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narawat lamaiin
2024-07-15 21:19:55 +07:00
parent 180bd16018
commit fdc50d1b90
9 changed files with 2396 additions and 591 deletions
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17
.vscode/launch.json vendored Normal file
View File

@@ -0,0 +1,17 @@
{
// Use IntelliSense to learn about possible attributes.
// Hover to view descriptions of existing attributes.
// For more information, visit: https://go.microsoft.com/fwlink/?linkid=830387
"version": "0.2.0",
"configurations": [
{
"type": "julia",
"request": "launch",
"name": "Run active Julia file",
"program": "${file}",
"stopOnEntry": false,
"cwd": "${workspaceFolder}",
"juliaEnv": "${command:activeJuliaEnvironment}"
}
]
}

275
Manifest.toml
View File

@@ -1,8 +1,8 @@
# This file is machine-generated - editing it directly is not advised # This file is machine-generated - editing it directly is not advised
julia_version = "1.10.3" julia_version = "1.10.4"
manifest_format = "2.0" manifest_format = "2.0"
project_hash = "d5182042dab089bafa4f01ef385efd46c01a0396" project_hash = "42fe76ec8191cf95e51733bee474db0f4870d573"
[[deps.AliasTables]] [[deps.AliasTables]]
deps = ["PtrArrays", "Random"] deps = ["PtrArrays", "Random"]
@@ -21,9 +21,20 @@ uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
[[deps.BitFlags]] [[deps.BitFlags]]
git-tree-sha1 = "2dc09997850d68179b69dafb58ae806167a32b1b" git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d"
uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35" uuid = "d1d4a3ce-64b1-5f1a-9ba4-7e7e69966f35"
version = "0.1.8" version = "0.1.9"
[[deps.CEnum]]
git-tree-sha1 = "eb4cb44a499229b3b8426dcfb5dd85333951ff90"
uuid = "fa961155-64e5-5f13-b03f-caf6b980ea82"
version = "0.4.2"
[[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.Calculus]] [[deps.Calculus]]
deps = ["LinearAlgebra"] deps = ["LinearAlgebra"]
@@ -39,9 +50,9 @@ version = "1.3.5"
[[deps.CodecZlib]] [[deps.CodecZlib]]
deps = ["TranscodingStreams", "Zlib_jll"] deps = ["TranscodingStreams", "Zlib_jll"]
git-tree-sha1 = "59939d8a997469ee05c4b4944560a820f9ba0d73" git-tree-sha1 = "b8fe8546d52ca154ac556809e10c75e6e7430ac8"
uuid = "944b1d66-785c-5afd-91f1-9de20f533193" uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
version = "0.7.4" version = "0.7.5"
[[deps.Compat]] [[deps.Compat]]
deps = ["TOML", "UUIDs"] deps = ["TOML", "UUIDs"]
@@ -60,9 +71,9 @@ version = "1.1.1+0"
[[deps.ConcurrentUtilities]] [[deps.ConcurrentUtilities]]
deps = ["Serialization", "Sockets"] deps = ["Serialization", "Sockets"]
git-tree-sha1 = "6cbbd4d241d7e6579ab354737f4dd95ca43946e1" git-tree-sha1 = "ea32b83ca4fefa1768dc84e504cc0a94fb1ab8d1"
uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
version = "2.4.1" version = "2.4.2"
[[deps.CondaPkg]] [[deps.CondaPkg]]
deps = ["JSON3", "Markdown", "MicroMamba", "Pidfile", "Pkg", "Preferences", "TOML"] deps = ["JSON3", "Markdown", "MicroMamba", "Pidfile", "Pkg", "Preferences", "TOML"]
@@ -70,11 +81,27 @@ git-tree-sha1 = "e81c4263c7ef4eca4d645ef612814d72e9255b41"
uuid = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" uuid = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
version = "0.2.22" version = "0.2.22"
[[deps.Crayons]]
git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15"
uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
version = "4.1.1"
[[deps.DBInterface]]
git-tree-sha1 = "a444404b3f94deaa43ca2a58e18153a82695282b"
uuid = "a10d1c49-ce27-4219-8d33-6db1a4562965"
version = "2.6.1"
[[deps.DataAPI]] [[deps.DataAPI]]
git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe" git-tree-sha1 = "abe83f3a2f1b857aac70ef8b269080af17764bbe"
uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a" uuid = "9a962f9c-6df0-11e9-0e5d-c546b8b5ee8a"
version = "1.16.0" version = "1.16.0"
[[deps.DataFrames]]
deps = ["Compat", "DataAPI", "DataStructures", "Future", "InlineStrings", "InvertedIndices", "IteratorInterfaceExtensions", "LinearAlgebra", "Markdown", "Missings", "PooledArrays", "PrecompileTools", "PrettyTables", "Printf", "REPL", "Random", "Reexport", "SentinelArrays", "SortingAlgorithms", "Statistics", "TableTraits", "Tables", "Unicode"]
git-tree-sha1 = "04c738083f29f86e62c8afc341f0967d8717bdb8"
uuid = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
version = "1.6.1"
[[deps.DataStructures]] [[deps.DataStructures]]
deps = ["Compat", "InteractiveUtils", "OrderedCollections"] deps = ["Compat", "InteractiveUtils", "OrderedCollections"]
git-tree-sha1 = "1d0a14036acb104d9e89698bd408f63ab58cdc82" git-tree-sha1 = "1d0a14036acb104d9e89698bd408f63ab58cdc82"
@@ -90,6 +117,23 @@ version = "1.0.0"
deps = ["Printf"] deps = ["Printf"]
uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
[[deps.Decimals]]
git-tree-sha1 = "e98abef36d02a0ec385d68cd7dadbce9b28cbd88"
uuid = "abce61dc-4473-55a0-ba07-351d65e31d42"
version = "0.4.1"
[[deps.DispatchDoctor]]
deps = ["MacroTools", "Preferences"]
git-tree-sha1 = "32d236e685d028f5bc808aae0634b58aac5128f0"
uuid = "8d63f2c5-f18a-4cf2-ba9d-b3f60fc568c8"
version = "0.4.10"
[deps.DispatchDoctor.extensions]
DispatchDoctorChainRulesCoreExt = "ChainRulesCore"
[deps.DispatchDoctor.weakdeps]
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
[[deps.Distributed]] [[deps.Distributed]]
deps = ["Random", "Serialization", "Sockets"] deps = ["Random", "Serialization", "Sockets"]
uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
@@ -133,6 +177,23 @@ git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a"
uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4"
version = "0.1.10" version = "0.1.10"
[[deps.ExprTools]]
git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec"
uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
version = "0.1.10"
[[deps.FileIO]]
deps = ["Pkg", "Requires", "UUIDs"]
git-tree-sha1 = "82d8afa92ecf4b52d78d869f038ebfb881267322"
uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
version = "1.16.3"
[[deps.FilePathsBase]]
deps = ["Compat", "Dates", "Mmap", "Printf", "Test", "UUIDs"]
git-tree-sha1 = "9f00e42f8d99fdde64d40c8ea5d14269a2e2c1aa"
uuid = "48062228-2e41-5def-b9a4-89aafe57970f"
version = "0.9.21"
[[deps.FileWatching]] [[deps.FileWatching]]
uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
@@ -148,8 +209,18 @@ weakdeps = ["PDMats", "SparseArrays", "Statistics"]
FillArraysSparseArraysExt = "SparseArrays" FillArraysSparseArraysExt = "SparseArrays"
FillArraysStatisticsExt = "Statistics" FillArraysStatisticsExt = "Statistics"
[[deps.FormatCorrector]]
deps = ["CSV", "CondaPkg", "DataFrames", "DataStructures", "Dates", "DispatchDoctor", "FileIO", "GeneralUtils", "HTTP", "JSON3", "LLMMCTS", "LibPQ", "MQTTClient", "PrettyPrinting", "PythonCall", "Random", "Revise", "URIs", "UUIDs"]
path = "../FormatCorrector"
uuid = "4aa99331-0491-47cc-864d-b8dfff346b60"
version = "0.1.0"
[[deps.Future]]
deps = ["Random"]
uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
[[deps.GeneralUtils]] [[deps.GeneralUtils]]
deps = ["DataStructures", "Dates", "Distributions", "JSON3", "MQTTClient", "Random", "Revise", "UUIDs"] deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "JSON3", "MQTTClient", "Random", "Revise", "UUIDs"]
path = "/appfolder/app/privatejuliapkg/GeneralUtils" path = "/appfolder/app/privatejuliapkg/GeneralUtils"
uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
version = "0.1.0" version = "0.1.0"
@@ -166,15 +237,50 @@ git-tree-sha1 = "f218fe3736ddf977e0e772bc9a586b2383da2685"
uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
version = "0.3.23" version = "0.3.23"
[[deps.Infinity]]
deps = ["Dates", "Random", "Requires"]
git-tree-sha1 = "cf8234411cbeb98676c173f930951ea29dca3b23"
uuid = "a303e19e-6eb4-11e9-3b09-cd9505f79100"
version = "0.2.4"
[[deps.InlineStrings]]
git-tree-sha1 = "45521d31238e87ee9f9732561bfee12d4eebd52d"
uuid = "842dd82b-1e85-43dc-bf29-5d0ee9dffc48"
version = "1.4.2"
[deps.InlineStrings.extensions]
ArrowTypesExt = "ArrowTypes"
ParsersExt = "Parsers"
[deps.InlineStrings.weakdeps]
ArrowTypes = "31f734f8-188a-4ce0-8406-c8a06bd891cd"
Parsers = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
[[deps.InteractiveUtils]] [[deps.InteractiveUtils]]
deps = ["Markdown"] deps = ["Markdown"]
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
[[deps.Intervals]]
deps = ["Dates", "Printf", "RecipesBase", "Serialization", "TimeZones"]
git-tree-sha1 = "ac0aaa807ed5eaf13f67afe188ebc07e828ff640"
uuid = "d8418881-c3e1-53bb-8760-2df7ec849ed5"
version = "1.10.0"
[[deps.InvertedIndices]]
git-tree-sha1 = "0dc7b50b8d436461be01300fd8cd45aa0274b038"
uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
version = "1.3.0"
[[deps.IrrationalConstants]] [[deps.IrrationalConstants]]
git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2" git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2"
uuid = "92d709cd-6900-40b7-9082-c6be49f344b6" uuid = "92d709cd-6900-40b7-9082-c6be49f344b6"
version = "0.2.2" version = "0.2.2"
[[deps.IterTools]]
git-tree-sha1 = "42d5f897009e7ff2cf88db414a389e5ed1bdd023"
uuid = "c8e1da08-722c-5040-9ed9-7db0dc04731e"
version = "1.10.0"
[[deps.IteratorInterfaceExtensions]] [[deps.IteratorInterfaceExtensions]]
git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856" git-tree-sha1 = "a3f24677c21f5bbe9d2a714f95dcd58337fb2856"
uuid = "82899510-4779-5014-852e-03e436cf321d" uuid = "82899510-4779-5014-852e-03e436cf321d"
@@ -200,16 +306,32 @@ version = "1.14.0"
[[deps.JuliaInterpreter]] [[deps.JuliaInterpreter]]
deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"] deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"]
git-tree-sha1 = "e9648d90370e2d0317f9518c9c6e0841db54a90b" git-tree-sha1 = "a6adc2dcfe4187c40dc7c2c9d2128e326360e90a"
uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a" uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a"
version = "0.9.31" version = "0.9.32"
[[deps.Kerberos_krb5_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
git-tree-sha1 = "60274b4ab38e8d1248216fe6b6ace75ae09b0502"
uuid = "b39eb1a6-c29a-53d7-8c32-632cd16f18da"
version = "1.19.3+0"
[[deps.LLMMCTS]] [[deps.LLMMCTS]]
deps = ["JSON3"] deps = ["GeneralUtils", "JSON3"]
path = "/appfolder/app/privatejuliapkg/LLMMCTS" path = "/appfolder/app/privatejuliapkg/LLMMCTS"
uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241" uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
version = "0.1.0" version = "0.1.0"
[[deps.LaTeXStrings]]
git-tree-sha1 = "50901ebc375ed41dbf8058da26f9de442febbbec"
uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
version = "1.3.1"
[[deps.LayerDicts]]
git-tree-sha1 = "6087ad3521d6278ebe5c27ae55e7bbb15ca312cb"
uuid = "6f188dcb-512c-564b-bc01-e0f76e72f166"
version = "1.0.0"
[[deps.LazyArtifacts]] [[deps.LazyArtifacts]]
deps = ["Artifacts", "Pkg"] deps = ["Artifacts", "Pkg"]
uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3" uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
@@ -233,6 +355,18 @@ deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"]
uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5" uuid = "e37daf67-58a4-590a-8e99-b0245dd2ffc5"
version = "1.6.4+0" version = "1.6.4+0"
[[deps.LibPQ]]
deps = ["CEnum", "DBInterface", "Dates", "Decimals", "DocStringExtensions", "FileWatching", "Infinity", "Intervals", "IterTools", "LayerDicts", "LibPQ_jll", "Libdl", "Memento", "OffsetArrays", "SQLStrings", "Tables", "TimeZones", "UTCDateTimes"]
git-tree-sha1 = "74feb1a63ebbcdcf1730016d2a4dfad0a655404f"
uuid = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
version = "1.17.1"
[[deps.LibPQ_jll]]
deps = ["Artifacts", "JLLWrappers", "Kerberos_krb5_jll", "Libdl", "OpenSSL_jll", "Pkg"]
git-tree-sha1 = "a299629703a93d8efcefccfc16b18ad9a073d131"
uuid = "08be9ffa-1c94-5ee5-a977-46a84ec9b350"
version = "14.3.0+1"
[[deps.LibSSH2_jll]] [[deps.LibSSH2_jll]]
deps = ["Artifacts", "Libdl", "MbedTLS_jll"] deps = ["Artifacts", "Libdl", "MbedTLS_jll"]
uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8" uuid = "29816b5a-b9ab-546f-933c-edad1886dfa8"
@@ -247,9 +381,9 @@ uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
[[deps.LogExpFunctions]] [[deps.LogExpFunctions]]
deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
git-tree-sha1 = "18144f3e9cbe9b15b070288eef858f71b291ce37" git-tree-sha1 = "a2d09619db4e765091ee5c6ffe8872849de0feea"
uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688" uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
version = "0.3.27" version = "0.3.28"
[deps.LogExpFunctions.extensions] [deps.LogExpFunctions.extensions]
LogExpFunctionsChainRulesCoreExt = "ChainRulesCore" LogExpFunctionsChainRulesCoreExt = "ChainRulesCore"
@@ -272,9 +406,9 @@ version = "1.0.3"
[[deps.LoweredCodeUtils]] [[deps.LoweredCodeUtils]]
deps = ["JuliaInterpreter"] deps = ["JuliaInterpreter"]
git-tree-sha1 = "c6a36b22d2cca0e1a903f00f600991f97bf5f426" git-tree-sha1 = "eeaedcf337f33c039f9f3a209a8db992deefd7e9"
uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b" uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
version = "2.4.6" version = "2.4.8"
[[deps.MQTTClient]] [[deps.MQTTClient]]
deps = ["Distributed", "Random", "Sockets"] deps = ["Distributed", "Random", "Sockets"]
@@ -307,6 +441,12 @@ deps = ["Artifacts", "Libdl"]
uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
version = "2.28.2+1" version = "2.28.2+1"
[[deps.Memento]]
deps = ["Dates", "Distributed", "Requires", "Serialization", "Sockets", "Test", "UUIDs"]
git-tree-sha1 = "bb2e8f4d9f400f6e90d57b34860f6abdc51398e5"
uuid = "f28f55f0-a522-5efc-85c2-fe41dfb9b2d9"
version = "1.4.1"
[[deps.MicroMamba]] [[deps.MicroMamba]]
deps = ["Pkg", "Scratch", "micromamba_jll"] deps = ["Pkg", "Scratch", "micromamba_jll"]
git-tree-sha1 = "011cab361eae7bcd7d278f0a7a00ff9c69000c51" git-tree-sha1 = "011cab361eae7bcd7d278f0a7a00ff9c69000c51"
@@ -322,6 +462,12 @@ version = "1.2.0"
[[deps.Mmap]] [[deps.Mmap]]
uuid = "a63ad114-7e13-5084-954f-fe012c677804" uuid = "a63ad114-7e13-5084-954f-fe012c677804"
[[deps.Mocking]]
deps = ["Compat", "ExprTools"]
git-tree-sha1 = "bf17d9cb4f0d2882351dfad030598f64286e5936"
uuid = "78c3b35d-d492-501b-9361-3d52fe80e533"
version = "0.7.8"
[[deps.MozillaCACerts_jll]] [[deps.MozillaCACerts_jll]]
uuid = "14a3606d-f60d-562e-9121-12d972cd8159" uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
version = "2023.1.10" version = "2023.1.10"
@@ -336,6 +482,17 @@ version = "1.0.2"
uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
version = "1.2.0" version = "1.2.0"
[[deps.OffsetArrays]]
git-tree-sha1 = "1a27764e945a152f7ca7efa04de513d473e9542e"
uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
version = "1.14.1"
[deps.OffsetArrays.extensions]
OffsetArraysAdaptExt = "Adapt"
[deps.OffsetArrays.weakdeps]
Adapt = "79e6a3ab-5dfb-504d-930d-738a2a938a0e"
[[deps.OpenBLAS_jll]] [[deps.OpenBLAS_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"] deps = ["Artifacts", "CompilerSupportLibraries_jll", "Libdl"]
uuid = "4536629a-c528-5b80-bd46-f80d51c5b363" uuid = "4536629a-c528-5b80-bd46-f80d51c5b363"
@@ -354,9 +511,9 @@ version = "1.4.3"
[[deps.OpenSSL_jll]] [[deps.OpenSSL_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl"] deps = ["Artifacts", "JLLWrappers", "Libdl"]
git-tree-sha1 = "3da7367955dcc5c54c1ba4d402ccdc09a1a3e046" git-tree-sha1 = "a12e56c72edee3ce6b96667745e6cbbe5498f200"
uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95" uuid = "458c3c95-2e84-50aa-8efc-19380b2a3a95"
version = "3.0.13+1" version = "1.1.23+0"
[[deps.OpenSpecFun_jll]] [[deps.OpenSpecFun_jll]]
deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"] deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
@@ -392,6 +549,12 @@ deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
version = "1.10.0" version = "1.10.0"
[[deps.PooledArrays]]
deps = ["DataAPI", "Future"]
git-tree-sha1 = "36d8b4b899628fb92c2749eb488d884a926614d3"
uuid = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
version = "1.4.3"
[[deps.PrecompileTools]] [[deps.PrecompileTools]]
deps = ["Preferences"] deps = ["Preferences"]
git-tree-sha1 = "5aa36f7049a63a1528fe8f7c3f2113413ffd4e1f" git-tree-sha1 = "5aa36f7049a63a1528fe8f7c3f2113413ffd4e1f"
@@ -409,6 +572,12 @@ git-tree-sha1 = "142ee93724a9c5d04d78df7006670a93ed1b244e"
uuid = "54e16d92-306c-5ea0-a30b-337be88ac337" uuid = "54e16d92-306c-5ea0-a30b-337be88ac337"
version = "0.4.2" version = "0.4.2"
[[deps.PrettyTables]]
deps = ["Crayons", "LaTeXStrings", "Markdown", "PrecompileTools", "Printf", "Reexport", "StringManipulation", "Tables"]
git-tree-sha1 = "66b20dd35966a748321d3b2537c4584cf40387c7"
uuid = "08abe8d2-0d0c-5749-adfa-8a2ac140af0d"
version = "2.3.2"
[[deps.Printf]] [[deps.Printf]]
deps = ["Unicode"] deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
@@ -438,6 +607,12 @@ uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
deps = ["SHA"] deps = ["SHA"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
[[deps.RecipesBase]]
deps = ["PrecompileTools"]
git-tree-sha1 = "5c3d09cc4f31f5fc6af001c250bf1278733100ff"
uuid = "3cdcf5f2-1ef4-517c-9805-6587b60abb01"
version = "1.3.4"
[[deps.Reexport]] [[deps.Reexport]]
git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b" git-tree-sha1 = "45e428421666073eab6f2da5c9d310d99bb12f9b"
uuid = "189a3867-3050-52da-a836-e630ba90ab69" uuid = "189a3867-3050-52da-a836-e630ba90ab69"
@@ -451,9 +626,9 @@ version = "1.3.0"
[[deps.Revise]] [[deps.Revise]]
deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Pkg", "REPL", "Requires", "UUIDs", "Unicode"] deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "Pkg", "REPL", "Requires", "UUIDs", "Unicode"]
git-tree-sha1 = "12aa2d7593df490c407a3bbd8b86b8b515017f3e" git-tree-sha1 = "85ddd93ea15dcd8493400600e09104a9e94bb18d"
uuid = "295af30f-e4ad-537b-8983-00126c2a3abe" uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
version = "3.5.14" version = "3.5.15"
[[deps.Rmath]] [[deps.Rmath]]
deps = ["Random", "Rmath_jll"] deps = ["Random", "Rmath_jll"]
@@ -471,12 +646,29 @@ version = "0.4.2+0"
uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce" uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
version = "0.7.0" version = "0.7.0"
[[deps.SQLLLM]]
deps = ["CSV", "CondaPkg", "DataFrames", "DataStructures", "Dates", "DispatchDoctor", "FileIO", "FormatCorrector", "GeneralUtils", "HTTP", "JSON3", "LLMMCTS", "LibPQ", "MQTTClient", "PrettyPrinting", "PythonCall", "Random", "Revise", "Tables", "URIs", "UUIDs"]
path = "/appfolder/app/privatejuliapkg/SQLLLM"
uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
version = "0.1.0"
[[deps.SQLStrings]]
git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
uuid = "af517c2e-c243-48fa-aab8-efac3db270f5"
version = "0.1.0"
[[deps.Scratch]] [[deps.Scratch]]
deps = ["Dates"] deps = ["Dates"]
git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386" git-tree-sha1 = "3bac05bc7e74a75fd9cba4295cde4045d9fe2386"
uuid = "6c6a2e73-6563-6170-7368-637461726353" uuid = "6c6a2e73-6563-6170-7368-637461726353"
version = "1.2.1" version = "1.2.1"
[[deps.SentinelArrays]]
deps = ["Dates", "Random"]
git-tree-sha1 = "ff11acffdb082493657550959d4feb4b6149e73a"
uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
version = "1.4.5"
[[deps.Serialization]] [[deps.Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
@@ -542,6 +734,12 @@ version = "1.3.1"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112" InverseFunctions = "3587e190-3f89-42d0-90ee-14403ec27112"
[[deps.StringManipulation]]
deps = ["PrecompileTools"]
git-tree-sha1 = "a04cabe79c5f01f4d723cc6704070ada0b9d46d5"
uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
version = "0.3.4"
[[deps.StructTypes]] [[deps.StructTypes]]
deps = ["Dates", "UUIDs"] deps = ["Dates", "UUIDs"]
git-tree-sha1 = "ca4bccb03acf9faaf4137a9abc1881ed1841aa70" git-tree-sha1 = "ca4bccb03acf9faaf4137a9abc1881ed1841aa70"
@@ -562,6 +760,12 @@ deps = ["Dates"]
uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76" uuid = "fa267f1f-6049-4f14-aa54-33bafae1ed76"
version = "1.0.3" version = "1.0.3"
[[deps.TZJData]]
deps = ["Artifacts"]
git-tree-sha1 = "1607ad46cf8d642aa779a1d45af1c8620dbf6915"
uuid = "dc5dba14-91b3-4cab-a142-028a31da12f7"
version = "1.2.0+2024a"
[[deps.TableTraits]] [[deps.TableTraits]]
deps = ["IteratorInterfaceExtensions"] deps = ["IteratorInterfaceExtensions"]
git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39" git-tree-sha1 = "c06b2f539df1c6efa794486abfb6ed2022561a39"
@@ -583,10 +787,20 @@ version = "1.10.0"
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
[[deps.TimeZones]]
deps = ["Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"]
git-tree-sha1 = "a6ae8d7a27940c33624f8c7bde5528de21ba730d"
uuid = "f269a46b-ccf7-5d73-abea-4c690281aa53"
version = "1.17.0"
weakdeps = ["RecipesBase"]
[deps.TimeZones.extensions]
TimeZonesRecipesBaseExt = "RecipesBase"
[[deps.TranscodingStreams]] [[deps.TranscodingStreams]]
git-tree-sha1 = "5d54d076465da49d6746c647022f3b3674e64156" git-tree-sha1 = "60df3f8126263c0d6b357b9a1017bb94f53e3582"
uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa" uuid = "3bb67fe8-82b1-5028-8e26-92a6c54297fa"
version = "0.10.8" version = "0.11.0"
weakdeps = ["Random", "Test"] weakdeps = ["Random", "Test"]
[deps.TranscodingStreams.extensions] [deps.TranscodingStreams.extensions]
@@ -597,6 +811,12 @@ git-tree-sha1 = "67db6cc7b3821e19ebe75791a9dd19c9b1188f2b"
uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" uuid = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
version = "1.5.1" 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.UUIDs]]
deps = ["Random", "SHA"] deps = ["Random", "SHA"]
uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" uuid = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
@@ -609,6 +829,17 @@ git-tree-sha1 = "c81331b3b2e60a982be57c046ec91f599ede674a"
uuid = "e17b2a0c-0bdf-430a-bd0c-3a23cae4ff39" uuid = "e17b2a0c-0bdf-430a-bd0c-3a23cae4ff39"
version = "1.0.0" 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.Zlib_jll]]
deps = ["Libdl"] deps = ["Libdl"]
uuid = "83775a58-1f1d-513f-b197-d71354ab007a" uuid = "83775a58-1f1d-513f-b197-d71354ab007a"

2
Project.toml
View File

@@ -15,5 +15,7 @@ MQTTClient = "985f35cc-2c3d-4943-b8c1-f0931d5f0959"
PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337" PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d" PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

1196
src/interface BACKUP.jl Normal file
View File

File diff suppressed because it is too large Load Diff

919
src/interface.jl
View File

@@ -39,8 +39,8 @@ macro executeStringFunction(functionStr, args...)
func_expr = Meta.parse(functionStr) func_expr = Meta.parse(functionStr)
# Create a new function with the parsed expression # Create a new function with the parsed expression
function_to_call = eval(Expr(:function, Expr(:call, func_expr, args...), function_to_call = eval(Expr(:function,
func_expr.args[2:end]...)) Expr(:call, func_expr, args...), func_expr.args[2:end]...))
# Call the newly created function with the provided arguments # Call the newly created function with the provided arguments
function_to_call(args...) function_to_call(args...)
@@ -97,169 +97,328 @@ julia> output_thoughtDict = Dict(
# Signature # Signature
""" """
function decisionMaker(config::T1, state::T2)::Dict{Symbol, Any} where {T1<:AbstractDict, T2<:AbstractDict} function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
customerinfo =
# lessonDict = copy(JSON3.read("lesson.json"))
# lesson =
# if isempty(lessonDict)
# ""
# else
# lessons = Dict{Symbol, Any}()
# for (k, v) in lessonDict
# lessons[k] = lessonDict[k][:lesson]
# end
# """
# You have attempted to help the user before and failed, either because your reasoning for the
# recommendation was incorrect or your response did not exactly match the user expectation.
# The following lesson(s) give a plan to avoid failing to help the user in the same way you
# did previously. Use them to improve your strategy to help the user.
# Here are some lessons in JSON format:
# $(JSON3.write(lessons))
# When providing the thought and action for the current trial, that into account these failed
# trajectories and make sure not to repeat the same mistakes and incorrect answers.
# """
# end
# _prompt =
# """
# You are a helpful sommelier working for a wine store.
# Your goal is to recommend the best wine from your inventory that match the user preferences.
# You are also keen to improve your recommendation with lesson(s).
# You must follow the following criteria:
# 1) Get to know how much the user willing to spend
# 2) Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
# 3) Get to know what occasion the user is buying wine for
# 4) Get to know what characteristics of wine the user is looking for
# e.g. tannin, sweetness, intensity, acidity
# 5) Get to know what food the user will have with wine
# 6) Check your inventory for the best wine that match the user preference
# 7) Recommend wine to the user
# You should only respond with interleaving Thought, Action, Observation steps.
# Thought can reason about the current situation, and Action can be three types:
# 1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
# 2) chatbox[text], which you can use to interact with the user.
# After each observation, provide the next Thought and next Action.
# You should only respond in JSON format as describe below:
# {
# "thought": "your reasoning",
# "action": {"name": "action to take", "input": "action input"},
# "observation": "result of the action"
# }
# Here are some examples:
# {
# "question": "I would like to buy a sedan with 8 seats.",
# "thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
# "action_1": {"name": "inventory", "input": "sedan with 8 seats."},
# "observation_1": "Several model has 8 seats. Available color are black, red green"
# }
# {
# "thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
# "action": {"name": "chatbox", "input": "Which color do you like?"}
# "observation": "I'll take black."
# }
# $lesson
# Let's begin!
# $(JSON3.write(state[:thoughtHistory]))
# {"thought"
# """
systemmsg =
""" """
I will give you the following information about customer: You are a helpful sommelier working for a wine store.
$(JSON3.write(state[:customerinfo])) Your task is to help the user choose the best wine that match the user preferences from your inventory.
You are also eager to improve your helpfulness.
You must follow the following guidelines:
- Get to know how much the user willing to spend
- Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
- Get to know what occasion the user is buying wine for
- Get to know what characteristics of wine the user is looking for e.g. tannin, sweetness, intensity, acidity
- Get to know what food the user will have with wine
At each round of conversation, the user will give you the current situation:
Context: ...
Your earlier conversation with the user: ...
You should then respond to the user with interleaving Thought, Plan, Action and Observation:
- thought:
1) State your reasoning about the current situation.
- plan: Based on the current situation, what would you do to complete the task? Be specific.
- action (Must be aligned with your plan): Can be one of the following functions:
1) CHATBOX[text], which you can use to talk with the user. "text" is in verbal English.
2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English.
- observation: result of the action.
You should only respond in format as described below:
thought: ...
plan: ...
action_name: ...
action_input: ...
observation: ...
Let's begin!
""" """
storeinfo = usermsg =
""" """
I will give you the following information about your store: Context: None
$(JSON3.write(state[:storeinfo])) Your earlier conversation with the user: $(chatHistoryToString(a))
""" """
lessonDict = copy(JSON3.read("lesson.json"))
lesson =
if isempty(lessonDict)
""
else
lessons = Dict{Symbol, Any}()
for (k, v) in lessonDict
lessons[k] = lessonDict[k][:lesson]
end
"""
You have attempted to help the user before and failed, either because your reasoning for the
recommendation was incorrect or your response did not exactly match the user expectation.
The following lesson(s) give a plan to avoid failing to help the user in the same way you
did previously. Use them to improve your strategy to help the user.
Here are some lessons in JSON format:
$(JSON3.write(lessons))
When providing the thought and action for the current trial, that into account these failed
trajectories and make sure not to repeat the same mistakes and incorrect answers.
"""
end
_prompt = _prompt =
""" [
You are a helpful sommelier working for a wine store. Dict(:name=> "system", :text=> systemmsg),
Your goal is to recommend the best wine from your inventory that match the user preferences. Dict(:name=> "user", :text=> usermsg)
You are also keen to improve your recommendation with lesson(s). ]
You must follow the following criteria:
1) Get to know how much the user willing to spend
2) Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
3) Get to know what occasion the user is buying wine for
4) Get to know what characteristics of wine the user is looking for
e.g. tannin, sweetness, intensity, acidity
5) Get to know what food the user will have with wine
6) Check your inventory for the best wine that match the user preference
7) Recommend wine to the user
You should only respond with interleaving Thought, Action, Observation steps.
Thought can reason about the current situation, and Action can be three types:
1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
2) chatbox[text], which you can use to interact with the user.
3) recommendbox[answer], which returns your wine recommendation to the user.
After each observation, provide the next Thought and next Action.
You should only respond in JSON format as describe below:
{
"thought": "your reasoning",
"action": {"name": "action to take", "input": "action input"},
"observation": "result of the action"
}
Here are some examples:
{
"question": "I would like to buy a sedan with 8 seats.",
"thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
"action_1": {"name": "inventory", "input": "sedan with 8 seats."},
"observation_1": "Several model has 8 seats. Available color are black, red green"
}
{
"thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
"action": {"name": "chatbox", "input": "Which color do you like?"}
"observation": "I'll take black."
}
$lesson # put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
Let's begin! prompt *=
"""
$(JSON3.write(state[:thoughtHistory])) <|start_header_id|>assistant<|end_header_id|>
{"thought" """
""" response = nothing # store for show when error msg show up
for attempt in 1:10
# apply LLM specific instruct format
externalService = config[:externalservice][:text2textinstruct]
llminfo = externalService[:llminfo]
prompt =
if llminfo[:name] == "llama3instruct"
formatLLMtext_llama3instruct("system", _prompt)
else
error("llm model name is not defied yet $(@__LINE__)")
end
msgMeta = GeneralUtils.generate_msgMeta(
externalService[:mqtttopic],
senderName= "decisionMaker",
senderId= string(uuid4()),
receiverName= "text2textinstruct",
mqttBroker= config[:mqttServerInfo][:broker],
mqttBrokerPort= config[:mqttServerInfo][:port],
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
:kwargs=> Dict(
:max_tokens=> 512,
:stop=> ["<|eot_id|>"],
)
)
)
@show outgoingMsg
for attempt in 1:5
try try
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) response = a.text2textInstructLLM(prompt)
_responseJsonStr = response[:response][:text] responsedict = GeneralUtils.textToDict(response,
expectedJsonExample = ["thought", "plan", "action_name", "action_input", "observation"],
""" rightmarker=":", symbolkey=true)
Here is an expected JSON format:
{
"thought": "...",
"action": {"name": "...", "input": "..."},
"observation": "..."
}
"""
responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
thoughtDict = copy(JSON3.read(responseJsonStr))
# check if dict has all required value if responsedict[:action_name] ∉ ["CHATBOX", "WINESTOCK"]
thought::AbstractString = thoughtDict[:thought] error("decisionMaker didn't use the given functions ", @__LINE__)
actionname::AbstractString = thoughtDict[:action][:name]
actioninput::AbstractString = thoughtDict[:action][:input]
if actionname ∈ ["winestock", "chatbox", "recommendbox"]
# LLM use available function
elseif thought == ""
error("DecisionMaker has no thought")
elseif length(actioninput) == 0
error("DecisionMaker has no actioninput")
else
error("DecisionMaker use wrong function")
end end
return thoughtDict for i ∈ [:thought, :plan, :action_name]
if length(JSON3.write(responsedict[i])) == 0
error("$i is empty ", @__LINE__)
end
end
# check if there are more than 1 key per categories
for i ∈ [:thought, :plan, :action_name, :action_input, :observation]
matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
if length(matchkeys) > 1
error("DecisionMaker has more than one key per categories")
end
end
println("--> 1")
pprintln(responsedict)
return responsedict
catch e catch e
io = IOBuffer() io = IOBuffer()
showerror(io, e) showerror(io, e)
errorMsg = String(take!(io)) errorMsg = String(take!(io))
st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace())) st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
println("") println("")
@warn "Attempt $attempt. Error occurred: $errorMsg\n$st" println("Attempt $attempt. Error occurred: $errorMsg\n$st")
println("") println("")
end end
end end
error("DecisionMaker failed to generate a thought") error("DecisionMaker failed to generate a thought ", response)
end end
# function decisionMaker(a::T)::Dict{Symbol, Any} where {T<:agent}
# # lessonDict = copy(JSON3.read("lesson.json"))
# # lesson =
# # if isempty(lessonDict)
# # ""
# # else
# # lessons = Dict{Symbol, Any}()
# # for (k, v) in lessonDict
# # lessons[k] = lessonDict[k][:lesson]
# # end
# # """
# # You have attempted to help the user before and failed, either because your reasoning for the
# # recommendation was incorrect or your response did not exactly match the user expectation.
# # The following lesson(s) give a plan to avoid failing to help the user in the same way you
# # did previously. Use them to improve your strategy to help the user.
# # Here are some lessons in JSON format:
# # $(JSON3.write(lessons))
# # When providing the thought and action for the current trial, that into account these failed
# # trajectories and make sure not to repeat the same mistakes and incorrect answers.
# # """
# # end
# _prompt =
# """
# You are a helpful sommelier working for a wine store.
# Your goal is to recommend the best wine from your inventory that match the user preferences.
# You are also keen to improve your recommendation with lesson(s).
# You must follow the following criteria:
# 1) Get to know how much the user willing to spend
# 2) Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
# 3) Get to know what occasion the user is buying wine for
# 4) Get to know what characteristics of wine the user is looking for
# e.g. tannin, sweetness, intensity, acidity
# 5) Get to know what food the user will have with wine
# 6) Check your inventory for the best wine that match the user preference
# 7) Recommend wine to the user
# You should only respond with interleaving Thought, Action, Observation steps.
# Thought can reason about the current situation, and Action can be three types:
# 1) winestock[query], which you can use to find wine in your inventory. The more input data the better.
# 2) chatbox[text], which you can use to interact with the user.
# After each observation, provide the next Thought and next Action.
# You should only respond in JSON format as describe below:
# {
# "thought": "your reasoning",
# "action": {"name": "action to take", "input": "action input"},
# "observation": "result of the action"
# }
# Here are some examples:
# {
# "question": "I would like to buy a sedan with 8 seats.",
# "thought_1": "Our showroom carries various vehicle model. But I'm not sure whether we have a models that fits the user demand, I need to check our inventory.",
# "action_1": {"name": "inventory", "input": "sedan with 8 seats."},
# "observation_1": "Several model has 8 seats. Available color are black, red green"
# }
# {
# "thought": "I have a few color for the user to choose from. I will ask him what color he likes.",
# "action": {"name": "chatbox", "input": "Which color do you like?"}
# "observation": "I'll take black."
# }
# $lesson
# Let's begin!
# $(JSON3.write(state[:thoughtHistory]))
# {"thought"
# """
# # apply LLM specific instruct format
# externalService = config[:externalservice][:text2textinstruct]
# llminfo = externalService[:llminfo]
# prompt =
# if llminfo[:name] == "llama3instruct"
# formatLLMtext_llama3instruct("system", _prompt)
# else
# error("llm model name is not defied yet $(@__LINE__)")
# end
# msgMeta = GeneralUtils.generate_msgMeta(
# externalService[:mqtttopic],
# senderName= "decisionMaker",
# 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|>"],
# )
# )
# )
# @show outgoingMsg
# for attempt in 1:5
# try
# response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# _responseJsonStr = response[:response][:text]
# expectedJsonExample =
# """
# Here is an expected JSON format:
# {
# "thought": "...",
# "action": {"name": "...", "input": "..."},
# "observation": "..."
# }
# """
# responseJsonStr = jsoncorrection(config, _responseJsonStr, expectedJsonExample)
# thoughtDict = copy(JSON3.read(responseJsonStr))
# # check if dict has all required value
# thought::AbstractString = thoughtDict[:thought]
# actionname::AbstractString = thoughtDict[:action][:name]
# actioninput::AbstractString = thoughtDict[:action][:input]
# if actionname ∈ ["winestock", "chatbox", "recommendbox"]
# # LLM use available function
# elseif thought == ""
# error("DecisionMaker has no thought")
# elseif length(actioninput) == 0
# error("DecisionMaker has no actioninput")
# else
# error("DecisionMaker use wrong function")
# end
# return thoughtDict
# catch e
# io = IOBuffer()
# showerror(io, e)
# errorMsg = String(take!(io))
# st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
# println("")
# @warn "Attempt $attempt. Error occurred: $errorMsg\n$st"
# println("")
# end
# end
# error("DecisionMaker failed to generate a thought")
# end
""" Assigns a scalar value to each new child node to be used for selec- """ Assigns a scalar value to each new child node to be used for selec-
@@ -551,97 +710,132 @@ function reflector(config::T1, state::T2)::String where {T1<:AbstractDict, T2<:A
end end
""" Get a new state
# Arguments
- `a::T1`
one of YiemAgent's agent
- `state::T2`
current game state
- `thoughtDict::T3`
contain Thought, Action, Observation
- `isterminal::Function`
a function to determine terminal state
# Return
- `(newNodeKey, newstate, isterminalstate, reward)::Tuple{String, Dict{Symbol, <:Any}, Bool, <:Number}`
# Example # """ Chat with llm.
```jldoctest
julia> state = Dict{Symbol, Dict{Symbol, Any}}(
:thoughtHistory => Dict(:question => "Hello, I want to buy a bottle of wine."),
:storeinfo => Dict(),
:customerinfo => Dict()
)
julia> thoughtDict = Dict(
:question=> "I want to buy a bottle of wine.",
:thought_1=> "The customer wants to buy a bottle of wine.",
:action_1=> Dict{Symbol, Any}(
:name=>"Chatbox",
:input=>"What occasion are you buying the wine for?",
),
:observation_1 => ""
)
```
# TODO # # Arguments
- [] add other actions # `a::agent`
- [WORKING] add embedding of newstate and store in newstate[:embedding] # an agent
# # Return
# None
# Signature # # Example
""" # ```jldoctest
function transition(state::T2, config::T1, decisionMaker::Function, evaluator::Function, # julia> using JSON3, UUIDs, Dates, FileIO, MQTTClient, ChatAgent
reflector::Function # julia> const mqttBroker = "mqtt.yiem.cc"
)::Tuple{String, Dict{Symbol, <:Any}, Integer} where {T1<:AbstractDict, T2<:AbstractDict} # julia> mqttclient, connection = MakeConnection(mqttBroker, 1883)
# julia> tools=Dict( # update input format
# "askbox"=>Dict(
# :description => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
# :input => "<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>",
# :output => "" ,
# :func => nothing,
# ),
# )
# julia> msgMeta = Dict(
# :msgPurpose=> "updateStatus",
# :from=> "agent",
# :to=> "llmAI",
# :requestresponse=> "request",
# :sendto=> "", # destination topic
# :replyTo=> "agent/api/v0.1.0/txt/response", # requester ask responseer to send reply to this topic
# :repondToMsgId=> "", # responseer is responseing to this msg id
# :taskstatus=> "", # "complete", "fail", "waiting" or other status
# :timestamp=> Dates.now(),
# :msgId=> "$(uuid4())",
# )
# julia> a = ChatAgent.agentReflex(
# "Jene",
# mqttclient,
# msgMeta,
# agentConfigTopic, # I need a function to send msg to config topic to get load balancer
# role=:sommelier,
# tools=tools
# )
# julia> newAgent = ChatAgent.agentReact(agent)
# julia> response = ChatAgent.conversation(newAgent, "Hi! how are you?")
# ```
thoughtDict = decisionMaker(config, state) # # TODO
# - [] update docstring
# - [x] MCTS() for planning
# - [] add recap to initialState for earlier completed question
# - [WORKING] conversation loop
actionname = thoughtDict[:action][:name] # # Signature
actioninput = thoughtDict[:action][:input] # """
# function conversation(a::T, userinput::Dict) where {T<:agent}
# config = deepcopy(a.config)
# pprint(config)
# if userinput[:text] == "newtopic"
# clearhistory(a)
# return "Okay. What shall we talk about?"
# else
# # add usermsg to a.chathistory
# addNewMessage(a, "user", userinput[:text])
# map action and input() to llm function # if isempty(a.plan[:currenttrajectory])
response, select, reward, isterminal =
if actionname == "chatbox"
# deepcopy(state[:virtualCustomerChatHistory]) because I want to keep it clean
# so that other simulation start from this same node is not contaminated with actioninput
virtualWineUserChatbox(config, actioninput, deepcopy(state[:virtualCustomerChatHistory])) # virtual customer
elseif actionname == "winestock"
winestock(config, actioninput)
elseif actionname == "recommendbox"
virtualWineUserRecommendbox(config, actioninput)
else
error("undefined LLM function. Requesting $actionname")
end
newNodeKey, newstate = LLMMCTS.makeNewState(state, thoughtDict, response, select, reward, # # initial state
isterminal) # a.plan[:currenttrajectory] = Dict{Symbol, Any}(
if actionname == "chatbox" # # deepcopy the info to prevent modifying the info unintentionally during MCTS planning
push!(newstate[:virtualCustomerChatHistory], Dict(:name=>"assistant", :text=> actioninput) ) # :customerinfo=> deepcopy(a.keywordinfo[:customerinfo]),
push!(newstate[:virtualCustomerChatHistory], Dict(:name=>"user", :text=> response)) # :storeinfo=> deepcopy(a.keywordinfo[:storeinfo]),
end # :userselect=> nothing,
# :reward=> 0,
# :isterminal=> false,
# :evaluation=> nothing,
# :lesson=> nothing,
stateevaluation, progressvalue = evaluator(config, newstate) # :totalTrajectoryReward=> nothing,
if newstate[:reward] < 0 # # contain question, thought_1, action_1, observation_1, thought_2, ...
pprint(newstate[:thoughtHistory]) # :thoughtHistory=> OrderedDict{Symbol, Any}(
newstate[:evaluation] = stateevaluation # #[] :recap=>,
newstate[:lesson] = reflector(config, newstate) # :question=> userinput[:text],
# ),
# store new lesson for later use # # store conversation for virtual customer because the virtual customer agent is just
lessonDict = copy(JSON3.read("lesson.json")) # # a function and stateless.
latestLessonKey, latestLessonIndice = # :virtualCustomerChatHistory=> Vector{Dict{Symbol, Any}}(
GeneralUtils.findHighestIndexKey(lessonDict, "lesson") # [Dict(:name=> "user", :text=> userinput[:text])]
nextIndice = latestLessonKey == :NA ? 1 : latestLessonIndice + 1 # ),
newLessonKey = Symbol("lesson_$(nextIndice)") # )
lessonDict[newLessonKey] = newstate # else
open("lesson.json", "w") do io # _, a.plan[:currenttrajectory] = makeNewState(a.plan[:currenttrajectory],
JSON3.pretty(io, lessonDict) # a.plan[:activeplan][:thoughtHistory], userinput[:text], userinput[:select],
end # userinput[:reward], userinput[:isterminal])
print("---> reflector()") # end
end # end
return (newNodeKey, newstate, progressvalue) # while true
end # bestNextState, besttrajectory = LLMMCTS.runMCTS(a.plan[:currenttrajectory],
# transition, config, decisionMaker, evaluator, reflector;
# totalsample=2, maxDepth=3, maxiterations=3, explorationweight=1.0)
# a.plan[:activeplan] = bestNextState
# latestActionKey, latestActionIndice =
# GeneralUtils.findHighestIndexKey(bestNextState[:thoughtHistory], "action")
# actionname = bestNextState[:thoughtHistory][latestActionKey][:name]
# actioninput = bestNextState[:thoughtHistory][latestActionKey][:input]
# # transition
# if actionname == "chatbox"
# # add usermsg to a.chathistory
# addNewMessage(a, "assistant", actioninput)
# return actioninput
# elseif actionname == "recommendbox"
# # add usermsg to a.chathistory
# addNewMessage(a, "assistant", actioninput)
# return actioninput
# else
# _, a.plan[:currenttrajectory] = transition(a, a.plan[:currenttrajectory], a.plan[:activeplan])
# end
# end
# end
@@ -700,8 +894,7 @@ julia> response = ChatAgent.conversation(newAgent, "Hi! how are you?")
# Signature # Signature
""" """
function conversation(a::T, userinput::Dict) where {T<:agent} function conversation(a::T, userinput::Dict) where {T<:agent}
config = deepcopy(a.config)
pprint(config)
if userinput[:text] == "newtopic" if userinput[:text] == "newtopic"
clearhistory(a) clearhistory(a)
return "Okay. What shall we talk about?" return "Okay. What shall we talk about?"
@@ -709,64 +902,246 @@ function conversation(a::T, userinput::Dict) where {T<:agent}
# add usermsg to a.chathistory # add usermsg to a.chathistory
addNewMessage(a, "user", userinput[:text]) addNewMessage(a, "user", userinput[:text])
if isempty(a.plan[:currenttrajectory]) thought = think(a)
# initial state # thought will be added to chat model via context
a.plan[:currenttrajectory] = Dict{Symbol, Any}( chatresponse = generatechat(a, thought)
# deepcopy the info to prevent modifying the info unintentionally during MCTS planning
:customerinfo=> deepcopy(a.keywordinfo[:customerinfo]),
:storeinfo=> deepcopy(a.keywordinfo[:storeinfo]),
:userselect=> nothing,
:reward=> 0,
:isterminal=> false,
:evaluation=> nothing,
:lesson=> nothing,
:totalTrajectoryReward=> nothing, return chatresponse
# contain question, thought_1, action_1, observation_1, thought_2, ...
:thoughtHistory=> OrderedDict{Symbol, Any}(
#[] :recap=>,
:question=> userinput[:text],
),
# store conversation for virtual customer because the virtual customer agent is just
# a function and stateless.
:virtualCustomerChatHistory=> Vector{Dict{Symbol, Any}}(
[Dict(:name=> "user", :text=> userinput[:text])]
),
)
else
_, a.plan[:currenttrajectory] = makeNewState(a.plan[:currenttrajectory],
a.plan[:activeplan][:thoughtHistory], userinput[:text], userinput[:select],
userinput[:reward], userinput[:isterminal])
end
end end
while true
bestNextState, besttrajectory = LLMMCTS.runMCTS(a.plan[:currenttrajectory],
transition, config, decisionMaker, evaluator, reflector;
totalsample=2, maxDepth=3, maxiterations=3, explorationweight=1.0)
a.plan[:activeplan] = bestNextState
latestActionKey, latestActionIndice = end
GeneralUtils.findHighestIndexKey(bestNextState[:thoughtHistory], "action") # function conversation(a::T, userinput::Dict) where {T<:agent}
actionname = bestNextState[:thoughtHistory][latestActionKey][:name] # config = deepcopy(a.config)
actioninput = bestNextState[:thoughtHistory][latestActionKey][:input] # pprint(config)
# if userinput[:text] == "newtopic"
# clearhistory(a)
# return "Okay. What shall we talk about?"
# else
# # add usermsg to a.chathistory
# addNewMessage(a, "user", userinput[:text])
# transition # if isempty(a.plan[:currenttrajectory])
if actionname == "chatbox"
# add usermsg to a.chathistory # # initial state
addNewMessage(a, "assistant", actioninput) # a.plan[:currenttrajectory] = Dict{Symbol, Any}(
return actioninput # # deepcopy the info to prevent modifying the info unintentionally during MCTS planning
elseif actionname == "recommendbox" # :customerinfo=> deepcopy(a.keywordinfo[:customerinfo]),
# add usermsg to a.chathistory # :storeinfo=> deepcopy(a.keywordinfo[:storeinfo]),
addNewMessage(a, "assistant", actioninput) # :userselect=> nothing,
return actioninput # :reward=> 0,
# :isterminal=> false,
# :evaluation=> nothing,
# :lesson=> nothing,
# :totalTrajectoryReward=> nothing,
# # contain question, thought_1, action_1, observation_1, thought_2, ...
# :thoughtHistory=> OrderedDict{Symbol, Any}(
# #[] :recap=>,
# :question=> userinput[:text],
# ),
# # store conversation for virtual customer because the virtual customer agent is just
# # a function and stateless.
# :virtualCustomerChatHistory=> Vector{Dict{Symbol, Any}}(
# [Dict(:name=> "user", :text=> userinput[:text])]
# ),
# )
# else
# _, a.plan[:currenttrajectory] = makeNewState(a.plan[:currenttrajectory],
# a.plan[:activeplan][:thoughtHistory], userinput[:text], userinput[:select],
# userinput[:reward], userinput[:isterminal])
# end
# end
# while true
# bestNextState, besttrajectory = LLMMCTS.runMCTS(a.plan[:currenttrajectory],
# transition, config, decisionMaker, evaluator, reflector;
# totalsample=2, maxDepth=3, maxiterations=3, explorationweight=1.0)
# a.plan[:activeplan] = bestNextState
# latestActionKey, latestActionIndice =
# GeneralUtils.findHighestIndexKey(bestNextState[:thoughtHistory], "action")
# actionname = bestNextState[:thoughtHistory][latestActionKey][:name]
# actioninput = bestNextState[:thoughtHistory][latestActionKey][:input]
# # transition
# if actionname == "chatbox"
# # add usermsg to a.chathistory
# addNewMessage(a, "assistant", actioninput)
# return actioninput
# elseif actionname == "recommendbox"
# # add usermsg to a.chathistory
# addNewMessage(a, "assistant", actioninput)
# return actioninput
# else
# _, a.plan[:currenttrajectory] = transition(a, a.plan[:currenttrajectory], a.plan[:activeplan])
# end
# end
# end
"""
# Arguments
# Return
# Example
```jldoctest
julia>
```
# TODO
- [] update docstring
- [x] implement the function
- [x] add try block. check result that it is expected before returning
# Signature
"""
function think(a::T) where {T<:agent}
thoughtDict = decisionMaker(a)
actionname = thoughtDict[:action_name]
actioninput = thoughtDict[:action_input]
# map action and input() to llm function
response =
if actionname == "CHATBOX"
(result=actioninput, errormsg=nothing, success=true)
elseif actionname == "WINESTOCK"
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
winestock(actioninput, DBconnection, a.text2textInstructLLM)
else else
_, a.plan[:currenttrajectory] = transition(a, a.plan[:currenttrajectory], a.plan[:activeplan]) error("undefined LLM function. Requesting $actionname")
end
# this section allow LLM functions above to have different return values.
result = haskey(response, :result) ? response[:result] : nothing
select = haskey(response, :select) ? response[:select] : nothing
reward::Integer = haskey(response, :reward) ? response[:reward] : 0
isterminal::Bool = haskey(response, :isterminal) ? response[:isterminal] : false
errormsg::Union{AbstractString, Nothing} = haskey(response, :errormsg) ? response[:errormsg] : nothing
success::Bool = haskey(response, :success) ? response[:success] : false
a.shortmem
return result
end
"""
# Arguments
- `a::T1`
one of ChatAgent's agent.
- `input::T2`
# Return
A JSON string of available wine
# Example
```jldoctest
julia>
```
# TODO
- [] update docs
- [WORKING] implement the function
# Signature
"""
function generatechat(a::T1, input::T2) where {T1<:agent, T2<:AbstractString}
systemmsg =
"""
You are a helpful sommelier working for a wine store.
Your task is to help the user choose the best wine that match the user preferences from your inventory.
You are also eager to improve your helpfulness.
You must follow the following guidelines:
- Get to know how much the user willing to spend
- Get to know type of wine the user is looking for e.g. red, white, sparkling, rose, dessert, fortified
- Get to know what occasion the user is buying wine for
- Get to know what characteristics of wine the user is looking for e.g. tannin, sweetness, intensity, acidity
- Get to know what food the user will have with wine
At each round of conversation, the user will give you:
Context: ...
Your thoughts: Your current thinking in your mind
Your earlier conversation with the user: ...
You should then respond to the user with:
- chat: what do you want to say to the user
You should only respond in format as described below:
chat: ...
Let's begin!
"""
usermsg =
"""
Context: None
Your thoughts: $input
Your earlier conversation with the user: $(chatHistoryToString(a))
"""
_prompt =
[
Dict(:name=> "system", :text=> systemmsg),
Dict(:name=> "user", :text=> usermsg)
]
# put in model format
prompt = GeneralUtils.formatLLMtext(_prompt, "llama3instruct")
prompt *=
"""
<|start_header_id|>assistant<|end_header_id|>
"""
for attempt in 1:5
try
response = text2textInstructLLM(prompt)
responsedict = GeneralUtils.textToDict(response,
["chat"],
rightmarker=":", symbolkey=true)
# check if dict has all required value
evaluationtext::AbstractString = responsedict[:evaluation]
responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
score::Integer = responsedict[:score]
accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
suggestion::AbstractString = responsedict[:suggestion]
# add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
state[:evaluation] = responsedict[:evaluation]
state[:evaluationscore] = responsedict[:score]
state[:accepted_as_answer] = responsedict[:accepted_as_answer]
state[:suggestion] = responsedict[:suggestion]
# mark as terminal state when the answer is achieved
if accepted_as_answer == "Yes"
state[:isterminal] = true
state[:reward] = 1
end
return responsedict[:score]
catch e
io = IOBuffer()
showerror(io, e)
errorMsg = String(take!(io))
st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
println("")
println("Attempt $attempt. Error occurred: $errorMsg\n$st")
println("")
end end
end end
error("evaluator failed to generate an evaluation")
end end

10
src/llmfunction.jl
View File

@@ -4,7 +4,7 @@ export virtualWineUserChatbox, jsoncorrection, winestock,
virtualWineUserRecommendbox, userChatbox, userRecommendbox virtualWineUserRecommendbox, userChatbox, userRecommendbox
using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs
using GeneralUtils using GeneralUtils, SQLLLM
using ..type, ..util using ..type, ..util
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
@@ -357,13 +357,13 @@ julia> result = winestock(agent, input)
``` ```
# TODO # TODO
[] update docs - [] update docs
[WORKING] implement the function - [WORKING] implement the function
# Signature # Signature
""" """
function winestock(config::T1, input::T2 function winestock(input::T, DBconnection, text2textInstructLLM::Function
)::Union{Tuple{String, Number, Number, Bool}, Tuple{String, Nothing, Number, Bool}} where {T1<:AbstractDict, T2<:AbstractString} )::Union{Tuple{String, Number, Number, Bool}, Tuple{String, Nothing, Number, Bool}} where {T<:AbstractString}
# SELECT * # SELECT *
# FROM food # FROM food

84
src/type.jl
View File

@@ -80,12 +80,8 @@ julia> agent = YiemAgent.bsommelier(
mutable struct sommelier <: agent mutable struct sommelier <: agent
name::String # agent name name::String # agent name
id::String # agent id id::String # agent id
config::Dict # agent config
tools::Dict tools::Dict
maxiterations::Integer # how many thinking round maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
totalsample::Integer # how many sample in each thinking round
maxDepth::Integer # how many step ahead to be simulated start from current state into the future
maxHistoryMsg::Integer # 21th and earlier messages will get summarized
""" Memory """ Memory
Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3 Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
@@ -97,76 +93,48 @@ mutable struct sommelier <: agent
""" """
chathistory::Vector{Dict{Symbol, Any}} chathistory::Vector{Dict{Symbol, Any}}
keywordinfo::Dict{Symbol, Any} shortmem::Dict{Symbol, Any}
# 1-historyPoint is in Dict{Symbol, Any} and compose of: # communication function
# state, statevalue, thought, action, observation text2textInstructLLM::Function
plan::Dict{Symbol, Any}
end end
function sommelier( function sommelier(
config::Dict = Dict( text2textInstructLLM::Function
:mqttServerInfo=> Dict(
:broker=> nothing,
:port=> nothing,
),
:receivemsg=> Dict(
:prompt=> nothing, # topic to receive prompt i.e. frontend send msg to this topic
:internal=> nothing,
),
:thirdPartyService=> Dict(
:text2textinstruct=> nothing,
:text2textchat=> nothing,
),
)
; ;
name::String= "Assistant", name::String= "Assistant",
id::String= string(uuid4()), id::String= string(uuid4()),
tools::Dict= Dict(
:chatbox=> Dict(
:name => "chatbox",
:description => "Useful for when you need to communicate with the user.",
:input => "Input should be a conversation to the user.",
:output => "" ,
:func => nothing,
),
),
maxiterations::Integer= 3,
totalsample::Integer= 3,
maxDepth::Integer= 3,
maxHistoryMsg::Integer= 20, maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{Symbol, Any}} = Vector{Dict{Symbol, Any}}(), chathistory::Vector{Dict{Symbol, Any}} = Vector{Dict{Symbol, Any}}(),
keywordinfo::Dict{Symbol, Any} = Dict{Symbol, Any}(
:customerinfo => Dict{Symbol, Any}(),
:storeinfo => Dict{Symbol, Any}(),
),
plan::Dict{Symbol, Any} = Dict{Symbol, Any}(
# store 3 to 5 best plan AI frequently used to avoid having to search MCTS all the time
# each plan is in [historyPoint_1, historyPoint_2, ...] format
:existingplan => Vector(),
:activeplan => Dict{Symbol, Any}(), # current using plan
:currenttrajectory=> Dict{Symbol, Any}(), # store question, thought, action, observation, ...
)
) )
#[NEXTVERSION] publish to a.config[:configtopic] to get a config. tools = Dict( # update input format
#[NEXTVERSION] get a config message in a.mqttMsg_internal "chatbox"=> Dict(
#[NEXTVERSION] set agent according to config :description => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>",
:input => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""",
:output => "" ,
),
"winestock"=> Dict(
:description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
:input => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
:output => """<output>Output are wines that match the search query in JSON format.""",
),
# "finalanswer"=> Dict(
# :description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
# :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
# :output => "" ,
# :func => nothing,
# ),
)
newAgent = sommelier( newAgent = sommelier(
name, name,
id, id,
config,
tools, tools,
maxiterations,
totalsample,
maxDepth,
maxHistoryMsg, maxHistoryMsg,
chathistory, chathistory,
keywordinfo, Dict{Symbol, Any}(),
plan, text2textInstructLLM,
) )
return newAgent return newAgent

407
src/util.jl
View File

@@ -1,7 +1,6 @@
module util module util
export clearhistory, addNewMessage, formatLLMtext, iterativeprompting, export clearhistory, addNewMessage, chatHistoryToString
formatLLMtext_llama3instruct, formatLLMtext_phi3instruct
using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3 using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3
using GeneralUtils using GeneralUtils
@@ -114,222 +113,242 @@ function addNewMessage(a::T1, name::String, text::T2;
end end
function chatHistoryToString(a)
""" Convert a single chat dictionary into LLM model instruct format. chatHistoryStr = ""
for i in a.chathistory
# Llama 3 instruct format example name = i[:name]
<|system|> text = i[:text]
You are a helpful AI assistant.<|end|> chatHistoryStr *= "$name> $text"
<|user|>
I am going to Paris, what should I see?<|end|>
<|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums."<|end|>
<|user|>
What is so great about #1?<|end|>
<|assistant|>
# Arguments
- `name::T`
message owner name e.f. "system", "user" or "assistant"
- `text::T`
# Return
- `formattedtext::String`
text formatted to model format
# Example
```jldoctest
julia> using Revise
julia> using YiemAgent
julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
julia> formattedtext = YiemAgent.formatLLMtext_phi3instruct(d[:name], d[:text])
```
Signature
"""
function formatLLMtext_phi3instruct(name::T, text::T) where {T<:AbstractString}
formattedtext =
"""
<|$name|>
$text<|end|>\n
"""
return formattedtext
end
""" Convert a single chat dictionary into LLM model instruct format.
# Llama 3 instruct format example
<|begin_of_text|>
<|start_header_id|>system<|end_header_id|>
You are a helpful assistant.
<|eot_id|>
<|start_header_id|>user<|end_header_id|>
Get me an icecream.
<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
Go buy it yourself at 7-11.
<|eot_id|>
# Arguments
- `name::T`
message owner name e.f. "system", "user" or "assistant"
- `text::T`
# Return
- `formattedtext::String`
text formatted to model format
# Example
```jldoctest
julia> using Revise
julia> using YiemAgent
julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
julia> formattedtext = YiemAgent.formatLLMtext_llama3instruct(d[:name], d[:text])
"<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n"
```
Signature
"""
function formatLLMtext_llama3instruct(name::T, text::T) where {T<:AbstractString}
formattedtext =
if name == "system"
"""
<|begin_of_text|>
<|start_header_id|>$name<|end_header_id|>
$text
<|eot_id|>
"""
else
"""
<|start_header_id|>$name<|end_header_id|>
$text
<|eot_id|>
"""
end end
return formattedtext return chatHistoryStr
end end
""" Convert a chat messages in vector of dictionary into LLM model instruct format.
# Arguments
- `messages::Vector{Dict{Symbol, T}}`
message owner name e.f. "system", "user" or "assistant"
- `formatname::T`
format name to be used
# Return
- `formattedtext::String`
text formatted to model format
# Example
```jldoctest
julia> using Revise
julia> using YiemAgent
julia> chatmessage = [
Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",), # """ Convert a single chat dictionary into LLM model instruct format.
Dict(:name=> "user",:text=> "list me all planets in our solar system.",),
Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",), # # Llama 3 instruct format example
] # <|system|>
julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct") # You are a helpful AI assistant.<|end|>
"<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n <|start_header_id|>user<|end_header_id|>\n list me all planets in our solar system.\n <|eot_id|>\n <|start_header_id|>assistant<|end_header_id|>\n I'm sorry. I don't know. You tell me.\n <|eot_id|>\n" # <|user|>
``` # I am going to Paris, what should I see?<|end|>
# <|assistant|>
# Paris, the capital of France, is known for its stunning architecture, art museums."<|end|>
# <|user|>
# What is so great about #1?<|end|>
# <|assistant|>
# # Arguments
# - `name::T`
# message owner name e.f. "system", "user" or "assistant"
# - `text::T`
# # Return
# - `formattedtext::String`
# text formatted to model format
# # Example
# ```jldoctest
# julia> using Revise
# julia> using YiemAgent
# julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
# julia> formattedtext = YiemAgent.formatLLMtext_phi3instruct(d[:name], d[:text])
# ```
# Signature # Signature
""" # """
function formatLLMtext(messages::Vector{Dict{Symbol, T}}, # function formatLLMtext_phi3instruct(name::T, text::T) where {T<:AbstractString}
formatname::String="llama3instruct") where {T<:Any} # formattedtext =
f = if formatname == "llama3instruct" # """
formatLLMtext_llama3instruct # <|$name|>
elseif formatname == "mistral" # $text<|end|>\n
# not define yet # """
elseif formatname == "phi3instruct"
formatLLMtext_phi3instruct
else
error("$formatname template not define yet")
end
str = "" # return formattedtext
for t in messages # end
str *= f(t[:name], t[:text])
end
# add <|assistant|> so that the model don't generate it and I don't need to clean it up later
if formatname == "phi3instruct"
str *= "<|assistant|>\n"
end
return str
end
""" # """ Convert a single chat dictionary into LLM model instruct format.
Arguments\n # # Llama 3 instruct format example
----- # <|begin_of_text|>
# <|start_header_id|>system<|end_header_id|>
# You are a helpful assistant.
# <|eot_id|>
# <|start_header_id|>user<|end_header_id|>
# Get me an icecream.
# <|eot_id|>
# <|start_header_id|>assistant<|end_header_id|>
# Go buy it yourself at 7-11.
# <|eot_id|>
# # Arguments
# - `name::T`
# message owner name e.f. "system", "user" or "assistant"
# - `text::T`
# # Return
# - `formattedtext::String`
# text formatted to model format
# # Example
# ```jldoctest
# julia> using Revise
# julia> using YiemAgent
# julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
# julia> formattedtext = YiemAgent.formatLLMtext_llama3instruct(d[:name], d[:text])
# "<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n"
# ```
# Signature
# """
# function formatLLMtext_llama3instruct(name::T, text::T) where {T<:AbstractString}
# formattedtext =
# if name == "system"
# """
# <|begin_of_text|>
# <|start_header_id|>$name<|end_header_id|>
# $text
# <|eot_id|>
# """
# else
# """
# <|start_header_id|>$name<|end_header_id|>
# $text
# <|eot_id|>
# """
# end
# return formattedtext
# end
# """ Convert a chat messages in vector of dictionary into LLM model instruct format.
# # Arguments
# - `messages::Vector{Dict{Symbol, T}}`
# message owner name e.f. "system", "user" or "assistant"
# - `formatname::T`
# format name to be used
# # Return
# - `formattedtext::String`
# text formatted to model format
# # Example
# ```jldoctest
# julia> using Revise
# julia> using YiemAgent
# julia> chatmessage = [
# Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",),
# Dict(:name=> "user",:text=> "list me all planets in our solar system.",),
# Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",),
# ]
# julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct")
# "<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n <|start_header_id|>user<|end_header_id|>\n list me all planets in our solar system.\n <|eot_id|>\n <|start_header_id|>assistant<|end_header_id|>\n I'm sorry. I don't know. You tell me.\n <|eot_id|>\n"
# ```
# # Signature
# """
# function formatLLMtext(messages::Vector{Dict{Symbol, T}},
# formatname::String="llama3instruct") where {T<:Any}
# f = if formatname == "llama3instruct"
# formatLLMtext_llama3instruct
# elseif formatname == "mistral"
# # not define yet
# elseif formatname == "phi3instruct"
# formatLLMtext_phi3instruct
# else
# error("$formatname template not define yet")
# end
# str = ""
# for t in messages
# str *= f(t[:name], t[:text])
# end
# # add <|assistant|> so that the model don't generate it and I don't need to clean it up later
# if formatname == "phi3instruct"
# str *= "<|assistant|>\n"
# end
# return str
# end
# """
# Arguments\n
# -----
Return\n # Return\n
----- # -----
Example\n # Example\n
----- # -----
```jldoctest # ```jldoctest
julia> # julia>
``` # ```
TODO\n # TODO\n
----- # -----
[] update docstring # [] update docstring
[PENDING] implement the function # [PENDING] implement the function
Signature\n # Signature\n
----- # -----
""" # """
function iterativeprompting(a::T, prompt::String, verification::Function) where {T<:agent} # function iterativeprompting(a::T, prompt::String, verification::Function) where {T<:agent}
msgMeta = GeneralUtils.generate_msgMeta( # msgMeta = GeneralUtils.generate_msgMeta(
a.config[:externalService][:text2textinstruct], # a.config[:externalService][:text2textinstruct],
senderName= "iterativeprompting", # senderName= "iterativeprompting",
senderId= a.id, # senderId= a.id,
receiverName= "text2textinstruct", # receiverName= "text2textinstruct",
) # )
outgoingMsg = Dict( # outgoingMsg = Dict(
:msgMeta=> msgMeta, # :msgMeta=> msgMeta,
:payload=> Dict( # :payload=> Dict(
:text=> prompt, # :text=> prompt,
) # )
) # )
success = nothing # success = nothing
result = nothing # result = nothing
critique = "" # critique = ""
# iteration loop # # iteration loop
while true # while true
# send prompt to LLM # # send prompt to LLM
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) # response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
error("--> iterativeprompting") # error("--> iterativeprompting")
# check for correctness and get feedback # # check for correctness and get feedback
success, _critique = verification(response) # success, _critique = verification(response)
if success # if success
result = response # result = response
break # break
else # else
# add critique to prompt # # add critique to prompt
critique *= _critique * "\n" # critique *= _critique * "\n"
replace!(prompt, "Critique: ..." => "Critique: $critique") # replace!(prompt, "Critique: ..." => "Critique: $critique")
end # end
end # end
return (success=success, result=result) # return (success=success, result=result)
end # end

77
test/runtest.jl
View File

@@ -13,60 +13,60 @@ client, connection = MakeConnection(config[:mqttServerInfo][:broker],
receiveUserMsgChannel = Channel{Dict}(4) receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4) receiveInternalMsgChannel = Channel{Dict}(4)
println(typeof(connection))
msgMeta = GeneralUtils.generate_msgMeta( msgMeta = GeneralUtils.generate_msgMeta(
"N/A", "N/A",
replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
) )
agentConfig = Dict( # commConfig = Dict(
:mqttServerInfo=> config[:mqttServerInfo], # :mqttServerInfo=> config[:mqttServerInfo],
:receivemsg=> Dict( # :receivemsg=> Dict(
:prompt=> config[:servicetopic][:mqtttopic], # topic to receive prompt i.e. frontend send msg to this topic # :prompt=> config[:servicetopic][:mqtttopic], # topic to receive prompt i.e. frontend send msg to this topic
:internal=> instanceInternalTopic, # ),
), # :externalservice=> config[:externalservice],
:externalservice=> config[:externalservice], # )
)
# Instantiate an agent function text2textInstructLLM(prompt::String)
tools=Dict( # update input format msgMeta = GeneralUtils.generate_msgMeta(
"askbox"=> Dict( config[:externalservice][:text2textinstruct][:mqtttopic],
:description => "<askbox tool description>Useful for when you need to ask the user for more context. Do not ask the user their own question.</askbox tool description>", senderName= "yiemagent",
:input => """<input>Input is a text in JSON format.</input><input example>{\"Q1\": \"How are you doing?\", \"Q2\": \"How may I help you?\"}</input example>""", senderId= string(uuid4()),
:output => "" , receiverName= "text2textinstruct",
:func => nothing, mqttBroker= config[:mqttServerInfo][:broker],
), mqttBrokerPort= config[:mqttServerInfo][:port],
# "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
# :input => """<input>Input is a JSON-formatted string that contains a detailed and precise search query.</input><input example>{\"wine type\": \"rose\", \"price\": \"max 35\", \"sweetness level\": \"sweet\", \"intensity level\": \"light bodied\", \"Tannin level\": \"low\", \"Acidity level\": \"low\"}</input example>""",
# :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock,
# ),
"finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
:input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" ,
:func => nothing,
),
) )
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
# Instantiate an agent
a = YiemAgent.sommelier( a = YiemAgent.sommelier(
agentConfig, text2textInstructLLM,
name="assistant", name="assistant",
id="testingSessionID", # agent instance id id="testingSessionID", # agent instance id
tools=tools,
) )
# response = YiemAgent.conversation(a, Dict(:text=> "newtopic",) ) # response = YiemAgent.conversation(a, Dict(:text=> "newtopic",) )
response = YiemAgent.conversation(a, Dict(:text=> "Hello, I would like a get a bottle of wine", response = YiemAgent.conversation(a, Dict(:text=> "Hello, I would like a get a bottle of wine."))
:select=> nothing,
:reward=> 0,
:isterminal=> false,
) )
println("---> YiemAgent: ", response) println("---> YiemAgent: ", response)
#BUG mcts do not start at current chat history
response = YiemAgent.conversation(a, Dict(:text=> "I'm having a graduation party this evening. I'll pay at most 30 bucks.", response = YiemAgent.conversation(a, Dict(:text=> "I'm having a graduation party this evening. I'll pay at most 30 bucks.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
@@ -96,9 +96,6 @@ response = YiemAgent.winestock(a, dummyinput)