update

2025-07-23 18:31:38 +07:00 · 2025-06-15 08:59:10 +07:00 · 2025-06-03 10:08:17 +07:00 · 2025-05-18 17:21:55 +07:00 · 2025-05-17 21:36:40 +07:00 · 2025-05-14 21:21:30 +07:00
23 changed files with 1511 additions and 4733 deletions
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,8 +1,8 @@
 # This file is machine-generated - editing it directly is not advised

-julia_version = "1.11.2"
+julia_version = "1.11.4"
 manifest_format = "2.0"
-project_hash = "6e88822413ea4a623cd914d84de127dc6c57fceb"
+project_hash = "9e0d7dca51b949f2ffa5477b895b90988ec62529"

 [[deps.AliasTables]]
 deps = ["PtrArrays", "Random"]
@@ -120,9 +120,9 @@ version = "1.11.0"

 [[deps.Distributions]]
 deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "3101c32aab536e7a27b1763c0797dba151b899ad"
+git-tree-sha1 = "03aa5d44647eaec98e1920635cdfed5d5560a8b9"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.113"
+version = "0.25.117"

    [deps.Distributions.extensions]
    DistributionsChainRulesCoreExt = "ChainRulesCore"
@@ -200,11 +200,9 @@ version = "1.11.0"

 [[deps.GeneralUtils]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "JSON3", "MQTTClient", "PrettyPrinting", "Random", "SHA", "UUIDs"]
-git-tree-sha1 = "978d9a5c3fc30205dd72d4a2a2ed4fa85ebee5cf"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/GeneralUtils"
+path = "../GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.1.0"
+version = "0.2.3"

 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "PrecompileTools", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"]
@@ -214,9 +212,9 @@ version = "1.10.13"

 [[deps.HypergeometricFunctions]]
 deps = ["LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"]
-git-tree-sha1 = "b1c2585431c382e3fe5805874bda6aea90a95de9"
+git-tree-sha1 = "2bd56245074fab4015b9174f24ceba8293209053"
 uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
-version = "0.3.25"
+version = "0.3.27"

 [[deps.ICU_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -305,12 +303,10 @@ uuid = "b39eb1a6-c29a-53d7-8c32-632cd16f18da"
 version = "1.19.3+0"

 [[deps.LLMMCTS]]
-deps = ["GeneralUtils", "JSON3"]
-git-tree-sha1 = "c8ad9715e78bbd19f5ac79e1f1cacf85f141449d"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/LLMMCTS"
+deps = ["GeneralUtils", "JSON3", "PrettyPrinting"]
+path = "../LLMMCTS"
 uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
-version = "0.1.2"
+version = "0.1.4"

 [[deps.LaTeXStrings]]
 git-tree-sha1 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c"
@@ -475,7 +471,7 @@ version = "0.3.27+1"
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
-version = "0.8.1+2"
+version = "0.8.1+4"

 [[deps.OpenSSL]]
 deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"]
@@ -493,7 +489,7 @@ version = "3.0.15+1"
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
 git-tree-sha1 = "13652491f6856acfd2db29360e1bbcd4565d04f1"
 uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
-version = "0.5.5+0"
+version = "0.5.5+2"

 [[deps.OrderedCollections]]
 git-tree-sha1 = "12f1439c4f986bb868acda6ea33ebc78e19b95ad"
@@ -502,9 +498,9 @@ version = "1.7.0"

 [[deps.PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65"
+git-tree-sha1 = "966b85253e959ea89c53a9abebbf2e964fbf593b"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.31"
+version = "0.11.32"

 [[deps.Parsers]]
 deps = ["Dates", "PrecompileTools", "UUIDs"]
@@ -556,15 +552,15 @@ uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 version = "1.11.0"

 [[deps.PtrArrays]]
-git-tree-sha1 = "77a42d78b6a92df47ab37e177b2deac405e1c88f"
+git-tree-sha1 = "1d36ef11a9aaf1e8b74dacc6a731dd1de8fd493d"
 uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d"
-version = "1.2.1"
+version = "1.3.0"

 [[deps.QuadGK]]
 deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "cda3b045cf9ef07a08ad46731f5a3165e56cf3da"
+git-tree-sha1 = "9da16da70037ba9d701192e27befedefb91ec284"
 uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.11.1"
+version = "2.11.2"

    [deps.QuadGK.extensions]
    QuadGKEnzymeExt = "Enzyme"
@@ -664,9 +660,9 @@ version = "1.11.0"

 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
-git-tree-sha1 = "2f5d4697f21388cbe1ff299430dd169ef97d7e14"
+git-tree-sha1 = "64cca0c26b4f31ba18f13f6c12af7c85f478cfde"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "2.4.0"
+version = "2.5.0"

    [deps.SpecialFunctions.extensions]
    SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"
--- a/Project.toml
+++ b/Project.toml
@@ -1,7 +1,7 @@
 name = "SQLLLM"
 uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.2.1"
+version = "0.2.4"

 [deps]
 CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
@@ -23,5 +23,4 @@ URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

 [compat]
-GeneralUtils = "0.1.0"
-LLMMCTS = "0.1.2"
+Dates = "1.11.0"
--- a/previousVersion/0.1/Manifest.toml
+++ b/previousVersion/0.1/Manifest.toml
@@ -1,879 +0,0 @@
-# This file is machine-generated - editing it directly is not advised
-
-julia_version = "1.11.0"
-manifest_format = "2.0"
-project_hash = "dbd62da0dcca1a1b2302848e770ef42c10a9d0d8"
-
-[[deps.AliasTables]]
-deps = ["PtrArrays", "Random"]
-git-tree-sha1 = "9876e1e164b144ca45e9e3198d0b689cadfed9ff"
-uuid = "66dad0bd-aa9a-41b7-9441-69ab47430ed8"
-version = "1.1.3"
-
-[[deps.ArgTools]]
-uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
-version = "1.1.2"
-
-[[deps.Artifacts]]
-uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
-version = "1.11.0"
-
-[[deps.Base64]]
-uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
-version = "1.11.0"
-
-[[deps.BitFlags]]
-git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d"
-uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35"
-version = "0.1.9"
-
-[[deps.CEnum]]
-git-tree-sha1 = "389ad5c84de1ae7cf0e28e381131c98ea87d54fc"
-uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
-version = "0.5.0"
-
-[[deps.CSV]]
-deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"]
-git-tree-sha1 = "6c834533dc1fabd820c1db03c839bf97e45a3fab"
-uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-version = "0.10.14"
-
-[[deps.CodeTracking]]
-deps = ["InteractiveUtils", "UUIDs"]
-git-tree-sha1 = "7eee164f122511d3e4e1ebadb7956939ea7e1c77"
-uuid = "da1fd8a2-8d9e-5ec2-8556-3022fb5608a2"
-version = "1.3.6"
-
-[[deps.CodecZlib]]
-deps = ["TranscodingStreams", "Zlib_jll"]
-git-tree-sha1 = "bce6804e5e6044c6daab27bb533d1295e4a2e759"
-uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
-version = "0.7.6"
-
-[[deps.Compat]]
-deps = ["TOML", "UUIDs"]
-git-tree-sha1 = "8ae8d32e09f0dcf42a36b90d4e17f5dd2e4c4215"
-uuid = "34da2185-b29b-5c13-b0c7-acf172513d20"
-version = "4.16.0"
-weakdeps = ["Dates", "LinearAlgebra"]
-
-    [deps.Compat.extensions]
-    CompatLinearAlgebraExt = "LinearAlgebra"
-
-[[deps.CompilerSupportLibraries_jll]]
-deps = ["Artifacts", "Libdl"]
-uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.1.1+0"
-
-[[deps.ConcurrentUtilities]]
-deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "ea32b83ca4fefa1768dc84e504cc0a94fb1ab8d1"
-uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.4.2"
-
-[[deps.CondaPkg]]
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-
-[[deps.Crayons]]
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-
-[[deps.DBInterface]]
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-
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-
-[[deps.DataFrames]]
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-git-tree-sha1 = "fb61b4812c49343d7ef0b533ba982c46021938a6"
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-
-[[deps.DataStructures]]
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-
-[[deps.Distributions]]
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-
-    [deps.Distributions.extensions]
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-    DistributionsDensityInterfaceExt = "DensityInterface"
-    DistributionsTestExt = "Test"
-
-    [deps.Distributions.weakdeps]
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-[[deps.StructTypes]]
-deps = ["Dates", "UUIDs"]
-git-tree-sha1 = "159331b30e94d7b11379037feeb9b690950cace8"
-uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
-version = "1.11.0"
-
-[[deps.StyledStrings]]
-uuid = "f489334b-da3d-4c2e-b8f0-e476e12c162b"
-version = "1.11.0"
-
-[[deps.SuiteSparse]]
-deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
-uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
-
-[[deps.SuiteSparse_jll]]
-deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
-uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
-version = "7.7.0+0"
-
-[[deps.TOML]]
-deps = ["Dates"]
-uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
-version = "1.0.3"
-
-[[deps.TZJData]]
-deps = ["Artifacts"]
-git-tree-sha1 = "36b40607bf2bf856828690e097e1c799623b0602"
-uuid = "dc5dba14-91b3-4cab-a142-028a31da12f7"
-version = "1.3.0+2024b"
-
-[[deps.TableTraits]]
-deps = ["IteratorInterfaceExtensions"]
-git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
-uuid = "3783bdb8-4a98-5b6b-af9a-565f29a5fe9c"
-version = "1.0.1"
-
-[[deps.Tables]]
-deps = ["DataAPI", "DataValueInterfaces", "IteratorInterfaceExtensions", "OrderedCollections", "TableTraits"]
-git-tree-sha1 = "598cd7c1f68d1e205689b1c2fe65a9f85846f297"
-uuid = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-version = "1.12.0"
-
-[[deps.Tar]]
-deps = ["ArgTools", "SHA"]
-uuid = "a4e569a6-e804-4fa4-b0f3-eef7a1d5b13e"
-version = "1.10.0"
-
-[[deps.Test]]
-deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
-uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
-version = "1.11.0"
-
-[[deps.TimeZones]]
-deps = ["Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"]
-git-tree-sha1 = "8323074bc977aa85cf5ad71099a83ac75b0ac107"
-uuid = "f269a46b-ccf7-5d73-abea-4c690281aa53"
-version = "1.18.1"
-weakdeps = ["RecipesBase"]
-
-    [deps.TimeZones.extensions]
-    TimeZonesRecipesBaseExt = "RecipesBase"
-
-[[deps.TranscodingStreams]]
-git-tree-sha1 = "0c45878dcfdcfa8480052b6ab162cdd138781742"
-uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
-version = "0.11.3"
-
-[[deps.URIs]]
-git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
-uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-version = "1.5.1"
-
-[[deps.UTCDateTimes]]
-deps = ["Dates", "TimeZones"]
-git-tree-sha1 = "4af3552bf0cf4a071bf3d14bd20023ea70f31b62"
-uuid = "0f7cfa37-7abf-4834-b969-a8aa512401c2"
-version = "1.6.1"
-
-[[deps.UUIDs]]
-deps = ["Random", "SHA"]
-uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-version = "1.11.0"
-
-[[deps.Unicode]]
-uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5"
-version = "1.11.0"
-
-[[deps.UnsafePointers]]
-git-tree-sha1 = "c81331b3b2e60a982be57c046ec91f599ede674a"
-uuid = "e17b2a0c-0bdf-430a-bd0c-3a23cae4ff39"
-version = "1.0.0"
-
-[[deps.WeakRefStrings]]
-deps = ["DataAPI", "InlineStrings", "Parsers"]
-git-tree-sha1 = "b1be2855ed9ed8eac54e5caff2afcdb442d52c23"
-uuid = "ea10d353-3f73-51f8-a26c-33c1cb351aa5"
-version = "1.4.2"
-
-[[deps.WorkerUtilities]]
-git-tree-sha1 = "cd1659ba0d57b71a464a29e64dbc67cfe83d54e7"
-uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
-version = "1.6.1"
-
-[[deps.Zlib_jll]]
-deps = ["Libdl"]
-uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
-version = "1.2.13+1"
-
-[[deps.Zstd_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "555d1076590a6cc2fdee2ef1469451f872d8b41b"
-uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
-version = "1.5.6+1"
-
-[[deps.libblastrampoline_jll]]
-deps = ["Artifacts", "Libdl"]
-uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
-version = "5.11.0+0"
-
-[[deps.micromamba_jll]]
-deps = ["Artifacts", "JLLWrappers", "LazyArtifacts", "Libdl"]
-git-tree-sha1 = "b4a5a3943078f9fd11ae0b5ab1bdbf7718617945"
-uuid = "f8abcde7-e9b7-5caa-b8af-a437887ae8e4"
-version = "1.5.8+0"
-
-[[deps.nghttp2_jll]]
-deps = ["Artifacts", "Libdl"]
-uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
-version = "1.59.0+0"
-
-[[deps.p7zip_jll]]
-deps = ["Artifacts", "Libdl"]
-uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
-version = "17.4.0+2"
--- a/previousVersion/0.1/Project.toml
+++ b/previousVersion/0.1/Project.toml
@@ -1,26 +0,0 @@
-name = "SQLLLM"
-uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
-authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.1.0"
-
-[deps]
-CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
-CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
-DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
-DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
-Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
-FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
-GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
-JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
-LLMMCTS = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
-LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
-MQTTClient = "985f35cc-2c3d-4943-b8c1-f0931d5f0959"
-PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
-PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
-Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
-Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
-StatsBase = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
-URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
-UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
--- a/previousVersion/0.1/config.json
+++ b/previousVersion/0.1/config.json
@@ -1,56 +0,0 @@
-{
-  "mqttServerInfo": {
-      "description": "mqtt server info",
-      "port": 1883,
-      "broker": "mqtt.yiem.cc"
-  },
-  "testingOrProduction": {
-      "value": "testing",
-      "description": "agent status, couldbe testing or production"
-  },
-  "agentid": {
-      "value": "2b74b87a-5413-4fe2-a4d3-405891051680",
-      "description": "a unique id for this agent"
-  },
-  "agentCentralConfigTopic": {
-      "mqtttopic": "/yiem_branch_1/agent/sommelier/backend/config/api/v1.1",
-      "description": "a central agent server's topic to get this agent config"
-  },
-  "servicetopic": {
-      "mqtttopic": [
-          "/yiem/branch_1/agent/wine/backend/prompt/api_v1/testing"
-      ],
-      "description": "a topic this agent are waiting for service request"
-  },
-  "role": {
-      "value": "sommelier",
-      "description": "agent role"
-  },
-  "organization": {
-      "value": "yiem_branch_1",
-      "description": "organization name"
-  },
-  "externalservice": {
-      "text2textinstruct": {
-          "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      },
-      "virtualWineCustomer_1": {
-          "mqtttopic": "/virtualenvironment/winecustomer",
-          "description": "text to text service with instruct LLM that act as wine customer",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      },
-      "text2textchat": {
-          "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
-          "llminfo": {
-              "name": "llama3instruct"
-          }
-      }
-  }
-}
--- a/previousVersion/0.1/src/SQLLLM.jl
+++ b/previousVersion/0.1/src/SQLLLM.jl
@@ -1,103 +0,0 @@
-module SQLLLM
-
-  # export
-
-
-  """ Order by dependencies of each file. The 1st included file must not depend on any other
-  files and each file can only depend on the file included before it. 
-  """
-
-  include("type.jl")
-  using .type
-
-  include("util.jl")
-  using .util
-
-  include("llmfunction.jl")
-  using .llmfunction
-
-  include("interface.jl")
-  using .interface
-  
-  
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-end # module SQLLLM
--- a/previousVersion/0.1/src/interface.jl
+++ b/previousVersion/0.1/src/interface.jl
--- a/previousVersion/0.1/src/llmfunction.jl
+++ b/previousVersion/0.1/src/llmfunction.jl
@@ -1,955 +0,0 @@
-module llmfunction
-
-export listAllTable_json, listAllTable_str, tableinfo, getdata, finalAnswerBox,
-  getTableNameFromSQL, extractContent_dataframe, SQLexecution
-
-using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, LibPQ, Tables, DataFrames, CSV,
-  DataStructures, StatsBase
-using GeneralUtils, LLMMCTS
-using ..util
-
-# ---------------------------------------------- 100 --------------------------------------------- #
-
-
-""" List all tables in the database and return in JSON format.
-
-# Arguments
-  - `executeSQL::Function`
-    A connection object to Postgres database
-    
-# Return
-  - `NamedTuple{(:result, :success), Tuple{DataFrame, Bool}}`
-
-# Example
-```jldoctest
-julia> using LibPQ, SQLLLM
-julia> function executeSQL(sql)
-          DBconnection = LibPQ.Connection("host=192.168.88.122 port=5432 dbname=xyz user=zyx password=1234")
-          result = LibPQ.execute(DBconnection, sql)
-          close(DBconnection)
-          return result
-        end
-julia> response = SQLLLM.listAllTable_json(executeSQL)
-julia> result = response[:result]
-```
-
-# Signature
-"""
-function listAllTable_json(executeSQL::Function
-)::NamedTuple{(:result, :success),Tuple{DataFrame,Bool}}
-
-  sql = """
-        SELECT 
-            table_name,
-            obj_description(relfilenode, 'pg_class') AS table_comment,
-            string_agg(column_name || ' (' || data_type || ')', ', ') AS columns
-        FROM 
-            information_schema.columns
-        JOIN 
-            pg_class ON table_name = relname
-        WHERE 
-            table_schema = 'public'
-        GROUP BY 
-            table_name, relfilenode
-        ORDER BY 
-            table_name;
-        """
-
-  result = executeSQL(sql)
-  df = DataFrame(result)
-  tablesinfo_df = df
-
-  return (result=tablesinfo_df, success=true)
-end
-
-
-function listAllTable_str(executeSQL::Function
-)::NamedTuple{(:result, :success),Tuple{String,Bool}}
-  sql = """
-        SELECT 
-            table_name,
-            obj_description(relfilenode, 'pg_class') AS table_comment,
-            string_agg(column_name || ' (' || data_type || ')', ', ') AS columns
-        FROM 
-            information_schema.columns
-        JOIN 
-            pg_class ON table_name = relname
-        WHERE 
-            table_schema = 'public'
-        GROUP BY 
-            table_name, relfilenode
-        ORDER BY 
-            table_name;
-        """
-  result = executeSQL(sql)
-  df = DataFrame(result)
-  tableinfo = "Here are a list of available tables in the database (each row is in this format: table name; table comment; table columns): \n"
-  for i in 1:size(df)[1]
-    table_name = df[i, 1]
-    table_comment = df[i, 2]
-    columns = df[i, 3]
-    tableinfo *= "$i. $table_name; $table_comment; $columns\n"
-  end
-  return (result=tableinfo, success=true)
-end
-
-
-""" Get table description, column comments and the first 3-rows of the table data
-
-# Arguments
-  - `executeSQL::Function`
-    A connection object to Postgres database
-    
-# Return
-  - `tableinfo::String`
-
-# Signature
-"""
-
-
-function tableinfo_str(executeSQL::Function, tablename::String)::NamedTuple{(:result, :success),Tuple{String,Bool}}
-
-  sql = """
-        SELECT 
-          column_name, 
-          data_type, 
-          col_description(format('%s.%s', table_schema, table_name)::regclass::oid, ordinal_position) AS column_comment
-        FROM 
-          information_schema.columns
-        WHERE 
-          table_name = '$tablename'
-          AND table_schema = 'public';
-        """
-
-  result = executeSQL(sql)
-  df = DataFrame(result)
-
-  tableinfo = "Here are info of table $tablename (each row is in this format: column name; data type; column comment):\n"
-  for i in 1:size(df)[1]
-    column_name = df[i, 1]
-    column_datatype = df[i, 2]
-    column_comment = df[i, 3]
-    tableinfo *= "$i. $column_name; $column_datatype; $column_comment \n"
-  end
-
-  return (result=tableinfo, success=true)
-end
-
-
-""" Get table description, column comments.
-
-# Arguments
-  - `executeSQL::Function`
-    A connection object to Postgres database
-  - `tablenames<:AbstractVector`
-    A list of table name to get description
-    
-# Return
-  - `NamedTuple{(:result), Tuple{String}}`
-    Text contain multiple table info 
-
-# Example
-```jldoctest
-julia> using SQLLLM, LibPQ
-julia> function executeSQL(sql)
-          DBconnection = LibPQ.Connection("host=192.168.88.122 port=5432 dbname=xyz user=zyx password=1234")
-          result = LibPQ.execute(DBconnection, sql)
-          close(DBconnection)
-          return result
-        end
-julia> response = SQLLLM.tableinfo(executeSQL, ["wine", "food"])
-julia> result = response[:result]
-```
-
-# Signature
-"""
-function tableinfo(executeSQL::Function, tablenames::T
-)::NamedTuple{(:result,),Tuple{String}} where {T<:AbstractVector}
-  # list all tables in a database
-  sql = """
-        SELECT pg_namespace.nspname AS schema_name,
-              relname AS table_name,
-              pg_catalog.obj_description(pg_class.oid) AS comment
-        FROM pg_class
-        INNER JOIN pg_namespace ON pg_namespace.oid = pg_class.relnamespace
-        WHERE pg_namespace.nspname = 'public' -- Replace 'public' with your desired schema
-          AND pg_class.relkind IN ('r', 't');
-        """
-
-  _result = executeSQL(sql)
-  df = DataFrame(_result)
-  alltable_df = df[:, [:table_name, :comment]]
-  tableNameList = alltable_df.table_name |> collect
-
-  # check if the requested table name exist in the database
-  notExistingTable = []
-  for i in tablenames
-    if i ∉ tableNameList
-      push!(notExistingTable, i)
-    end
-  end
-  if !isempty(notExistingTable)
-    result = "Error, the following tables does not exist in the database: $(JSON3.write(notExistingTable))"
-    return (result=result,)
-  end
-
-  tableInfoStr = ""
-  for i in tablenames
-    x, _ = tableinfo_str(executeSQL, i)
-    tableInfoStr *= x
-  end
-
-  return (result=tableInfoStr,)
-end
-
-
-
-
-""" Convert a query process in English into SQL, execute and get the result from the database.
-
-# Arguments
-  - `query<:AbstractString`
-    A query to a database in SQL.
-  - `context::Union{Dict, Nothing}`
-    A context to be available at transition()
-  - `executeSQL::Function`
-    A connection object connected to the database
-  - `text2textInstructLLM::Function`
-    A function that handles communication to LLM service.
-
-# Return
-  - `NamedTuple{(:result, :errormsg, success), Tuple{String, String, Bool}}`
-  
-# TODO
-  - [x] getdata directly using sql execute
-
-# Signature
-"""
-function getdata(query::T, context::Union{Dict,Nothing}, executeSQL::Function,
-  text2textInstructLLM::Function;
-) where {T<:AbstractString}
-
-  response = SQLexecution(executeSQL, query)
-  if response[:success]
-    extracted = extractContent_dataframe(response[:result], context, text2textInstructLLM)
-    response_ = (result=extracted, errormsg=nothing, success=true)
-    return response_
-  else
-    response_ = (result=nothing, errormsg=response[:errormsg], success=false)
-    return response_
-  end
-end
-# function getdata(query::T, context::Union{Dict, Nothing}, executeSQL::Function,
-#   text2textInstructLLM::Function;
-#   )::NamedTuple{(:result, :errormsg, :success), Tuple{String, Union{String, Nothing}, Bool}} where {T<:AbstractString}
-
-#   # get table info here because it'll be called only 1-time. If this function is in 
-#   # getdata_decisionMaker(), it'll be called everytime
-#   mentionedtable = getTableNameFromSQL(query, text2textInstructLLM)
-#   mentionedTableInfo = tableinfo(executeSQL, mentionedtable)[:result]
-#   context[:mentionedTableInfo] = mentionedTableInfo
-
-#   initialstate = Dict{Symbol, Any}(
-#     :reward=> 0,
-#     :isterminal=> false,
-#     :evaluation=> nothing,
-#     :errormsg=> nothing,
-#     :errorexplain=> nothing,
-
-#     :question=> query,
-#     :code=> nothing,
-#     :response=> nothing,
-#     )
-
-#   transitionargs = (
-#                     # decisionMaker=getdata_decisionMaker, 
-#                     # evaluator=getdata_evaluator, 
-#                     # reflector=getdata_reflector,
-#                     context=context,
-#                     executeSQL=executeSQL, 
-#                     text2textInstructLLM=text2textInstructLLM
-#                   )
-#   result_1, result_2 = LLMMCTS.runMCTS(initialstate, getdata_transition, transitionargs;
-#                           totalsample=1, maxdepth=3, maxiterations=1, explorationweight=1.0)
-
-#   if result_2[:isterminal] == true
-#     return (result=result_2[:response], errormsg=nothing, success=true) # succues=true to finish getdata()
-#   else
-#     # return (response="Failed to act with the following error message: $(result_2[:errorexplain])", select=nothing, reward=0, success=false)
-#     return (result="Failed to get the data. $(result_1[:errormsg])", 
-#           errormsg=result_1[:errormsg], success=false)
-#   end
-# end
-
-
-"""
-
-# Arguments
-  `v::Integer`
-    dummy variable
-    
-# Return
-
-# Example
-```jldoctest
-julia> 
-```
-
-# TODO
-  - [] update docstring
-  - [PENDING] implement the function
-
-# Signature
-"""
-function getdata_evaluator(newstate, config)
-
-  return (evaluation="None", score=0)
-end
-
-
-""" State transition
-
-# Arguments
-  - `state<:AbstractDict`
-    A game state
-  - `args::NamedTuple`
-    Arguments for various function within transition()
-    
-# Return
-  - `NamedTuple{(:newNodeKey, :newstate, :progressvalue), Tuple{String, T, Integer}}`
-
-# Signature
-"""
-function getdata_transition(state::T, args::NamedTuple
-)::NamedTuple{(:newNodeKey, :newstate, :progressvalue),Tuple{String,T,Integer}} where {T<:AbstractDict}
-
-
-  # decisionMaker::Function = args[:decisionMaker]
-  # evaluator::Function = args[:evaluator]
-  # reflector::Function = args[:reflector]
-  context = args[:context]
-  executeSQL::Function = args[:executeSQL]
-  text2textInstructLLM::Function = args[:text2textInstructLLM]
-
-  thought, sql =
-    if state[:code] !== nothing
-      result = getdata_decisionMaker(state, context, text2textInstructLLM)
-      result[:thought], result[:code]
-    else
-      nothing, state[:question]
-    end
-
-  # make new state
-  newNodeKey = GeneralUtils.uuid4snakecase()
-  newstate = deepcopy(state)
-
-  response, success, errormsg, reward, isterminal =
-    if sql !== nothing
-      response, success, errormsg, reward, isterminal = SQLexecution(executeSQL, sql)
-    else
-      (result=nothing,
-        success=false,
-        errormsg="SQL execution failed. An unexpected error occurred. Please try again.",
-        reward=0,
-        isterminal=false)
-    end
-  println("getdata_transition() 1 ", @__FILE__, " ", @__LINE__)
-  newstate[:code] = sql
-  newstate[:response] = response
-  newstate[:errorexplain] = thought
-  newstate[:errormsg] = errormsg
-  newstate[:reward] = reward
-  newstate[:isterminal] = isterminal
-  if response !== nothing
-    extracted = extractContent_dataframe(response, context, text2textInstructLLM)
-    newstate[:response] = extracted
-  end
-  println("getdata_transition() 2 ", @__FILE__, " ", @__LINE__)
-  stateevaluation = "None"
-  progressvalue = 0
-
-  return (newNodeKey=newNodeKey, newstate=newstate, progressvalue=progressvalue)
-end
-
-
-""" Make a decision using LLM
-
-# Arguments
-  - `state::Dict`
-    A game state
-  - `context::Dict`
-    Additional context for LLM to use
-  - `text2textInstructLLM::Function`
-    A function to handles communication to LLM
-    
-# Return
-  - `NamedTuple{(:thought, :code, :success, :errormsg), Tuple{String, String, Bool, Union{String, Nothing}}}`
-
-# Signature
-"""
-function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM::Function
-)::NamedTuple{(:thought, :code, :success, :errormsg),Tuple{Union{String,Nothing},Union{String,Nothing},Bool,Union{String,Nothing}}}
-
-  Hints = "None"
-
-  # """
-  # Here are some useful SQL programs:
-  # $usefulSQL
-  # """
-
-  # systemmsg = 
-  # """
-  # You are an assistant helping the user to execute SQL code from the user's query.
-
-  # At each round of conversation, the user will give you:
-  # Context: ...
-  # User intention: ...
-  # Code executed from the last round: ...
-  # Execution error: execution error of the last round code.
-
-  # You should consider the following guidelines:
-  # - Text information in the database is sometimes stored in lower case. If your search returns empty, try using lower case to search.
-
-  # You should then respond to the user with:
-  # - thought: Why the code does not complete the task. What does the execution error imply exactly?
-  # - plan: Step-by-step instructions of how to complete the task.
-  #     1) Focus on improving the code from the last round.
-  #     2) Do not create any table in the database.
-  # - code:
-  #     1) Write new improved code.
-  #     2) Do not wrap the code and no comment as it will be executed directly without any modification against the database.
-
-  # You should only respond in format as described below and nothing more:
-  # thought: ...
-  # plan: 
-  #   1) ...
-  #   2) ... 
-  #   ...
-  # code: ...
-
-  # Let's begin!
-  # """
-
-  systemmsg = """
-              You are an assistant helping the user to execute SQL code from the user's query.
-
-              At each round of conversation, the user will give you:
-              Context: ...
-              User intention: ...
-              Code executed from the last round: ...
-              Execution error: execution error of the last round code.
-
-              You should consider the following guidelines:
-              - Text information in the database is sometimes stored in lower case. If your search returns empty, try using lower case to search.
-
-              You should then respond to the user with:
-              1) Understanding: 
-                    - State your understanding about the current situation.
-              2) Reasoning: 
-                  - State your step by step reasoning about the current situation.
-              3) Plan: Step-by-step instructions of how to complete the task.
-                  - Focus on improving the code from the last round.
-                  - Do not create any table in the database.
-              4) Code:
-                  - Write new improved code.
-                  - Do not wrap the code and no comment as it will be executed directly without any modification against the database.
-
-              You should only respond in format as described below and nothing more:
-              Understanding: ...
-              Reasoning: ...
-              Plan: 
-                1) ...
-                2) ... 
-                ...
-              Code: ...
-
-              Let's begin!
-              """
-
-  noise = ""
-  note_flag = ""
-  for attempt in 1:10
-    usermsg = """
-              Context: 
-              $(context[:mentionedTableInfo])
-              User intention: $(context[:userintention])
-              Code executed from the last round: $(state[:code])
-              Execution error: $(state[:errormsg])
-              $noise
-              $note_flag
-              """
-
-    _prompt =
-      [
-        Dict(:name => "system", :text => systemmsg),
-        Dict(:name => "user", :text => usermsg)
-      ]
-
-    # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
-    try
-      response = text2textInstructLLM(prompt)
-      responsedict = GeneralUtils.textToDict(response,
-        ["Understanding", "Reasoning", "Plan", "Code"];
-        rightmarker=":", symbolkey=true, lowercasekey=true)
-      _code = responsedict[:code]
-      code = strip(_code)
-
-      if length(code) < 2
-        error("No code available.")
-      elseif code == state[:code]
-        error("generated code is the same as earlier.")
-      else
-      end
-
-      # check code
-      if occursin("CREATE TABLE", code)
-        note_flag = "Note: Create new table is not allowed."
-        error("create table is not allowed")
-      elseif occursin("```", code)
-        error("Note: code contains backtick ` which is not allowed")
-      elseif code[end] != ';'
-        error("SQL does not ending with ';'")
-      elseif count(';', code) > 1
-        error("Multiple SQL statement are not allowed")
-      else
-      end
-
-      println("\n~~~ getdata_decisionMaker() ", @__FILE__, " ", @__LINE__)
-      pprintln(Dict(responsedict))
-      return (thought=responsedict[:reasoning], code=code, success=true, errormsg=nothing)
-    catch e
-      io = IOBuffer()
-      showerror(io, e)
-      errorMsg = String(take!(io))
-      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      print("Attempt $attempt. Error occurred: $errorMsg\n$st")
-      println("")
-      noise = GeneralUtils.randstrings(3, 5)
-    end
-  end
-  return (thought=nothing, code=nothing, success=false,
-    errormsg="Failed to generate SQL after numerous attempts.")
-end
-
-""" Execute a given SQL.
-
-# Arguments
-  - `sql::T<:AbstractString`
-    A SQL command
-  - `executeSQL::Function`
-    A connection object to a database
-    
-# Return
-  - `NamedTuple{(:result, :errormsg, :reward, :isterminal), Tuple{Union{Nothing, DataFrame}, String, Integer, Bool}}`
-
-# Example
-```jldoctest
-julia> using LibPQ, SQLLLM
-julia> function executeSQL(sql)
-          DBconnection = LibPQ.Connection("host=192.168.88.122 port=5432 dbname=xyz user=zyx password=1234")
-          result = LibPQ.execute(DBconnection, sql)
-          close(DBconnection)
-          return result
-        end
-julia> response = SQLLLM.SQLexecution(executeSQL, sql)
-```
-
-# Signature
-"""
-# function SQLexecution(executeSQL::Function, sql::T
-#   )::NamedTuple{(:result, :success, :errormsg, :reward, :isterminal), Tuple{Union{DataFrame, Nothing}, Bool, Union{String, Nothing}, Integer, Bool}} where {T<:AbstractString}
-#   println("\n~~~ 1-01 ", @__FILE__, " ", @__LINE__) 
-#   #XXX dummy SQL. use for testing
-#   # sql = "SELECT w.wine_name FROM wine w JOIN wine_food wf ON w.wine_id = wf.wine_id JOIN food f ON wf.food_id = f.food_id WHERE f.\"food_name\" = 'lamb';"
-#   # sql = " SELECT      w.wine_name FROM      wine w JOIN      food f ON f.food_name = 'lamb' JOIN      wine_food wf ON w.wine_id = wf.wine_id AND f.food_id = wf.food_id GROUP BY      w.wine_name ORDER BY      COUNT(DISTINCT w.wine_id) DESC;"
-#   # sql = " SELECT COUNT(DISTINCT wf.wine_id) FROM wine w JOIN wine_food wf ON w.wine_id = wf.wine_id JOIN food f ON wf.food_id = f.food_id WHERE f.food_name ILIKE '%lamb%'"
-
-#   #XXX use for package testing, remove when done
-#   # ans = "1.schilfwein zweigelt 2.cabernet sauvignon reserve limited edition"
-#   # ans = "There are 1500 wines that can be paired with lamb."
-#   # ans = "1500"
-#   # return (response=ans, errormsg=nothing, reward=1, isterminal=true)
-
-#   # add LIMIT to the SQL to prevent loading large data
-#   sql = strip(sql)
-#   println("\n~~~ SQL 1", @__FILE__, " ", @__LINE__)
-#   println(sql)
-#   println("\n~~~ 1-02 ", @__FILE__, " ", @__LINE__) 
-
-#   if sql[end] != ';'
-#     errorMsg = "Error, SQL execution failed because it does not ended with ';'"
-#     return (result=nothing, success=false, errormsg=errorMsg, reward=0, isterminal=false)
-#   end
-#   println("\n~~~ 1-03 ", @__FILE__, " ", @__LINE__) 
-#   if !occursin("LIMIT", sql)
-#     # sql = sql[1:end-1] * " LIMIT 100;"
-#     sql = sql[1:end-1] * " ORDER BY RANDOM() LIMIT 2;"
-#   end
-
-#   println("\n~~~ SQL 2", @__FILE__, " ", @__LINE__)
-#   println(sql)
-#   println("\n~~~ 1-1 ", @__FILE__, " ", @__LINE__) 
-#   result = executeSQL(sql)
-#   println("\n~~~ 1-2 ", @__FILE__, " ", @__LINE__) 
-#   df = DataFrame(result)
-#   println("\n~~~ raw df ", df)
-#   tablesize = size(df)
-#   println("\n~~~ df size ", tablesize)
-#   println("\n~~~ 6 ", @__FILE__, " ", @__LINE__) 
-#   row = tablesize[1]
-#   println("\n~~~ 7 ", @__FILE__, " ", @__LINE__) 
-#   if row == 0  # if 0 row
-#     errorMsg = "The resulting table has 0 row. Possible causes: 1) SQL is incorrect 2) There is no data that match your search criteria."
-#     return (result=nothing, success=false, errormsg=errorMsg, reward=0, isterminal=false)
-#   end
-#   println("\n~~~ 8 ", @__FILE__, " ", @__LINE__) 
-#   df1 =   
-#     if row > 2
-#       # ramdom row to pick
-#       df[sample(1:nrow(df), 2, replace=false), :] # random select 2 rows from df
-#     else
-#       df
-#     end
-
-#   println("\n~~~ SQLexecution result ", @__FILE__, " ", @__LINE__)   
-#   println(df1)
-#   return (result=df1, success=true, errormsg=nothing, reward=1, isterminal=true)
-# end
-function SQLexecution(executeSQL::Function, sql::T
-) where {T<:AbstractString}
-
-  try
-    #XXX dummy SQL. use for testing
-    # sql = "SELECT w.wine_name FROM wine w JOIN wine_food wf ON w.wine_id = wf.wine_id JOIN food f ON wf.food_id = f.food_id WHERE f.\"food_name\" = 'lamb';"
-    # sql = " SELECT      w.wine_name FROM      wine w JOIN      food f ON f.food_name = 'lamb' JOIN      wine_food wf ON w.wine_id = wf.wine_id AND f.food_id = wf.food_id GROUP BY      w.wine_name ORDER BY      COUNT(DISTINCT w.wine_id) DESC;"
-    # sql = " SELECT COUNT(DISTINCT wf.wine_id) FROM wine w JOIN wine_food wf ON w.wine_id = wf.wine_id JOIN food f ON wf.food_id = f.food_id WHERE f.food_name ILIKE '%lamb%'"
-
-    #XXX use for package testing, remove when done
-    # ans = "1.schilfwein zweigelt 2.cabernet sauvignon reserve limited edition"
-    # ans = "There are 1500 wines that can be paired with lamb."
-    # ans = "1500"
-    # return (response=ans, errormsg=nothing, reward=1, isterminal=true)
-
-    # add LIMIT to the SQL to prevent loading large data
-    sql = strip(sql)
-    if sql[end] == ';'
-      if !occursin("LIMIT", sql)
-        # sql = sql[1:end-1] * " LIMIT 100;"
-        sql = sql[1:end-1] * " ORDER BY RANDOM() LIMIT 2;"
-      end
-    else
-      sql = sql * ";"
-    end
-    println("\n~~~ SQLexecution() SQL: ", @__FILE__, " ", @__LINE__)
-    println(sql)
-
-    result = executeSQL(sql)
-    df = DataFrame(result)
-
-    tablesize = size(df)
-    row, column = tablesize
-    if row == 0  # if 0 row
-      error("The resulting table has 0 row. Possible causes: 1) You might be searching in the wrong place 2) There could be a typo in your search query.")
-    elseif column > 30
-      error("SQL execution failed. An unexpected error occurred. Please try again.")
-    end
-
-    df1 =
-      if row > 2
-        # ramdom row to pick
-        df[sample(1:nrow(df), 2, replace=false), :] # random select 2 rows from df
-      else
-        df
-      end
-
-    println("\n~~~ SQLexecution() result: ", @__FILE__, " ", @__LINE__)
-    println(df1)
-    return (result=df1, success=true, errormsg=nothing)
-  catch e
-    io = IOBuffer()
-    showerror(io, e)
-    errorMsg = String(take!(io))
-    st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-    println(errorMsg)
-    response = (result=nothing, success=false, errormsg=errorMsg)
-    return response
-  end
-end
-
-
-""" Extract content from a dataframe with LLM.
-
-# Arguments
-  - `df::DataFrame`
-    A dataframe to be read.
-  - `context::Dict`
-    A dictionary to give LLM more context
-  - `text2textInstructLLM::Function`
-    A function that handles communication to LLM service
-    
-# Return
-  - `result::String`
-
-# Signature
-"""
-function extractContent_dataframe(df::DataFrame, context::Dict, text2textInstructLLM::Function
-)::String
-  tablesize = size(df)
-  row = tablesize[1]
-  column = tablesize[2]
-  #[PENDING] Since selected column  depend on the question, there should be a better way to select column on the fly, not hard coded like this.
-  # df1 =
-  #   if column > 10  # assuming if columns > 10, agent is getting wine info but the info is too much
-  #     selectedcolumn = ["wine_id",
-  #       "wine_name",
-  #       "winery",
-  #       "region",
-  #       "country",
-  #       "wine_type",
-  #       "grape",
-  #       "serving_temperature",
-  #       "intensity",
-  #       "sweetness",
-  #       "tannin",
-  #       "acidity",
-  #       "fizziness",
-  #       "tasting_notes"]
-  #     df1 = df[:, selectedcolumn]
-  #   else
-  #     df
-  #   end
-
-  df1 = df
-
-  dfstr = dfToString(df1)
-
-  systemmsg = """
-              You are an assistant that readouts the resulting table after the user executing SQL command.
-
-              At each round of conversation, the user will give you:
-              - User intention: ...
-              - Resulting table dimension: ...
-              - Resulting table: The resulting table after executing the user's intention.
-
-              You should then respond to the user with:
-              - About_resulting_table:
-                  1) What is the resulting table represent?
-              - Search_summary:
-                  1) Summarize the table's content based on the user intension in verbal English.
-                    Here are some example:
-                    Bad example (you are not Summarize the table content): there are 2 columns in the table i.e. "cash" and "number".
-                  2) Do not generate additional text.
-
-              You should only respond in format as described below:
-              About_resulting_table: ...
-              Search_summary: ...
-
-              Let's begin!
-              """
-  usermsg = """
-            User intention: $(context[:userintention])
-            Resulting table: $dfstr
-            """
-  _prompt =
-    [
-      Dict(:name => "system", :text => systemmsg),
-      Dict(:name => "user", :text => usermsg)
-    ]
-
-  # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-  prompt *= """
-            <|start_header_id|>assistant<|end_header_id|>
-            """
-  for i in 1:5
-    response = text2textInstructLLM(prompt)
-    responsedict = GeneralUtils.textToDict(response, ["About_resulting_table", "Search_summary"],
-      rightmarker=":", symbolkey=true)
-
-    # result = dfstr
-    result = """
-             Summary: $(responsedict[:Search_summary]) 
-             More details: $dfstr
-             """
-
-    if row > 2
-      result *= "There are many more rows, but they are truncated because there are too many of them."
-    end
-
-    println("\n~~~ extractContent_dataframe() ", @__FILE__, " ", @__LINE__)
-    println(result)
-
-    return result
-  end
-  error("Failed to get Code part.")
-end
-
-
-""" Extract a database's table name that mentioned in SQL
-
-# Arguments
-  - `sql<:AbstractString`
-    SQL command
-  - `text2textInstructLLM::Function`
-    A function that handles communication to LLM service
-    
-# Return
-  - `tablename::Vector{String}`
-    A list of table name
-
-# Example
-```jldoctest
-julia> using SQLLLM, UUIDs, GeneralUtils
-julia> sql = "Get all rows from the \"food\" table where the description contains the word \"lamb\". Then, join this result with the \"wine_food\" table on the \"food_id\" column to get a list of wines that can be paired with lamb. Finally, group the result by the \"wine_id\" column and count the number of unique wines."
-julia> function text2textInstructLLM(prompt::String)
-    config = Dict(
-      :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")
-        ),
-      )
-    )
-
-    # apply LLM specific instruct format
-    externalService = config[:externalservice][:text2textinstruct]
-
-    msgMeta = GeneralUtils.generate_msgMeta(
-      externalService[:mqtttopic],
-      senderName= "SQLLLM",
-      senderId= string(uuid4()),
-      receiverName= "text2textinstruct",
-      mqttBroker= config[:mqttServerInfo][:broker],
-      mqttBrokerPort= config[:mqttServerInfo][:port],
-    )
-
-    outgoingMsg = Dict(
-      :msgMeta=> msgMeta,
-      :payload=> Dict(
-        :text=> prompt,
-        :kwargs=> Dict(
-          :max_tokens=> 512,
-          :stop=> ["<|eot_id|>"],
-          :temperature=> 0.2,
-          )
-        )
-      )
-
-    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-    response = _response[:response][:text]
-
-    return response
-  end
-julia> result = SQLLLM.getTableNameFromSQL(sql, text2textInstructLLM)
-```
-
-# Signature
-"""
-function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{String} where {T<:AbstractString}
-  systemmsg = """
-              Extract table name out of the user query.
-
-              At each round of conversation, the user will give you:
-              Query: ...
-
-              You should then respond to the user with:
-              - table_name: a list of table name that the user mentioned in the query. 
-                  For example, ["color", "type"]
-
-              You must only respond in format as described below:
-              table_name: ["...", "...", ...]
-
-              Let's begin!
-              """
-
-  usermsg = """
-            Query: $sql
-            """
-
-  _prompt =
-    [
-      Dict(:name => "system", :text => systemmsg),
-      Dict(:name => "user", :text => usermsg)
-    ]
-
-  # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-  prompt *= """
-            <|start_header_id|>assistant<|end_header_id|>
-            """
-
-  for attempt in 1:5
-    try
-      response = text2textInstructLLM(prompt)
-      responsedict = GeneralUtils.textToDict(response,
-        ["table_name"],
-        rightmarker=":", symbolkey=true)
-      response = copy(JSON3.read(responsedict[:table_name]))
-
-      return response
-    catch e
-      io = IOBuffer()
-      showerror(io, e)
-      errorMsg = String(take!(io))
-      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      println("")
-      println("Attempt $attempt. Error occurred: $errorMsg\n$st")
-      println("")
-    end
-  end
-  error("getTableNameFromSQL failed to generate a thought")
-end
-
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-end # module llmfunction
--- a/previousVersion/0.1/src/type.jl
+++ b/previousVersion/0.1/src/type.jl
@@ -1,81 +0,0 @@
-module type
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-end # module type
--- a/previousVersion/0.1/src/util.jl
+++ b/previousVersion/0.1/src/util.jl
@@ -1,116 +0,0 @@
-module util
-
-export getDataFrameValue, dfRowtoString, dfToString
-
-using DataFrames
-
-
-""" get a value from a dataframe row by a given key
-"""
-getDataFrameValue(row::DataFrameRow, key::Symbol) = row.:($key)
-
-
-""" convert df row into key:value string
-"""
-function dfRowtoString(row::DataFrameRow)::String
-  str = ""
-  for key in keys(row)
-    value = getDataFrameValue(row, key)
-    str *= "$key: $value, "
-  end
-  result = str[1:end-2] # remove ", " at the end of row
-  return result
-end
-
-
-""" convert df table into string
-"""
-function dfToString(df::DataFrame)
-  dfstr = ""
-  for (i, row) in enumerate(eachrow(df))
-    rowstr = dfRowtoString(row)
-    dfstr *= "$i) $rowstr\n"
-  end
-  return dfstr
-end
-
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--- a/previousVersion/0.1/test/test_1.jl
+++ b/previousVersion/0.1/test/test_1.jl
@@ -1,88 +0,0 @@
-using Revise
-using LibPQ, JSON3, PrettyPrinting, UUIDs, DataFrames, DataStructures, Dates, MQTTClient, Random
-using SQLLLM, GeneralUtils
-
-
-function executeSQL(sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=5433 dbname=SQLVectorDB user=yiemtechnologies@gmail.com password=yiem@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-
-sql = 
-"""
-CREATE TABLE items (id bigserial PRIMARY KEY, embedding vector(3));
-"""
-
-result = executeSQL(sql)
-
-
-
-sql = 
-"""
-INSERT INTO items (embedding) VALUES ('[[1,2,3], [1,2,3], [1,2,3]]'), ('[4,5,6]');
-"""
-result = executeSQL(sql)
-
-
-sql = 
-"""
-SELECT * FROM items ORDER BY embedding <-> '[3,1,2]' LIMIT 1;
-"""
-result = executeSQL(sql)
-df = DataFrame(result)
-
-
-
-
-
-
-config = copy(JSON3.read("config.json"))
-
-msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose= "embedding",
-    senderName= "yiemagent",
-    senderId= string(uuid4()),
-    receiverName= "text2textinstruct",
-    mqttBrokerAddress= "mqtt.yiem.cc",
-    mqttBrokerPort= 1883,
-  )
-
-text = ["hello world"]
-
-outgoingMsg = Dict(
-  :msgMeta=> msgMeta,
-  :payload=> Dict(
-    :text=> text,
-    :kwargs=> Dict(
-      :max_tokens=> 2048,
-      :stop=> ["<|eot_id|>"],
-      :temperature=> 0.2,
-      )
-    )
-  )
-
-
-
-response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
--- a/previousVersion/0.1/test/test_2.jl
+++ b/previousVersion/0.1/test/test_2.jl
@@ -1,23 +0,0 @@
-using Revise
-
-function testf(a)::NamedTuple{(:a, :b), Tuple{Union{Nothing, Int}, Int}}
-  if a == 1
-    return (a=nothing, b=5)
-  else
-    return (a=5, b=5)
-  end
-end
-
-
-q = testf(1)
-w = testf(2)
-
-
-
-
-
-
-
-
-
-
--- a/previousVersion/0.1/testfile.csv
+++ b/previousVersion/0.1/testfile.csv
@@ -1,8 +0,0 @@
-table_name,comment
-customer,"The customer table stores information about customers. It includes details such as first name, last name, display name, username, password, gender, country, telephone number, email, birthdate, additional_search_term, other attributes (in JSON format) and a description."
-wine,"The wine table stores information about different wines. It includes details namely id, name, brand, manufacturer, region, country, wine_type, grape_variety, serving_temperature, intensity, sweetness, tannin, acidity, fizziness, additional_search_term, other attributes (in JSON format) and a description."
-wine_food,"The wine_food table represents the association between wines and food items. It establishes a many-to-many relationship, allowing us to link specific wines with various food items."
-food,"The food table represents various food items. It stores information related to food names, country of origin, taste attributes (spiciness, sweetness, sourness, savoriness, and bitterness), serving temperature, additional_search_term, other attributes (in JSON format) and a description."
-retailer,"The retailer table stores information about different retailers. It includes details related to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description."
-retailer_wine,"The retailer_wine table represents the relationship between retailers and wines. It stores information about the wines available from which retailers, including vintage, their price, and the currency."
-retailer_food,"The retailer_food table represents the relationship between retailers and food items. It stores information about the food items available from which retailers, including their price and the currency."
--- a/src/interface.jl
+++ b/src/interface.jl
--- a/src/llmfunction.jl
+++ b/src/llmfunction.jl
@@ -1,10 +1,10 @@
 module llmfunction

 export listAllTable_json, listAllTable_str, tableinfo, getdata, finalAnswerBox,
-  getTableNameFromSQL, extractContent_dataframe, SQLexecution
+  getTableNameFromSQL, extractContent_dataframe, SQLexecution, compareState

 using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, LibPQ, Tables, DataFrames, CSV,
-  DataStructures, StatsBase
+  DataStructures, StatsBase, Dates
 using GeneralUtils, LLMMCTS
 using ..util

@@ -36,7 +36,7 @@ julia> result = response[:result]
 # Signature
 """
 function listAllTable_json(executeSQL::Function
-)::NamedTuple{(:result, :success),Tuple{DataFrame,Bool}}
+  )::NamedTuple{(:result, :success),Tuple{DataFrame,Bool}}

  sql = """
        SELECT 
@@ -347,84 +347,42 @@ end

 # Signature
 """
-function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM::Function
-)::NamedTuple{(:thought, :code, :success, :errormsg),Tuple{Union{String,Nothing},Union{String,Nothing},Bool,Union{String,Nothing}}}
+function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM::Function,
+  llmFormatName::String
+  )::NamedTuple{(:thought, :code, :success, :errormsg),Tuple{Union{String,Nothing},Union{String,Nothing},Bool,Union{String,Nothing}}}

  Hints = "None"

-  # """
-  # Here are some useful SQL programs:
-  # $usefulSQL
-  # """
+  systemmsg = 
+  """
+  You are an assistant helping the user to execute SQL code from the user's query.

-  # systemmsg = 
-  # """
-  # You are an assistant helping the user to execute SQL code from the user's query.
+  At each round of conversation, the user will give you:
+  Context: ...
+  User intention: ...
+  Code executed from the last round: ...
+  Execution error: execution error of the last round code.

-  # At each round of conversation, the user will give you:
-  # Context: ...
-  # User intention: ...
-  # Code executed from the last round: ...
-  # Execution error: execution error of the last round code.
+  You should consider the following guidelines:
+  - Text information in the database is sometimes stored in lower case. If your search returns empty, try using lower case to search.

-  # You should consider the following guidelines:
-  # - Text information in the database is sometimes stored in lower case. If your search returns empty, try using lower case to search.
+  You should then respond to the user with:
+  1) Plan: Step-by-step instructions of how to complete the task.
+      - Focus on improving the code from the last round.
+      - Do not create any table in the database.
+  2) Code:
+      - Write new improved code.
+      - Do not wrap the code and no comment as it will be executed directly without any modification against the database.

-  # You should then respond to the user with:
-  # - thought: Why the code does not complete the task. What does the execution error imply exactly?
-  # - plan: Step-by-step instructions of how to complete the task.
-  #     1) Focus on improving the code from the last round.
-  #     2) Do not create any table in the database.
-  # - code:
-  #     1) Write new improved code.
-  #     2) Do not wrap the code and no comment as it will be executed directly without any modification against the database.
+  You should only respond in format as described below and nothing more:
+  Plan: 
+    1) ...
+    2) ... 
+    ...
+  Code: ...

-  # You should only respond in format as described below and nothing more:
-  # thought: ...
-  # plan: 
-  #   1) ...
-  #   2) ... 
-  #   ...
-  # code: ...
-
-  # Let's begin!
-  # """
-
-  systemmsg = """
-              You are an assistant helping the user to execute SQL code from the user's query.
-
-              At each round of conversation, the user will give you:
-              Context: ...
-              User intention: ...
-              Code executed from the last round: ...
-              Execution error: execution error of the last round code.
-
-              You should consider the following guidelines:
-              - Text information in the database is sometimes stored in lower case. If your search returns empty, try using lower case to search.
-
-              You should then respond to the user with:
-              1) Understanding: 
-                    - State your understanding about the current situation.
-              2) Reasoning: 
-                  - State your step by step reasoning about the current situation.
-              3) Plan: Step-by-step instructions of how to complete the task.
-                  - Focus on improving the code from the last round.
-                  - Do not create any table in the database.
-              4) Code:
-                  - Write new improved code.
-                  - Do not wrap the code and no comment as it will be executed directly without any modification against the database.
-
-              You should only respond in format as described below and nothing more:
-              Understanding: ...
-              Reasoning: ...
-              Plan: 
-                1) ...
-                2) ... 
-                ...
-              Code: ...
-
-              Let's begin!
-              """
+  Let's begin!
+  """

  noise = ""
  note_flag = ""
@@ -446,15 +404,17 @@ function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM:
      ]

    # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-    prompt *= """
-              <|start_header_id|>assistant<|end_header_id|>
-              """
+    prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
    try
-      response = text2textInstructLLM(prompt)
-      responsedict = GeneralUtils.textToDict(response,
-        ["Understanding", "Reasoning", "Plan", "Code"];
-        rightmarker=":", symbolkey=true, lowercasekey=true)
+      response = text2textInstructLLM(prompt, modelsize="medium")
+      response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
+      think, response = GeneralUtils.extractthink(response)
+      
+      header = ["Plan:", "Code:"]
+      dictkey = ["plan", "code"]
+
+      responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
      _code = responsedict[:code]
      code = strip(_code)

@@ -480,7 +440,7 @@ function getdata_decisionMaker(state::Dict, context::Dict, text2textInstructLLM:

      println("\n~~~ getdata_decisionMaker() ", @__FILE__, " ", @__LINE__)
      pprintln(Dict(responsedict))
-      return (thought=responsedict[:reasoning], code=code, success=true, errormsg=nothing)
+      return (thought=responsedict[:comprehension], code=code, success=true, errormsg=nothing)
    catch e
      io = IOBuffer()
      showerror(io, e)
@@ -520,65 +480,6 @@ julia> response = SQLLLM.SQLexecution(executeSQL, sql)

 # Signature
 """
-# function SQLexecution(executeSQL::Function, sql::T
-#   )::NamedTuple{(:result, :success, :errormsg, :reward, :isterminal), Tuple{Union{DataFrame, Nothing}, Bool, Union{String, Nothing}, Integer, Bool}} where {T<:AbstractString}
-#   println("\n~~~ 1-01 ", @__FILE__, " ", @__LINE__) 
-#   #XXX dummy SQL. use for testing
-#   # sql = "SELECT w.wine_name FROM wine w JOIN wine_food wf ON w.wine_id = wf.wine_id JOIN food f ON wf.food_id = f.food_id WHERE f.\"food_name\" = 'lamb';"
-#   # sql = " SELECT      w.wine_name FROM      wine w JOIN      food f ON f.food_name = 'lamb' JOIN      wine_food wf ON w.wine_id = wf.wine_id AND f.food_id = wf.food_id GROUP BY      w.wine_name ORDER BY      COUNT(DISTINCT w.wine_id) DESC;"
-#   # sql = " SELECT COUNT(DISTINCT wf.wine_id) FROM wine w JOIN wine_food wf ON w.wine_id = wf.wine_id JOIN food f ON wf.food_id = f.food_id WHERE f.food_name ILIKE '%lamb%'"
-
-#   #XXX use for package testing, remove when done
-#   # ans = "1.schilfwein zweigelt 2.cabernet sauvignon reserve limited edition"
-#   # ans = "There are 1500 wines that can be paired with lamb."
-#   # ans = "1500"
-#   # return (response=ans, errormsg=nothing, reward=1, isterminal=true)
-
-#   # add LIMIT to the SQL to prevent loading large data
-#   sql = strip(sql)
-#   println("\n~~~ SQL 1", @__FILE__, " ", @__LINE__)
-#   println(sql)
-#   println("\n~~~ 1-02 ", @__FILE__, " ", @__LINE__) 
-
-#   if sql[end] != ';'
-#     errorMsg = "Error, SQL execution failed because it does not ended with ';'"
-#     return (result=nothing, success=false, errormsg=errorMsg, reward=0, isterminal=false)
-#   end
-#   println("\n~~~ 1-03 ", @__FILE__, " ", @__LINE__) 
-#   if !occursin("LIMIT", sql)
-#     # sql = sql[1:end-1] * " LIMIT 100;"
-#     sql = sql[1:end-1] * " ORDER BY RANDOM() LIMIT 2;"
-#   end
-
-#   println("\n~~~ SQL 2", @__FILE__, " ", @__LINE__)
-#   println(sql)
-#   println("\n~~~ 1-1 ", @__FILE__, " ", @__LINE__) 
-#   result = executeSQL(sql)
-#   println("\n~~~ 1-2 ", @__FILE__, " ", @__LINE__) 
-#   df = DataFrame(result)
-#   println("\n~~~ raw df ", df)
-#   tablesize = size(df)
-#   println("\n~~~ df size ", tablesize)
-#   println("\n~~~ 6 ", @__FILE__, " ", @__LINE__) 
-#   row = tablesize[1]
-#   println("\n~~~ 7 ", @__FILE__, " ", @__LINE__) 
-#   if row == 0  # if 0 row
-#     errorMsg = "The resulting table has 0 row. Possible causes: 1) SQL is incorrect 2) There is no data that match your search criteria."
-#     return (result=nothing, success=false, errormsg=errorMsg, reward=0, isterminal=false)
-#   end
-#   println("\n~~~ 8 ", @__FILE__, " ", @__LINE__) 
-#   df1 =   
-#     if row > 2
-#       # ramdom row to pick
-#       df[sample(1:nrow(df), 2, replace=false), :] # random select 2 rows from df
-#     else
-#       df
-#     end
-
-#   println("\n~~~ SQLexecution result ", @__FILE__, " ", @__LINE__)   
-#   println(df1)
-#   return (result=df1, success=true, errormsg=nothing, reward=1, isterminal=true)
-# end
 function SQLexecution(executeSQL::Function, sql::T
 ) where {T<:AbstractString}

@@ -596,9 +497,12 @@ function SQLexecution(executeSQL::Function, sql::T

    # add LIMIT to the SQL to prevent loading large data
    sql = strip(sql)
+
+    # remove DISTINCT keyword because it is incompatible with RANDOM()
+    sql = replace(sql, "DISTINCT" => "")  
+
    if sql[end] == ';'
      if !occursin("LIMIT", sql)
-        # sql = sql[1:end-1] * " LIMIT 100;"
        sql = sql[1:end-1] * " ORDER BY RANDOM() LIMIT 2;"
      end
    else
@@ -612,10 +516,10 @@ function SQLexecution(executeSQL::Function, sql::T

    tablesize = size(df)
    row, column = tablesize
-    if row == 0  # if 0 row
-      error("The resulting table has 0 row. Possible causes: 1) Your search criteria might be too specific. Relaxing some conditions could yield better results. Remember, you can always refine your search later. 2) There could be a typo in your search query. 3) You might be searching in the wrong place.")
+    if row == 0
+      error("\nThe resulting table has 0 row. Please try again.")
    elseif column > 30
-      error("SQL execution failed. An unexpected error occurred. Please try again.")
+      error("\nSQL execution failed. An unexpected error occurred. Please try again.")
    end

    df1 =
@@ -656,8 +560,9 @@ end

 # Signature
 """
-function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function
-)::String
+function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function, action::String,
+  llmFormatName::String
+  )::String
  tablesize = size(df)
  row = tablesize[1]
  column = tablesize[2]
@@ -687,32 +592,35 @@ function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function

  dfstr = GeneralUtils.dfToString(df1)

-  systemmsg = """
-              You are an assistant that readouts the resulting table after the user executing SQL command.
+  systemmsg = 
+  """
+  You are an assistant that readouts the resulting table after the user executing SQL command.

-              At each round of conversation, the user will give you:
-              - User intention: ...
-              - Resulting table dimension: ...
-              - Resulting table: The resulting table after executing the user's intention.
+  At each round of conversation, the user will give you:
+  - User SQL: the SQL query user executed.
+  - Resulting table: The resulting table after executing the user's intention.

-              You should then respond to the user with:
-              - About_resulting_table:
-                  1) What is the resulting table represent?
-              - Search_summary:
-                  1) Summarize the table's content based on the user intension in verbal English.
-                    Here are some example:
-                    Bad example (you are not Summarize the table content): there are 2 columns in the table i.e. "cash" and "number".
-                  2) Do not generate additional text.
+  You should then respond to the user with:
+  - About_resulting_table:
+      1) What is the resulting table represent?
+  - Search_summary:
+      1) Summarize the table's content based on the user intension in verbal English.
+        Here are some example:
+        Bad example (you are not Summarize the table content): there are 2 columns in the table i.e. "cash" and "number".
+      2) Do not generate additional text.

-              You should only respond in format as described below:
-              About_resulting_table: ...
-              Search_summary: ...
+  You should only respond in format as described below:
+  About_resulting_table: ...
+  Search_summary: ...

-              Let's begin!
-              """
-  usermsg = """
-            Resulting table: $dfstr
-            """
+  Let's begin!
+  """
+  
+  usermsg = 
+  """
+  User SQL: $action
+  Resulting table: $dfstr
+  """
  _prompt =
    [
      Dict(:name => "system", :text => systemmsg),
@@ -720,20 +628,37 @@ function extractContent_dataframe(df::DataFrame, text2textInstructLLM::Function
    ]

  # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-  prompt *= """
-            <|start_header_id|>assistant<|end_header_id|>
-            """
+  prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
+  header = ["About_resulting_table:", "Search_summary:"]
+  dictkey = ["about_resulting_table", "search_summary"]
+
  for i in 1:5
-    response = text2textInstructLLM(prompt)
-    responsedict = GeneralUtils.textToDict(response, ["About_resulting_table", "Search_summary"],
-      rightmarker=":", symbolkey=true)
+    response = text2textInstructLLM(prompt, modelsize="medium")
+    response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
+    think, response = GeneralUtils.extractthink(response)
+
+    # check whether response has all header
+    detected_kw = GeneralUtils.detectKeywordVariation(header, response)
+    missingkeys = [k for (k, v) in detected_kw if v === nothing]
+    if !isempty(missingkeys)
+      errornote = "$missingkeys are missing from your previous response"
+      println("\nERROR SQLLLM extractContent_dataframe() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    elseif sum([length(i) for i in values(detected_kw)]) > length(header)
+      errornote = "\nYour previous attempt has duplicated points according to the required response format"
+      println("\nERROR SQLLLM extractContent_dataframe() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)

    # result = dfstr
-    result = """
-             Summary: $(responsedict[:Search_summary]) 
-             More details: $dfstr
-             """
+    result = 
+    """
+    Summary: $(responsedict[:search_summary]) 
+    More details: $dfstr
+    """

    if row > 2
      result *= "There are many more rows, but they are truncated because there are too many of them."
@@ -814,7 +739,9 @@ julia> result = SQLLLM.getTableNameFromSQL(sql, text2textInstructLLM)

 # Signature
 """
-function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{String} where {T<:AbstractString}
+function getTableNameFromSQL(sql::T, text2textInstructLLM::Function,
+  llmFormatName::String
+  )::Vector{String} where {T<:AbstractString}
  systemmsg = """
              Extract table name out of the user query.

@@ -822,11 +749,11 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
              Query: ...

              You should then respond to the user with:
-              - table_name: a list of table name that the user mentioned in the query. 
+              - Table_name: a list of table name that the user mentioned in the query. 
                  For example, ["color", "type"]

              You must only respond in format as described below:
-              table_name: ["...", "...", ...]
+              Table_name: ["...", "...", ...]

              Let's begin!
              """
@@ -842,17 +769,16 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
    ]

  # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-  prompt *= """
-            <|start_header_id|>assistant<|end_header_id|>
-            """
+  prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
+  header = ["Table_name:"]
+  dictkey = ["table_name"]

  for attempt in 1:5
    try
-      response = text2textInstructLLM(prompt)
-      responsedict = GeneralUtils.textToDict(response,
-        ["table_name"],
-        rightmarker=":", symbolkey=true)
+      response = text2textInstructLLM(prompt, modelsize="medium")
+      response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
+      responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
      response = copy(JSON3.read(responsedict[:table_name]))

      return response
@@ -870,6 +796,174 @@ function getTableNameFromSQL(sql::T, text2textInstructLLM::Function)::Vector{Str
 end


+""" Compare multiple solution attempts and select the most accurate one.
+
+This function evaluates multiple solution attempts for a given question and determines which attempt 
+provides the most accurate and relevant response. It uses an LLM to analyze and compare the attempts,
+considering their actions and observations.
+
+# Arguments
+- `question::String`
+  The original question or task that was attempted to be solved
+- `highValueStateList::Vector{Dict}`
+  List of states containing different solution attempts and their results
+- `text2textInstructLLM::Function`
+  A function that handles communication to LLM service
+
+# Returns
+- `Integer`
+  The index of the selected best response (1-based indexing)
+
+# Example
+```jldoctest
+julia> 
+```
+
+# Notes
+- The function makes up to 10 attempts to get a valid response from the LLM
+- Each state in highValueStateList should contain a thoughtHistory with action_input and observation
+- The LLM evaluates attempts based on accuracy and relevance to the original question
+"""
+function compareState(question::String, highValueStateList::Vector{T},
+  text2textInstructLLM::Function, llmFormatName::String
+  )::Integer where {T<:AbstractDict}
+
+  systemmsg = 
+  """
+    Your profile:
+    - You are a helpful assistant
+    Situation:
+    - The user has made multiple attempts to solve the question, resulting in various answers
+    Your mission:
+    - Identify and select the most accurate and relevant response from these multiple results for the user
+    At each round of conversation, you will be given the following:
+      Question: the question the user is trying to answer
+      Attempt: the user's attempted actions and their corresponding results
+    You should then respond to the user with the following:
+      Comparison: detailed comparison of all results from all attempts from various aspects.
+      Rationale: a brief explanation of why the selected response is the most accurate and relevant
+      Selected_response_number: the number the selected response in the list of results (e.g., 1, 2, 3, ...)
+    You should only respond in format as described below:
+      Comparison: ...
+      Rationale: ...
+      Selected_response_number: ...
+    Here are some examples:
+      User's question: "How many German wines do you have?"
+      Attempt 1)
+      Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany'
+      Result: 100 wines
+      Attempt 2)
+      Action: SELECT COUNT(*) FROM wines WHERE country = 'Germany' AND type = 'Red'
+      Result: 50 red wines
+      Comparison: The second attempt counts only German red wines while the first attempt includes all German wines.
+      Rationale: The user is asking for the number of German wines without specifying a type, so the most accurate response is the first attempt because it includes all German wines.
+      Selected_response_number:1
+
+    Let's begin!
+  """
+
+  potentialSolution = []
+  keys = [:action_input, :observation]
+  # extract the last action_name, action_input, observation of each state in highValueStateList and store them in a dictionary then push into potentialSolution
+  for state in highValueStateList
+    thoughtHistory = state[:thoughtHistory]
+    _, currentstate_latestIndice = 
+      GeneralUtils.findHighestIndexKey(thoughtHistory, keys[1])
+    latestKeys = makekey.(keys, currentstate_latestIndice)
+    d = Dict()
+    # get the last action_name, action_input, observation of currentstate
+    for (i,v) in enumerate(keys)
+      d[v] = thoughtHistory[latestKeys[i]]
+    end
+    push!(potentialSolution, d)
+  end
+
+  """
+  # put potential solutions from potentialSolution into the following form
+  Attempt 1)
+    action_name: 
+    action_input:
+    observation:
+  Attempt 2)
+    action_name:` 
+    action_input:
+    observation:`
+  ...
+  """
+  potentialSolutionStr = ""
+  for (i, state) in enumerate(potentialSolution)
+    potentialSolutionStr *= "Attempt $i)\n"
+    for k in keys
+      potentialSolutionStr *= "$k: $(state[k])\n"
+      println("")
+    end
+  end
+
+  errornote = "N/A"
+
+  for attempt in 1:10
+    errorFlag = false
+
+    usermsg =
+    """
+    Question: $question
+    Attempts: $potentialSolutionStr
+    P.S. $errornote
+    """
+
+    _prompt = 
+    [
+      Dict(:name=> "system", :text=> systemmsg),
+      Dict(:name=> "user", :text=> usermsg)
+    ]
+
+    # put in model format
+    prompt = GeneralUtils.formatLLMtext(_prompt, llmFormatName)
+
+    header = ["Comparison:", "Rationale:", "Selected_response_number:"]
+    dictkey = ["comparison", "rationale", "selected_response_number"]
+
+    response = text2textInstructLLM(prompt, modelsize="medium")
+
+    # sometime LLM output something like **Comprehension**: which is not expected
+    response = replace(response, "**"=>"")
+    response = replace(response, "***"=>"")
+    response = GeneralUtils.deFormatLLMtext(response, llmFormatName)
+    think, response = GeneralUtils.extractthink(response)
+
+    # check whether response has all header
+    detected_kw = GeneralUtils.detectKeywordVariation(header, response)
+    missingkeys = [k for (k, v) in detected_kw if v === nothing]
+    if !isempty(missingkeys)
+      errornote = "$missingkeys are missing from your previous response"
+      println("\nERROR SQLLLM extractContent_dataframe() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    elseif sum([length(i) for i in values(detected_kw)]) > length(header)
+      errornote = "\nYour previous attempt has duplicated points according to the required response format"
+      println("\nERROR SQLLLM extractContent_dataframe() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    responsedict = GeneralUtils.textToDict(response, header; dictKey=dictkey, symbolkey=true)
+
+    responsedict[:selected_response_number] = responsedict[:selected_response_number][1]  # some time "6\nThe trajectories are incomplete" is generated but I only need the number.
+    try
+      responsedict[:selected_response_number] = parse(Int, responsedict[:selected_response_number]) # convert string "5" into integer 5
+    catch
+      errornote = "In your previous attempt, Selected_response_number was not a number. It must be a number."
+      println("\nERROR SQLLLM compareState() Attempt $attempt. $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    println("\n~~~ compareState() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    pprintln(Dict(responsedict))
+
+    return responsedict[:selected_response_number]
+  end
+  error("compareState() failed to generate an evaluation, Response: \n$response\n<|End of error|>", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+end
+
+



@@ -906,8 +1000,6 @@ end



-
-



--- a/src/util.jl
+++ b/src/util.jl
@@ -1,6 +1,8 @@
 module util

+export makekey

+makekey(key, indice) = Symbol("$(key)_$indice")



--- a/system_prompt_template.jl
+++ b/system_prompt_template.jl
@@ -0,0 +1,160 @@
+"""
+# -------------------------------- Default system message template ------------------------------- #
+
+<Your role>
+  - You are a helpful assistant
+</Your role>
+<Situation>
+  - Describe the current situation
+  Ex. The world use enormous energy from non-sustainable sources. This leads to climate change.
+</Situation>
+<Your vision>
+  - state your vision of how the situation will evolve, what would you want the situation to evolve into
+  Ex. To be the leading innovator in sustainable technology by 2030, transforming global energy systems.
+</Your vision>
+<Your mission>
+  - state the goal
+  Ex. Empowering communities through clean energy solutions to create a sustainable future.
+</Your mission>
+<Your mission's objective includes>
+  - Specific, measurable, and time-bound goals that directly support the mission.
+  Ex. Launch 50 solar-powered water purification systems in 3 regions by 2025.
+</Your mission's objective includes>
+<Your responsibility includes>
+  - state the mini goals that fall under your responsibility
+</Your responsibility includes>
+<Your responsibility does NOT includes>
+  - 
+</Your responsibility does NOT includes>
+<At each round of conversation, you will be given the following information>
+  -
+</At each round of conversation, you will be given the following information>
+<You must follow the following policy>
+  -
+</You must follow the following policy>
+<You should follow the following guidelines>
+  -
+</You should follow the following guidelines>
+<You should then respond to the user with interleaving Comprehension, Plan, Action_name, Action_input>
+  Comprehension: State your comprehension about the current situation.
+  Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
+  Action_name: (Typically corresponds to the execution of the first step in your plan) Can be one of the following function names:
+    - CHATBOX which you can use to talk with the user. The input is your intentions for the dialogue. Be specific.
+    - CHECKRESOURCES which you can use to check resources
+    - IMPLEMENT which you can use to implement the solution
+  Action_input: Detail the input for the action.
+</You should then respond to the user with interleaving Comprehension, Plan, Action_name, Action_input>
+<You should only respond in format as described below>
+  Comprehension: ...
+  Plan: ...
+  Action_name: ...
+  Action_input: ...
+</You should only respond in format as described below>
+<Here are some examples>
+
+</Here are some examples>
+
+Let's begin!
+
+
+
+
+
+
+
+
+
+# ------------------------------------------- Example: ------------------------------------------- #
+
+<Your profile>
+- You are a founder of a tech startup
+</Your profile>
+<Situation>
+- The global rise in bedridden patients, driven by an aging population, presents significant challenges for caregivers. Family members often become primary caretakers, leading to physical and emotional strain. This situation frequently forces caregivers to make difficult choices, including leaving their careers to provide full-time care, which impacts both family finances and personal well-being.
+</Situation>
+<Your vision>
+  - We want to develop a system that can help people with bedridden patients and their families so that they could go on with their lives.
+</Your vision>
+<Your mission>
+  - To create an innovative caregiving support platform that reduces the physical and emotional burden on family caregivers while ensuring quality care for bedridden patients
+</Your mission>
+<Your mission's objectives include>
+  - Develop smart monitoring systems for patient safety
+  - Create automated alert mechanisms for critical situations
+  - Design user-friendly interfaces for remote patient monitoring
+  - Implement AI-driven predictive care recommendations
+  - Build a support network connecting caregivers with healthcare professionals
+  - Establish training modules for family caregivers
+</Your mission's objectives include>
+<Your responsibilities include>
+  - Lead product vision and strategy development
+  - Oversee technical implementation and system architecture
+  - Coordinate with healthcare experts for medical validation
+  - Ensure compliance with healthcare regulations
+  - Manage stakeholder relationships
+  - Drive fundraising and business development
+</Your responsibilities include>
+<At each round of conversation, you will be given the following>
+  Challenges: user's specific caregiving challenges
+  Context: context and severity of the situation
+  Feedback: comments from family caregivers
+  Solutions: potential solution based on immediate and long-term impact
+</At each round of conversation, you will be given the following>
+<You must follow the following guidelines>
+  - Always prioritize patient safety and well-being
+  - Maintain empathy and understanding in all interactions
+  - Focus on practical, implementable solutions
+  - Consider both immediate needs and long-term sustainability
+  - Respect privacy and confidentiality of all stakeholders
+  - Follow healthcare regulations and best practices
+</You must follow the following guidelines>
+<You should then respond to the user with interleaving Comprehension, Plan, Action_name, Action_input>
+  Comprehension: State your comprehension about the current situation.
+  Plan: Given the current circumstances, outline a detailed, step-by-step plan to accomplish the task. Be specific.
+  Action_name: (Typically corresponds to the execution of the first step in your plan)
+    Can be one of the following function names:
+    - CHATBOX which you can use to talk with the user. The input is your intentions for the dialogue. Be specific.
+    - CHECKRESOURCES which you can use to check resources
+    - IMPLEMENT which you can use to implement the solution
+  Action_input: Detail the input for the action.
+</You should then respond to the user with interleaving Comprehension, Plan, Action_name, Action_input>
+<You should only respond in format as described below>
+  Comprehension: ...
+  Plan: ...
+  Action_name: ...
+  Action_input: ...
+</You should only respond in format as described below>
+<Here are some examples>
+  Example 1:
+  Challenges: "My mother needs constant monitoring at night, but I'm exhausted from lack of sleep."
+  Context: Elderly patient with dementia, requires 24/7 supervision
+  Feedback: "Need urgent solution for night monitoring"
+  Solutions: Smart monitoring system with motion sensors and alerts
+
+  Comprehension: The caregiver is experiencing severe sleep deprivation due to nighttime monitoring requirements
+  Plan: 
+    1. Assess current monitoring needs
+    2. Propose smart monitoring system installation
+    3. Set up emergency alert system
+    4. Train family on system usage
+  Action_name: CHATBOX
+  Action_input: Discuss specific nighttime behaviors and incidents to determine optimal sensor placement and alert thresholds
+
+  Example 2:
+  Challenges: "Managing medication schedules is becoming overwhelming"
+  Context: Patient on multiple medications with complex timing requirements
+  Feedback: "Need help with medication management"
+  Solutions: Automated medication reminder and tracking system
+
+  Comprehension: Caregiver struggling with complex medication management tasks
+  Plan:
+    1. Review current medication schedule
+    2. Implement automated reminder system
+    3. Set up medication tracking log
+    4. Connect with pharmacy for refill automation
+  Action_name: IMPLEMENT
+  Action_input: Deploy medication management module with smart alerts and compliance tracking
+</Here are some examples>
+
+Let's begin!
+"""
--- a/test/Manifest.toml
+++ b/test/Manifest.toml
@@ -0,0 +1,41 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.11.4"
+manifest_format = "2.0"
+project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
+
+[[deps.Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+version = "1.11.0"
+
+[[deps.InteractiveUtils]]
+deps = ["Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+version = "1.11.0"
+
+[[deps.Logging]]
+uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
+version = "1.11.0"
+
+[[deps.Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+version = "1.11.0"
+
+[[deps.Random]]
+deps = ["SHA"]
+uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+version = "1.11.0"
+
+[[deps.SHA]]
+uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+version = "0.7.0"
+
+[[deps.Serialization]]
+uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+version = "1.11.0"
+
+[[deps.Test]]
+deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
+uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+version = "1.11.0"
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -0,0 +1,2 @@
+[deps]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
--- a/test/runtest.jl
+++ b/test/runtest.jl
@@ -1,305 +0,0 @@
-using Revise
-using LibPQ, JSON3, PrettyPrinting, UUIDs, DataFrames, DataStructures, Base64
-using GeneralUtils, SQLLLM
-
-
-config = copy(JSON3.read("config.json"))
-
-function executeSQL(sql::T) where {T<:AbstractString}
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function text2textInstructLLM(prompt::String)
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose="inference",
-    senderName="yiemagent",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-    mqttBrokerPort=config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta => msgMeta,
-    :payload => Dict(
-      :text => prompt,
-      :kwargs => Dict(
-        :num_ctx => 20480,
-        :temperature => 0.2,
-      )
-    )
-  )
-
-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
-  response = _response[:response][:text]
-
-  return response
-end
-
-
-function executeSQLVectorDB(sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function addSQLVectorDB(state)
-  # get embedding of the query
-  query = [state[:thoughtHistory][:question]]
-  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose= "embedding",
-    senderName= "yiemagent",
-    senderId= string(uuid4()),
-    receiverName= "text2textinstruct",
-    mqttBrokerAddress= config[:mqttServerInfo][:broker],
-    mqttBrokerPort= config[:mqttServerInfo][:port],
-  )
-
-  outgoingMsg = Dict(
-    :msgMeta=> msgMeta,
-    :payload=> Dict(
-      :text=> query
-      )
-    )
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-  embedding = response[:response][:embeddings][1]
-
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-  sql =
-    """
-    SELECT *, embedding <-> '$embedding' as distance
-    FROM sql_statement_repository
-    ORDER BY distance LIMIT 1;
-    """
-  response = executeSQLVectorDB(sql)
-  df = DataFrame(response)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > 10 # no close enough SQL stored in the database
-    latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input)
-    _sqlStatement = state[:thoughtHistory][latestKey]
-    if occursin("SELECT", _sqlStatement)  # make sure it is an SQL statement before adding into DB
-      sqlStatementBase64 = base64encode(_sqlStatement)
-      sqlStatement = replace(_sqlStatement, "'"=>"")
-      sql =
-      """
-      INSERT INTO sql_statement_repository (question, sql_statement, sql_statement_base64, embedding) VALUES ('$query', '$sqlStatement', '$sqlStatementBase64', '$embedding');
-      """
-      _ = executeSQLVectorDB(sql)
-      println("--> added new SQL statement to vectorDB ", @__FILE__, " ", @__LINE__)
-      println(sqlStatement)
-    end
-  end
-end
-
-function querySQLVectorDB(state)
-
-  # provide similarSQL at the first time thinking only
-  latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input)
-  if latestKey === nothing
-    # get embedding of the query
-    query = [state[:thoughtHistory][:question]]
-    msgMeta = GeneralUtils.generate_msgMeta(
-      config[:externalservice][:text2textinstruct][:mqtttopic];
-      msgPurpose= "embedding",
-      senderName= "yiemagent",
-      senderId= string(uuid4()),
-      receiverName= "text2textinstruct",
-      mqttBrokerAddress= config[:mqttServerInfo][:broker],
-      mqttBrokerPort= config[:mqttServerInfo][:port],
-    )
-
-    outgoingMsg = Dict(
-      :msgMeta=> msgMeta,
-      :payload=> Dict(
-        :text=> query
-        )
-      )
-    response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-    embedding = response[:response][:embeddings][1]
-
-    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-    sql =
-    """
-    SELECT *, embedding <-> '$embedding' as distance
-    FROM sql_statement_repository
-    ORDER BY distance LIMIT 1;
-    """
-    response = executeSQLVectorDB(sql)
-    df = DataFrame(response)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row != 0 && distance < 100
-      # if there is usable SQL, return it.
-      sqlStatementBase64 = df[1, :sql_statement_base64]
-      sqlStatement = String(base64decode(sqlStatementBase64))
-      return sqlStatement
-    else
-      return nothing
-    end
-  end
-  return nothing
-end
-
-
-
-
-# query = Dict(:text=> "How many wines from France do you have that can be paired with lamb?")
-# query = "How many wines are from United States?"
-query = "retailer: Yiem, wine_type: red, sweetness: 1-2, intensity: 4-5, wine price: 20-40"
-# query = "wine_type: white, country: United States, sweetness: 1-2, tannin: 3, food to be served with wine: pizza"
-# query = "wine_type: white, country: Austria, food to be served with wine: pork"
-# query = "wine price: less than 25, wine_type: rose, country: France, sweetness: 2, tannin: 3, food to be served with wine: pizza"
-# query = Dict(:text=> "wine_type: white, country: France, sweetness: 1")
-result = SQLLLM.query(query, executeSQL, text2textInstructLLM;
-                      addSQLVectorDB=addSQLVectorDB, 
-                      querySQLVectorDB=querySQLVectorDB)
-
-println(result)
-error(555)
-
-
-
-"""
-CREATE TABLE sql_statement_repository (id bigserial PRIMARY KEY, question text, sql_statement text, sql_statement_base64 text, embedding vector(768));
-
-SELECT * FROM wine WHERE wine_type = 'red' AND country = 'France' AND sweetness >= 1 AND sweetness <= 2 AND intensity >= 4 AND intensity <= 5 ORDER BY RANDOM() LIMIT 2;
-
-
-"""
-
-
-# sql = 
-# """
-# SELECT COUNT(*)  FROM wine_food  JOIN wine ON wine_food.wine_id = wine.wine_id  JOIN food ON wine_food.food_id = food.food_id  WHERE food.description LIKE '%lamb%';
-# """
-# response = SQLLLM.SQLexecution(executeSQL, sql);
-# result = response[:result]
-# userintention = 
-# """
-# Since this is the first round, there's no execution error to analyze. However, we can think about how to improve the query to achieve the desired result.
-# 1) We need to join the wine_food table with the food table on the food_id column.\n  2) We want to filter the results to include only wines that can be paired with lamb by checking if the food_name or additional_search_term matches 'lamb'.\n  3) We'll use a COUNT(DISTINCT) function to count the number of unique wine_id values that meet the condition.
-# """
-# userintention_dict = Dict(:userintention=>userintention)
-
-
-
-# sql = 
-# """
-# SELECT DISTINCT wf.wine_id, COUNT(wf.wine_id) AS wine_count FROM wine_food wf JOIN food f ON wf.food_id = f.food_id WHERE f.description LIKE '%lamb%' GROUP BY wf.wine_id ORDER BY wine_count DESC;
-# """
-# response = SQLLLM.SQLexecution(executeSQL, sql);
-# result = response[:result]
-# userintention = 
-# """
-# 1. Use TABLEINFO function to get information about the columns in the wine_food table.\n2. Use GETDATA function to retrieve data from the wine_food table that contains information about wines paired with lamb.\n3. Join the retrieved data with the wine table on the wine_id column to get information about the wines that can be paired with lamb.\n4. Count the number of unique wines associated with lamb through the wine_food junction table. 1. Use TABLEINFO function to get information about the columns in the wine_food table.\n2. Use GETDATA function to retrieve data from the wine_food table that contains information about wines paired with lamb.\n3. Join the retrieved data with the wine table on the wine_id column to get information about the wines that can be paired with lamb.\n4. Count the number of unique wines associated with lamb through the wine_food junction table.
-# """
-# userintention_dict = Dict(:userintention=>userintention)
-
-
-
-# sql = 
-# """
-# SELECT COUNT(DISTINCT w.wine_name) FROM (SELECT * FROM wine_food wf JOIN food f ON wf.food_id = f.food_id WHERE f.food_name = 'lamb') AS temp_table JOIN wine w ON temp_table.wine_id = w.wine_id;
-# """
-# response = SQLLLM.SQLexecution(executeSQL, sql);
-# result = response[:result]
-# userintention = 
-# """
-# 1. Join the wine_food table with the food table using the food_id column in both tables.\n2. Filter the results to only include rows where the associated food is 'lamb'.\n3. Join the resulting table with the wine table using the wine_id column in both tables.\n4. Count the number of unique wines that can be paired with lamb. 1. Join the wine_food table with the food table using the food_id column in both tables.\n2. Filter the results to only include rows where the associated food is 'lamb'.\n3. Join the resulting table with the wine table using the wine_id column in both tables.\n4. Count the number of unique wines that can be paired with lamb.
-# """
-# userintention_dict = Dict(:userintention=>userintention)
-
-
-
-# sql = 
-# """
-# SELECT * FROM wine WHERE country = 'France' AND sweetness = 1 AND wine_type = 'white' LIMIT 2;
-# """
-# response = SQLLLM.SQLexecution(executeSQL, sql);
-# result = response[:result]
-# userintention = 
-# """
-# "- Identify the primary key in the wine table.\n- Filter the results to only include wines with type white, from France and level of sweetness 1.\n- Retrieve the information about wines that match the specified criteria. - Identify the primary key in the wine table.\n- Filter the results to only include wines with type white, from France and level of sweetness 1.\n- Retrieve the information about wines that match the specified criteria.
-# """
-# userintention_dict = Dict(:userintention=>userintention)
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-# readout = SQLLLM.extractContent_dataframe(result, userintention_dict, text2textInstructLLM)
-
-# println("runtest.jl is done")
-
-
-
-
-
-
-# sql = 
-# """
-# SELECT * FROM wine WHERE country = 'France' AND sweetness = 1 AND wine_type = 'white' LIMIT 2;
-# """
-# _result = executeSQL(sql)
-# df2 = DataFrame(_result)
-
-
-
-
-
-
-
-
-
-
-
-# state = Dict(
-#   :isterminal => true,
-#   :lesson => nothing,
-#   :reward => 1,
-#   :evaluation =>
-#       "The user's question is to search the database for wines that have a type of \"white\", are from \"France\", and have a sweetness level of 1. The thought is correct in identifying the conditions needed to filter the wine table. The action taken is to execute a SQL query to retrieve the desired data, which is also correct. The observation provides a search summary and two search results that match the user's question. Each result includes details about the wine such as ID, name, brand, manufacturer, region, country, type, grape variety, serving temperature, intensity, sweetness, tannin, and acidity.",
-#   :accepted_as_answer => "Yes",
-#   :thoughtHistory =>
-#       OrderedDict{Symbol, Any}(:question => "Search the database for wine_type: white, country: France, sweetness: 1", :thought_1 => "The user wants to search the database for wines that have a type of \"white\", are from \"France\", and have a sweetness level of 1. To achieve this, we need to filter the wine table based on these conditions.", :action_name_1 => "GETDATA", :action_input_1 => "SELECT * FROM wine WHERE wine.wine_type = 'white' AND wine.country = 'France' AND wine.sweetness = 1;", :observation_1 => "\"Search summary: The resulting table represents wines.\\nSearch result: 1) wine_id: 5b6b6df9-d87c-4f33-8995-7249c2ecc917, wine_name: corton-charlemagne grand cru, brand: domaine des croix, manufacturer: domaine des croix, region: bourgogne, country: France, wine_type: white, grape_variety: cote de beaune blanc, serving_temperature: 11 to 13 Celsius, intensity: 4, sweetness: 1, tannin: missing, acidity: 3, fizziness: missing\\n2) wine_id: 1ad27d16-ef64-4907-acf1-40631630c143, wine_name: puligny-montrachet 1er cru 'les demoiselles', brand: amiot guy, manufacturer: amiot guy, region: bourgogne, country: France, wine_type: white, grape_variety: cote de beaune blanc, serving_temperature: 11 to 13 Celsius, intensity: 4, sweetness: 1, tannin: missing, acidity: 3, fizziness: missing\\n\\n\""),
-#   :evaluationscore => 9,
-#   :select => nothing,
-#   :suggestion => "None")
-
-
-
-# result = SQLLLM.evaluator(state, text2textInstructLLM)
-
-
-
-
-
-
-
-
-
-
-
-
-println("runtest.jl done")
-
-
-
-
--- a/previousVersion/0.1/test/runtest.jl
+++ b/previousVersion/0.1/test/runtest.jl
@@ -1,24 +1,47 @@
 using Revise
-using LibPQ, JSON3, PrettyPrinting, UUIDs, DataFrames, DataStructures, Base64
+using LibPQ, Dates, JSON3, PrettyPrinting, UUIDs, DataFrames, DataStructures, Base64
 using GeneralUtils, SQLLLM


-config = copy(JSON3.read("config.json"))
+config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")

 function executeSQL(sql::T) where {T<:AbstractString}
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
+  host = config[:externalservice][:wineDB][:host]
+  port = config[:externalservice][:wineDB][:port]
+  dbname = config[:externalservice][:wineDB][:dbname]
+  user = config[:externalservice][:wineDB][:user]
+  password = config[:externalservice][:wineDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
  result = LibPQ.execute(DBconnection, sql)
  close(DBconnection)
  return result
 end

-function text2textInstructLLM(prompt::String)
+function executeSQLVectorDB(sql)
+  host = config[:externalservice][:SQLVectorDB][:host]
+  port = config[:externalservice][:SQLVectorDB][:port]
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+  user = config[:externalservice][:SQLVectorDB][:user]
+  password = config[:externalservice][:SQLVectorDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
+  result = LibPQ.execute(DBconnection, sql)
+  close(DBconnection)
+  return result
+end
+
+function text2textInstructLLM(prompt::String; maxattempt::Integer=3, modelsize::String="medium",
+    senderId=GeneralUtils.uuid4snakecase(), timeout=180,
+    llmkwargs=Dict(
+      :num_ctx => 32768,
+      :temperature => 0.5,
+    )
+  )
  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+    config[:externalservice][:loadbalancer][:mqtttopic];
    msgPurpose="inference",
    senderName="yiemagent",
-    senderId=string(uuid4()),
-    receiverName="text2textinstruct",
+    senderId=senderId,
+    receiverName="text2textinstruct_$modelsize",
    mqttBrokerAddress=config[:mqttServerInfo][:broker],
    mqttBrokerPort=config[:mqttServerInfo][:port],
  )
@@ -27,139 +50,191 @@ function text2textInstructLLM(prompt::String)
    :msgMeta => msgMeta,
    :payload => Dict(
      :text => prompt,
-      :kwargs => Dict(
-        :num_ctx => 20480,
-        :temperature => 0.2,
-      )
+      :kwargs => llmkwargs
    )
  )

-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120)
-  response = _response[:response][:text]
+  response = nothing
+  for attempts in 1:maxattempt
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=timeout, maxattempt=maxattempt)
+    payload = _response[:response]
+    if _response[:success] && payload[:text] !== nothing
+      response = _response[:response][:text]
+      break
+    else
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(outgoingMsg)
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      sleep(3)
+    end
+  end

  return response
 end

-
-function executeSQLVectorDB(sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-function addSQLVectorDB(state)
-  # get embedding of the query
-  query = [state[:thoughtHistory][:question]]
+# get text embedding from a LLM service
+function getEmbedding(text::T) where {T<:AbstractString}
  msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
-    msgPurpose= "embedding",
-    senderName= "yiemagent",
-    senderId= string(uuid4()),
-    receiverName= "text2textinstruct",
-    mqttBrokerAddress= config[:mqttServerInfo][:broker],
-    mqttBrokerPort= config[:mqttServerInfo][:port],
+    config[:externalservice][:loadbalancer][:mqtttopic];
+    msgPurpose="embedding",
+    senderName="yiemagent",
+    senderId=sessionId,
+    receiverName="textembedding",
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    mqttBrokerPort=config[:mqttServerInfo][:port],
  )

  outgoingMsg = Dict(
-    :msgMeta=> msgMeta,
-    :payload=> Dict(
-      :text=> query
-      )
+    :msgMeta => msgMeta,
+    :payload => Dict(
+      :text => [text] # must be a vector of string
    )
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-  embedding = response[:response][:embeddings][1]
+  )

+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
+  embedding = response[:response][:embeddings]
+  return embedding
+end
+
+function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
+  vectorDB::Function; limit::Integer=1
+  )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+  # get embedding from LLM service
+  embedding = getEmbedding(text)[1]
  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-  sql =
-    """
-    SELECT *, embedding <-> '$embedding' as distance
-    FROM sql_statement_repository
-    ORDER BY distance LIMIT 1;
-    """
-  response = executeSQLVectorDB(sql)
+  sql = """
+        SELECT *, $embeddingColumnName <-> '$embedding' as distance
+        FROM $tablename
+        ORDER BY distance LIMIT $limit;
+        """
+  response = vectorDB(sql)
  df = DataFrame(response)
+  return df
+end
+
+function similarSQLVectorDB(query; maxdistance::Integer=100)
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  # println(df[1, [:id, :function_output]])
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]  
+  # distance = 100  # CHANGE this is for testing only
+  if row != 0 && distance < maxdistance
+    # if there is usable SQL, return it.
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    output_str = String(base64decode(output_b64))
+    rowid = df[1, :id]
+    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=output_str, distance=distance)
+  else
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    return (dict=nothing, distance=nothing)
+  end
+end
+
+function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
+  tablename = "sqlllm_decision_repository"
+  # get embedding of the query
+  # query = state[:thoughtHistory][:question]
+  df = findSimilarTextFromVectorDB(query, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
  row, col = size(df)
  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > 10 # no close enough SQL stored in the database
-    latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input)
-    _sqlStatement = state[:thoughtHistory][latestKey]
-    if occursin("SELECT", _sqlStatement)  # make sure it is an SQL statement before adding into DB
-      sqlStatementBase64 = base64encode(_sqlStatement)
-      sqlStatement = replace(_sqlStatement, "'"=>"")
-      sql =
-      """
-      INSERT INTO sql_statement_repository (question, sql_statement, sql_statement_base64, embedding) VALUES ('$query', '$sqlStatement', '$sqlStatementBase64', '$embedding');
-      """
-      _ = executeSQLVectorDB(sql)
-      println("--> added new SQL statement to vectorDB ", @__FILE__, " ", @__LINE__)
-      println(sqlStatement)
-    end
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    query_embedding = getEmbedding(query)[1]
+    query = replace(query, "'" => "")
+    sql_base64 = base64encode(SQL)
+    sql_ = replace(SQL, "'" => "")
+
+    sql = """
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+          """
+    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # println(sql)
+    _ = executeSQLVectorDB(sql)
  end
 end

-function querySQLVectorDB(state)

-  # provide similarSQL at the first time thinking only
-  latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input)
-  if latestKey === nothing
-    # get embedding of the query
-    query = [state[:thoughtHistory][:question]]
-    msgMeta = GeneralUtils.generate_msgMeta(
-      config[:externalservice][:text2textinstruct][:mqtttopic];
-      msgPurpose= "embedding",
-      senderName= "yiemagent",
-      senderId= string(uuid4()),
-      receiverName= "text2textinstruct",
-      mqttBrokerAddress= config[:mqttServerInfo][:broker],
-      mqttBrokerPort= config[:mqttServerInfo][:port],
-    )
-
-    outgoingMsg = Dict(
-      :msgMeta=> msgMeta,
-      :payload=> Dict(
-        :text=> query
-        )
-      )
-    response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
-    embedding = response[:response][:embeddings][1]
-
-    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-    sql =
-    """
-    SELECT *, embedding <-> '$embedding' as distance
-    FROM sql_statement_repository
-    ORDER BY distance LIMIT 1;
-    """
-    response = executeSQLVectorDB(sql)
-    df = DataFrame(response)
-    row, col = size(df)
-    distance = row == 0 ? Inf : df[1, :distance]
-    if row != 0 && distance < 100
-      # if there is usable SQL, return it.
-      sqlStatementBase64 = df[1, :sql_statement_base64]
-      sqlStatement = String(base64decode(sqlStatementBase64))
-      return sqlStatement
-    else
-      return nothing
-    end
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
+  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row != 0 && distance < maxdistance
+    # if there is usable decision, return it.
+    rowid = df[1, :id]
+    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    _output_str = String(base64decode(output_b64))
+    output = copy(JSON3.read(_output_str))
+    return output
+  else
+    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+    return nothing
  end
-  return nothing
 end


+function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+  ) where {T1<:AbstractString, T2<:AbstractDict}
+  tablename = "sommelier_decision_repository"
+  # find similar
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+                                  "function_input_embedding", executeSQLVectorDB)
+  row, col = size(df)
+  distance = row == 0 ? Inf : df[1, :distance]
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+    recentevents_embedding = getEmbedding(recentevents)[1]
+    recentevents = replace(recentevents, "'" => "")
+    decision_json = JSON3.write(decision)
+    decision_base64 = base64encode(decision_json)
+    decision = replace(decision_json, "'" => "")
+    
+    sql = """
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+          """
+    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+    println(sql)
+    _ = executeSQLVectorDB(sql)
+  else
+    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+  end
+end
+
+sessionId = GeneralUtils.uuid4snakecase()
+d = Dict(:id => sessionId)
+filepath = "/appfolder/app/sessionid.json"
+open(filepath, "w") do io
+  JSON3.pretty(io, d)
+end
+
+
+
+
+# query = "How many German wines do you have?"
+# highValueStateList = copy(JSON3.read("/appfolder/app/highValueState_1.json"))
+# selectedState = SQLLLM.compareState(query, highValueStateList, text2textInstructLLM)
+


 # query = Dict(:text=> "How many wines from France do you have that can be paired with lamb?")
-# query = "How many wines are from United States?"
-query = "retailer: Yiem, wine_type: red, sweetness: 1-2, intensity: 4-5, wine price: 20-40"
+# query = "How many French wines from Yiem store under 100 dollars do you have?"
+# query = "retailer: Yiem, wine_type: red, sweetness: 1-2, intensity: 4-5, wine price: 20-40"
+query = "from Yiem retailer, red wine from France. price 100 to 1000 USD. sweetness: 1-2, intensity: 4-5"
 # query = "wine_type: white, country: United States, sweetness: 1-2, tannin: 3, food to be served with wine: pizza"
 # query = "wine_type: white, country: Austria, food to be served with wine: pork"
 # query = "wine price: less than 25, wine_type: rose, country: France, sweetness: 2, tannin: 3, food to be served with wine: pizza"
 # query = Dict(:text=> "wine_type: white, country: France, sweetness: 1")
 result = SQLLLM.query(query, executeSQL, text2textInstructLLM;
-                      addSQLVectorDB=addSQLVectorDB, 
-                      querySQLVectorDB=querySQLVectorDB)
+                      insertSQLVectorDB=insertSQLVectorDB, 
+                      similarSQLVectorDB=similarSQLVectorDB)

 println(result)
 error(555)
--- a/test/test_2.jl
+++ b/test/test_2.jl
@@ -1,16 +1,70 @@
-using Revise
+# using Revise
+# using SQLLLM, LLMMCTS, DataStructures, JSON3
+
+# query = "How many German wines do you have?"
+# highValueStateList = copy(JSON3.read("/appfolder/app/highValueState_1.json"))
+# selectedState = SQLLLM.compareState(query, highValueStateList)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+

-function testf(a)::NamedTuple{(:a, :b), Tuple{Union{Nothing, Int}, Int}}
-  if a == 1
-    return (a=nothing, b=5)
-  else
-    return (a=5, b=5)
-  end
-end
-
-
-q = testf(1)
-w = testf(2)



--- a/testfile.csv
+++ b/testfile.csv
@@ -1,8 +0,0 @@
-table_name,comment
-customer,"The customer table stores information about customers. It includes details such as first name, last name, display name, username, password, gender, country, telephone number, email, birthdate, additional_search_term, other attributes (in JSON format) and a description."
-wine,"The wine table stores information about different wines. It includes details namely id, name, brand, manufacturer, region, country, wine_type, grape_variety, serving_temperature, intensity, sweetness, tannin, acidity, fizziness, additional_search_term, other attributes (in JSON format) and a description."
-wine_food,"The wine_food table represents the association between wines and food items. It establishes a many-to-many relationship, allowing us to link specific wines with various food items."
-food,"The food table represents various food items. It stores information related to food names, country of origin, taste attributes (spiciness, sweetness, sourness, savoriness, and bitterness), serving temperature, additional_search_term, other attributes (in JSON format) and a description."
-retailer,"The retailer table stores information about different retailers. It includes details related to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description."
-retailer_wine,"The retailer_wine table represents the relationship between retailers and wines. It stores information about the wines available from which retailers, including vintage, their price, and the currency."
-retailer_food,"The retailer_food table represents the relationship between retailers and food items. It stores information about the food items available from which retailers, including their price and the currency."
Author	SHA1	Message	Date
narawat	a4227ec165	update	2025-07-23 18:31:38 +07:00
narawat lamaiin	21416f4b13	update	2025-06-15 08:59:10 +07:00
narawat lamaiin	ff4db039ab	update	2025-06-03 10:08:17 +07:00
narawat lamaiin	b3537a83e0	update	2025-05-18 17:21:55 +07:00
narawat lamaiin	0a0e36d86a	update	2025-05-17 21:36:40 +07:00
narawat lamaiin	8c5b1b6938	update	2025-05-14 21:21:30 +07:00
narawat lamaiin	aeda7e0baf	update	2025-05-06 06:49:21 +07:00
narawat lamaiin	2541223bbb	update	2025-05-04 20:56:55 +07:00
narawat lamaiin	c8f5983620	update	2025-05-04 13:30:08 +07:00
narawat lamaiin	5112701dc2	update	2025-05-01 07:59:18 +07:00
narawat lamaiin	bf223b64b2	update	2025-04-27 22:32:22 +07:00
narawat lamaiin	d9c842bba5	update	2025-04-25 21:13:12 +07:00
narawat lamaiin	b8fd331c1a	update	2025-04-13 21:45:58 +07:00
narawat lamaiin	00b0ab01a4	update	2025-04-04 15:05:16 +07:00
narawat lamaiin	fd5ac82662	update	2025-04-01 21:17:03 +07:00
narawat lamaiin	bc0f735ab7	update	2025-03-22 20:26:41 +07:00
tonaerospace	3d03a4d351	update	2025-03-21 10:04:22 +07:00
tonaerospace	568e0ff54f	update	2025-03-20 16:08:40 +07:00
tonaerospace	83a20faab6	update	2025-03-19 19:11:06 +07:00
tonaerospace	418c543d44	update	2025-03-19 11:29:31 +07:00
tonaerospace	e6ce6f9954	update	2025-03-18 21:22:12 +07:00
tonaerospace	7fd0d6269a	update	2025-03-18 08:37:35 +07:00
tonaerospace	e391547991	update	2025-03-18 07:54:23 +07:00
tonaerospace	7c9ceb06f8	update	2025-03-18 07:34:51 +07:00
tonaerospace	14c881741e	update	2025-03-16 22:11:23 +07:00
tonaerospace	0873b1341f	mark new version	2025-03-15 11:43:05 +07:00
ton	00ec7328e7	Merge pull request 'v0.2.3' (#2 ) from v0.2.3 into main Reviewed-on: #2	2025-03-15 01:45:32 +00:00
tonaerospace	83ef7d52b2	update	2025-03-15 08:29:33 +07:00
tonaerospace	323c232121	update	2025-03-14 21:57:27 +07:00
tonaerospace	696a77a483	update	2025-03-14 19:15:42 +07:00
tonaerospace	94f2c9479a	update	2025-03-14 13:06:26 +07:00
tonaerospace	200a1d3e23	update	2025-03-14 12:32:09 +07:00
tonaerospace	a22f9c52d2	update	2025-03-13 19:10:02 +07:00
tonaerospace	2036a07f46	update	2025-03-11 10:28:38 +07:00
tonaerospace	d09e9c1071	update	2025-03-11 00:13:50 +07:00
tonaerospace	4f1917e01b	update	2025-03-09 18:36:57 +07:00
tonaerospace	7dd6b56e4c	update	2025-03-09 11:26:55 +07:00
narawat lamaiin	e9f9e431a9	update	2025-03-07 12:32:49 +07:00
narawat lamaiin	2f38f3cd0d	update	2025-02-24 06:28:46 +07:00
ton	c1ac00829c	Merge pull request 'WIP v0.2.2-dev' (#1 ) from v0.2.2-dev into main Reviewed-on: #1	2025-01-25 07:38:21 +00:00
narawat lamaiin	b3345514ca	update	2025-01-25 14:21:52 +07:00
narawat lamaiin	112db2929c	update	2025-01-15 08:35:25 +07:00
narawat lamaiin	aa7973ca7e	update	2025-01-11 16:57:00 +07:00
narawat lamaiin	bba3c26301	update	2025-01-06 13:13:16 +07:00
narawat lamaiin	dcf57420d1	update	2025-01-05 13:34:41 +07:00
tonaerospace	4fa16c4b76	update	2025-01-04 16:11:20 +07:00
tonaerospace	370f3501b9	update	2025-01-01 07:53:18 +07:00
tonaerospace	210aecb183	update	2024-12-27 20:47:22 +07:00