@ function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothin
    #   timeline *= "Event_$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})
+    reply = NATS.request(agent_conn, 
-  payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
-  _, msg_envelope_json_str = msghandler.smartpack(
+                        msg_envelope_json_str, timeout=120)
        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, 
+    incoming_env_json_str = String(reply.payload)
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
-                      msg_envelope_json_str, timeout=120)
+    _llm_response = incoming_env["payloads"][1][2]
-
+    llm_response = _llm_response["choices"][1]["message"]["content"]
-  incoming_env_json_str = String(reply.payload)
+    return llm_response
  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)
  end
 end
-#TESTING
+  """ get a single text embedding from a LLM service
-function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
+  Example
-  tablename = "sqlllm_decision_repository"
+  text = ["hello"]
-  # get embedding of the query
+  embedding = get_embedding(text)
-  # query = state[:thoughtHistory][:question]
+  """
-  df = find_similar_text_from_vectordb(query, tablename, 
+  function get_embedding(text::AbstractArray{String})
-                                  "function_input_embedding", execute_sql_vectordb)
+    documents_dict = Dict("documents" => text)
-  row, col = size(df)
+    payloads = [("documents", documents_dict, "dictionary")]
-  distance = row == 0 ? Inf : df[1, :distance]
+    _, msg_envelope_json_str = msghandler.smartpack(
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      config["externalservice"]["servicesloadbalancer"]["nats"], 
-    _query_embedding = get_embedding([query])[1]
+      payloads;   
-    query_embedding = _query_embedding["data"][1]["embedding"]
+      msg_purpose="embedding",  
-    query = replace(query, "'" => "")
+      broker_url=config["nats_server_info"]["url"],
-    sql_base64 = base64encode(SQL)
+      fileserver_url=config["externalservice"]["fileserver"]["url"])
    sql_ = replace(SQL, "'" => "")
-    sql = """
+    reply = NATS.request(agent_conn, 
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
-          """
+                        msg_envelope_json_str, timeout=120)
-    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    incoming_env_json_str = String(reply.payload)
-    # println(sql)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
-    _ = execute_sql_vectordb(sql)
+    embedding_response = incoming_env["payloads"][1][2]
    return embedding_response
  end
 end
-#TESTING
+  """ sql = "SELECT * FROM wine;"
-function execute_sql_vectordb(sql::T) where {T<:AbstractString}
+  result = execute_sql_winedb(sql)
-  host_url, _port = split(config["SQLVectorDB"]["url"], ':')
+  """
-  port = parse(Int, _port)
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
-  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-  user = config[:externalservice][:SQLVectorDB][:user]
+    port = parse(Int, _port)
-  password = config[:externalservice][:SQLVectorDB][:password]
+    dbname = "winedb"
-  DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    user = config["externalservice"]["sommpanion_db"]["user"]
-  result = LibPQ.execute(DBconnection, sql)
+    password = config["externalservice"]["sommpanion_db"]["password"]
-  close(DBconnection)
+    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-  return result
+    result = nothing
-end
+    try
-
+      result = LibPQ.execute(db_connection, sql)  
-
+    catch e
-function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
+      LibPQ.close(db_connection)
-  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+    end
  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 = 
+    LibPQ.close(db_connection)
-    """
+    return result
    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
  """ 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"
+backend_session_topic = "sommpanion.testsubject"
 config = JSON.parsefile("./dummy_config.json")
 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
+  # can't instantiate
-agent = YiemAgent.sommelier(
+  agent = YiemAgent.sommelier(
-  agent_context;
+    agent_context;
-  name="Janie",
+    name="Janie",
-  id=sessionId,  # agent instance id
+    id=sessionId,  # agent instance id
-  retailername="Yiem",
+    retailername="Yiem Wine Ltd.",
-  llmFormatName=""
+    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)
+result = YiemAgent.conversation(agent; userinput=message)
-println(result)
+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	1c829ad854	update	2026-07-17 10:14:13 +07:00
ton	da98baddb6	update	2026-07-17 10:06:32 +07:00
ton	c5fbaabf42	Merge pull request 'v0.7.0' (#25 ) from v0.7.0 into main Reviewed-on: #25	2026-07-17 00:03:01 +00:00
ton	8080905bad	Merge pull request 'v0.7.0-output_openai_msg' (#24 ) from v0.7.0-output_openai_msg into v0.7.0 Reviewed-on: #24	2026-07-17 00:02:47 +00:00
ton	b349c3a8b6	update	2026-07-17 07:01:55 +07:00
ton	0148e03d6a	update	2026-07-16 23:31:36 +07:00
ton	e718cc4a5c	update	2026-07-16 23:13:18 +07:00
ton	87bc6a46a1	update	2026-07-16 22:31:40 +07:00
ton	44bb8baf7c	Merge pull request 'update' (#23 ) from v0.6.0-output_text_image into main Reviewed-on: #23	2026-07-15 11:51:43 +00:00
ton	18b2d54ba7	update	2026-07-15 18:51:21 +07:00
ton	b3c3bb9b75	Merge pull request 'update' (#22 ) from v0.6.0-output_text_image into main Reviewed-on: #22	2026-07-15 11:50:09 +00:00
ton	d004193b19	update	2026-07-15 18:49:51 +07:00
ton	8898226825	Merge pull request 'v0.6.0-output_text_image' (#21 ) from v0.6.0-output_text_image into main Reviewed-on: #21	2026-07-15 11:48:20 +00:00
ton	686b9b2e92	update	2026-07-15 18:47:18 +07:00
ton	3acf46964b	update	2026-07-15 14:25:04 +07:00
ton	5c7caf0b49	Merge pull request 'update' (#20 ) from v0.6.0-output_text_image into main Reviewed-on: #20	2026-07-15 07:01:48 +00:00
ton	ad917ea8d0	update	2026-07-15 14:01:32 +07:00
ton	edeef4ed2a	Merge pull request 'v0.6.0-output_text_image' (#19 ) from v0.6.0-output_text_image into main Reviewed-on: #19	2026-07-15 06:59:56 +00:00
ton	31daa805f3	update	2026-07-15 13:59:28 +07:00
ton	c9937ab5d7	update	2026-07-15 13:59:01 +07:00
ton	4610137f04	Merge pull request 'update' (#18 ) from v0.6.0-output_text_image into main Reviewed-on: #18	2026-07-15 05:20:58 +00:00
ton	9d7eed7cde	update	2026-07-15 12:20:44 +07:00
ton	aedc53bf86	Merge pull request 'update' (#17 ) from v0.6.0-output_text_image into main Reviewed-on: #17	2026-07-15 05:17:56 +00:00
ton	286da3cf2c	update	2026-07-15 12:16:59 +07:00
ton	7fa988313d	Merge pull request 'update' (#16 ) from v0.6.0-output_text_image into main Reviewed-on: #16	2026-07-15 05:15:31 +00:00
ton	e5b19dd268	update	2026-07-15 12:14:32 +07:00
ton	0df4159261	Merge pull request 'update' (#15 ) from v0.6.0-output_text_image into main Reviewed-on: #15	2026-07-15 05:11:05 +00:00
ton	45e8ded111	update	2026-07-15 12:10:36 +07:00
ton	9167ece0c0	Merge pull request 'v0.6.0' (#14 ) from v0.6.0 into main Reviewed-on: #14	2026-07-15 04:57:47 +00:00
ton	a6a9395ecc	Merge pull request 'update' (#13 ) from v0.6.0-output_text_image into v0.6.0 Reviewed-on: #13	2026-07-15 04:57:11 +00:00
ton	a503d4d759	update	2026-07-15 11:56:44 +07:00
ton	f45a036971	Merge pull request 'v0.6.0' (#12 ) from v0.6.0 into main Reviewed-on: #12	2026-07-15 04:38:48 +00:00
ton	24b85be58b	Merge pull request 'updatet' (#11 ) from v0.6.0-output_text_image into v0.6.0 Reviewed-on: #11	2026-07-15 04:38:18 +00:00
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
ton	4d57f0146b	update	2026-07-09 07:56:33 +07:00
ton	d33aa14dc8	use md system prompt	2026-07-07 07:54:24 +07:00
ton	0ed3edd48a	update	2026-07-06 06:10:12 +07:00
ton	fb91b51573	update	2026-07-05 20:48:24 +07:00