update

Reviewed-on: #30

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "09bd5c43d6ad954d8be233d27fc343ea1149c0b0"
+project_hash = "dc7878808bbc4637a12e709dd495979a784824a5"
 
 [[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"
@@ -224,11 +244,21 @@ git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec"
 uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
 version = "0.1.10"
 
+[[deps.EzXML]]
+deps = ["Printf", "XML2_jll"]
+git-tree-sha1 = "7ea1aa5869e2626ccae84480e4f37185bc6f41d3"
+uuid = "8f5d6c58-4d21-5cfd-889c-e3ad7ee6a615"
+version = "1.2.3"
+
 [[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 +280,7 @@ 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 +288,6 @@ 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 +299,31 @@ 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 +348,11 @@ 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 +449,7 @@ 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"
@@ -463,6 +506,12 @@ version = "1.11.3+1"
 uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
 version = "1.11.0"
 
+[[deps.Libiconv_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl"]
+git-tree-sha1 = "be484f5c92fad0bd8acfef35fe017900b0b73809"
+uuid = "94ce4f54-9a6c-5748-9c1c-f9c7231a4531"
+version = "1.18.0+0"
+
 [[deps.LinearAlgebra]]
 deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"]
 uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
@@ -645,15 +694,17 @@ 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 +785,9 @@ 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 +811,11 @@ 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"
@@ -789,10 +840,22 @@ git-tree-sha1 = "084c47c7c5ce5cfecefa0a98dff69eb3646b5a80"
 uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
 version = "1.4.10"
 
+[[deps.Serde]]
+deps = ["CSV", "Dates", "EzXML", "JSON", "TOML", "UUIDs", "YAML"]
+git-tree-sha1 = "f397fc8779cc53e4677c2708f3802c6996f28d00"
+uuid = "db9b398d-9517-45f8-9a95-92af99003e0e"
+version = "3.7.2"
+
 [[deps.Serialization]]
 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 +889,25 @@ 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 +944,18 @@ 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.StringEncodings]]
+deps = ["Libiconv_jll"]
+git-tree-sha1 = "b765e46ba27ecf6b44faf70df40c57aa3a547dcb"
+uuid = "69024149-9ee7-55f6-a4c4-859efe599b68"
+version = "0.3.7"
+
 [[deps.StringManipulation]]
 deps = ["PrecompileTools"]
 git-tree-sha1 = "d05693d339e37d6ab134c5ab53c29fce5ee5d7d5"
@@ -982,11 +1076,23 @@ git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
 uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 
+[[deps.XML2_jll]]
+deps = ["Artifacts", "JLLWrappers", "Libdl", "Libiconv_jll", "Zlib_jll"]
+git-tree-sha1 = "3f3315d89fc954a28f5b471bce698ed6e27481be"
+uuid = "02c8fc9c-b97f-50b9-bbe4-9be30ff0a78a"
+version = "2.15.3+0"
+
+[[deps.YAML]]
+deps = ["Base64", "Dates", "Printf", "StringEncodings"]
+git-tree-sha1 = "a1c0c7585346251353cddede21f180b96388c403"
+uuid = "ddb6d928-2868-570f-bddf-ab3f9cf99eb6"
+version = "0.4.16"
+
 [[deps.YiemAgent]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs"]
+deps = ["Base64", "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 = "0.7.2"
 
 [[deps.Zlib_jll]]
 deps = ["Libdl"]

Project.toml

@@ -1,9 +1,10 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.7.4"
 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"
@@ -18,16 +19,19 @@ PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
 Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
 SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
+Serde = "db9b398d-9517-45f8-9a95-92af99003e0e"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 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"
+Serde = "3.7.2"

aisystemprompt.md

@@ -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

appconfig.json

@@ -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",

etc.jl

@@ -1,93 +1,200 @@
-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"]
+password = config["externalservice"]["sommpanion_db"]["password"]
+pg_conn_str = "host=$host_url port=$port dbname=$dbname user=$user password=$password"
 
-        # 1. Process and normalize all keys from the input dictionary
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
-        processed_dict = OrderedDict{keytype, Any}()
+    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-        for (k, v) in x
+    port = parse(Int, _port)
-            if keytype === String
+    dbname = "winedb"
-                newk = string(k)
+    user = config["externalservice"]["sommpanion_db"]["user"]
-            elseif keytype === Symbol
+    password = config["externalservice"]["sommpanion_db"]["password"]
-                newk = Symbol(string(k))
+    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = nothing
+    try
+      result = LibPQ.execute(db_connection, sql)  
+    catch e
+      LibPQ.close(db_connection)
+    end
+    
+    LibPQ.close(db_connection)
+    return result
+  end
+
+
+
+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
-                newk = k
+      nothing
     end
-            processed_dict[newk] = dictify(v; keytype=keytype, sort_order=sort_order)
+  d["image"] = image_base64
 end
 
-        # 2. If a sort order is specified, apply it
+
-        if !isnothing(sort_order)
+
-            # Normalize the sort_order elements to match the requested keytype
+
-            normalized_order = map(sort_order) do tk
+
-                if keytype === String
+
-                    return string(tk)
+
-                elseif keytype === Symbol
+
-                    return Symbol(string(tk))
+
+
+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
+
+
+
+
+
+
+
+
+
+
+
+function generate_wine_retail_sql(conditions::Dict{String, Any})::String
+    # 1. Base SQL structure
+    base_query = """
+SELECT
+  w.winery,
+  w.wine_name,
+  w.wine_id,
+  w.vintage,
+  w.region,
+  w.country,
+  w.wine_type,
+  w.grape,
+  w.serving_temperature,
+  w.sweetness,
+  w.intensity,
+  w.tannin,
+  w.acidity,
+  w.tasting_notes,
+  rw.price,
+  rw.currency,
+  w.image_url,
+  NULL AS retailer_name,
+  rw.retailer_id
+FROM wine AS w
+JOIN retailer_wine AS rw
+  ON w.wine_id = rw.wine_id
+"""
+
+    # 2. Dynamic WHERE Clause Builder
+    where_clauses = String[]
+
+    # Iterate over each table condition provided
+    for (table_name, table_conditions) in conditions
+        
+        # Determine table alias
+        alias = if table_name == "wine"
+            "w"
+        elseif table_name == "retailer_wine"
+            "rw"
         else
-                    return tk
+            continue # Skip unsupported tables
+        end
+        
+        # Process condition dictionaries
+        if isa(table_conditions, Dict) && !isempty(table_conditions)
+            
+            for (column_name, filter_details) in table_conditions
+                
+                if isa(filter_details, Dict) && haskey(filter_details, "operator")
+                    op = filter_details["operator"]
+                    raw_val = filter_details["value"]
+                    
+                    # --- Value Type Handling ---
+                    # Use tryparse instead of try/catch for cleaner, faster parsing
+                    final_val = raw_val
+                    
+                    if op in ("=", "<", ">", "<=", ">=")
+                        str_val = string(raw_val)
+                        num_val = tryparse(Float64, str_val)
+                        
+                        if !isnothing(num_val)
+                            final_val = isinteger(num_val) ? round(Int, num_val) : num_val
                         end
                     end
                     
-            # First, insert keys that match the requested order
+                    # --- SQL Formatting ---
-            for target_key in normalized_order
+                    if isa(final_val, Number)
-                if haskey(processed_dict, target_key)
+                        clause = "$(alias).$(column_name) $(op) $(final_val)"
-                    out[target_key] = processed_dict[target_key]
-                end
-            end
-
-            # Then, append any remaining keys that weren't in the sort_order
-            for (k, v) in processed_dict
-                if !haskey(out, k)
-                    out[k] = v
-                end
-            end
                     else
-            # If no sort order is given, just use the processed dict
+                        # Escape single quotes within string values
-            out = processed_dict
+                        escaped_val = replace(string(final_val), "'" => "''")
+                        clause = "$(alias).$(column_name) $(op) '$(escaped_val)'"
                     end
 
-        return out
+                    push!(where_clauses, clause)
-
+                end
-    # Arrays / vectors: map elements recursively
+            end
-    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
 
+    # 3. Assemble Final Query
+    where_sql = isempty(where_clauses) ? "" : "WHERE " * join(where_clauses, " AND ")
     
-
+    return string(base_query, where_sql, ";")
-
-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")
 end
 
 
-
-
-
-

src/interface.jl

@@ -4,7 +4,7 @@ export addNewMessage, conversation, decisionMaker, reflector, generatechat,
   generalconversation, detectWineryName, generateSituationReport
 
 using JSON, DataStructures, Dates, UUIDs, HTTP, Random, PrettyPrinting, Serialization,
-      DataFrames, CSV
+      DataFrames, Serde
 using GeneralUtils
 using ..type, ..util, ..llmfunction
 
@@ -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
+function decisionMaker(a::T; recentevents::Integer=20, maxattempt=3
   ) 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>
     """
 
@@ -147,63 +122,67 @@ function decisionMaker(a::T; recentevents::Integer=20, maxattempt=10
   errornote = "N/A"
   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",
       "messages" => a.chathistory,
       "temperature" => 0.7
     )
 
+  for attempt in 1:maxattempt
     response = a.context.text2textInstructLLM(a.id, msg)
-    response = GeneralUtils.clean_json_response(response)
-
     response = GeneralUtils.remove_french_accents(response)
-    think, response = GeneralUtils.extractthink(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
-    responsedict = nothing
+    while occursin('$', response)
-    if occursin(requiredKeys[2], response) 
+      response = replace(response, '$' => "USD")
-      try
-        _responsedict = JSON.parse(response)
-        responsedict = GeneralUtils.dictify(_responsedict; keytype=String, sort_order=requiredKeys)
-      catch
-        println("\nERROR YiemAgent decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-        continue
     end
 
-    # fall back to normal text because LLM default to natural chat when it didn't use action_call
+    responsedict = nothing
-    else  
+    try
-      responsedict = OrderedDict(
+      responsedict = Serde.parse_yaml(response)
-        "plan"=> "I will talk to the user",
+    catch e
-        "action_name"=> "CHAT_BOX",
+      println("\nERROR YiemAgent decisionMaker() Error: $e --(not qualify response)-> $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-        "action_input"=> response[2:end-1]  # remove { } at the front and back that added by clean_json_response
+      continue
-      )
     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)
+
+  # in case decisionMaker failed, force to use generatechat!()
+  responsedict = OrderedDict(
+    "plan"=> "N/A",
+    "action_name"=> "CHAT_BOX",
+    "action_input"=> "N/A"
+  )
+  return responsedict
 end
 
 
@@ -291,9 +270,9 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
     context =
     """
     <context>
-      <trajectory>
+      <assistant_trajectories>
       $timeline
-      </trajectory>
+      </assistant_trajectories>
       <evaluatee_context>
       $evaluateecontext
       </evaluatee_context>
@@ -342,36 +321,11 @@ function evaluator(a::T1, timeline, decisiondict, evaluateecontext
 
     println("\nEvaluator() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     pprintln(Dict(responsedict))
-
-    # # read sessionId
-    # sessionid = a.id
-    # # save to filename ./log/decisionlog.txt
-    # println("saving SQLLLM evaluator() to disk")
-    # filename = "agent_evaluator_log_$(sessionid[:id]).json"
-    # filepath = "/appfolder/app/log/$filename"
-    # # check whether there is a file path exists before writing to it
-    # if !isfile(filepath)
-    #   decisionlist = [responsedict]
-    #   println("Creating file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # else
-    #   # read the file and append new data
-    #   decisionlist = copy(JSON.parsefile(filepath))
-    #   push!(decisionlist, responsedict)
-    #   println("Appending new data to file $filepath")
-    #   open(filepath, "w") do io
-    #     JSON.pretty(io, decisionlist)
-    #   end
-    # end
-
     return responsedict
   end
   error("Evaluator failed to generate an evaluation, Response: \n$response\n<|End of error|>")
 end
 
-
 """ Chat with llm.
 
 # Example userinput
@@ -396,92 +350,163 @@ 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
+
-      end
+        thoughtdict, result_raw = generatechat!(a)
-    end
         assistant_response = Dict{String, Any}(
           "role" => "assistant",
-      "content" => [Dict("type" => "text", "text" => chatresponse),]
+          "content" => [Dict("type" => "text", "text" => thoughtdict["action_input"]),]
         )
         addNewMessage(a, "assistant", assistant_response; maximumMsg=maximumMsg)
 
-    return chatresponse
+        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"])
+            @info "YiemAgent conversation() shortmem: $(length(a.memory["shortmem"]["items_info"])), items_info: $(length(items_info)), item name: $(item["wine_name"])  " @__LINE__
+            if haskey(item, "wine_name") && occursin(item["wine_name"], thoughtdict["action_input"])
+              push!(items_info, deepcopy(item))
+              push!(send_item_ind, i)
+              @info "YiemAgent conversation() shortmem: $(length(a.memory["shortmem"]["items_info"])), items_info: $(length(items_info)) " @__LINE__
+            end
+          end
+          # remove sent items
+          deleteat!(a.memory["shortmem"]["items_info"], send_item_ind)
+          @info "YiemAgent conversation() shortmem: $(length(a.memory["shortmem"]["items_info"])), items_info: $(length(items_info)) " @__LINE__
+        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"])
+            @info "YiemAgent conversation() shortmem: $(length(a.memory["shortmem"]["items_info"])), items_info: $(length(items_info)), item name: $(item["wine_name"])  " @__LINE__
+            if haskey(item, "wine_name") && occursin(item["wine_name"], thoughtdict["action_input"])
+              push!(items_info, deepcopy(item))
+              push!(send_item_ind, i)
+              @info "YiemAgent conversation() shortmem: $(length(a.memory["shortmem"]["items_info"])), items_info: $(length(items_info)) " @__LINE__
+            end
+          end
+          # remove sent items
+          deleteat!(a.memory["shortmem"]["items_info"], send_item_ind)
+          @info "YiemAgent conversation() shortmem: $(length(a.memory["shortmem"]["items_info"])), items_info: $(length(items_info)) " @__LINE__
+        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
   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
@@ -494,41 +519,65 @@ 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"] ∈ ["WINE_PRESENTATION_GUIDELINE"]
-  elseif thoughtDict["action_name"] == "END_CONVER_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}
   
-    # add guideline in to context
   guideline =
     """
     <end_conversation_guideline>
@@ -540,19 +589,14 @@ 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
+  return (thoughtdict=thoughtdict, result_raw=nothing)
-        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
+function wine_presentation_guideline!(a::T, thoughtdict::AbstractDict
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
   
-    # add guideline in to context
   guideline =
     """
     <wine_presentation_guideline>
@@ -589,296 +633,170 @@ 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
 
+    # your responsibility does NOT includes
+      - 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.
+      - Processing sales orders or engaging in any other sales-related activities. 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.
 
+    # 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 "CHAT_BOX
+      3) "action_input", Dialogue you want to chat with the user according to your plan.
+      After the action is executed you gets "action_result". It is the output from the action you selected.
 
-  <At each round of conversation, you will be given the following information>
+    # you should only respond in JSON format as described below
-    Name of the wines that needs to be introduced: name of wines you are going to introduce to the user
+      "plan": "...",
-    Database search result: the result of a database search using SQL commands you have found so far
+      "action_name": "...",
-  </At each round of conversation, you will be given the following information>
+      "action_input": "..."
-  <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!
     """
-  requiredKeys = [:dialogue]
-  database_search_result = 
-      if length(a.memory["shortmem"][:db_search_result]) != 0
-        availableWineToText(a.memory["shortmem"][:db_search_result])
-      else
-        "N/A"
-      end
 
-  # chathistory = chatHistoryToText(a.chathistory)
+  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"]
+
   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
+    if occursin(requiredKeys[2], response) 
       try
-      responsedict = copy(JSON.parsefile(response))  
+        _responsedict = JSON.parse(response)
+        responsedict = GeneralUtils.dictify(_responsedict; keytype=String, sort_order=requiredKeys)
       catch
-      println("\nERROR YiemAgent presentbox() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        println("\nERROR YiemAgent generatechat() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
         continue
       end
+    else
+      println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      continue
+    end
+
 
     # check whether all answer's key points are in responsedict
-    ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
+    ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
     if !ispass
       errornote = errormsg
-      println("\nERROR YiemAgent presentbox() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
       continue
     end
 
-    # check if Context: is in dialogue
+    # if responsedict["action_name"] ∉ ["CHAT_BOX", "SEARCH_WINE_DATABASE", "PRESENT_WINE_GUIDELINE", "END_CONVER_GUIDELINE"]
-    if occursin("Context:", responsedict["dialogue"])
+    #   errornote = "Your previous attempt didn't use the given functions"
-      errornote = "Your previous response contains 'Context:' which is not allowed"
+    #   println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)--> $(responsedict["action_name"])", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      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
-    end
-
-    result = responsedict["dialogue"]
-    return result
-  end
-  error("presentbox() failed to generate a 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
+    # println("\nYiem decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#     ispass, errormsg = checkAgentResponse_JSON(responsedict, requiredKeys)
+    # pprintln(responsedict)
-#     if !ispass
+    responsedict["action_result"] = "Action result is the next user dialogue."
-#       errornote = errormsg
+    @info "YiemAgent generatechat!() end " @__LINE__
-#       println("\nERROR YiemAgent generatechat() $errornote --(not qualify response)> $responsedict", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+    return (thoughtdict=responsedict, result_raw=responsedict["action_input"])
-#       continue
+  end
-#     end
+  @info "YiemAgent generatechat() failed to generate a thought " @__LINE__
-
+  error("YiemAgent generatechat() failed to generate a thought ", response)
-#     # sometime the model response like this "here's how I would respond: ..."
+end
-#     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

src/type.jl

@@ -16,83 +16,18 @@ 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
 
-mutable struct companion <: agent
-  name::String  # agent name
-  id::String    # agent id
-  systemmsg::String # system message
-  tools::Dict  # tools
-  maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
-  chathistory::Vector{Dict{String, Any}}
-  memory::Dict{String, Any}
-  context::NamedTuple  # NamedTuple of functions 
-  llmFormatName::String
-end
-
-function companion(
-  context::agentcontext  # NamedTuple of functions
-  ;
-  name::String= "Assistant",
-  id::String= GeneralUtils.uuid4snakecase(), 
-  maxHistoryMsg::Integer= 20,
-  chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
-  llmFormatName::String= "granite3",
-  systemmsg::String=
-    """
-      Your name: $name
-      Your sex: Female
-      Your role: You are a helpful assistant.
-      You should follow the following guidelines:
-      - Focus on the latest conversation.
-      - Your like to be short and concise.
-
-      Let's begin!
-      """,
-  )
-
-  tools = Dict( # update input format
-      "CHAT_BOX"=> Dict(
-        "description" => "- CHAT_BOX which you can use to talk with the user. The input is your intentions for the dialogue. Be specific.",
-      ),
-    )
-
-  """ Memory
-    Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
-    NO "system" message in chathistory because I want to add it at the inference time
-    chathistory= [
-      Dict("name"=>"user", "text"=> "Wassup!", "timestamp"=> Dates.now()),
-      Dict("name"=>"assistant", "text"=> "Hi I'm your assistant.", "timestamp"=> Dates.now()),
-    ]
-  """
-  memory = Dict{String, Any}(
-            "events"=> Vector{Dict{String, Any}}(),
-            "state"=> Dict{String, Any}(), # state of the agent
-            "recap"=> OrderedDict{String, Any}(),  # recap summary of the conversation
-          )
-
-  newAgent = companion(
-    name,
-    id,
-    systemmsg,
-    tools,
-    maxHistoryMsg,
-    chathistory,
-    memory,
-    context,
-    llmFormatName
-    )
-  
-  return newAgent
-end
-
-
 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 +75,12 @@ 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"
@@ -205,17 +141,14 @@ function sommelier(
   memory = Dict{String, Any}(
             "shortmem"=> OrderedDict{String, Any}(),
             "scratchpad"=> "",
-            "events"=> Vector{Dict{String, Any}}(),
-            "state"=> Dict{String, Any}(
-            ),
             "recap"=> OrderedDict{String, Any}(),
-            
           )
 
   newAgent = sommelier(
     name,
     id,
     retailername,
+    retailerid,
     tools,
     maxHistoryMsg,
     chathistory,
@@ -223,10 +156,9 @@ 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 +170,8 @@ 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 +179,46 @@ 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 YAML format as described below
-      "plan": "...",
+      plan: "..."
-      "action_name": "...",
+      action_name: "..."
-      "action_input": "..."
+      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 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(

src/util.jl

@@ -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"

test/runtests.jl

@@ -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>"
         """
         ),
       ]

test/test1.jl

@@ -2,19 +2,18 @@ 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"], 
+          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"])
+          fileserver_url=config["externalservice"]["fileserver"]["url"])
 
     reply = NATS.request(agent_conn, 
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
                         msg_envelope_json_str, timeout=120)
 
     incoming_env_json_str = String(reply.payload)
@@ -24,19 +23,23 @@ function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any}
     return llm_response
   end
 
-#TESTING get text embedding from a LLM service
+  """ get a single text embedding from a LLM service
+  Example
+  text = ["hello"]
+  embedding = get_embedding(text)
+  """
   function get_embedding(text::AbstractArray{String})
     documents_dict = Dict("documents" => text)
     payloads = [("documents", documents_dict, "dictionary")]
     _, msg_envelope_json_str = msghandler.smartpack(
-    config["externalService"]["servicesloadbalancer"]["nats"], 
+      config["externalservice"]["servicesloadbalancer"]["nats"], 
       payloads;   
       msg_purpose="embedding",  
       broker_url=config["nats_server_info"]["url"],
-    fileserver_url=config["externalService"]["fileserver"]["url"])
+      fileserver_url=config["externalservice"]["fileserver"]["url"])
 
     reply = NATS.request(agent_conn, 
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
+                        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)
@@ -45,44 +48,61 @@ function get_embedding(text::AbstractArray{String})
     return embedding_response
   end
 
-#TESTING
+  """ sql = "SELECT * FROM wine;"
-function execute_sql_winedb(config::JSON.Object, sql::T) where {T<:AbstractString}
+  result = execute_sql_winedb(sql)
+  """
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
     host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
     port = parse(Int, _port)
     dbname = "winedb"
     user = config["externalservice"]["sommpanion_db"]["user"]
     password = config["externalservice"]["sommpanion_db"]["password"]
     db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = nothing
+    try
       result = LibPQ.execute(db_connection, sql)  
+    catch e
+      LibPQ.close(db_connection)
+    end
+    
     LibPQ.close(db_connection)
     return result
   end
 
-#TESTING
+  """ find similar sql from vector database
-function similar_sql_vectordb(query; maxdistance::Integer=100)
+  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(query, tablename, 
+    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]  
-  # 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())")
+      println("\n--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    return (dict=output_str, distance=distance)
+      pprintln(output_str)
+      return (result=output_str, distance=distance)
     else
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    return (dict=nothing, distance=nothing)
+      return (result=nothing, distance=nothing)
     end
   end
 
-#TESTING
+  """ insert query and sql into vector database
-function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
+  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]
@@ -91,40 +111,49 @@ function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Integer=3) where {
     row, col = size(df)
     distance = row == 0 ? Inf : df[1, :distance]
     if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    _query_embedding = get_embedding([query])[1]
+      _query_embedding = 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 = """
+      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("\n--| added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
       # println(sql)
       _ = execute_sql_vectordb(sql)
     end
   end
 
-#TESTING
+  """ 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["SQLVectorDB"]["url"], ':')
+    host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
     port = parse(Int, _port)
-  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+    dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
-  user = config[:externalservice][:SQLVectorDB][:user]
+    user = config["externalservice"]["sommpanion_vectordb"]["user"]
-  password = config[:externalservice][:SQLVectorDB][:password]
+    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
-  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)
@@ -132,24 +161,29 @@ function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
     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__)
+      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__)
+      println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
       return nothing
     end
   end
 
-#TESTING
+  """ 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])[1]
+    _embedding = get_embedding([text])
-  embedding = _embedding["data"][1]["embedding"]
+    _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
@@ -158,10 +192,12 @@ function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColum
           """
     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"
@@ -182,20 +218,20 @@ function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::
       """
       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("\n--| added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
       println(sql)
       _ = execute_sql_vectordb(sql)
     else
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+      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
@@ -215,27 +251,29 @@ agent = YiemAgent.sommelier(
     agent_context;
     name="Janie",
     id=sessionId,  # agent instance id
-  retailername="Yiem",
+    retailername="Yiem Wine Ltd.",
     llmFormatName=""
   )
 
 
-
-
-# 1. Read local file and encode to base64 string
 image1_path = "test/large_image.png"
 image1_bytes = read(image1_path)
 image1_base64_string = base64encode(image1_bytes)
-
-# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
 mime_type = "image/png" 
 data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
 
+# 1. Read local file and encode to base64 string
+image2_path = "test/small_image.png"
+image2_bytes = read(image2_path)
+image2_base64_string = base64encode(image2_bytes)
+mime_type = "image/png" 
+data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
+
 # 3. Construct payload with the Data URI
-usermsg = Dict{String, Any}(
+message = Dict(
     "role" => "user",
     "content" => [
-      Dict("type" => "text", "text" => "รู้จักไวน์ที่อยู่ในรูปมั้ย"),
+      Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
       Dict(
         "type" => "image_url",
         "image_url" => Dict("url" => data1_uri)
@@ -243,8 +281,80 @@ usermsg = Dict{String, Any}(
     ]
   )
 
-result = YiemAgent.conversation(agent; userinput=usermsg)
+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")
+
+
+
+
+
+
+
+
+
+
+
+
+
+