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This commit is contained in:
narawat lamaiin
2024-12-09 20:48:45 +07:00
parent 4f1280daa3
commit 9aef993813
47 changed files with 16623 additions and 12554 deletions
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32
.vscode/launch.json vendored
View File

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

187
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 = "1.10.5" julia_version = "1.11.2"
manifest_format = "2.0" manifest_format = "2.0"
project_hash = "bf6c32becbc917fa1c33558e7aa59c1aac5237e3" project_hash = "3fbf548b167fd8368831ce3bd276589c6cddf72e"
[[deps.AliasTables]] [[deps.AliasTables]]
deps = ["PtrArrays", "Random"] deps = ["PtrArrays", "Random"]
@@ -12,13 +12,15 @@ version = "1.1.3"
[[deps.ArgTools]] [[deps.ArgTools]]
uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f" uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
version = "1.1.1" version = "1.1.2"
[[deps.Artifacts]] [[deps.Artifacts]]
uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33" uuid = "56f22d72-fd6d-98f1-02f0-08ddc0907c33"
version = "1.11.0"
[[deps.Base64]] [[deps.Base64]]
uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f" uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
version = "1.11.0"
[[deps.BitFlags]] [[deps.BitFlags]]
git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d" git-tree-sha1 = "0691e34b3bb8be9307330f88d1a3c3f25466c24d"
@@ -32,9 +34,9 @@ version = "0.5.0"
[[deps.CSV]] [[deps.CSV]]
deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"] deps = ["CodecZlib", "Dates", "FilePathsBase", "InlineStrings", "Mmap", "Parsers", "PooledArrays", "PrecompileTools", "SentinelArrays", "Tables", "Unicode", "WeakRefStrings", "WorkerUtilities"]
git-tree-sha1 = "6c834533dc1fabd820c1db03c839bf97e45a3fab" git-tree-sha1 = "deddd8725e5e1cc49ee205a1964256043720a6c3"
uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b" uuid = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
version = "0.10.14" version = "0.10.15"
[[deps.CodeTracking]] [[deps.CodeTracking]]
deps = ["InteractiveUtils", "UUIDs"] deps = ["InteractiveUtils", "UUIDs"]
@@ -69,12 +71,6 @@ git-tree-sha1 = "ea32b83ca4fefa1768dc84e504cc0a94fb1ab8d1"
uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb" uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
version = "2.4.2" version = "2.4.2"
[[deps.CondaPkg]]
deps = ["JSON3", "Markdown", "MicroMamba", "Pidfile", "Pkg", "Preferences", "TOML"]
git-tree-sha1 = "8f7faef2ca039ee068cd971a80ccd710d23fb2eb"
uuid = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
version = "0.2.23"
[[deps.Crayons]] [[deps.Crayons]]
git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15" git-tree-sha1 = "249fe38abf76d48563e2f4556bebd215aa317e15"
uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f" uuid = "a8cc5b0e-0ffa-5ad4-8c14-923d3ee1735f"
@@ -110,6 +106,7 @@ version = "1.0.0"
[[deps.Dates]] [[deps.Dates]]
deps = ["Printf"] deps = ["Printf"]
uuid = "ade2ca70-3891-5945-98fb-dc099432e06a" uuid = "ade2ca70-3891-5945-98fb-dc099432e06a"
version = "1.11.0"
[[deps.Decimals]] [[deps.Decimals]]
git-tree-sha1 = "e98abef36d02a0ec385d68cd7dadbce9b28cbd88" git-tree-sha1 = "e98abef36d02a0ec385d68cd7dadbce9b28cbd88"
@@ -119,12 +116,13 @@ version = "0.4.1"
[[deps.Distributed]] [[deps.Distributed]]
deps = ["Random", "Serialization", "Sockets"] deps = ["Random", "Serialization", "Sockets"]
uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b" uuid = "8ba89e20-285c-5b6f-9357-94700520ee1b"
version = "1.11.0"
[[deps.Distributions]] [[deps.Distributions]]
deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"] deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
git-tree-sha1 = "d7477ecdafb813ddee2ae727afa94e9dcb5f3fb0" git-tree-sha1 = "3101c32aab536e7a27b1763c0797dba151b899ad"
uuid = "31c24e10-a181-5473-b8eb-7969acd0382f" uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
version = "0.25.112" version = "0.25.113"
[deps.Distributions.extensions] [deps.Distributions.extensions]
DistributionsChainRulesCoreExt = "ChainRulesCore" DistributionsChainRulesCoreExt = "ChainRulesCore"
@@ -149,21 +147,15 @@ version = "1.6.0"
[[deps.ExceptionUnwrapping]] [[deps.ExceptionUnwrapping]]
deps = ["Test"] deps = ["Test"]
git-tree-sha1 = "dcb08a0d93ec0b1cdc4af184b26b591e9695423a" git-tree-sha1 = "d36f682e590a83d63d1c7dbd287573764682d12a"
uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4" uuid = "460bff9d-24e4-43bc-9d9f-a8973cb893f4"
version = "0.1.10" version = "0.1.11"
[[deps.ExprTools]] [[deps.ExprTools]]
git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec" git-tree-sha1 = "27415f162e6028e81c72b82ef756bf321213b6ec"
uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04" uuid = "e2ba6199-217a-4e67-a87a-7c52f15ade04"
version = "0.1.10" version = "0.1.10"
[[deps.FileIO]]
deps = ["Pkg", "Requires", "UUIDs"]
git-tree-sha1 = "62ca0547a14c57e98154423419d8a342dca75ca9"
uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
version = "1.16.4"
[[deps.FilePathsBase]] [[deps.FilePathsBase]]
deps = ["Compat", "Dates"] deps = ["Compat", "Dates"]
git-tree-sha1 = "7878ff7172a8e6beedd1dea14bd27c3c6340d361" git-tree-sha1 = "7878ff7172a8e6beedd1dea14bd27c3c6340d361"
@@ -177,6 +169,7 @@ weakdeps = ["Mmap", "Test"]
[[deps.FileWatching]] [[deps.FileWatching]]
uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee" uuid = "7b1f6079-737a-58dc-b8bc-7a2ca5c1b5ee"
version = "1.11.0"
[[deps.FillArrays]] [[deps.FillArrays]]
deps = ["LinearAlgebra"] deps = ["LinearAlgebra"]
@@ -193,24 +186,27 @@ weakdeps = ["PDMats", "SparseArrays", "Statistics"]
[[deps.Future]] [[deps.Future]]
deps = ["Random"] deps = ["Random"]
uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820" uuid = "9fa8497b-333b-5362-9e8d-4d0656e87820"
version = "1.11.0"
[[deps.GeneralUtils]] [[deps.GeneralUtils]]
deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "JSON3", "MQTTClient", "PrettyPrinting", "Random", "SHA", "UUIDs"] deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "JSON3", "MQTTClient", "PrettyPrinting", "Random", "SHA", "UUIDs"]
path = "/appfolder/app/privatejuliapkg/GeneralUtils" git-tree-sha1 = "978d9a5c3fc30205dd72d4a2a2ed4fa85ebee5cf"
repo-rev = "main"
repo-url = "https://git.yiem.cc/ton/GeneralUtils"
uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
version = "0.1.0" version = "0.1.0"
[[deps.HTTP]] [[deps.HTTP]]
deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"] deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "PrecompileTools", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"]
git-tree-sha1 = "d1d712be3164d61d1fb98e7ce9bcbc6cc06b45ed" git-tree-sha1 = "6c22309e9a356ac1ebc5c8a217045f9bae6f8d9a"
uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3" uuid = "cd3eb016-35fb-5094-929b-558a96fad6f3"
version = "1.10.8" version = "1.10.13"
[[deps.HypergeometricFunctions]] [[deps.HypergeometricFunctions]]
deps = ["LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"] deps = ["LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"]
git-tree-sha1 = "7c4195be1649ae622304031ed46a2f4df989f1eb" git-tree-sha1 = "b1c2585431c382e3fe5805874bda6aea90a95de9"
uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a" uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
version = "0.3.24" version = "0.3.25"
[[deps.ICU_jll]] [[deps.ICU_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -240,6 +236,7 @@ version = "1.4.2"
[[deps.InteractiveUtils]] [[deps.InteractiveUtils]]
deps = ["Markdown"] deps = ["Markdown"]
uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240" uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
version = "1.11.0"
[[deps.Intervals]] [[deps.Intervals]]
deps = ["Dates", "Printf", "RecipesBase", "Serialization", "TimeZones"] deps = ["Dates", "Printf", "RecipesBase", "Serialization", "TimeZones"]
@@ -287,9 +284,9 @@ version = "1.14.1"
[[deps.JuliaInterpreter]] [[deps.JuliaInterpreter]]
deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"] deps = ["CodeTracking", "InteractiveUtils", "Random", "UUIDs"]
git-tree-sha1 = "2984284a8abcfcc4784d95a9e2ea4e352dd8ede7" git-tree-sha1 = "10da5154188682e5c0726823c2b5125957ec3778"
uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a" uuid = "aa1ae85d-cabe-5617-a682-6adf51b2e16a"
version = "0.9.36" version = "0.9.38"
[[deps.Kerberos_krb5_jll]] [[deps.Kerberos_krb5_jll]]
deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"] deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -297,12 +294,6 @@ git-tree-sha1 = "60274b4ab38e8d1248216fe6b6ace75ae09b0502"
uuid = "b39eb1a6-c29a-53d7-8c32-632cd16f18da" uuid = "b39eb1a6-c29a-53d7-8c32-632cd16f18da"
version = "1.19.3+0" version = "1.19.3+0"
[[deps.LLMMCTS]]
deps = ["GeneralUtils", "JSON3"]
path = "/appfolder/app/privatejuliapkg/LLMMCTS"
uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
version = "0.1.0"
[[deps.LaTeXStrings]] [[deps.LaTeXStrings]]
git-tree-sha1 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c" git-tree-sha1 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c"
uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f" uuid = "b964fa9f-0449-5b57-a5c2-d3ea65f4040f"
@@ -313,10 +304,6 @@ git-tree-sha1 = "6087ad3521d6278ebe5c27ae55e7bbb15ca312cb"
uuid = "6f188dcb-512c-564b-bc01-e0f76e72f166" uuid = "6f188dcb-512c-564b-bc01-e0f76e72f166"
version = "1.0.0" version = "1.0.0"
[[deps.LazyArtifacts]]
deps = ["Artifacts", "Pkg"]
uuid = "4af54fe1-eca0-43a8-85a7-787d91b784e3"
[[deps.LibCURL]] [[deps.LibCURL]]
deps = ["LibCURL_jll", "MozillaCACerts_jll"] deps = ["LibCURL_jll", "MozillaCACerts_jll"]
uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21" uuid = "b27032c2-a3e7-50c8-80cd-2d36dbcbfd21"
@@ -325,16 +312,17 @@ version = "0.6.4"
[[deps.LibCURL_jll]] [[deps.LibCURL_jll]]
deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"] deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll", "Zlib_jll", "nghttp2_jll"]
uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0" uuid = "deac9b47-8bc7-5906-a0fe-35ac56dc84c0"
version = "8.4.0+0" version = "8.6.0+0"
[[deps.LibGit2]] [[deps.LibGit2]]
deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"] deps = ["Base64", "LibGit2_jll", "NetworkOptions", "Printf", "SHA"]
uuid = "76f85450-5226-5b5a-8eaa-529ad045b433" uuid = "76f85450-5226-5b5a-8eaa-529ad045b433"
version = "1.11.0"
[[deps.LibGit2_jll]] [[deps.LibGit2_jll]]
deps = ["Artifacts", "LibSSH2_jll", "Libdl", "MbedTLS_jll"] 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 = "1.7.2+0"
[[deps.LibPQ]] [[deps.LibPQ]]
deps = ["CEnum", "DBInterface", "Dates", "Decimals", "DocStringExtensions", "FileWatching", "Infinity", "Intervals", "IterTools", "LayerDicts", "LibPQ_jll", "Libdl", "Memento", "OffsetArrays", "SQLStrings", "Tables", "TimeZones", "UTCDateTimes"] deps = ["CEnum", "DBInterface", "Dates", "Decimals", "DocStringExtensions", "FileWatching", "Infinity", "Intervals", "IterTools", "LayerDicts", "LibPQ_jll", "Libdl", "Memento", "OffsetArrays", "SQLStrings", "Tables", "TimeZones", "UTCDateTimes"]
@@ -355,10 +343,12 @@ version = "1.11.0+1"
[[deps.Libdl]] [[deps.Libdl]]
uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb" uuid = "8f399da3-3557-5675-b5ff-fb832c97cbdb"
version = "1.11.0"
[[deps.LinearAlgebra]] [[deps.LinearAlgebra]]
deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"] deps = ["Libdl", "OpenBLAS_jll", "libblastrampoline_jll"]
uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e" uuid = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
version = "1.11.0"
[[deps.LogExpFunctions]] [[deps.LogExpFunctions]]
deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"] deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
@@ -378,18 +368,19 @@ version = "0.3.28"
[[deps.Logging]] [[deps.Logging]]
uuid = "56ddb016-857b-54e1-b83d-db4d58db5568" uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
version = "1.11.0"
[[deps.LoggingExtras]] [[deps.LoggingExtras]]
deps = ["Dates", "Logging"] deps = ["Dates", "Logging"]
git-tree-sha1 = "c1dd6d7978c12545b4179fb6153b9250c96b0075" git-tree-sha1 = "f02b56007b064fbfddb4c9cd60161b6dd0f40df3"
uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36" uuid = "e6f89c97-d47a-5376-807f-9c37f3926c36"
version = "1.0.3" version = "1.1.0"
[[deps.LoweredCodeUtils]] [[deps.LoweredCodeUtils]]
deps = ["JuliaInterpreter"] deps = ["JuliaInterpreter"]
git-tree-sha1 = "260dc274c1bc2cb839e758588c63d9c8b5e639d1" git-tree-sha1 = "688d6d9e098109051ae33d126fcfc88c4ce4a021"
uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b" uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
version = "3.0.5" version = "3.1.0"
[[deps.MQTTClient]] [[deps.MQTTClient]]
deps = ["Distributed", "Random", "Sockets"] deps = ["Distributed", "Random", "Sockets"]
@@ -401,15 +392,10 @@ weakdeps = ["PrecompileTools"]
[deps.MQTTClient.extensions] [deps.MQTTClient.extensions]
PrecompileMQTT = "PrecompileTools" PrecompileMQTT = "PrecompileTools"
[[deps.MacroTools]]
deps = ["Markdown", "Random"]
git-tree-sha1 = "2fa9ee3e63fd3a4f7a9a4f4744a52f4856de82df"
uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
version = "0.5.13"
[[deps.Markdown]] [[deps.Markdown]]
deps = ["Base64"] deps = ["Base64"]
uuid = "d6f4376e-aef5-505a-96c1-9c027394607a" uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
version = "1.11.0"
[[deps.MbedTLS]] [[deps.MbedTLS]]
deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"] deps = ["Dates", "MbedTLS_jll", "MozillaCACerts_jll", "NetworkOptions", "Random", "Sockets"]
@@ -420,7 +406,7 @@ version = "1.1.9"
[[deps.MbedTLS_jll]] [[deps.MbedTLS_jll]]
deps = ["Artifacts", "Libdl"] deps = ["Artifacts", "Libdl"]
uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1" uuid = "c8ffd9c3-330d-5841-b78e-0817d7145fa1"
version = "2.28.2+1" version = "2.28.6+0"
[[deps.Memento]] [[deps.Memento]]
deps = ["Dates", "Distributed", "Requires", "Serialization", "Sockets", "Test", "UUIDs"] deps = ["Dates", "Distributed", "Requires", "Serialization", "Sockets", "Test", "UUIDs"]
@@ -428,12 +414,6 @@ git-tree-sha1 = "bb2e8f4d9f400f6e90d57b34860f6abdc51398e5"
uuid = "f28f55f0-a522-5efc-85c2-fe41dfb9b2d9" uuid = "f28f55f0-a522-5efc-85c2-fe41dfb9b2d9"
version = "1.4.1" version = "1.4.1"
[[deps.MicroMamba]]
deps = ["Pkg", "Scratch", "micromamba_jll"]
git-tree-sha1 = "011cab361eae7bcd7d278f0a7a00ff9c69000c51"
uuid = "0b3b1443-0f03-428d-bdfb-f27f9c1191ea"
version = "0.1.14"
[[deps.Missings]] [[deps.Missings]]
deps = ["DataAPI"] deps = ["DataAPI"]
git-tree-sha1 = "ec4f7fbeab05d7747bdf98eb74d130a2a2ed298d" git-tree-sha1 = "ec4f7fbeab05d7747bdf98eb74d130a2a2ed298d"
@@ -442,6 +422,7 @@ version = "1.2.0"
[[deps.Mmap]] [[deps.Mmap]]
uuid = "a63ad114-7e13-5084-954f-fe012c677804" uuid = "a63ad114-7e13-5084-954f-fe012c677804"
version = "1.11.0"
[[deps.Mocking]] [[deps.Mocking]]
deps = ["Compat", "ExprTools"] deps = ["Compat", "ExprTools"]
@@ -451,16 +432,16 @@ version = "0.8.1"
[[deps.MozillaCACerts_jll]] [[deps.MozillaCACerts_jll]]
uuid = "14a3606d-f60d-562e-9121-12d972cd8159" uuid = "14a3606d-f60d-562e-9121-12d972cd8159"
version = "2023.1.10" version = "2023.12.12"
[[deps.NetworkOptions]] [[deps.NetworkOptions]]
uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908" uuid = "ca575930-c2e3-43a9-ace4-1e988b2c1908"
version = "1.2.0" version = "1.2.0"
[[deps.OffsetArrays]] [[deps.OffsetArrays]]
git-tree-sha1 = "1a27764e945a152f7ca7efa04de513d473e9542e" git-tree-sha1 = "39d000d9c33706b8364817d8894fae1548f40295"
uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881" uuid = "6fe1bfb0-de20-5000-8ca7-80f57d26f881"
version = "1.14.1" version = "1.14.2"
[deps.OffsetArrays.extensions] [deps.OffsetArrays.extensions]
OffsetArraysAdaptExt = "Adapt" OffsetArraysAdaptExt = "Adapt"
@@ -471,7 +452,7 @@ version = "1.14.1"
[[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"
version = "0.3.23+4" version = "0.3.27+1"
[[deps.OpenLibm_jll]] [[deps.OpenLibm_jll]]
deps = ["Artifacts", "Libdl"] deps = ["Artifacts", "Libdl"]
@@ -497,9 +478,9 @@ uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
version = "0.5.5+0" version = "0.5.5+0"
[[deps.OrderedCollections]] [[deps.OrderedCollections]]
git-tree-sha1 = "dfdf5519f235516220579f949664f1bf44e741c5" git-tree-sha1 = "12f1439c4f986bb868acda6ea33ebc78e19b95ad"
uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d" uuid = "bac558e1-5e72-5ebc-8fee-abe8a469f55d"
version = "1.6.3" version = "1.7.0"
[[deps.PDMats]] [[deps.PDMats]]
deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"] deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
@@ -513,16 +494,14 @@ git-tree-sha1 = "8489905bcdbcfac64d1daa51ca07c0d8f0283821"
uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0" uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
version = "2.8.1" version = "2.8.1"
[[deps.Pidfile]]
deps = ["FileWatching", "Test"]
git-tree-sha1 = "2d8aaf8ee10df53d0dfb9b8ee44ae7c04ced2b03"
uuid = "fa939f87-e72e-5be4-a000-7fc836dbe307"
version = "1.3.0"
[[deps.Pkg]] [[deps.Pkg]]
deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Serialization", "TOML", "Tar", "UUIDs", "p7zip_jll"] deps = ["Artifacts", "Dates", "Downloads", "FileWatching", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "Random", "SHA", "TOML", "Tar", "UUIDs", "p7zip_jll"]
uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f" uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
version = "1.10.0" version = "1.11.0"
weakdeps = ["REPL"]
[deps.Pkg.extensions]
REPLExt = "REPL"
[[deps.PooledArrays]] [[deps.PooledArrays]]
deps = ["DataAPI", "Future"] deps = ["DataAPI", "Future"]
@@ -556,18 +535,13 @@ version = "2.4.0"
[[deps.Printf]] [[deps.Printf]]
deps = ["Unicode"] deps = ["Unicode"]
uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7" uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
version = "1.11.0"
[[deps.PtrArrays]] [[deps.PtrArrays]]
git-tree-sha1 = "77a42d78b6a92df47ab37e177b2deac405e1c88f" git-tree-sha1 = "77a42d78b6a92df47ab37e177b2deac405e1c88f"
uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d" uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d"
version = "1.2.1" version = "1.2.1"
[[deps.PythonCall]]
deps = ["CondaPkg", "Dates", "Libdl", "MacroTools", "Markdown", "Pkg", "REPL", "Requires", "Serialization", "Tables", "UnsafePointers"]
git-tree-sha1 = "06a778ec6d6e76b0c2fb661436a18bce853ec45f"
uuid = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
version = "0.9.23"
[[deps.QuadGK]] [[deps.QuadGK]]
deps = ["DataStructures", "LinearAlgebra"] deps = ["DataStructures", "LinearAlgebra"]
git-tree-sha1 = "cda3b045cf9ef07a08ad46731f5a3165e56cf3da" git-tree-sha1 = "cda3b045cf9ef07a08ad46731f5a3165e56cf3da"
@@ -581,12 +555,14 @@ version = "2.11.1"
Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9" Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
[[deps.REPL]] [[deps.REPL]]
deps = ["InteractiveUtils", "Markdown", "Sockets", "Unicode"] deps = ["InteractiveUtils", "Markdown", "Sockets", "StyledStrings", "Unicode"]
uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb" uuid = "3fa0cd96-eef1-5676-8a61-b3b8758bbffb"
version = "1.11.0"
[[deps.Random]] [[deps.Random]]
deps = ["SHA"] deps = ["SHA"]
uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
version = "1.11.0"
[[deps.RecipesBase]] [[deps.RecipesBase]]
deps = ["PrecompileTools"] deps = ["PrecompileTools"]
@@ -607,9 +583,9 @@ version = "1.3.0"
[[deps.Revise]] [[deps.Revise]]
deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "REPL", "Requires", "UUIDs", "Unicode"] deps = ["CodeTracking", "Distributed", "FileWatching", "JuliaInterpreter", "LibGit2", "LoweredCodeUtils", "OrderedCollections", "REPL", "Requires", "UUIDs", "Unicode"]
git-tree-sha1 = "7f4228017b83c66bd6aa4fddeb170ce487e53bc7" git-tree-sha1 = "470f48c9c4ea2170fd4d0f8eb5118327aada22f5"
uuid = "295af30f-e4ad-537b-8983-00126c2a3abe" uuid = "295af30f-e4ad-537b-8983-00126c2a3abe"
version = "3.6.2" version = "3.6.4"
[[deps.Rmath]] [[deps.Rmath]]
deps = ["Random", "Rmath_jll"] deps = ["Random", "Rmath_jll"]
@@ -627,12 +603,6 @@ version = "0.5.1+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", "FileIO", "GeneralUtils", "HTTP", "JSON3", "LLMMCTS", "LibPQ", "MQTTClient", "PrettyPrinting", "PythonCall", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
path = "/appfolder/app/privatejuliapkg/SQLLLM"
uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
version = "0.1.0"
[[deps.SQLStrings]] [[deps.SQLStrings]]
git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c" git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
uuid = "af517c2e-c243-48fa-aab8-efac3db270f5" uuid = "af517c2e-c243-48fa-aab8-efac3db270f5"
@@ -646,12 +616,13 @@ version = "1.2.1"
[[deps.SentinelArrays]] [[deps.SentinelArrays]]
deps = ["Dates", "Random"] deps = ["Dates", "Random"]
git-tree-sha1 = "ff11acffdb082493657550959d4feb4b6149e73a" git-tree-sha1 = "d0553ce4031a081cc42387a9b9c8441b7d99f32d"
uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c" uuid = "91c51154-3ec4-41a3-a24f-3f23e20d615c"
version = "1.4.5" version = "1.4.7"
[[deps.Serialization]] [[deps.Serialization]]
uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b" uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
version = "1.11.0"
[[deps.SimpleBufferStream]] [[deps.SimpleBufferStream]]
git-tree-sha1 = "f305871d2f381d21527c770d4788c06c097c9bc1" git-tree-sha1 = "f305871d2f381d21527c770d4788c06c097c9bc1"
@@ -660,6 +631,7 @@ version = "1.2.0"
[[deps.Sockets]] [[deps.Sockets]]
uuid = "6462fe0b-24de-5631-8697-dd941f90decc" uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
version = "1.11.0"
[[deps.SortingAlgorithms]] [[deps.SortingAlgorithms]]
deps = ["DataStructures"] deps = ["DataStructures"]
@@ -670,7 +642,7 @@ version = "1.2.1"
[[deps.SparseArrays]] [[deps.SparseArrays]]
deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"] deps = ["Libdl", "LinearAlgebra", "Random", "Serialization", "SuiteSparse_jll"]
uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf" uuid = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
version = "1.10.0" version = "1.11.0"
[[deps.SpecialFunctions]] [[deps.SpecialFunctions]]
deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"] deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
@@ -685,9 +657,14 @@ version = "2.4.0"
ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4" ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
[[deps.Statistics]] [[deps.Statistics]]
deps = ["LinearAlgebra", "SparseArrays"] deps = ["LinearAlgebra"]
git-tree-sha1 = "ae3bb1eb3bba077cd276bc5cfc337cc65c3075c0"
uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2" uuid = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
version = "1.10.0" version = "1.11.1"
weakdeps = ["SparseArrays"]
[deps.Statistics.extensions]
SparseArraysExt = ["SparseArrays"]
[[deps.StatsAPI]] [[deps.StatsAPI]]
deps = ["LinearAlgebra"] deps = ["LinearAlgebra"]
@@ -727,6 +704,10 @@ git-tree-sha1 = "159331b30e94d7b11379037feeb9b690950cace8"
uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4" uuid = "856f2bd8-1eba-4b0a-8007-ebc267875bd4"
version = "1.11.0" version = "1.11.0"
[[deps.StyledStrings]]
uuid = "f489334b-da3d-4c2e-b8f0-e476e12c162b"
version = "1.11.0"
[[deps.SuiteSparse]] [[deps.SuiteSparse]]
deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"] deps = ["Libdl", "LinearAlgebra", "Serialization", "SparseArrays"]
uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9" uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
@@ -734,7 +715,7 @@ uuid = "4607b0f0-06f3-5cda-b6b1-a6196a1729e9"
[[deps.SuiteSparse_jll]] [[deps.SuiteSparse_jll]]
deps = ["Artifacts", "Libdl", "libblastrampoline_jll"] deps = ["Artifacts", "Libdl", "libblastrampoline_jll"]
uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c" uuid = "bea87d4a-7f5b-5778-9afe-8cc45184846c"
version = "7.2.1+1" version = "7.7.0+0"
[[deps.TOML]] [[deps.TOML]]
deps = ["Dates"] deps = ["Dates"]
@@ -767,12 +748,13 @@ version = "1.10.0"
[[deps.Test]] [[deps.Test]]
deps = ["InteractiveUtils", "Logging", "Random", "Serialization"] deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40" uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
version = "1.11.0"
[[deps.TimeZones]] [[deps.TimeZones]]
deps = ["Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"] deps = ["Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"]
git-tree-sha1 = "8323074bc977aa85cf5ad71099a83ac75b0ac107" git-tree-sha1 = "33c771f2157712ff4c85931186a4984efbe58934"
uuid = "f269a46b-ccf7-5d73-abea-4c690281aa53" uuid = "f269a46b-ccf7-5d73-abea-4c690281aa53"
version = "1.18.1" version = "1.19.0"
weakdeps = ["RecipesBase"] weakdeps = ["RecipesBase"]
[deps.TimeZones.extensions] [deps.TimeZones.extensions]
@@ -797,14 +779,11 @@ 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"
version = "1.11.0"
[[deps.Unicode]] [[deps.Unicode]]
uuid = "4ec0a83e-493e-50e2-b9ac-8f72acf5a8f5" 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.WeakRefStrings]]
deps = ["DataAPI", "InlineStrings", "Parsers"] deps = ["DataAPI", "InlineStrings", "Parsers"]
@@ -833,16 +812,10 @@ deps = ["Artifacts", "Libdl"]
uuid = "8e850b90-86db-534c-a0d3-1478176c7d93" uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
version = "5.11.0+0" 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.nghttp2_jll]]
deps = ["Artifacts", "Libdl"] deps = ["Artifacts", "Libdl"]
uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d" uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
version = "1.52.0+1" version = "1.59.0+0"
[[deps.p7zip_jll]] [[deps.p7zip_jll]]
deps = ["Artifacts", "Libdl"] deps = ["Artifacts", "Libdl"]

7
Project.toml
View File

@@ -4,20 +4,19 @@ authors = ["narawat lamaiin <narawat@outlook.com>"]
version = "0.1.0" version = "0.1.0"
[deps] [deps]
CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3" HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
LLMMCTS = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1" LibPQ = "194296ae-ab2e-5f79-8cd4-7183a0a5a0d1"
MQTTClient = "985f35cc-2c3d-4943-b8c1-f0931d5f0959" MQTTClient = "985f35cc-2c3d-4943-b8c1-f0931d5f0959"
PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337" PrettyPrinting = "54e16d92-306c-5ea0-a30b-337be88ac337"
PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c" Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe" Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b" Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
[compat]
GeneralUtils = "0.1.0"

12
README.md
View File

@@ -1,7 +1,7 @@
version 0.1.0 version 0.1.0
TODO: TODO:
[WORKING] build MCTS() for planning [WORKING] build MCTS() for planning
[] executeplan() to execute the plan [] executeplan() to execute the plan
Change from version: 0.0.9 Change from version: 0.0.9
- -

1260
previousversion/0.1/Manifest.toml
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36
previousversion/0.1/Project.toml
View File

@@ -1,18 +1,18 @@
name = "YiemAgent" name = "YiemAgent"
uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2" uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
authors = ["narawat lamaiin <narawat@outlook.com>"] authors = ["narawat lamaiin <narawat@outlook.com>"]
version = "0.1.0" version = "0.1.0"
[deps] [deps]
CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab" CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8" DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a" Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe" GeneralUtils = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3" HTTP = "cd3eb016-35fb-5094-929b-558a96fad6f3"
JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1" JSON3 = "0f8b85d8-7281-11e9-16c2-39a750bddbf1"
MQTTClient = "985f35cc-2c3d-4943-b8c1-f0931d5f0959" 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"
URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4" URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4" UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

1072
previousversion/0.1/src/MCTSexamplePrompt.py
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100
previousversion/0.1/src/YiemAgent.jl
View File

@@ -1,50 +1,50 @@
module YiemAgent module YiemAgent
# export agent # export agent
""" Order by dependencies of each file. The 1st included file must not depend on any other """ 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. files and each file can only depend on the file included before it.
""" """
include("type.jl") include("type.jl")
using .type using .type
include("util.jl") include("util.jl")
using .util using .util
include("llmfunction.jl") include("llmfunction.jl")
using .llmfunction using .llmfunction
include("mcts.jl") include("mcts.jl")
using .mcts using .mcts
include("interface.jl") include("interface.jl")
using .interface using .interface
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
end # module YiemAgent_v1 end # module YiemAgent_v1

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module type module type
export agent, sommelier export agent, sommelier
using Dates, UUIDs, DataStructures, JSON3 using Dates, UUIDs, DataStructures, JSON3
using GeneralUtils using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
abstract type agent end abstract type agent end
""" A sommelier agent. """ A sommelier agent.
# Arguments # Arguments
- `mqttClient::Client` - `mqttClient::Client`
MQTTClient's client MQTTClient's client
- `msgMeta::Dict{Symbol, Any}` - `msgMeta::Dict{Symbol, Any}`
A dict contain info about a message. A dict contain info about a message.
- `config::Dict{Symbol, Any}` - `config::Dict{Symbol, Any}`
Config info for an agent. Contain mqtt topic for internal use and other info. Config info for an agent. Contain mqtt topic for internal use and other info.
# Keyword Arguments # Keyword Arguments
- `name::String` - `name::String`
Agent's name Agent's name
- `id::String` - `id::String`
Agent's ID Agent's ID
- `tools::Dict{Symbol, Any}` - `tools::Dict{Symbol, Any}`
Agent's tools Agent's tools
- `maxHistoryMsg::Integer` - `maxHistoryMsg::Integer`
max history message max history message
# Return # Return
- `nothing` - `nothing`
# Example # Example
```jldoctest ```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils julia> using YiemAgent, MQTTClient, GeneralUtils
julia> msgMeta = GeneralUtils.generate_msgMeta( julia> msgMeta = GeneralUtils.generate_msgMeta(
"N/A", "N/A",
replyTopic = "/testtopic/prompt" replyTopic = "/testtopic/prompt"
) )
julia> tools= Dict( julia> tools= Dict(
:chatbox=>Dict( :chatbox=>Dict(
:name => "chatbox", :name => "chatbox",
:description => "Useful only for when you need to ask the user for more info or context. Do not ask the user their own question.", :description => "Useful only for when you need to ask the user for more info or context. Do not ask the user their own question.",
:input => "Input should be a text.", :input => "Input should be a text.",
:output => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
) )
julia> agentConfig = Dict( julia> agentConfig = Dict(
:receiveprompt=>Dict( :receiveprompt=>Dict(
:mqtttopic=> "/testtopic/prompt", # topic to receive prompt i.e. frontend send msg to this topic :mqtttopic=> "/testtopic/prompt", # topic to receive prompt i.e. frontend send msg to this topic
), ),
:receiveinternal=>Dict( :receiveinternal=>Dict(
:mqtttopic=> "/testtopic/internal", # receive topic for model's internal :mqtttopic=> "/testtopic/internal", # receive topic for model's internal
), ),
:text2text=>Dict( :text2text=>Dict(
:mqtttopic=> "/text2text/receive", :mqtttopic=> "/text2text/receive",
), ),
) )
julia> client, connection = MakeConnection("test.mosquitto.org", 1883) julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> agent = YiemAgent.bsommelier( julia> agent = YiemAgent.bsommelier(
client, client,
msgMeta, msgMeta,
agentConfig, agentConfig,
name= "assistant", name= "assistant",
id= "555", # agent instance id id= "555", # agent instance id
tools=tools, tools=tools,
) )
``` ```
# TODO # TODO
- [] update docstring - [] update docstring
- [x] implement the function - [x] implement the function
# Signature # Signature
""" """
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 config::Dict # agent config
tools::Dict tools::Dict
maxiterations::Integer # how many thinking round maxiterations::Integer # how many thinking round
totalsample::Integer # how many sample in each thinking round 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 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 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
NO "system" message in chathistory because I want to add it at the inference time NO "system" message in chathistory because I want to add it at the inference time
chathistory= [ chathistory= [
Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()), Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()), Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
] ]
""" """
chathistory::Vector{Dict{Symbol, Any}} chathistory::Vector{Dict{Symbol, Any}}
keywordinfo::Dict{Symbol, Any} keywordinfo::Dict{Symbol, Any}
# 1-historyPoint is in Dict{Symbol, Any} and compose of: # 1-historyPoint is in Dict{Symbol, Any} and compose of:
# state, statevalue, thought, action, observation # state, statevalue, thought, action, observation
plan::Dict{Symbol, Any} plan::Dict{Symbol, Any}
mctsWorkDict::Dict{Symbol, Any} mctsWorkDict::Dict{Symbol, Any}
end end
function sommelier( function sommelier(
config::Dict = Dict( config::Dict = Dict(
:mqttServerInfo=> Dict( :mqttServerInfo=> Dict(
:broker=> nothing, :broker=> nothing,
:port=> nothing, :port=> nothing,
), ),
:receivemsg=> Dict( :receivemsg=> Dict(
:prompt=> nothing, # topic to receive prompt i.e. frontend send msg to this topic :prompt=> nothing, # topic to receive prompt i.e. frontend send msg to this topic
:internal=> nothing, :internal=> nothing,
), ),
:thirdPartyService=> Dict( :thirdPartyService=> Dict(
:text2textinstruct=> nothing, :text2textinstruct=> nothing,
:text2textchat=> nothing, :text2textchat=> nothing,
), ),
) )
; ;
name::String= "Assistant", name::String= "Assistant",
id::String= string(uuid4()), id::String= string(uuid4()),
tools::Dict= Dict( tools::Dict= Dict(
:chatbox=> Dict( :chatbox=> Dict(
:name => "chatbox", :name => "chatbox",
:description => "Useful for when you need to communicate with the user.", :description => "Useful for when you need to communicate with the user.",
:input => "Input should be a conversation to the user.", :input => "Input should be a conversation to the user.",
:output => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
), ),
maxiterations::Integer= 3, maxiterations::Integer= 3,
totalsample::Integer= 3, totalsample::Integer= 3,
maxDepth::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}( keywordinfo::Dict{Symbol, Any} = Dict{Symbol, Any}(
:customerinfo => Dict{Symbol, Any}(), :customerinfo => Dict{Symbol, Any}(),
:storeinfo => Dict{Symbol, Any}(), :storeinfo => Dict{Symbol, Any}(),
), ),
plan::Dict{Symbol, Any} = 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 # 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 # each plan is in [historyPoint_1, historyPoint_2, ...] format
:existingplan => Vector(), :existingplan => Vector(),
:activeplan => Dict{Symbol, Any}(), # current using plan :activeplan => Dict{Symbol, Any}(), # current using plan
:currenttrajectory=> Dict{Symbol, Any}(), # store question, thought, action, observation, ... :currenttrajectory=> Dict{Symbol, Any}(), # store question, thought, action, observation, ...
), ),
mctsWorkDict::Dict{Symbol, Any} = Dict{Symbol, Any}() mctsWorkDict::Dict{Symbol, Any} = Dict{Symbol, Any}()
) )
#[NEXTVERSION] publish to a.config[:configtopic] to get a config. #[NEXTVERSION] publish to a.config[:configtopic] to get a config.
#[NEXTVERSION] get a config message in a.mqttMsg_internal #[NEXTVERSION] get a config message in a.mqttMsg_internal
#[NEXTVERSION] set agent according to config #[NEXTVERSION] set agent according to config
newAgent = sommelier( newAgent = sommelier(
name, name,
id, id,
config, config,
tools, tools,
maxiterations, maxiterations,
totalsample, totalsample,
maxDepth, maxDepth,
maxHistoryMsg, maxHistoryMsg,
chathistory, chathistory,
keywordinfo, keywordinfo,
plan, plan,
mctsWorkDict, mctsWorkDict,
) )
return newAgent return newAgent
end end
end # module type end # module type

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module util module util
export clearhistory, addNewMessage, formatLLMtext, iterativeprompting, export clearhistory, addNewMessage, formatLLMtext, iterativeprompting,
formatLLMtext_llama3instruct, formatLLMtext_phi3instruct formatLLMtext_llama3instruct, formatLLMtext_phi3instruct
using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3 using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3
using GeneralUtils using GeneralUtils
using ..type using ..type
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
""" Clear agent chat history. """ Clear agent chat history.
# Arguments # Arguments
- `a::agent` - `a::agent`
an agent an agent
# Return # Return
- nothing - nothing
# Example # Example
```jldoctest ```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883) julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection) julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic") julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict( julia> agentConfig = Dict(
:receiveprompt=>Dict( :receiveprompt=>Dict(
:mqtttopic=> "testtopic/receive", :mqtttopic=> "testtopic/receive",
), ),
:receiveinternal=>Dict( :receiveinternal=>Dict(
:mqtttopic=> "testtopic/internal", :mqtttopic=> "testtopic/internal",
), ),
:text2text=>Dict( :text2text=>Dict(
:mqtttopic=> "testtopic/text2text", :mqtttopic=> "testtopic/text2text",
), ),
) )
julia> a = YiemAgent.sommelier( julia> a = YiemAgent.sommelier(
client, client,
msgMeta, msgMeta,
agentConfig, agentConfig,
) )
julia> YiemAgent.addNewMessage(a, "user", "hello") julia> YiemAgent.addNewMessage(a, "user", "hello")
julia> YiemAgent.clearhistory(a) julia> YiemAgent.clearhistory(a)
``` ```
# Signature # Signature
""" """
function clearhistory(a::T) where {T<:agent} function clearhistory(a::T) where {T<:agent}
empty!(a.chathistory) empty!(a.chathistory)
empty!(a.mctstree) empty!(a.mctstree)
empty!(a.plan[:activeplan]) empty!(a.plan[:activeplan])
empty!(a.plan[:currenttrajectory]) empty!(a.plan[:currenttrajectory])
end end
""" Add new message to agent. """ Add new message to agent.
Arguments\n Arguments\n
----- -----
a::agent a::agent
an agent an agent
role::String role::String
message sender role i.e. system, user or assistant message sender role i.e. system, user or assistant
text::String text::String
message text message text
Return\n Return\n
----- -----
nothing nothing
Example\n Example\n
----- -----
```jldoctest ```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883) julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection) julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic") julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict( julia> agentConfig = Dict(
:receiveprompt=>Dict( :receiveprompt=>Dict(
:mqtttopic=> "testtopic/receive", :mqtttopic=> "testtopic/receive",
), ),
:receiveinternal=>Dict( :receiveinternal=>Dict(
:mqtttopic=> "testtopic/internal", :mqtttopic=> "testtopic/internal",
), ),
:text2text=>Dict( :text2text=>Dict(
:mqtttopic=> "testtopic/text2text", :mqtttopic=> "testtopic/text2text",
), ),
) )
julia> a = YiemAgent.sommelier( julia> a = YiemAgent.sommelier(
client, client,
msgMeta, msgMeta,
agentConfig, agentConfig,
) )
julia> YiemAgent.addNewMessage(a, "user", "hello") julia> YiemAgent.addNewMessage(a, "user", "hello")
``` ```
Signature\n Signature\n
----- -----
""" """
function addNewMessage(a::T1, name::String, text::T2; function addNewMessage(a::T1, name::String, text::T2;
maximumMsg::Integer=20) where {T1<:agent, T2<:AbstractString} maximumMsg::Integer=20) where {T1<:agent, T2<:AbstractString}
if name ∉ ["system", "user", "assistant"] # guard against typo if name ∉ ["system", "user", "assistant"] # guard against typo
error("name is not in agent.availableRole $(@__LINE__)") error("name is not in agent.availableRole $(@__LINE__)")
end end
#[] summarize the oldest 10 message #[] summarize the oldest 10 message
if length(a.chathistory) > maximumMsg if length(a.chathistory) > maximumMsg
summarize(a.chathistory) summarize(a.chathistory)
else else
d = Dict(:name=> name, :text=> text, :timestamp=> Dates.now()) d = Dict(:name=> name, :text=> text, :timestamp=> Dates.now())
push!(a.chathistory, d) push!(a.chathistory, d)
end end
end end
""" Convert a single chat dictionary into LLM model instruct format. """ Convert a single chat dictionary into LLM model instruct format.
# Llama 3 instruct format example # Llama 3 instruct format example
<|system|> <|system|>
You are a helpful AI assistant.<|end|> You are a helpful AI assistant.<|end|>
<|user|> <|user|>
I am going to Paris, what should I see?<|end|> I am going to Paris, what should I see?<|end|>
<|assistant|> <|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums."<|end|> Paris, the capital of France, is known for its stunning architecture, art museums."<|end|>
<|user|> <|user|>
What is so great about #1?<|end|> What is so great about #1?<|end|>
<|assistant|> <|assistant|>
# Arguments # Arguments
- `name::T` - `name::T`
message owner name e.f. "system", "user" or "assistant" message owner name e.f. "system", "user" or "assistant"
- `text::T` - `text::T`
# Return # Return
- `formattedtext::String` - `formattedtext::String`
text formatted to model format text formatted to model format
# Example # Example
```jldoctest ```jldoctest
julia> using Revise julia> using Revise
julia> using YiemAgent julia> using YiemAgent
julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",) julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
julia> formattedtext = YiemAgent.formatLLMtext_phi3instruct(d[:name], d[:text]) julia> formattedtext = YiemAgent.formatLLMtext_phi3instruct(d[:name], d[:text])
``` ```
Signature Signature
""" """
function formatLLMtext_phi3instruct(name::T, text::T) where {T<:AbstractString} function formatLLMtext_phi3instruct(name::T, text::T) where {T<:AbstractString}
formattedtext = formattedtext =
""" """
<|$name|> <|$name|>
$text<|end|>\n $text<|end|>\n
""" """
return formattedtext return formattedtext
end end
""" Convert a single chat dictionary into LLM model instruct format. """ Convert a single chat dictionary into LLM model instruct format.
# Llama 3 instruct format example # Llama 3 instruct format example
<|begin_of_text|> <|begin_of_text|>
<|start_header_id|>system<|end_header_id|> <|start_header_id|>system<|end_header_id|>
You are a helpful assistant. You are a helpful assistant.
<|eot_id|> <|eot_id|>
<|start_header_id|>user<|end_header_id|> <|start_header_id|>user<|end_header_id|>
Get me an icecream. Get me an icecream.
<|eot_id|> <|eot_id|>
<|start_header_id|>assistant<|end_header_id|> <|start_header_id|>assistant<|end_header_id|>
Go buy it yourself at 7-11. Go buy it yourself at 7-11.
<|eot_id|> <|eot_id|>
# Arguments # Arguments
- `name::T` - `name::T`
message owner name e.f. "system", "user" or "assistant" message owner name e.f. "system", "user" or "assistant"
- `text::T` - `text::T`
# Return # Return
- `formattedtext::String` - `formattedtext::String`
text formatted to model format text formatted to model format
# Example # Example
```jldoctest ```jldoctest
julia> using Revise julia> using Revise
julia> using YiemAgent julia> using YiemAgent
julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",) julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
julia> formattedtext = YiemAgent.formatLLMtext_llama3instruct(d[:name], d[:text]) 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" "<|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 Signature
""" """
function formatLLMtext_llama3instruct(name::T, text::T) where {T<:AbstractString} function formatLLMtext_llama3instruct(name::T, text::T) where {T<:AbstractString}
formattedtext = formattedtext =
if name == "system" if name == "system"
""" """
<|begin_of_text|> <|begin_of_text|>
<|start_header_id|>$name<|end_header_id|> <|start_header_id|>$name<|end_header_id|>
$text $text
<|eot_id|> <|eot_id|>
""" """
else else
""" """
<|start_header_id|>$name<|end_header_id|> <|start_header_id|>$name<|end_header_id|>
$text $text
<|eot_id|> <|eot_id|>
""" """
end end
return formattedtext return formattedtext
end end
""" Convert a chat messages in vector of dictionary into LLM model instruct format. """ Convert a chat messages in vector of dictionary into LLM model instruct format.
# Arguments # Arguments
- `messages::Vector{Dict{Symbol, T}}` - `messages::Vector{Dict{Symbol, T}}`
message owner name e.f. "system", "user" or "assistant" message owner name e.f. "system", "user" or "assistant"
- `formatname::T` - `formatname::T`
format name to be used format name to be used
# Return # Return
- `formattedtext::String` - `formattedtext::String`
text formatted to model format text formatted to model format
# Example # Example
```jldoctest ```jldoctest
julia> using Revise julia> using Revise
julia> using YiemAgent julia> using YiemAgent
julia> chatmessage = [ julia> chatmessage = [
Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",), 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=> "user",:text=> "list me all planets in our solar system.",),
Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",), Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",),
] ]
julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct") 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" "<|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 # Signature
""" """
function formatLLMtext(messages::Vector{Dict{Symbol, T}}, function formatLLMtext(messages::Vector{Dict{Symbol, T}},
formatname::String="llama3instruct") where {T<:Any} formatname::String="llama3instruct") where {T<:Any}
f = if formatname == "llama3instruct" f = if formatname == "llama3instruct"
formatLLMtext_llama3instruct formatLLMtext_llama3instruct
elseif formatname == "mistral" elseif formatname == "mistral"
# not define yet # not define yet
elseif formatname == "phi3instruct" elseif formatname == "phi3instruct"
formatLLMtext_phi3instruct formatLLMtext_phi3instruct
else else
error("$formatname template not define yet") error("$formatname template not define yet")
end end
str = "" str = ""
for t in messages for t in messages
str *= f(t[:name], t[:text]) str *= f(t[:name], t[:text])
end end
# add <|assistant|> so that the model don't generate it and I don't need to clean it up later # add <|assistant|> so that the model don't generate it and I don't need to clean it up later
if formatname == "phi3instruct" if formatname == "phi3instruct"
str *= "<|assistant|>\n" str *= "<|assistant|>\n"
end end
return str return str
end end
""" """
Arguments\n 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
end # module util end # module util

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using Revise using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures using YiemAgent, GeneralUtils, JSON3, DataStructures
thoughtDict = OrderedDict( thoughtDict = OrderedDict(
:Question=> "Hello, I would like a get a bottle of wine", :Question=> "Hello, I would like a get a bottle of wine",
:Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.", :Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.",
:Action_1=> Dict( :Action_1=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What occasion are you buying the wine for?", :input=> "What occasion are you buying the wine for?",
), ),
:Observation_1=> "We are having a wedding pary this weekend.", :Observation_1=> "We are having a wedding pary this weekend.",
:Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.", :Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.",
:Action_2=> Dict( :Action_2=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What type of food will you be serving with the wine?", :input=> "What type of food will you be serving with the wine?",
), ),
:Observation_2=> "I think it is Thai dishes", :Observation_2=> "I think it is Thai dishes",
:Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.", :Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.",
:Action_3=> Dict( :Action_3=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What is your budget for this wine?", :input=> "What is your budget for this wine?",
), ),
:Observation_3=> "50 bucks", :Observation_3=> "50 bucks",
:Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.", :Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.",
:Action_4=> Dict( :Action_4=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)", :input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)",
), ),
:Observation_4=> "Sparkling please.", :Observation_4=> "Sparkling please.",
:Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.", :Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.",
:Action_5=> Dict( :Action_5=> Dict(
:name=> "winestock", :name=> "winestock",
:input=> "wine with budget \$50, Thai dishes, sparkling, wedding party", :input=> "wine with budget \$50, Thai dishes, sparkling, wedding party",
), ),
:Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}", :Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}",
:Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.", :Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.",
:Action_6=> Dict( :Action_6=> Dict(
:name=> "recommendation", :name=> "recommendation",
:input=> "I recommend Zena Crown Vista for its sparkling and affordable price.", :input=> "I recommend Zena Crown Vista for its sparkling and affordable price.",
), ),
:Observation_6=> "I don't like it. Do you have another option?", :Observation_6=> "I don't like it. Do you have another option?",
) )
_thoughtJsonStr = JSON3.write(thoughtDict) _thoughtJsonStr = JSON3.write(thoughtDict)
thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end
# @show thoughtJsonStr # @show thoughtJsonStr
_, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought") _, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
nextThoughtIndice = latestThoughtIndice + 1 nextThoughtIndice = latestThoughtIndice + 1
_prompt = _prompt =
""" """
You are a helpful sommelier working for a wine store. You are a helpful sommelier working for a wine store.
Your goal is to reccommend the best wine from your inventory that match the user preferences. Your goal is to reccommend the best wine from your inventory that match the user preferences.
You must follow the following criteria: You must follow the following criteria:
1) Get to know what occasion the user is buying wine for 1) Get to know what occasion the user is buying wine for
2) Get to know what food the user will have with wine 2) Get to know what food the user will have with wine
3) Get to know how much the user willing to spend 3) Get to know how much the user willing to spend
4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified 4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified
5) Get to know what characteristics of wine the user is looking for 5) Get to know what characteristics of wine the user is looking for
e.g. tannin, sweetness, intensity, acidity e.g. tannin, sweetness, intensity, acidity
6) Check your inventory for the best wine that match the user preference 6) Check your inventory for the best wine that match the user preference
7) Recommend wine to the user 7) Recommend wine to the user
You should only respond with interleaving Thought, Action, Observation steps. You should only respond with interleaving Thought, Action, Observation steps.
Thought can reason about the current situation, and Action can be three types: 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. 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. 2) chatbox[text], which you can use to interact with the user.
3) recommendation[answer], which returns your wine reccommendation to the user. 3) recommendation[answer], which returns your wine reccommendation to the user.
You should only respond in JSON format as describe below: You should only respond in JSON format as describe below:
{ {
"Thought": "your reasoning", "Thought": "your reasoning",
"Action": {"name": "action to take", "input": "Action input"}, "Action": {"name": "action to take", "input": "Action input"},
"Observation": "result of the action" "Observation": "result of the action"
} }
Here are some examples: Here are some examples:
{ {
"Question": "I would like to buy a sedan with 8 seats.", "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.", "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."}, "Action_1": {"name": "inventory", "input": "sedan with 8 seats."},
"Observation_1": "Several model has 8 seats. Available color are black, red green" "Observation_1": "Several model has 8 seats. Available color are black, red green"
} }
{ {
"Thought_2": "I have to ask the user what color he likes.", "Thought_2": "I have to ask the user what color he likes.",
"Action_2": {"name": "chatbox", "input": "Which color do you like?"} "Action_2": {"name": "chatbox", "input": "Which color do you like?"}
"Observation_2": "I'll take black." "Observation_2": "I'll take black."
} }
{ {
"Thought_3": "There is only one model that fits the user preference. It's Yiem model A", "Thought_3": "There is only one model that fits the user preference. It's Yiem model A",
"Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"} "Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"}
} }
Let's begin! Let's begin!
$(JSON3.write(thoughtDict)) $(JSON3.write(thoughtDict))
{Thought_$nextThoughtIndice {Thought_$nextThoughtIndice
""" """
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt) prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt @show prompt
msgMeta = Dict(:requestResponse => nothing, msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing, :msgPurpose => nothing,
:receiverId => nothing, :receiverId => nothing,
:getPost => nothing, :getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be", :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing, :acknowledgestatus => nothing,
:replyToMsgId => nothing, :replyToMsgId => nothing,
:msgFormatVersion => nothing, :msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"), :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice", :sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct", :receiverName => "text2textinstruct",
:replyTopic => nothing, :replyTopic => nothing,
:senderName => "decisionMaker", :senderName => "decisionMaker",
:senderSelfnote => nothing, :senderSelfnote => nothing,
:senderId => "testingSessionID", :senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561" :timeStamp => "2024-05-04T08:06:23.561"
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> prompt, :text=> prompt,
) )
) )
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
thoughtJsonStr = _response[:response][:text] thoughtJsonStr = _response[:response][:text]

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@@ -1,87 +1,87 @@
using Revise # remove when this package is completed using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
using Base.Threads using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json")) config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1" instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker], client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port]) config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4) receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4) receiveInternalMsgChannel = Channel{Dict}(4)
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( agentConfig = 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, :internal=> instanceInternalTopic,
), ),
:externalservice=> config[:externalservice], :externalservice=> config[:externalservice],
) )
# Instantiate an agent # Instantiate an agent
tools=Dict( # update input format tools=Dict( # update input format
"askbox"=> Dict( "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>", :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>""", :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 => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
# "winestock"=> Dict( # "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>", # :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>""", # :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.""", # :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock, # :func => ChatAgent.winestock,
# ), # ),
"finalanswer"=> Dict( "finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>", :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>""", :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
) )
a = YiemAgent.sommelier( a = YiemAgent.sommelier(
receiveUserMsgChannel, receiveUserMsgChannel,
receiveInternalMsgChannel, receiveInternalMsgChannel,
agentConfig, agentConfig,
name= "assistant", name= "assistant",
id= "testingSessionID", # agent instance id id= "testingSessionID", # agent instance id
tools=tools, tools=tools,
) )
input = input =
OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.") OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.")
result = YiemAgent.jsoncorrection(a, input) result = YiemAgent.jsoncorrection(a, input)

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@@ -1,112 +1,112 @@
using Revise # remove when this package is completed using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs
using Base.Threads using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json")) config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1" instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker], client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port]) config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4) receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4) receiveInternalMsgChannel = Channel{Dict}(4)
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( agentConfig = 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, :internal=> instanceInternalTopic,
), ),
:externalservice=> config[:externalservice], :externalservice=> config[:externalservice],
) )
# Instantiate an agent # Instantiate an agent
tools=Dict( # update input format tools=Dict( # update input format
"askbox"=> Dict( "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>", :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>""", :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 => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
# "winestock"=> Dict( # "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>", # :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>""", # :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.""", # :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock, # :func => ChatAgent.winestock,
# ), # ),
"finalanswer"=> Dict( "finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>", :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>""", :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
) )
a = YiemAgent.sommelier( a = YiemAgent.sommelier(
agentConfig, agentConfig,
name="assistant", name="assistant",
id="testingSessionID", # agent instance id id="testingSessionID", # agent instance id
tools=tools, 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, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) ) ) )
println("---> YiemAgent: ", response) println("---> YiemAgent: ", response)
#BUG mcts do not start at current chat history #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,
:isterminal=> false, :isterminal=> false,
) ) ) )
println("---> YiemAgent: ", response) println("---> YiemAgent: ", response)
dummyinput = "price below 50, full-bodied red wine with sweetness level 2, low tannin level and medium acidity level, Thai dishes" dummyinput = "price below 50, full-bodied red wine with sweetness level 2, low tannin level and medium acidity level, Thai dishes"
response = YiemAgent.winestock(a, dummyinput) response = YiemAgent.winestock(a, dummyinput)
""" """
Write me SQL command to search the database for wines that fits: {"wine_type": "red","sweetness": 2,"acidity": 3,"tannin": 1,"intensity": 5} Write me SQL command to search the database for wines that fits: {"wine_type": "red","sweetness": 2,"acidity": 3,"tannin": 1,"intensity": 5}
""" """
mctsparam = Dict{Symbol, Any}() mctsparam = Dict{Symbol, Any}()

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@@ -1,190 +1,190 @@
using Revise using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures using YiemAgent, GeneralUtils, JSON3, DataStructures
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
msgMeta = Dict(:requestResponse => nothing, msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing, :msgPurpose => nothing,
:receiverId => nothing, :receiverId => nothing,
:getPost => nothing, :getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be", :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing, :acknowledgestatus => nothing,
:replyToMsgId => "dummyid", :replyToMsgId => "dummyid",
:msgFormatVersion => nothing, :msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"), :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/testingSessionID", :sendTopic => "/testingSessionID",
:receiverName => "wineassistant", :receiverName => "wineassistant",
:replyTopic => nothing, :replyTopic => nothing,
:senderName => "test_1", :senderName => "test_1",
:senderSelfnote => nothing, :senderSelfnote => nothing,
:senderId => nothing, :senderId => nothing,
:timeStamp => "2024-05-04T08:06:23.561" :timeStamp => "2024-05-04T08:06:23.561"
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "We are holding a wedding party", :text=> "We are holding a wedding party",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "It will be Thai dishes.", :text=> "It will be Thai dishes.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I would spend up to 50 bucks.", :text=> "I would spend up to 50 bucks.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I like dry, full-bodied red wine with low tannin.", :text=> "I like dry, full-bodied red wine with low tannin.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "What do you have?", :text=> "What do you have?",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "Dry wine please.", :text=> "Dry wine please.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "You did not gave me any choice.", :text=> "You did not gave me any choice.",
:select=> nothing, :select=> nothing,
:reward=> -1, :reward=> -1,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "You don't need to ask me. Just tell me already!", :text=> "You don't need to ask me. Just tell me already!",
:select=> nothing, :select=> nothing,
:reward=> -1, :reward=> -1,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I like dry wine with fruity flavors.", :text=> "I like dry wine with fruity flavors.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "OK, I'll take it.", :text=> "OK, I'll take it.",
:select=> 1, :select=> 1,
:reward=> 1, :reward=> 1,
:isterminal=> true, :isterminal=> true,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "Yes.", :text=> "Yes.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I like dry wine with medium acidity.", :text=> "I like dry wine with medium acidity.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)

260
previousversion/0.1/test/test_2.jl
View File

@@ -1,130 +1,130 @@
using Revise # remove when this package is completed using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
using Base.Threads using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json")) config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1" instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker], client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port]) config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4) receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4) receiveInternalMsgChannel = Channel{Dict}(4)
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( agentConfig = 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, :internal=> instanceInternalTopic,
), ),
:externalservice=> config[:externalservice], :externalservice=> config[:externalservice],
) )
# Instantiate an agent # Instantiate an agent
tools=Dict( # update input format tools=Dict( # update input format
"askbox"=> Dict( "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>", :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>""", :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 => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
# "winestock"=> Dict( # "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>", # :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>""", # :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.""", # :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock, # :func => ChatAgent.winestock,
# ), # ),
"finalanswer"=> Dict( "finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>", :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>""", :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
) )
a = YiemAgent.sommelier( a = YiemAgent.sommelier(
receiveUserMsgChannel, receiveUserMsgChannel,
receiveInternalMsgChannel, receiveInternalMsgChannel,
agentConfig, agentConfig,
name= "assistant", name= "assistant",
id= "testingSessionID", # agent instance id id= "testingSessionID", # agent instance id
tools=tools, tools=tools,
) )
input = input =
OrderedDict{Symbol, Any}( OrderedDict{Symbol, Any}(
:question => "Hello, I would like a get a bottle of wine", :question => "Hello, I would like a get a bottle of wine",
:thought_1 => "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.", :thought_1 => "A customer wants to buy a bottle of wine. Before making a recommendation, I need to know more about their preferences.",
:action_1 => Dict{Symbol, Any}( :action_1 => Dict{Symbol, Any}(
:name => "chatbox", :name => "chatbox",
:input => "What is the occasion for which you're buying this wine?" :input => "What is the occasion for which you're buying this wine?"
), ),
:observation_1 => "We are holding a wedding party", :observation_1 => "We are holding a wedding party",
:thought_2 => "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.", :thought_2 => "A wedding party, that's a great occasion! The customer might be looking for a celebratory drink. Let me ask some more questions to narrow down the options.",
:action_2 => Dict{Symbol, Any}( :action_2 => Dict{Symbol, Any}(
:name => "chatbox", :name => "chatbox",
:input => "What type of food will you be serving at the wedding?" :input => "What type of food will you be serving at the wedding?"
), ),
:observation_2 => "It will be Thai dishes.", :observation_2 => "It will be Thai dishes.",
:thought_3 => "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.", :thought_3 => "With Thai food, I should recommend a wine that complements its spicy and savory flavors. And since it's a celebratory occasion, the customer might prefer a full-bodied wine.",
:action_3 => Dict{Symbol, Any}( :action_3 => Dict{Symbol, Any}(
:name => "chatbox", :name => "chatbox",
:input => "What is your budget for this bottle of wine?" :input => "What is your budget for this bottle of wine?"
), ),
:observation_3 => "I would spend up to 50 bucks.", :observation_3 => "I would spend up to 50 bucks.",
:thought_4 => "Now that I have some more information, it's time to narrow down the options.", :thought_4 => "Now that I have some more information, it's time to narrow down the options.",
:action_4 => Dict{Symbol, Any}( :action_4 => Dict{Symbol, Any}(
:name => "winestock", :name => "winestock",
:input => "red wine with full body, pairs well with spicy food, budget \$50"), :input => "red wine with full body, pairs well with spicy food, budget \$50"),
:observation_4 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :observation_4 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n",
:thought_5 => "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.", :thought_5 => "Now that I have a list of potential wines, I need to know more about the customer's taste preferences.",
:action_5 => Dict{Symbol, Any}( :action_5 => Dict{Symbol, Any}(
:name => "chatbox", :name => "chatbox",
:input => "What type of wine characteristics are you looking for? (e.g. t.e.g. tannin level, sweetness, intensity, acidity)" :input => "What type of wine characteristics are you looking for? (e.g. t.e.g. tannin level, sweetness, intensity, acidity)"
), ),
:observation_5 => "I like full-bodied Red wine with low tannin.", :observation_5 => "I like full-bodied Red wine with low tannin.",
:thought_6 => "Now that I have more information about the customer's preferences, it's time to make a recommendation.", :thought_6 => "Now that I have more information about the customer's preferences, it's time to make a recommendation.",
:action_6 => Dict{Symbol, Any}( :action_6 => Dict{Symbol, Any}(
:name => "recommendbox", :name => "recommendbox",
:input => "El Enemigo Cabernet Franc 2019" :input => "El Enemigo Cabernet Franc 2019"
), ),
:observation_6 => "I don't like the one you recommend. I want dry wine." :observation_6 => "I don't like the one you recommend. I want dry wine."
) )
result = YiemAgent.reflector(a, input) result = YiemAgent.reflector(a, input)

850
previousversion/0.2/Manifest.toml Normal file
View File

@@ -0,0 +1,850 @@
# This file is machine-generated - editing it directly is not advised
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manifest_format = "2.0"
project_hash = "bf6c32becbc917fa1c33558e7aa59c1aac5237e3"
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StatsFunsInverseFunctionsExt = "InverseFunctions"
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deps = ["Dates", "Downloads", "InlineStrings", "Mocking", "Printf", "Scratch", "TZJData", "Unicode", "p7zip_jll"]
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[deps.TimeZones.extensions]
TimeZonesRecipesBaseExt = "RecipesBase"
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23
previousversion/0.2/Project.toml Normal file
View File

@@ -0,0 +1,23 @@
name = "YiemAgent"
uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
authors = ["narawat lamaiin <narawat@outlook.com>"]
version = "0.1.0"
[deps]
CondaPkg = "992eb4ea-22a4-4c89-a5bb-47a3300528ab"
DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
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"
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PythonCall = "6099a3de-0909-46bc-b1f4-468b9a2dfc0d"
Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
Revise = "295af30f-e4ad-537b-8983-00126c2a3abe"
SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
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UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"

7
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version 0.1.0
TODO:
[WORKING] build MCTS() for planning
[] executeplan() to execute the plan
Change from version: 0.0.9
-

537
previousversion/0.2/src/MCTSexamplePrompt.py Normal file
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https://github.com/andyz245/LanguageAgentTreeSearch/blob/main/hotpot/hotpot.py
standard_prompt = '''
Write a coherent passage of 4 short paragraphs. The end sentence of each paragraph must be: {input}
'''
reflection_prompt = '''You are an advanced reasoning agent that can improve based on self refection. You will be given a previous reasoning trial in which you were given access to an Docstore API environment and a question to answer. You were unsuccessful in answering the question either because you guessed the wrong answer with Finish[<answer>], or you used up your set number of reasoning steps. In a few sentences, Diagnose a possible reason for failure and devise a new, concise, high level plan that aims to mitigate the same failure. Use complete sentences.
Here are some examples:
Previous Trial:
Question: The Rome Protocols were signed by three Prime Ministers one of which was assassinated as part of what?
Thought 1: I need to search Rome Protocols, find the three Prime Ministers, then find what they were assassinated as part of.
Action 1: Search[Rome Protocols]
Observation 1: The Rome Protocols were a series of three international agreements concluded in Rome on 17 March 1934 between the governments of Austria, Hungary and Italy. They were signed by Italian Prime Minister Benito Mussolini, Austrian Chancellor Engelbert Dollfuss and Hungarian Prime Minister Gyula Gömbös. All of the protocols went into effect on 12 July 1934 and were registered in League of Nations Treaty Series on 12 December 1934.
Thought 2: The three Prime Ministers were Benito Mussolini, Engelbert Dollfuss, and Gyula Gömbös. I need to search Benito Mussolini and find what he was assassinated as part of.
Action 2: Search[Benito Mussolini]
Observation 2: Benito Amilcare Andrea Mussolini (UK: MU(U)SS-ə-LEE-nee, US: MOOSS-, Italian: [beˈniːto aˈmilkare anˈdrɛːa mussoˈliːni]; 29 July 1883 – 28 April 1945) was an Italian politician and journalist who founded and led the National Fascist Party (PNF). He was Prime Minister of Italy from the March on Rome in 1922 until his deposition in 1943, as well as "Duce" of Italian fascism from the establishment of the Italian Fasces of Combat in 1919 until his summary execution in 1945 by Italian partisans. As dictator of Italy and principal founder of fascism, Mussolini inspired and supported the international spread of fascist movements during the inter-war period.Mussolini was originally a socialist politician and a journalist at the Avanti! newspaper. In 1912, he became a member of the National Directorate of the Italian Socialist Party (PSI), but he was expelled from the PSI for advocating military intervention in World War I, in opposition to the party's stance on neutrality. In 1914, Mussolini founded a new journal, Il Popolo d'Italia, and served in the Royal Italian Army during the war until he was wounded and discharged in 1917. Mussolini denounced the PSI, his views now centering on Italian nationalism instead of socialism, and later founded the fascist movement which came to oppose egalitarianism and class conflict, instead advocating "revolutionary nationalism" transcending class lines. On 31 October 1922, following the March on Rome (28–30 October), Mussolini was appointed prime minister by King Victor Emmanuel III, becoming the youngest individual to hold the office up to that time. After removing all political opposition through his secret police and outlawing labor strikes, Mussolini and his followers consolidated power through a series of laws that transformed the nation into a one-party dictatorship. Within five years, Mussolini had established dictatorial authority by both legal and illegal means and aspired to create a totalitarian state. In 1929, Mussolini signed the Lateran Treaty with the Holy See to establish Vatican City.
Mussolini's foreign policy aimed to restore the ancient grandeur of the Roman Empire by expanding Italian colonial possessions and the fascist sphere of influence. In the 1920s, he ordered the Pacification of Libya, instructed the bombing of Corfu over an incident with Greece, established a protectorate over Albania, and incorporated the city of Fiume into the Italian state via agreements with Yugoslavia. In 1936, Ethiopia was conquered following the Second Italo-Ethiopian War and merged into Italian East Africa (AOI) with Eritrea and Somalia. In 1939, Italian forces annexed Albania. Between 1936 and 1939, Mussolini ordered the successful Italian military intervention in Spain in favor of Francisco Franco during the Spanish Civil War. Mussolini's Italy initially tried to avoid the outbreak of a second global war, sending troops at the Brenner Pass to delay Anschluss and taking part in the Stresa Front, the Lytton Report, the Treaty of Lausanne, the Four-Power Pact and the Munich Agreement. However, Italy then alienated itself from Britain and France by aligning with Germany and Japan. Germany invaded Poland on 1 September 1939, resulting in declarations of war by France and the UK and the start of World War II.
On 10 June 1940, Mussolini decided to enter the war on the Axis side. Despite initial success, the subsequent Axis collapse on multiple fronts and eventual Allied invasion of Sicily made Mussolini lose the support of the population and members of the Fascist Party. As a consequence, early on 25 July 1943, the Grand Council of Fascism passed a motion of no confidence in Mussolini; later that day King Victor Emmanuel III dismissed him as head of government and had him placed in custody, appointing Pietro Badoglio to succeed him as Prime Minister. After the king agreed to an armistice with the Allies, on 12 September 1943 Mussolini was rescued from captivity in the Gran Sasso raid by German paratroopers and Waffen-SS commandos led by Major Otto-Harald Mors. Adolf Hitler, after meeting with the rescued former dictator, then put Mussolini in charge of a puppet regime in northern Italy, the Italian Social Republic (Italian: Repubblica Sociale Italiana, RSI), informally known as the Salò Republic, causing a civil war. In late April 1945, in the wake of near total defeat, Mussolini and his mistress Clara Petacci attempted to flee to Switzerland, but both were captured by Italian communist partisans and summarily executed by firing squad on 28 April 1945 near Lake Como. The bodies of Mussolini and his mistress were then taken to Milan, where they were hung upside down at a service station to publicly confirm their demise.
Thought 3: Benito Mussolini was assassinated as part of the collapse of the Axis powers during World War II. So the Rome Protocols were signed by three Prime Ministers one of which was assassinated as part of World War II.
Action 3: Finish[World War II]
Reflection: I searched one of the prime ministers involved in the signing, then attemted to answer right away. I should have searched each of the prime ministers, then looked up 'death' on each of their pages in order to get more information before answering.
Previous Trial:
Question: Kam Heskin plays Paige Morgan in a 2004 film directed by who?
Thought 1: I need to search Kam Heskin and Paige Morgan, find the 2004 film they are in, then find the director of the film.
Action 1: Search[Kam Heskin]
Observation 1: Kam Erika Heskin (born May 8, 1973) is an American actress. She began her career playing Caitlin Deschanel on the NBC daytime soap opera Sunset Beach (1998–1999), before appearing in films Planet of the Apes (2001) and Catch Me If You Can (2002). Heskin went on in 2003 to play Elizabeth Bennet in an independent film Pride & Prejudice: A Latter-Day Comedy, Robin in Charmed (2004), and Paige Morgan in The Prince and Me film franchise (2006–2010).
Thought 2: Kam Heskin plays Paige Morgan in The Prince and Me film franchise. I need to search The Prince and Me film franchise and find the director of the 2004 film.
Action 2: Search[The Prince and Me film franchise]
Observation 2: Could not find [The Prince and Me film franchise]. Similar: ['Kam Heskin', 'Prince of Persia', 'Harry Potter and the Half-Blood Prince (film)', 'Prince of Persia: The Sands of Time (film)', 'The Chronicles of Narnia (film series)', 'Majestic Prince (franchise)', 'Kingsman (franchise)', 'The Little Prince (2015 film)', 'Shrek (franchise)', 'Harry Potter (film series)']
Thought 3: I need to search The Prince and Me (2004 film) instead.
Action 3: Search[The Prince and Me (2004 film)]
Observation 3: Could not find [The Prince and Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Kam Heskin', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince of Persia', 'Brooklynn Prince', 'Prince (musician)']
Thought 4: I need to search The Prince & Me (2004 film).
Action 4: Search[The Prince & Me (2004 film)]
Observation 4: Could not find [The Prince & Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince (musician)', 'Prince of Persia', 'Kam Heskin', 'Brooklynn Prince']
Thought 5: I need to search The Prince & Me (2004 film) instead.
Action 5: Search[The Prince & Me (2004 film)]
Observation 5: Could not find [The Prince & Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince (musician)', 'Prince of Persia', 'Kam Heskin', 'Brooklynn Prince']
Thought 6: I need to search The Prince & Me (2004 film).
Action 6: Search[The Prince & Me (2004 film)]
Observation 6: Could not find [The Prince & Me (2004 film)]. Similar: ['The Prince & Me', 'The Prince & Me 2: The Royal Wedding', 'Prince of Darkness (film)', 'Prince of Persia: The Sands of Time (film)', 'Rob Knox', 'Alexander (2004 film)', 'Prince (musician)', 'Prince of Persia', 'Kam Heskin', 'Brooklynn Prince']
Reflection: I got stuck in a loop where I kept trying to search 'The Prince & Me (2004 film)' but the page could not be found. Instead I should have tried to search the similar results that had a similar name to see and they were made in 2004.
Previous Trial:
Question: The Oberoi family is part of a hotel company that has a head office in what city?
Thought 1: I need to search the Oberoi family and find the city where the hotel company's head office is located.
Action 1: Search[Oberoi family]
Observation 1: The Oberoi Group is a luxury hotel group with its head office in New Delhi, India.[2] Founded in 1934, the company owns and operates 32 luxury hotels and two river cruise ships in 7 countries, primarily under its Oberoi Hotels & Resorts and Trident brands.[3] The group also operates The Oberoi Centre for Learning and Development, which is regarded as one of Asia's top institutions for hospitality education.[4]. The foundations of the Oberoi Group date back to 1934 when The Rai Bahadur Mohan Singh Oberoi, the founder of the group, bought two properties: the Maidens in Delhi and the Clarke's in Shimla.[5] In the following years Oberoi, assisted by his two sons, Tilak Raj Singh Oberoi and Prithvi Raj Singh Oberoi (P.R.S. Oberoi
Thought 2: The Oberoi Group has its head office in New Delhi, India. I just need to finish with New Delhi.
Action 2: Finish[New Delhi]
Observation 2: Episode finished, reward = 0
Reflection: I appeared to have retrieved the correct information about The Oberoi Family and the location of it's head office, and provided a corresponding answer. However this answer does not exactly match the ground truth answer so I should try a different wording, such as Delhi.
Previous trial:
{trajectory}Reflection:'''
cot_prompt = '''
Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Guitars for Wounded Warriors is an album that was recorded in the village in which New York county?
Thought 1: I need to search Guitars for Wounded Warriors, then find in which New York county it was recorded.
Action 1: Search[Guitars for Wounded Warriors]
Observation 1: Guitars for Wounded Warriors is the sixth solo studio album by guitarist Brian Tarquin, released in August 2014 by Cleopatra Records. In 2016, it received a Best Compilation Album nomination by the Independent Music Awards. All tracks were recorded at Tarquin's Jungle Room Studios in New Paltz (village), New York. Being moved by the lack of support for returning veterans through his life Tarquin decided to compose a heavy instrumental rock album as a way to show his appreciation to all veterans. So once again he enlisted top notch players to guest with him on the album, Billy Sheehan on Black Hawk, Ron \"Bumblefoot\" Thal and Reb Beach on Taliban Terror, Steve Morse on Freedom, Chris Poland on Charlie Surfs and Hunting, Chuck Loeb on Escape Kabul, Hal Lindes on Sand & Blood, Gary Hoey on 5 Klicks To Hell and Baghdad, Alex De Rosso Dokken on Broken Arrow, and The Flyin' Ryan Brothers on Surgical Strike. The entire album was engineered, produced and composed by Tarquin especially for each one of the guest guitarists. Partial proceeds are donated to the Fisher House Foundation from sales.
Thought 2: Guitars for Wounded Warriors was recorded in New Paltz (village), New York. I need to search New Paltz (village), New York and find which county it is in
Action 2: Search[New Paltz (village), New York]
Observation 2: New Paltz is a village in Ulster County located in the U.S. state of New York. It is approximately 80 miles (130 km) north of New York City and 70 miles (110 km) south of Albany. The population was 7,324 at the 2020 census.[3]
Thought 3: New Paltz (village), New York is in Ulster County, so the answer is Ulster County.
Action 3: Finish[Ulster County]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
{input}
'''
cot_prompt_short = '''
Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
{input}
'''
cot_prompt_feedback_short = '''You are also an advanced reasoning agent that can improve based on self refection. Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
You have attempted to answer the following question before and failed. The following reflection(s) give a plan to avoid failing to answer the question in the same way you did previously. Use them to improve your strategy of correctly answering the given question.
{trajectories}
{input}
'''
cot_prompt_feedback = '''You are also an advanced reasoning agent that can improve based on self refection. Solve a question answering task with interleaving Thought, Action, Observation steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
After each observation, provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Observation 1: The Colorado orogeny was an episode of mountain building (an orogeny) in Colorado and surrounding areas. This took place from 1780 to 1650 million years ago (Mya), during the Paleoproterozoic (Statherian Period). It is recorded in the Colorado orogen, a >500-km-wide belt of oceanic arc rock that extends southward into New Mexico. The Colorado orogeny was likely part of the larger Yavapai orogeny.
Thought 2: It does not mention the eastern sector of the Colorado orogeny. I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Observation 2: (Result 1 / 1) The Colorado orogen, formerly called the Colorado province, is a >500-km-wide belt of oceanic arc rock (1.78\u20131.65 Ga) that extends southward into New Mexico and composes a major part of the Proterozoic provinces of southwestern United States. This transcontinental collisional event occurred during the Paleoproterozoic (Statherian Period).[1] The Wyoming sector of the Colorado orogeny was formerly called the Medicine Bow orogeny. The eastern sector extends into the High Plains and is called the Central Plains orogeny. The boundary between the Colorado orogeny and the Wyoming craton is the Cheyenne belt, a 5-km-wide mylonitic shear zone that verges northward. The Cheyenne belt transects and cuts off the south edge of the older Trans-Hudson orogeny.[2]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]Observation 3: High Plains refers to one of two distinct land regions:
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Observation 4: The High Plains are a subregion of the Great Plains, mainly in the Western United States, but also partly in the Midwest states of Nebraska, Kansas, and South Dakota, generally encompassing the western part of the Great Plains before the region reaches the Rocky Mountains. The High Plains are located in eastern Montana, southeastern Wyoming, southwestern South Dakota, western Nebraska, eastern Colorado, western Kansas, eastern New Mexico, western Oklahoma, and to just south of the Texas Panhandle.[4] The southern region of the Western High Plains ecology region contains the geological formation known as Llano Estacado which can be seen from a short distance or on satellite maps.[5] From east to west, the High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130 m).[3]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Guitars for Wounded Warriors is an album that was recorded in the village in which New York county?
Thought 1: I need to search Guitars for Wounded Warriors, then find in which New York county it was recorded.
Action 1: Search[Guitars for Wounded Warriors]
Observation 1: Guitars for Wounded Warriors is the sixth solo studio album by guitarist Brian Tarquin, released in August 2014 by Cleopatra Records. In 2016, it received a Best Compilation Album nomination by the Independent Music Awards. All tracks were recorded at Tarquin's Jungle Room Studios in New Paltz (village), New York. Being moved by the lack of support for returning veterans through his life Tarquin decided to compose a heavy instrumental rock album as a way to show his appreciation to all veterans. So once again he enlisted top notch players to guest with him on the album, Billy Sheehan on Black Hawk, Ron \"Bumblefoot\" Thal and Reb Beach on Taliban Terror, Steve Morse on Freedom, Chris Poland on Charlie Surfs and Hunting, Chuck Loeb on Escape Kabul, Hal Lindes on Sand & Blood, Gary Hoey on 5 Klicks To Hell and Baghdad, Alex De Rosso Dokken on Broken Arrow, and The Flyin' Ryan Brothers on Surgical Strike. The entire album was engineered, produced and composed by Tarquin especially for each one of the guest guitarists. Partial proceeds are donated to the Fisher House Foundation from sales.
Thought 2: Guitars for Wounded Warriors was recorded in New Paltz (village), New York. I need to search New Paltz (village), New York and find which county it is in
Action 2: Search[New Paltz (village), New York]
Observation 2: New Paltz is a village in Ulster County located in the U.S. state of New York. It is approximately 80 miles (130 km) north of New York City and 70 miles (110 km) south of Albany. The population was 7,324 at the 2020 census.[3]
Thought 3: New Paltz (village), New York is in Ulster County, so the answer is Ulster County.
Action 3: Finish[Ulster County]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
You have attempted to answer the following question before and failed, either because your reasoning for the answer was incorrect or the phrasing of your response did not exactly match the answer. The following reflection(s) give a plan to avoid failing to answer the question in the same way you did previously. Use them to improve your strategy of correctly answering the given question.
{trajectories}
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.
{input}
'''
vote_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by pairs of thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a list of trajectories, decide which trajectory is most promising. Analyze each trajectory in detail and consider possible errors, then conclude in the last line "The best trajectory is {s}", where s the integer id of the trajectory.
'''
compare_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by pairs of thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Briefly analyze the correctness of the following two trajectories. Conclude in the last line "The more correct trajectory is 1", "The more correct trajectory is 2", or "The two trajectories are similarly correct".
'''
score_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by pairs of thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, analyze the following trajectory, then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
'''
value_prompt = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
Thus the correctness score is 3
Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?
Thought 1: I need to search for the football manager who recruited David Beckham, and then find out the timeframe in which he managed Manchester United.
Action 1: Search[David Beckham's football manager]
Observation 1: Could not find David Beckham's football manager. Similar: ['David Beckham', 'Inter Miami CF (redirect from Miami Beckham United)', 'Victoria Beckham', 'The Beckham Experiment', 'Miami Freedom Park'].
Thought 2: I couldn't find the information on David Beckham's football manager. I need to search Manchester United and find out the timeframe of the manager.
Action 2: Search[Manchester United]
Observation 2: Manchester United Football Club, commonly referred to as Man United (often stylised as Man Utd), or simply United, is a professional football club based in Old Trafford, Greater Manchester, England. The club competes in the Premier League, the top division in the English football league system. Nicknamed the Red Devils, they were founded as Newton Heath LYR Football Club in 1878, but changed their name to Manchester United in 1902. After a spell playing in Clayton, Manchester, the club moved to their current stadium, Old Trafford, in 1910.. Domestically, Manchester United have won a record 20 league titles, 12 FA Cups, six League Cups and a record 21 FA Community Shields.
Thought 3: The information about David Beckham's football manager is not available. I will search for Manchester United's managers to find the timeframe.
Action 3: Search[Manchester United managers]
Observation 3: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 1886–87 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 2022–23 season, Manchester United have had 23 full-time managers.
Thought 4: The information about David Beckham's football manager is not available. The question asks for the timeframe when the manager managed Manchester United, so I need to find a list of Manchester United managers.
Action 4: Search[list of Manchester United managers]
Observation 4: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 1886–87 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 2022–23 season, Manchester United have had 23 full-time managers.
Thus the correctness score is 4
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
Thus the correctness score is 10
{input}
'''
value_prompt_feedback = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
Thus the correctness score is 3
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
Thus the correctness score is 10
{trajectories}
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
Thus the correctness score is 10
{input}
'''
value_prompt_reasoning = '''You are an advanced reasoning agent that can improve based on self refection. Analyze the trajectories of your previous solutions to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
This trajectory is incorrect as my search should be related to Mexican Formula One race car drivers, not winners of the Mexican Grand Prix, a seperate event. A better search would have been for the List of Formula One Grand Prix winners, as suggested.
Thus the correctness score is 3
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
This trajectory is correct as it is reasonable to search for the first magazine provided in the question. It is also better to have simple searches corresponding to a single entity, making this the best action.
Thus the correctness score is 10
Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?
Thought 1: I need to search for the football manager who recruited David Beckham, and then find out the timeframe in which he managed Manchester United.
Action 1: Search[David Beckham's football manager]
Observation 1: Could not find David Beckham's football manager. Similar: ['David Beckham', 'Inter Miami CF (redirect from Miami Beckham United)', 'Victoria Beckham', 'The Beckham Experiment', 'Miami Freedom Park'].
Thought 2: I couldn't find the information on David Beckham's football manager. I need to search Manchester United and find out the timeframe of the manager.
Action 2: Search[Manchester United]
Observation 2: Manchester United Football Club, commonly referred to as Man United (often stylised as Man Utd), or simply United, is a professional football club based in Old Trafford, Greater Manchester, England. The club competes in the Premier League, the top division in the English football league system. Nicknamed the Red Devils, they were founded as Newton Heath LYR Football Club in 1878, but changed their name to Manchester United in 1902. After a spell playing in Clayton, Manchester, the club moved to their current stadium, Old Trafford, in 1910.. Domestically, Manchester United have won a record 20 league titles, 12 FA Cups, six League Cups and a record 21 FA Community Shields.
Thought 3: The information about David Beckham's football manager is not available. I will search for Manchester United's managers to find the timeframe.
Action 3: Search[Manchester United managers]
Observation 3: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 1886–87 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 2022–23 season, Manchester United have had 23 full-time managers.
Thought 4: The information about David Beckham's football manager is not available. The question asks for the timeframe when the manager managed Manchester United, so I need to find a list of Manchester United managers.
Action 4: Search[list of Manchester United managers]
Observation 4: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 1886–87 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 2022–23 season, Manchester United have had 23 full-time managers.
This trajectory is mostly incorrect as I was not able to retrieve the relevant information to answer the question. The searches are too broad and not relevant to the specific player recruited, David Beckham. The last two actions are also too similar. It would be better to first search for David Beckham and lookup information related to his manager.
Thus the correctness score is 4
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
This trajectory is correct as all of my thoughts and actions are correct. It makes sense to search for Milhouse first as it is the central subject of the question. It is also correct to directly look up the relevant information in the article, instead of trying another search.
Thus the correctness score is 10
{input}
'''
value_prompt_reasoning_feedback = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
This trajectory is incorrect as my search should be related to Mexican Formula One race car drivers, not winners it is reasonable to checkof the Mexican Grand Prix, a seperate event. A better search would have been for the List of Formula One Grand Prix winners, as suggested.
Thus the correctness score is 3
Question: Which magazine was started first Arthur's Magazine or First for Women?
Thought 1: I need to search Arthur's Magazine and First for Women, and find which was started first.
Action 1: Search[Arthur's Magazine]
Observation 1: Arthur's Magazine (1844\u00e2\u0080\u00931846) was an American literary periodical published in Philadelphia in the 19th century. Edited by Timothy Shay Arthur, it featured work by Edgar A. Poe, J.H. Ingraham, Sarah Josepha Hale, Thomas G. Spear, and others.[1][2] In May 1846 it was merged into Godey's Lady's Book.[3]
This trajectory is correct as it is reasonable to search for the first magazine provided in the question. It is also better to have simple searches corresponding to a single entity, making this the best action.
Thus the correctness score is 10
Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?Question: The football manager who recruited David Beckham managed Manchester United during what timeframe?
Thought 1: I need to search for the football manager who recruited David Beckham, and then find out the timeframe in which he managed Manchester United.
Action 1: Search[David Beckham's football manager]
Observation 1: Could not find David Beckham's football manager. Similar: ['David Beckham', 'Inter Miami CF (redirect from Miami Beckham United)', 'Victoria Beckham', 'The Beckham Experiment', 'Miami Freedom Park'].
Thought 2: I couldn't find the information on David Beckham's football manager. I need to search Manchester United and find out the timeframe of the manager.
Action 2: Search[Manchester United]
Observation 2: Manchester United Football Club, commonly referred to as Man United (often stylised as Man Utd), or simply United, is a professional football club based in Old Trafford, Greater Manchester, England. The club competes in the Premier League, the top division in the English football league system. Nicknamed the Red Devils, they were founded as Newton Heath LYR Football Club in 1878, but changed their name to Manchester United in 1902. After a spell playing in Clayton, Manchester, the club moved to their current stadium, Old Trafford, in 1910.. Domestically, Manchester United have won a record 20 league titles, 12 FA Cups, six League Cups and a record 21 FA Community Shields.
Thought 3: The information about David Beckham's football manager is not available. I will search for Manchester United's managers to find the timeframe.
Action 3: Search[Manchester United managers]
Observation 3: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 1886–87 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 2022–23 season, Manchester United have had 23 full-time managers.
Thought 4: The information about David Beckham's football manager is not available. The question asks for the timeframe when the manager managed Manchester United, so I need to find a list of Manchester United managers.
Action 4: Search[list of Manchester United managers]
Observation 4: Manchester United Football Club is a professional football club based in Old Trafford, Greater Manchester, England. The club was formed in Newton Heath in 1878 as Newton Heath LYR F.C., and played their first competitive match in October 1886, when they entered the First Round of the 1886–87 FA Cup. The club was renamed Manchester United F.C. in 1902, and moved to Old Trafford in 1910.. From the beginning of the club's official managerial records in 1892 to the start of the 2022–23 season, Manchester United have had 23 full-time managers.
This trajectory is mostly incorrect as I was not able to retrieve the relevant information to answer the question. The searches are too broad and not relevant to the specific player recruited, David Beckham. The last two actions are also too similar. It would be better to first search for David Beckham and lookup information related to his manager.
Thus the correctness score is 4
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
This trajectory is correct as all of my thoughts and actions are correct. It makes sense to search for Milhouse first as it is the central subject of the question. It is also correct to directly look up the relevant information in the article, instead of trying another search.
Thus the correctness score is 10
{trajectories}
{input}
'''
value_prompt_reasoning_feedback_short = '''Analyze the trajectories of a solution to a question answering task. The trajectories are labeled by environmental observations about the situation, thoughts that can reason about the current situation and actions that can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Given a question and a trajectory, evaluate its correctness and provide your reasoning and analysis in detail. Focus on the latest thought, action, and observation. Incomplete trajectories can be correct if the thoughts and actions so far are correct, even if the answer is not found yet. Do not generate additional thoughts or actions. Then at the last line conclude "Thus the correctness score is {s}", where s is an integer from 1 to 10.
Question: Which other Mexican Formula One race car driver has held the podium besides the Force India driver born in 1990?
Thought 1: I need to search for a list of Mexican Formula One race car drivers, then eliminate the one who drove for Force India and was born in 1990.
Action 1: Search[Formula One drivers from Mexico]
Observation 1: There have been six Formula One drivers from Mexico who have taken part in races since the championship began in 1950. Sergio P\u00e9rez is the most successful, having won six Grands Prix. Since 2021, P\u00e9rez drives for the Red Bull team.. P\u00e9rez is the only Mexican F1 driver to achieve a pole position. He took his first pole position at the 2022 Saudi Arabian Grand Prix, in his 219th Grand Prix meeting (a record for the most Grands Prix before a pole).
Thought 2: Sergio P\u00e9rez, born in 1990 and drove for Force India, is not the Mexican driver we are looking for. The passage does not give me enough information about other Mexican drivers who have achieved a podium finish. I need to look up more information.
Action 2: Lookup[Formula One drivers from Mexico]
Observation 2: No more results.
Thought 3: The available information doesn't allow me to determine which other Mexican Formula One race car driver, besides Sergio P\u00e9rez, has held the podium. With the search function failing to provide a full list of Mexican Formula One drivers, I need to return different search results. Let's try searching for Mexican Grand Prix winners, as they would have held the podium.
Action 3: Search[Mexican Grand Prix winners]
Observation 3: Could not find Mexican Grand Prix winners. Similar: ['Mexican Grand Prix', 'List of Formula One Grand Prix winners', '1990 Mexican Grand Prix', '2018 Mexican Grand Prix', '2019 Mexican Grand Prix'].
Thought 4: Given the difficulties in finding a direct list of Mexican Grand Prix winners, I should search for the Mexican Grand Prix to get a broader picture of the race's history. This might include winners.
Action 4: Search[Mexican Grand Prix]
This trajectory is incorrect as my search should be related to Mexican Formula One race car drivers, not winners of the Mexican Grand Prix, a seperate event. A better search would have been for the List of Formula One Grand Prix winners, as suggested.
Thus the correctness score is 3
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Observation 1: Milhouse Mussolini Van Houten is a recurring character in the Fox animated television series The Simpsons voiced by Pamela Hayden and created by Matt Groening.
Thought 2: The paragraph does not tell who Milhouse is named after, maybe I can look up "named after".
Action 2: Lookup[named after]
Observation 2: (Result 1 / 1) Milhouse was named after U.S. president Richard Nixon, whose middle name was Milhous.
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
This trajectory is correct as all of my thoughts and actions are correct. It makes sense to search for Milhouse first as it is the central subject of the question. It is also correct to directly look up the relevant information in the article, instead of trying another search.
Thus the correctness score is 10
{trajectories}
{input}
'''
rap_prompt = '''
Solve a question answering task with interleaving Thought and Action steps. Thought can reason about the current situation, and Action can be three types:
(1) Search[entity], which searches the exact entity on Wikipedia and returns the first paragraph if it exists. If not, it will return some similar entities to search.
(2) Lookup[keyword], which returns the next sentence containing keyword in the current passage.
(3) Finish[answer], which returns the answer and finishes the task.
Provide the next Thought and next Action. Here are some examples:
Question: What is the elevation range for the area that the eastern sector of the Colorado orogeny extends into?
Thought 1: I need to search Colorado orogeny, find the area that the eastern sector of the Colorado orogeny extends into, then find the elevation range of the area.
Action 1: Search[Colorado orogeny]
Thought 2: Then I need to look up eastern sector.
Action 2: Lookup[eastern sector]
Thought 3: The eastern sector of Colorado orogeny extends into the High Plains, so High Plains is the area. I need to search High Plains and find its elevation range.
Action 3: Search[High Plains]
Thought 4: I need to search High Plains (United States).
Action 4: Search[High Plains (United States)]
Thought 5: High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer is 1,800 to 7,000 ft.
Action 5: Finish[1,800 to 7,000 ft]
Question: Guitars for Wounded Warriors is an album that was recorded in the village in which New York county?
Thought 1: I need to search Guitars for Wounded Warriors, then find in which New York county it was recorded.
Action 1: Search[Guitars for Wounded Warriors]
Thought 2: I need to search New Paltz (village), New York and find which county it is in
Action 2: Search[New Paltz (village), New York]
Thought 3: New Paltz (village), New York is in Ulster County, so the answer is Ulster County.
Action 3: Finish[Ulster County]
Question: Musician and satirist Allie Goertz wrote a song about the "The Simpsons" character Milhouse, who Matt Groening named after who?
Thought 1: The question simplifies to "The Simpsons" character Milhouse is named after who. I only need to search Milhouse and find who it is named after.
Action 1: Search[Milhouse]
Thought 2: I can look up "named after" for finding the specific individual Milhouse is named after.
Action 2: Lookup[named after]
Thought 3: Milhouse was named after U.S. president Richard Nixon, so the answer is Richard Nixon.
Action 3: Finish[President Richard Nixon]
{input}
'''

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module YiemAgent
# export agent
""" 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 YiemAgent_v1

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module type
export agent, sommelier, companion
using Dates, UUIDs, DataStructures, JSON3
using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- #
abstract type agent end
mutable struct companion <: agent
name::String # agent name
id::String # agent id
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
""" Memory
Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
NO "system" message in chathistory because I want to add it at the inference time
chathistory= [
Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
]
"""
chathistory::Vector{Dict{Symbol, Any}}
memory::Dict{Symbol, Any}
# communication function
text2textInstructLLM::Function
end
function companion(
text2textInstructLLM::Function
;
name::String= "Assistant",
id::String= string(uuid4()),
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
)
memory = Dict{Symbol, Any}(
:chatbox=> "",
:shortmem=> Vector{Dict{Symbol, String}}(),
:events=> Vector{Dict{Symbol, Any}}()
)
newAgent = companion(
name,
id,
maxHistoryMsg,
chathistory,
memory,
text2textInstructLLM
)
return newAgent
end
""" A sommelier agent.
# Arguments
- `mqttClient::Client`
MQTTClient's client
- `msgMeta::Dict{Symbol, Any}`
A dict contain info about a message.
- `config::Dict{Symbol, Any}`
Config info for an agent. Contain mqtt topic for internal use and other info.
# Keyword Arguments
- `name::String`
Agent's name
- `id::String`
Agent's ID
- `tools::Dict{Symbol, Any}`
Agent's tools
- `maxHistoryMsg::Integer`
max history message
# Return
- `nothing`
# Example
```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils
julia> msgMeta = GeneralUtils.generate_msgMeta(
"N/A",
replyTopic = "/testtopic/prompt"
)
julia> tools= Dict(
:chatbox=>Dict(
:name => "chatbox",
:description => "Useful only for when you need to ask the user for more info or context. Do not ask the user their own question.",
:input => "Input should be a text.",
:output => "" ,
:func => nothing,
),
)
julia> agentConfig = Dict(
:receiveprompt=>Dict(
:mqtttopic=> "/testtopic/prompt", # topic to receive prompt i.e. frontend send msg to this topic
),
:receiveinternal=>Dict(
:mqtttopic=> "/testtopic/internal", # receive topic for model's internal
),
:text2text=>Dict(
:mqtttopic=> "/text2text/receive",
),
)
julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> agent = YiemAgent.bsommelier(
client,
msgMeta,
agentConfig,
name= "assistant",
id= "555", # agent instance id
tools=tools,
)
```
# TODO
- [] update docstring
- [x] implement the function
# Signature
"""
mutable struct sommelier <: agent
name::String # agent name
id::String # agent id
retailername::String
tools::Dict
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
""" Memory
Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
NO "system" message in chathistory because I want to add it at the inference time
chathistory= [
Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
]
"""
chathistory::Vector{Dict{Symbol, Any}}
memory::Dict{Symbol, Any}
# communication function
text2textInstructLLM::Function
executeSQL::Function
querySQLVectorDB::Function
addSQLVectorDB::Function
end
function sommelier(
text2textInstructLLM::Function,
executeSQL::Function,
querySQLVectorDB::Function,
addSQLVectorDB::Function
;
name::String= "Assistant",
id::String= string(uuid4()),
retailername::String= "retailer_name",
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
)
tools = Dict( # update input format
"chatbox"=> 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 => "" ,
),
"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,
# ),
)
memory = Dict{Symbol, Any}(
:chatbox=> "",
:shortmem=> Vector{Dict{Symbol, String}}(),
:events=> Vector{Dict{Symbol, Any}}()
)
newAgent = sommelier(
name,
id,
retailername,
tools,
maxHistoryMsg,
chathistory,
memory,
text2textInstructLLM,
executeSQL,
querySQLVectorDB,
addSQLVectorDB
)
return newAgent
end
end # module type

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module util
export clearhistory, addNewMessage, vectorOfDictToText, eventdict, noises
using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3
using GeneralUtils
using ..type
# ---------------------------------------------- 100 --------------------------------------------- #
""" Clear agent chat history.
# Arguments
- `a::agent`
an agent
# Return
- nothing
# Example
```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict(
:receiveprompt=>Dict(
:mqtttopic=> "testtopic/receive",
),
:receiveinternal=>Dict(
:mqtttopic=> "testtopic/internal",
),
:text2text=>Dict(
:mqtttopic=> "testtopic/text2text",
),
)
julia> a = YiemAgent.sommelier(
client,
msgMeta,
agentConfig,
)
julia> YiemAgent.addNewMessage(a, "user", "hello")
julia> YiemAgent.clearhistory(a)
```
# TODO
- [PENDING] clear memory
# Signature
"""
function clearhistory(a::T) where {T<:agent}
empty!(a.chathistory)
empty!(a.memory[:shortmem])
empty!(a.memory[:events])
a.memory[:chatbox] = ""
end
""" Add new message to agent.
Arguments\n
-----
a::agent
an agent
role::String
message sender role i.e. system, user or assistant
text::String
message text
Return\n
-----
nothing
Example\n
-----
```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict(
:receiveprompt=>Dict(
:mqtttopic=> "testtopic/receive",
),
:receiveinternal=>Dict(
:mqtttopic=> "testtopic/internal",
),
:text2text=>Dict(
:mqtttopic=> "testtopic/text2text",
),
)
julia> a = YiemAgent.sommelier(
client,
msgMeta,
agentConfig,
)
julia> YiemAgent.addNewMessage(a, "user", "hello")
```
Signature\n
-----
"""
function addNewMessage(a::T1, name::String, text::T2;
maximumMsg::Integer=20) where {T1<:agent, T2<:AbstractString}
if name ∉ ["system", "user", "assistant"] # guard against typo
error("name is not in agent.availableRole $(@__LINE__)")
end
#[] summarize the oldest 10 message
if length(a.chathistory) > maximumMsg
summarize(a.chathistory)
else
d = Dict(:name=> name, :text=> text, :timestamp=> Dates.now())
push!(a.chathistory, d)
end
end
"""
# Arguments
- `v::Integer`
dummy variable
# Return
# Example
```jldoctest
julia>
```
# TODO
- [] update docstring
- [x] implement the function
# Signature
"""
function vectorOfDictToText(vecd::Vector; withkey=true)
text = ""
if withkey
for d in vecd
name = d[:name]
_text = d[:text]
text *= "$name> $_text \n"
end
else
for d in vecd
for (k, v) in d
text *= "$v \n"
end
end
end
return text
end
function eventdict(;
event_description::Union{String, Nothing}=nothing,
timestamp::Union{DateTime, Nothing}=nothing,
subject::Union{String, Nothing}=nothing,
action_or_dialogue::Union{String, Nothing}=nothing,
location::Union{String, Nothing}=nothing,
equipment_used::Union{String, Nothing}=nothing,
material_used::Union{String, Nothing}=nothing,
outcome::Union{String, Nothing}=nothing,
note::Union{String, Nothing}=nothing,
)
return Dict{Symbol, Any}(
:event_description=> event_description,
:timestamp=> timestamp,
:subject=> subject,
:action_or_dialogue=> action_or_dialogue,
:location=> location,
:equipment_used=> equipment_used,
:material_used=> material_used,
:outcome=> outcome,
:note=> note,
)
end
noise(n::Integer) = String(rand('a':'z', n))
function noises(totalword::Integer, wordlength::Integer)
noises = ""
for i in 1:totalword
noises *= noise(wordlength) * " "
end
noises = strip(noises)
return noises
end
# """ Convert a single chat dictionary into LLM model instruct format.
# # Llama 3 instruct format example
# <|system|>
# You are a helpful AI assistant.<|end|>
# <|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|>
# $text
# <|eot_id|>
# """
# end
# return formattedtext
# end
# """ Convert a chat messages in vector of dictionary into LLM model instruct format.
# # Arguments
# - `messages::Vector{Dict{Symbol, T}}`
# message owner name e.f. "system", "user" or "assistant"
# - `formatname::T`
# format name to be used
# # Return
# - `formattedtext::String`
# text formatted to model format
# # Example
# ```jldoctest
# julia> using Revise
# julia> using YiemAgent
# julia> chatmessage = [
# Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",),
# Dict(:name=> "user",:text=> "list me all planets in our solar system.",),
# Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",),
# ]
# julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct")
# "<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n <|start_header_id|>user<|end_header_id|>\n list me all planets in our solar system.\n <|eot_id|>\n <|start_header_id|>assistant<|end_header_id|>\n I'm sorry. I don't know. You tell me.\n <|eot_id|>\n"
# ```
# # Signature
# """
# function formatLLMtext(messages::Vector{Dict{Symbol, T}},
# formatname::String="llama3instruct") where {T<:Any}
# f = if formatname == "llama3instruct"
# formatLLMtext_llama3instruct
# elseif formatname == "mistral"
# # not define yet
# elseif formatname == "phi3instruct"
# formatLLMtext_phi3instruct
# else
# error("$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
# -----
# Example\n
# -----
# ```jldoctest
# julia>
# ```
# TODO\n
# -----
# [] update docstring
# [PENDING] implement the function
# Signature\n
# -----
# """
# function iterativeprompting(a::T, prompt::String, verification::Function) where {T<:agent}
# msgMeta = GeneralUtils.generate_msgMeta(
# a.config[:externalService][:text2textinstruct],
# senderName= "iterativeprompting",
# senderId= a.id,
# receiverName= "text2textinstruct",
# )
# outgoingMsg = Dict(
# :msgMeta=> msgMeta,
# :payload=> Dict(
# :text=> prompt,
# )
# )
# success = nothing
# result = nothing
# critique = ""
# # iteration loop
# while true
# # send prompt to LLM
# response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# error("--> iterativeprompting")
# # check for correctness and get feedback
# success, _critique = verification(response)
# if success
# result = response
# break
# else
# # add critique to prompt
# critique *= _critique * "\n"
# replace!(prompt, "Critique: ..." => "Critique: $critique")
# end
# end
# return (success=success, result=result)
# end
end # module util

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using GeneralUtils
response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria."
responsedict = GeneralUtils.textToDict(response,
["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"],
rightmarker=":", symbolkey=true)

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using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures
thoughtDict = OrderedDict(
:Question=> "Hello, I would like a get a bottle of wine",
:Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.",
:Action_1=> Dict(
:name=> "chatbox",
:input=> "What occasion are you buying the wine for?",
),
:Observation_1=> "We are having a wedding pary this weekend.",
:Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.",
:Action_2=> Dict(
:name=> "chatbox",
:input=> "What type of food will you be serving with the wine?",
),
:Observation_2=> "I think it is Thai dishes",
:Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.",
:Action_3=> Dict(
:name=> "chatbox",
:input=> "What is your budget for this wine?",
),
:Observation_3=> "50 bucks",
:Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.",
:Action_4=> Dict(
:name=> "chatbox",
:input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)",
),
:Observation_4=> "Sparkling please.",
:Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.",
:Action_5=> Dict(
:name=> "winestock",
:input=> "wine with budget \$50, Thai dishes, sparkling, wedding party",
),
:Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}",
:Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.",
:Action_6=> Dict(
:name=> "recommendation",
:input=> "I recommend Zena Crown Vista for its sparkling and affordable price.",
),
:Observation_6=> "I don't like it. Do you have another option?",
)
_thoughtJsonStr = JSON3.write(thoughtDict)
thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end
# @show thoughtJsonStr
_, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
nextThoughtIndice = latestThoughtIndice + 1
_prompt =
"""
You are a helpful sommelier working for a wine store.
Your goal is to reccommend the best wine from your inventory that match the user preferences.
You must follow the following criteria:
1) Get to know what occasion the user is buying wine for
2) Get to know what food the user will have with wine
3) Get to know how much the user willing to spend
4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified
5) Get to know what characteristics of wine the user is looking for
e.g. tannin, sweetness, intensity, acidity
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) recommendation[answer], which returns your wine reccommendation to the user.
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_2": "I have to ask the user what color he likes.",
"Action_2": {"name": "chatbox", "input": "Which color do you like?"}
"Observation_2": "I'll take black."
}
{
"Thought_3": "There is only one model that fits the user preference. It's Yiem model A",
"Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"}
}
Let's begin!
$(JSON3.write(thoughtDict))
{Thought_$nextThoughtIndice
"""
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => nothing,
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct",
:replyTopic => nothing,
:senderName => "decisionMaker",
:senderSelfnote => nothing,
:senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
thoughtJsonStr = _response[:response][:text]

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using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4)
msgMeta = GeneralUtils.generate_msgMeta(
"N/A",
replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
)
agentConfig = Dict(
:mqttServerInfo=> config[:mqttServerInfo],
:receivemsg=> Dict(
:prompt=> config[:servicetopic][:mqtttopic], # topic to receive prompt i.e. frontend send msg to this topic
:internal=> instanceInternalTopic,
),
:externalservice=> config[:externalservice],
)
# Instantiate an agent
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,
),
# "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,
),
)
a = YiemAgent.sommelier(
receiveUserMsgChannel,
receiveInternalMsgChannel,
agentConfig,
name= "assistant",
id= "testingSessionID", # agent instance id
tools=tools,
)
input =
OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.")
result = YiemAgent.jsoncorrection(a, input)

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using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures, LibPQ
using SQLLLM
# _prompt =
# """
# You are a helpful assistant.
# answer the following question:
# From the following CSV text:
# "{\"tabledescription\":[\"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.\",\"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.\",\"The wine_food table represents the association between wines and food items. It estab" ⋯ 477 bytes ⋯ "ed to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description.\",\"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.\",\"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.\"],\"tablename\":[\"customer\",\"wine\",\"wine_food\",\"food\",\"retailer\",\"retailer_wine\",\"retailer_food\"]}"
# What is the description of table wine?
# """
# prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
# @show prompt
# msgMeta = Dict(:requestResponse => nothing,
# :msgPurpose => nothing,
# :receiverId => nothing,
# :getPost => nothing,
# :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
# :acknowledgestatus => nothing,
# :replyToMsgId => nothing,
# :msgFormatVersion => nothing,
# :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
# :sendTopic => "/loadbalancer/requestingservice",
# :receiverName => "text2textinstruct",
# :replyTopic => nothing,
# :senderName => "decisionMaker",
# :senderSelfnote => nothing,
# :senderId => "testingSessionID",
# :timeStamp => "2024-05-04T08:06:23.561"
# )
# outgoingMsg = Dict(
# :msgMeta=> msgMeta,
# :payload=> Dict(
# :text=> prompt,
# )
# )
# _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# result = _response[:response][:text]
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
tableinfo, df1, df2, df3 = SQLLLM.tableinfo(DBconnection, "wine")
_prompt =
"""
You are a helpful assistant helping to answer user question from a database table.
$tableinfo
Are there any chardonnay?
"""
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt
msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing,
:receiverId => nothing,
:getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing,
:replyToMsgId => nothing,
:msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct",
:replyTopic => nothing,
:senderName => "decisionMaker",
:senderSelfnote => nothing,
:senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561"
)
outgoingMsg = Dict(
:msgMeta=> msgMeta,
:payload=> Dict(
:text=> prompt,
)
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
result2 = _response[:response][:text]

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using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, LibPQ, Base64, DataFrames
using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json"))
# instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
# client, connection = MakeConnection(config[:mqttServerInfo][:broker],
# config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4)
# receiveInternalMsgChannel = Channel{Dict}(4)
# println(typeof(connection))
# msgMeta = GeneralUtils.generate_msgMeta(
# "N/A",
# replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
# )
function executeSQL(sql::T) where {T<:AbstractString}
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@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(
:max_tokens=> 2048,
:stop=> ["<|eot_id|>"],
: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=5433 dbname=SQLVectorDB user=yiemtechnologies@gmail.com password=yiem@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))
println("--> getting SQL statement from vectorDB ", @__FILE__, " ", @__LINE__)
println(sqlStatement)
return sqlStatement
else
return nothing
end
end
return nothing
end
# Instantiate an agent
a = YiemAgent.sommelier(
text2textInstructLLM,
executeSQL,
querySQLVectorDB,
addSQLVectorDB;
name= "Jene",
id= "tempId", # agent instance id
)
function main()
for i in 1:10
userinput = ""
for i in 1:3
if userinput == ""
println("")
println("--> user input:")
userinput = readline()
else
break
end
end
response = YiemAgent.conversation(a, Dict(:text=> userinput))
println("")
println("--> assistant response: \n", response)
end
end
main()
"""
I'm joining a graduation party this evening. I want to get a bottle of white wine from the US to celebrate. I'm ok with any price range.
Well, the party is small casual with close friends and no food serving.
I'm open to suggestion since I have no specific idea.
I'm ok with any region.
The input is instructions on how you want the presentation to be conducted.
"""
wines =
"""
Summary: This table contains two wine records, both from the United States, with white wine types, moderate sweetness (2), and high intensity (5).
More details: 1) wine_id: add9824f-81b0-47da-a08a-ee20498bc6c8, wine_name: Belle Cote Chardonnay, brand: Peter Michael, manufacturer: Peter Michael, region: Californian, country: United States, wine_type: white, grape_variety: Chardonnay, serving_temperature: 11 to 13 Celsius, intensity: 5, sweetness: 2, tannin: missing, acidity: 3, fizziness: missing, tasting_notes: oak, butter, vanilla, cream, oil, lemon curd, pear, peach, apple
2) wine_id: ff9a494c-e916-44c4-9385-1c18b23aa825, wine_name: Ma Belle-Fille Chardonnay, brand: Peter Michael, manufacturer: Peter Michael, region: Californian, country: United States, wine_type: white, grape_variety: Chardonnay, serving_temperature: 11 to 13 Celsius, intensity: 5, sweetness: 2, tannin: missing, acidity: 3, fizziness: missing, tasting_notes: oak, butter, vanilla, cream, banana, cheese, apricot, peach, apple
"""
# response = YiemAgent.conversation(a, Dict(:text=> "newtopic",) )
# response = YiemAgent.conversation(a, Dict(:text=> "Hello, I would like a get a bottle of wine."))
# println("---> YiemAgent: ", response)
# response = YiemAgent.conversation(a, Dict(:text=> "I'm having a graduation party this evening. I'll pay at most 30 bucks."))
# # input = "query=\"off dry, medium tannin, French Rosé\""
# input = "Search the database for wine type: white, country: France, sweetness level: 1"
# YiemAgent.winestock(a, input)
# input = "French dry white wines with medium body"
# input = "query=\"medium-bodied dry white wine\""
# # input = "the customer is looking for a medium-bodied, dry white wine."
# result = YiemAgent.checkinventory(a, input)
error("test done")
function run_with_timeout(f, args...; timeout=5)
result = Ref{Any}()
task = Threads.@spawn try
result[] = f(args...)
catch e
println("Task interrupted: ", e)
end
Timer(timeout) do _
if !istaskdone(task)
schedule(task, InterruptException())
println("Task did not complete in time. Aborting.")
else
println("Task completed within the timeout.")
end
end
return result[]
end
# Example function that takes arguments and returns a value
function example_function(x, y)
sleep(10) # Simulate a long-running task
return x + y
end
# Example usage
result = run_with_timeout(example_function, 3, 4; timeout=5)
println("Result: ", result)
a = `$"hello\nworld"`
a = $"hello\nworld"
a = """$("hello\nworld")"""

384
test/test_1.jl → previousversion/0.2/test/test_1.jl
View File

@@ -1,192 +1,192 @@
using Revise using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures using YiemAgent, GeneralUtils, JSON3, DataStructures
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
msgMeta = Dict(:requestResponse => nothing, msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing, :msgPurpose => nothing,
:receiverId => nothing, :receiverId => nothing,
:getPost => nothing, :getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be", :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing, :acknowledgestatus => nothing,
:replyToMsgId => "dummyid", :replyToMsgId => "dummyid",
:msgFormatVersion => nothing, :msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"), :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/testingSessionID", :sendTopic => "/testingSessionID",
:receiverName => "wineassistant", :receiverName => "wineassistant",
:replyTopic => nothing, :replyTopic => nothing,
:senderName => "test_1", :senderName => "test_1",
:senderSelfnote => nothing, :senderSelfnote => nothing,
:senderId => nothing, :senderId => nothing,
:timeStamp => "2024-05-04T08:06:23.561" :timeStamp => "2024-05-04T08:06:23.561"
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "We are holding a wedding party", :text=> "We are holding a wedding party",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "It will be Thai dishes.", :text=> "It will be Thai dishes.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I would spend up to 50 bucks.", :text=> "I would spend up to 50 bucks.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I like dry, full-bodied red wine with low tannin.", :text=> "I like dry, full-bodied red wine with low tannin.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "What do you have?", :text=> "What do you have?",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "Dry wine please.", :text=> "Dry wine please.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "You did not gave me any choice.", :text=> "You did not gave me any choice.",
:select=> nothing, :select=> nothing,
:reward=> -1, :reward=> -1,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "You don't need to ask me. Just tell me already!", :text=> "You don't need to ask me. Just tell me already!",
:select=> nothing, :select=> nothing,
:reward=> -1, :reward=> -1,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I like dry wine with fruity flavors.", :text=> "I like dry wine with fruity flavors.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "OK, I'll take it.", :text=> "OK, I'll take it.",
:select=> 1, :select=> 1,
:reward=> 1, :reward=> 1,
:isterminal=> true, :isterminal=> true,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "Yes.", :text=> "Yes.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> "I like dry wine with medium acidity.", :text=> "I like dry wine with medium acidity.",
:select=> nothing, :select=> nothing,
:reward=> 0, :reward=> 0,
:isterminal=> false, :isterminal=> false,
) )
) )
result = GeneralUtils.sendMqttMsg(outgoingMsg) result = GeneralUtils.sendMqttMsg(outgoingMsg)

68
test/test_2.jl → previousversion/0.2/test/test_2.jl
View File

@@ -1,34 +1,34 @@
import requests import requests
# URL of the API endpoint # URL of the API endpoint
url = 'https://api.yiem.cc/wine/agent/sommelier/prompt/apiv1' url = 'https://api.yiem.cc/wine/agent/sommelier/prompt/apiv1'
# Data to be sent in the JSON request # Data to be sent in the JSON request
data = { data = {
'sid': 'dummySID', 'sid': 'dummySID',
'txt': 'hello' 'txt': 'hello'
} }
# Sending the POST request # Sending the POST request
response = requests.post(url, json=data) response = requests.post(url, json=data)
# Displaying the response # Displaying the response
print('Status Code:', response.status_code) print('Status Code:', response.status_code)
print('Response JSON:', response.json()) print('Response JSON:', response.json())

1072
src/MCTSexamplePrompt.py
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94
src/YiemAgent.jl
View File

@@ -1,47 +1,47 @@
module YiemAgent module YiemAgent
# export agent # export agent
""" Order by dependencies of each file. The 1st included file must not depend on any other """ 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. files and each file can only depend on the file included before it.
""" """
include("type.jl") include("type.jl")
using .type using .type
include("util.jl") include("util.jl")
using .util using .util
include("llmfunction.jl") include("llmfunction.jl")
using .llmfunction using .llmfunction
include("interface.jl") include("interface.jl")
using .interface using .interface
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
end # module YiemAgent_v1 end # module YiemAgent_v1

1196
src/interface BACKUP.jl
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2746
src/interface.jl
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2118
src/llmfunction.jl
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528
src/type.jl
View File

@@ -1,268 +1,262 @@
module type module type
export agent, sommelier, companion export agent, sommelier, companion
using Dates, UUIDs, DataStructures, JSON3 using Dates, UUIDs, DataStructures, JSON3
using GeneralUtils using GeneralUtils
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
abstract type agent end abstract type agent end
mutable struct companion <: agent mutable struct companion <: agent
name::String # agent name name::String # agent name
id::String # agent id id::String # agent id
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized maxHistoryMsg::Integer # e.g. 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
NO "system" message in chathistory because I want to add it at the inference time NO "system" message in chathistory because I want to add it at the inference time
chathistory= [ chathistory= [
Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()), Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()), Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
] ]
""" """
chathistory::Vector{Dict{Symbol, Any}} chathistory::Vector{Dict{Symbol, Any}}
memory::Dict{Symbol, Any} memory::Dict{Symbol, Any}
# communication function # communication function
text2textInstructLLM::Function text2textInstructLLM::Function
end end
function companion( function companion(
text2textInstructLLM::Function text2textInstructLLM::Function
; ;
name::String= "Assistant", name::String= "Assistant",
id::String= string(uuid4()), id::String= string(uuid4()),
maxHistoryMsg::Integer= 20, maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(), chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
) )
memory = Dict{Symbol, Any}( memory = Dict{Symbol, Any}(
:chatbox=> "", :chatbox=> "",
:shortmem=> Vector{Dict{Symbol, String}}(), :shortmem=> OrderedDict{Symbol, Any}(),
:events=> Vector{Dict{Symbol, Any}}() :events=> Vector{Dict{Symbol, Any}}(),
) :state=> Dict{Symbol, Any}(),
)
newAgent = companion(
name, newAgent = companion(
id, name,
maxHistoryMsg, id,
chathistory, maxHistoryMsg,
memory, chathistory,
text2textInstructLLM memory,
) text2textInstructLLM
)
return newAgent
end return newAgent
end
""" A sommelier agent.
""" A sommelier agent.
# Arguments
- `mqttClient::Client` # Arguments
MQTTClient's client - `mqttClient::Client`
- `msgMeta::Dict{Symbol, Any}` MQTTClient's client
A dict contain info about a message. - `msgMeta::Dict{Symbol, Any}`
- `config::Dict{Symbol, Any}` A dict contain info about a message.
Config info for an agent. Contain mqtt topic for internal use and other info. - `config::Dict{Symbol, Any}`
Config info for an agent. Contain mqtt topic for internal use and other info.
# Keyword Arguments
- `name::String` # Keyword Arguments
Agent's name - `name::String`
- `id::String` Agent's name
Agent's ID - `id::String`
- `tools::Dict{Symbol, Any}` Agent's ID
Agent's tools - `tools::Dict{Symbol, Any}`
- `maxHistoryMsg::Integer` Agent's tools
max history message - `maxHistoryMsg::Integer`
max history message
# Return
- `nothing` # Return
- `nothing`
# Example
```jldoctest # Example
julia> using YiemAgent, MQTTClient, GeneralUtils ```jldoctest
julia> msgMeta = GeneralUtils.generate_msgMeta( julia> using YiemAgent, MQTTClient, GeneralUtils
"N/A", julia> msgMeta = GeneralUtils.generate_msgMeta(
replyTopic = "/testtopic/prompt" "N/A",
) replyTopic = "/testtopic/prompt"
julia> tools= Dict( )
:chatbox=>Dict( julia> tools= Dict(
:name => "chatbox", :chatbox=>Dict(
:description => "Useful only for when you need to ask the user for more info or context. Do not ask the user their own question.", :name => "chatbox",
:input => "Input should be a text.", :description => "Useful only for when you need to ask the user for more info or context. Do not ask the user their own question.",
:output => "" , :input => "Input should be a text.",
:func => nothing, :output => "" ,
), :func => nothing,
) ),
julia> agentConfig = Dict( )
:receiveprompt=>Dict( julia> agentConfig = Dict(
:mqtttopic=> "/testtopic/prompt", # topic to receive prompt i.e. frontend send msg to this topic :receiveprompt=>Dict(
), :mqtttopic=> "/testtopic/prompt", # topic to receive prompt i.e. frontend send msg to this topic
:receiveinternal=>Dict( ),
:mqtttopic=> "/testtopic/internal", # receive topic for model's internal :receiveinternal=>Dict(
), :mqtttopic=> "/testtopic/internal", # receive topic for model's internal
:text2text=>Dict( ),
:mqtttopic=> "/text2text/receive", :text2text=>Dict(
), :mqtttopic=> "/text2text/receive",
) ),
julia> client, connection = MakeConnection("test.mosquitto.org", 1883) )
julia> agent = YiemAgent.bsommelier( julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
client, julia> agent = YiemAgent.bsommelier(
msgMeta, client,
agentConfig, msgMeta,
name= "assistant", agentConfig,
id= "555", # agent instance id name= "assistant",
tools=tools, id= "555", # agent instance id
) tools=tools,
``` )
```
# TODO
- [] update docstring # TODO
- [x] implement the function - [] update docstring
- [x] implement the function
# Signature
""" # Signature
mutable struct sommelier <: agent """
name::String # agent name mutable struct sommelier <: agent
id::String # agent id name::String # agent name
retailername::String id::String # agent id
tools::Dict retailername::String
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized tools::Dict
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
""" Memory
Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3 """ Memory
NO "system" message in chathistory because I want to add it at the inference time Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
chathistory= [ NO "system" message in chathistory because I want to add it at the inference time
Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()), chathistory= [
Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()), Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
] Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
]
"""
chathistory::Vector{Dict{Symbol, Any}} """
memory::Dict{Symbol, Any} chathistory::Vector{Dict{Symbol, Any}}
memory::Dict{Symbol, Any}
# communication function
text2textInstructLLM::Function func # NamedTuple of functions
executeSQL::Function end
querySQLVectorDB::Function
addSQLVectorDB::Function function sommelier(
end func, # NamedTuple of functions
;
function sommelier( name::String= "Assistant",
text2textInstructLLM::Function, id::String= string(uuid4()),
executeSQL::Function, retailername::String= "retailer_name",
querySQLVectorDB::Function, maxHistoryMsg::Integer= 20,
addSQLVectorDB::Function chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
; )
name::String= "Assistant",
id::String= string(uuid4()), tools = Dict( # update input format
retailername::String= "retailer_name", "chatbox"=> Dict(
maxHistoryMsg::Integer= 20, :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>",
chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(), :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 => "" ,
),
tools = Dict( # update input format "winestock"=> Dict(
"chatbox"=> Dict( :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>",
: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 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>""",
: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 => """<output>Output are wines that match the search query in JSON format.""",
:output => "" , ),
), # "finalanswer"=> Dict(
"winestock"=> Dict( # :description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>",
:description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>", # :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
: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>Output are wines that match the search query in JSON format.""", # :func => nothing,
), # ),
# "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>""", memory = Dict{Symbol, Any}(
# :output => "" , :chatbox=> "",
# :func => nothing, :shortmem=> OrderedDict{Symbol, Any}(),
# ), :events=> Vector{Dict{Symbol, Any}}(),
) :state=> Dict{Symbol, Any}(
:wine_presented_to_user=> "None",
memory = Dict{Symbol, Any}( ),
:chatbox=> "", )
:shortmem=> Vector{Dict{Symbol, String}}(),
:events=> Vector{Dict{Symbol, Any}}() newAgent = sommelier(
) name,
id,
newAgent = sommelier( retailername,
name, tools,
id, maxHistoryMsg,
retailername, chathistory,
tools, memory,
maxHistoryMsg, func
chathistory, )
memory,
text2textInstructLLM, return newAgent
executeSQL, end
querySQLVectorDB,
addSQLVectorDB
)
return newAgent
end
end # module type end # module type

901
src/util.jl
View File

@@ -1,450 +1,453 @@
module util module util
export clearhistory, addNewMessage, vectorOfDictToText, eventdict, noises export clearhistory, addNewMessage, vectorOfDictToText, eventdict, noises
using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3 using UUIDs, Dates, DataStructures, HTTP, MQTTClient, JSON3
using GeneralUtils using GeneralUtils
using ..type using ..type
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
""" Clear agent chat history. """ Clear agent chat history.
# Arguments # Arguments
- `a::agent` - `a::agent`
an agent an agent
# Return # Return
- nothing - nothing
# Example # Example
```jldoctest ```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883) julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection) julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic") julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict( julia> agentConfig = Dict(
:receiveprompt=>Dict( :receiveprompt=>Dict(
:mqtttopic=> "testtopic/receive", :mqtttopic=> "testtopic/receive",
), ),
:receiveinternal=>Dict( :receiveinternal=>Dict(
:mqtttopic=> "testtopic/internal", :mqtttopic=> "testtopic/internal",
), ),
:text2text=>Dict( :text2text=>Dict(
:mqtttopic=> "testtopic/text2text", :mqtttopic=> "testtopic/text2text",
), ),
) )
julia> a = YiemAgent.sommelier( julia> a = YiemAgent.sommelier(
client, client,
msgMeta, msgMeta,
agentConfig, agentConfig,
) )
julia> YiemAgent.addNewMessage(a, "user", "hello") julia> YiemAgent.addNewMessage(a, "user", "hello")
julia> YiemAgent.clearhistory(a) julia> YiemAgent.clearhistory(a)
``` ```
# TODO # TODO
- [PENDING] clear memory - [PENDING] clear memory
# Signature # Signature
""" """
function clearhistory(a::T) where {T<:agent} function clearhistory(a::T) where {T<:agent}
empty!(a.chathistory) empty!(a.chathistory)
empty!(a.memory[:shortmem]) empty!(a.memory[:shortmem])
empty!(a.memory[:events]) empty!(a.memory[:events])
a.memory[:chatbox] = "" a.memory[:chatbox] = ""
end end
""" Add new message to agent. """ Add new message to agent.
Arguments\n Arguments\n
----- -----
a::agent a::agent
an agent an agent
role::String role::String
message sender role i.e. system, user or assistant message sender role i.e. system, user or assistant
text::String text::String
message text message text
Return\n Return\n
----- -----
nothing nothing
Example\n Example\n
----- -----
```jldoctest ```jldoctest
julia> using YiemAgent, MQTTClient, GeneralUtils julia> using YiemAgent, MQTTClient, GeneralUtils
julia> client, connection = MakeConnection("test.mosquitto.org", 1883) julia> client, connection = MakeConnection("test.mosquitto.org", 1883)
julia> connect(client, connection) julia> connect(client, connection)
julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic") julia> msgMeta = GeneralUtils.generate_msgMeta("testtopic")
julia> agentConfig = Dict( julia> agentConfig = Dict(
:receiveprompt=>Dict( :receiveprompt=>Dict(
:mqtttopic=> "testtopic/receive", :mqtttopic=> "testtopic/receive",
), ),
:receiveinternal=>Dict( :receiveinternal=>Dict(
:mqtttopic=> "testtopic/internal", :mqtttopic=> "testtopic/internal",
), ),
:text2text=>Dict( :text2text=>Dict(
:mqtttopic=> "testtopic/text2text", :mqtttopic=> "testtopic/text2text",
), ),
) )
julia> a = YiemAgent.sommelier( julia> a = YiemAgent.sommelier(
client, client,
msgMeta, msgMeta,
agentConfig, agentConfig,
) )
julia> YiemAgent.addNewMessage(a, "user", "hello") julia> YiemAgent.addNewMessage(a, "user", "hello")
``` ```
Signature\n Signature\n
----- -----
""" """
function addNewMessage(a::T1, name::String, text::T2; function addNewMessage(a::T1, name::String, text::T2;
maximumMsg::Integer=20) where {T1<:agent, T2<:AbstractString} maximumMsg::Integer=20) where {T1<:agent, T2<:AbstractString}
if name ∉ ["system", "user", "assistant"] # guard against typo if name ∉ ["system", "user", "assistant"] # guard against typo
error("name is not in agent.availableRole $(@__LINE__)") error("name is not in agent.availableRole $(@__LINE__)")
end end
#[] summarize the oldest 10 message #[] summarize the oldest 10 message
if length(a.chathistory) > maximumMsg if length(a.chathistory) > maximumMsg
summarize(a.chathistory) summarize(a.chathistory)
else else
d = Dict(:name=> name, :text=> text, :timestamp=> Dates.now()) d = Dict(:name=> name, :text=> text, :timestamp=> Dates.now())
push!(a.chathistory, d) push!(a.chathistory, d)
end end
end end
""" """ Converts a vector of dictionaries to a formatted string.
This function takes in a vector of dictionaries and outputs a single string where each dictionary's keys are prefixed by their values.
# Arguments
- `v::Integer` # Arguments
dummy variable - `vecd::Vector`
a vector of dictionaries
# Return - `withkey::Bool`
whether to include the key in the output text. Default is true
# Example
```jldoctest # Return
julia> a string with the formatted dictionaries
```
# Example
# TODO ```jldoctest
- [] update docstring julia> using Revise
- [x] implement the function julia> using GeneralUtils
julia> vecd = [Dict(:name => "John", :text => "Hello"), Dict(:name => "Jane", :text => "Goodbye")]
# Signature julia> GeneralUtils.vectorOfDictToText(vecd, withkey=true)
""" "John> Hello\nJane> Goodbye\n"
function vectorOfDictToText(vecd::Vector; withkey=true) ```
text = "" # Signature
"""
if withkey function vectorOfDictToText(vecd::Vector; withkey=true)::String
for d in vecd # Initialize an empty string to hold the final text
name = d[:name] text = ""
_text = d[:text]
text *= "$name> $_text \n" # Determine whether to include the key in the output text or not
end if withkey
else # Loop through each dictionary in the input vector
for d in vecd for d in vecd
for (k, v) in d # Extract the 'name' and 'text' keys from the dictionary
text *= "$v \n" name = d[:name]
end _text = d[:text]
end
end # Append the formatted string to the text variable
text *= "$name> $_text \n"
return text end
end else
# Loop through each dictionary in the input vector
for d in vecd
function eventdict(; # Iterate over all key-value pairs in the dictionary
event_description::Union{String, Nothing}=nothing, for (k, v) in d
timestamp::Union{DateTime, Nothing}=nothing, # Append the formatted string to the text variable
subject::Union{String, Nothing}=nothing, text *= "$v \n"
action_or_dialogue::Union{String, Nothing}=nothing, end
location::Union{String, Nothing}=nothing, end
equipment_used::Union{String, Nothing}=nothing, end
material_used::Union{String, Nothing}=nothing,
outcome::Union{String, Nothing}=nothing, # Return the final text
note::Union{String, Nothing}=nothing, return text
) end
return Dict{Symbol, Any}(
:event_description=> event_description,
:timestamp=> timestamp, function eventdict(;
:subject=> subject, event_description::Union{String, Nothing}=nothing,
:action_or_dialogue=> action_or_dialogue, timestamp::Union{DateTime, Nothing}=nothing,
:location=> location, subject::Union{String, Nothing}=nothing,
:equipment_used=> equipment_used, action_or_dialogue::Union{String, Nothing}=nothing,
:material_used=> material_used, location::Union{String, Nothing}=nothing,
:outcome=> outcome, equipment_used::Union{String, Nothing}=nothing,
:note=> note, material_used::Union{String, Nothing}=nothing,
) outcome::Union{String, Nothing}=nothing,
end note::Union{String, Nothing}=nothing,
)
return Dict{Symbol, Any}(
noise(n::Integer) = String(rand('a':'z', n)) :event_description=> event_description,
function noises(totalword::Integer, wordlength::Integer) :timestamp=> timestamp,
noises = "" :subject=> subject,
for i in 1:totalword :action_or_dialogue=> action_or_dialogue,
noises *= noise(wordlength) * " " :location=> location,
end :equipment_used=> equipment_used,
noises = strip(noises) :material_used=> material_used,
return noises :outcome=> outcome,
end :note=> note,
)
end
# """ Convert a single chat dictionary into LLM model instruct format.
# # Llama 3 instruct format example
# <|system|> # """ Convert a single chat dictionary into LLM model instruct format.
# You are a helpful AI assistant.<|end|>
# <|user|> # # Llama 3 instruct format example
# I am going to Paris, what should I see?<|end|> # <|system|>
# <|assistant|> # You are a helpful AI assistant.<|end|>
# Paris, the capital of France, is known for its stunning architecture, art museums."<|end|> # <|user|>
# <|user|> # I am going to Paris, what should I see?<|end|>
# What is so great about #1?<|end|> # <|assistant|>
# <|assistant|> # Paris, the capital of France, is known for its stunning architecture, art museums."<|end|>
# <|user|>
# What is so great about #1?<|end|>
# # Arguments # <|assistant|>
# - `name::T`
# message owner name e.f. "system", "user" or "assistant"
# - `text::T` # # Arguments
# - `name::T`
# # Return # message owner name e.f. "system", "user" or "assistant"
# - `formattedtext::String` # - `text::T`
# text formatted to model format
# # Return
# # Example # - `formattedtext::String`
# ```jldoctest # text formatted to model format
# julia> using Revise
# julia> using YiemAgent # # Example
# julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",) # ```jldoctest
# julia> formattedtext = YiemAgent.formatLLMtext_phi3instruct(d[:name], d[:text]) # 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 = # Signature
# """ # """
# <|$name|> # function formatLLMtext_phi3instruct(name::T, text::T) where {T<:AbstractString}
# $text<|end|>\n # formattedtext =
# """ # """
# <|$name|>
# return formattedtext # $text<|end|>\n
# end # """
# return formattedtext
# """ Convert a single chat dictionary into LLM model instruct format. # end
# # Llama 3 instruct format example
# <|begin_of_text|> # """ Convert a single chat dictionary into LLM model instruct format.
# <|start_header_id|>system<|end_header_id|>
# You are a helpful assistant. # # Llama 3 instruct format example
# <|eot_id|> # <|begin_of_text|>
# <|start_header_id|>user<|end_header_id|> # <|start_header_id|>system<|end_header_id|>
# Get me an icecream. # You are a helpful assistant.
# <|eot_id|> # <|eot_id|>
# <|start_header_id|>assistant<|end_header_id|> # <|start_header_id|>user<|end_header_id|>
# Go buy it yourself at 7-11. # Get me an icecream.
# <|eot_id|> # <|eot_id|>
# <|start_header_id|>assistant<|end_header_id|>
# # Arguments # Go buy it yourself at 7-11.
# - `name::T` # <|eot_id|>
# message owner name e.f. "system", "user" or "assistant"
# - `text::T` # # Arguments
# - `name::T`
# # Return # message owner name e.f. "system", "user" or "assistant"
# - `formattedtext::String` # - `text::T`
# text formatted to model format
# # Return
# # Example # - `formattedtext::String`
# ```jldoctest # text formatted to model format
# julia> using Revise
# julia> using YiemAgent # # Example
# julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",) # ```jldoctest
# julia> formattedtext = YiemAgent.formatLLMtext_llama3instruct(d[:name], d[:text]) # julia> using Revise
# "<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n" # 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])
# Signature # "<|begin_of_text|>\n <|start_header_id|>system<|end_header_id|>\n You are a helpful, respectful and honest assistant.\n <|eot_id|>\n"
# """ # ```
# function formatLLMtext_llama3instruct(name::T, text::T) where {T<:AbstractString}
# formattedtext = # Signature
# if name == "system" # """
# """ # function formatLLMtext_llama3instruct(name::T, text::T) where {T<:AbstractString}
# <|begin_of_text|> # formattedtext =
# <|start_header_id|>$name<|end_header_id|> # if name == "system"
# $text # """
# <|eot_id|> # <|begin_of_text|>
# """ # <|start_header_id|>$name<|end_header_id|>
# else # $text
# """ # <|eot_id|>
# <|start_header_id|>$name<|end_header_id|> # """
# $text # else
# <|eot_id|> # """
# """ # <|start_header_id|>$name<|end_header_id|>
# end # $text
# <|eot_id|>
# return formattedtext # """
# end # end
# return formattedtext
# end
# """ Convert a chat messages in vector of dictionary into LLM model instruct format.
# # Arguments
# - `messages::Vector{Dict{Symbol, T}}` # """ Convert a chat messages in vector of dictionary into LLM model instruct format.
# message owner name e.f. "system", "user" or "assistant"
# - `formatname::T` # # Arguments
# format name to be used # - `messages::Vector{Dict{Symbol, T}}`
# message owner name e.f. "system", "user" or "assistant"
# # Return # - `formatname::T`
# - `formattedtext::String` # format name to be used
# text formatted to model format
# # Return
# # Example # - `formattedtext::String`
# ```jldoctest # text formatted to model format
# julia> using Revise
# julia> using YiemAgent # # Example
# julia> chatmessage = [ # ```jldoctest
# Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",), # julia> using Revise
# Dict(:name=> "user",:text=> "list me all planets in our solar system.",), # julia> using YiemAgent
# Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",), # julia> chatmessage = [
# ] # Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",),
# julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct") # Dict(:name=> "user",:text=> "list me all planets in our solar system.",),
# "<|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" # Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",),
# ``` # ]
# julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct")
# # Signature # "<|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"
# """ # ```
# function formatLLMtext(messages::Vector{Dict{Symbol, T}},
# formatname::String="llama3instruct") where {T<:Any} # # Signature
# f = if formatname == "llama3instruct" # """
# formatLLMtext_llama3instruct # function formatLLMtext(messages::Vector{Dict{Symbol, T}},
# elseif formatname == "mistral" # formatname::String="llama3instruct") where {T<:Any}
# # not define yet # f = if formatname == "llama3instruct"
# elseif formatname == "phi3instruct" # formatLLMtext_llama3instruct
# formatLLMtext_phi3instruct # elseif formatname == "mistral"
# else # # not define yet
# error("$formatname template not define yet") # elseif formatname == "phi3instruct"
# end # formatLLMtext_phi3instruct
# else
# str = "" # error("$formatname template not define yet")
# for t in messages # end
# str *= f(t[:name], t[:text])
# end # str = ""
# for t in messages
# # add <|assistant|> so that the model don't generate it and I don't need to clean it up later # str *= f(t[:name], t[:text])
# if formatname == "phi3instruct" # end
# str *= "<|assistant|>\n"
# end # # add <|assistant|> so that the model don't generate it and I don't need to clean it up later
# if formatname == "phi3instruct"
# return str # str *= "<|assistant|>\n"
# end # end
# return str
# """ # end
# Arguments\n
# ----- # """
# Return\n # Arguments\n
# ----- # -----
# Example\n # Return\n
# ----- # -----
# ```jldoctest
# julia> # Example\n
# ``` # -----
# ```jldoctest
# TODO\n # julia>
# ----- # ```
# [] update docstring
# [PENDING] implement the function # TODO\n
# -----
# Signature\n # [] update docstring
# ----- # [PENDING] implement the function
# """
# function iterativeprompting(a::T, prompt::String, verification::Function) where {T<:agent} # Signature\n
# msgMeta = GeneralUtils.generate_msgMeta( # -----
# a.config[:externalService][:text2textinstruct], # """
# senderName= "iterativeprompting", # function iterativeprompting(a::T, prompt::String, verification::Function) where {T<:agent}
# senderId= a.id, # msgMeta = GeneralUtils.generate_msgMeta(
# receiverName= "text2textinstruct", # a.config[:externalService][:text2textinstruct],
# ) # senderName= "iterativeprompting",
# senderId= a.id,
# outgoingMsg = Dict( # receiverName= "text2textinstruct",
# :msgMeta=> msgMeta, # )
# :payload=> Dict(
# :text=> prompt, # outgoingMsg = Dict(
# ) # :msgMeta=> msgMeta,
# ) # :payload=> Dict(
# :text=> prompt,
# success = nothing # )
# result = nothing # )
# critique = ""
# success = nothing
# # iteration loop # result = nothing
# while true # critique = ""
# # send prompt to LLM
# response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) # # iteration loop
# error("--> iterativeprompting") # while true
# # check for correctness and get feedback # # send prompt to LLM
# success, _critique = verification(response) # response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# error("--> iterativeprompting")
# if success # # check for correctness and get feedback
# result = response # success, _critique = verification(response)
# break
# else # if success
# # add critique to prompt # result = response
# critique *= _critique * "\n" # break
# replace!(prompt, "Critique: ..." => "Critique: $critique") # else
# end # # add critique to prompt
# end # critique *= _critique * "\n"
# replace!(prompt, "Critique: ..." => "Critique: $critique")
# return (success=success, result=result) # end
# end # end
# return (success=success, result=result)
# end
end # module util end # module util

18
test/etc.jl
View File

@@ -1,9 +1,9 @@
using GeneralUtils using GeneralUtils
response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria." response = "trajectory_evaluation:\nThe trajectory is correct so far. The thought accurately reflects the user's question, and the action taken is a valid attempt to retrieve data from the database that matches the specified criteria.\n\nanswer_evaluation:\nThe observation provides information about two red wines from Bordeaux rive droite in France, which partially answers the question. However, it does not provide a complete answer as it only lists the wine names and characteristics, but does not explicitly state whether there are any other wines that match the criteria.\n\naccepted_as_answer: No\n\nscore: 6\nThe trajectory is mostly correct, but the observation does not fully address the question.\n\nsuggestion: Consider adding more filters or parameters to the database query to retrieve a complete list of wines that match the specified criteria."
responsedict = GeneralUtils.textToDict(response, responsedict = GeneralUtils.textToDict(response,
["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"],
rightmarker=":", symbolkey=true) rightmarker=":", symbolkey=true)

318
test/prompttest.jl
View File

@@ -1,159 +1,159 @@
using Revise using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures using YiemAgent, GeneralUtils, JSON3, DataStructures
thoughtDict = OrderedDict( thoughtDict = OrderedDict(
:Question=> "Hello, I would like a get a bottle of wine", :Question=> "Hello, I would like a get a bottle of wine",
:Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.", :Thought_1=> "The customer wants to buy a bottle of wine, but we need more information about their preferences.",
:Action_1=> Dict( :Action_1=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What occasion are you buying the wine for?", :input=> "What occasion are you buying the wine for?",
), ),
:Observation_1=> "We are having a wedding pary this weekend.", :Observation_1=> "We are having a wedding pary this weekend.",
:Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.", :Thought_2=> "A wedding party is a great occasion to have a good bottle of wine.",
:Action_2=> Dict( :Action_2=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What type of food will you be serving with the wine?", :input=> "What type of food will you be serving with the wine?",
), ),
:Observation_2=> "I think it is Thai dishes", :Observation_2=> "I think it is Thai dishes",
:Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.", :Thought_3=> "Now that I know the occasion and food, I need to ask about the budget.",
:Action_3=> Dict( :Action_3=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What is your budget for this wine?", :input=> "What is your budget for this wine?",
), ),
:Observation_3=> "50 bucks", :Observation_3=> "50 bucks",
:Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.", :Thought_4=> "With a budget of \$50, we have a wide range of options. Now that I know it's a wedding party and Thai dishes, I need to ask about the type of wine they prefer.",
:Action_4=> Dict( :Action_4=> Dict(
:name=> "chatbox", :name=> "chatbox",
:input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)", :input=> "What type of wine are you looking for? (Red, White, Sparkling, Rose, Dessert, Fortified)",
), ),
:Observation_4=> "Sparkling please.", :Observation_4=> "Sparkling please.",
:Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.", :Thought_5=> "Now that I know the occasion, food, budget and preferred type of wine, it's time to check our inventory for the best matching wine.",
:Action_5=> Dict( :Action_5=> Dict(
:name=> "winestock", :name=> "winestock",
:input=> "wine with budget \$50, Thai dishes, sparkling, wedding party", :input=> "wine with budget \$50, Thai dishes, sparkling, wedding party",
), ),
:Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}", :Observation_5=> "I found the following wine in stock {1 : Zena Crown Vista, 2 : Schrader Cabernet Sauvignon}",
:Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.", :Thought_6=> "Now that I have all the information, it's time to recommend a wine that fits their preferences.",
:Action_6=> Dict( :Action_6=> Dict(
:name=> "recommendation", :name=> "recommendation",
:input=> "I recommend Zena Crown Vista for its sparkling and affordable price.", :input=> "I recommend Zena Crown Vista for its sparkling and affordable price.",
), ),
:Observation_6=> "I don't like it. Do you have another option?", :Observation_6=> "I don't like it. Do you have another option?",
) )
_thoughtJsonStr = JSON3.write(thoughtDict) _thoughtJsonStr = JSON3.write(thoughtDict)
thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end thoughtJsonStr = _thoughtJsonStr[1:end-1] # remove } at the end
# @show thoughtJsonStr # @show thoughtJsonStr
_, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought") _, latestThoughtIndice = GeneralUtils.findHighestIndexKey(thoughtDict, "Thought")
nextThoughtIndice = latestThoughtIndice + 1 nextThoughtIndice = latestThoughtIndice + 1
_prompt = _prompt =
""" """
You are a helpful sommelier working for a wine store. You are a helpful sommelier working for a wine store.
Your goal is to reccommend the best wine from your inventory that match the user preferences. Your goal is to reccommend the best wine from your inventory that match the user preferences.
You must follow the following criteria: You must follow the following criteria:
1) Get to know what occasion the user is buying wine for 1) Get to know what occasion the user is buying wine for
2) Get to know what food the user will have with wine 2) Get to know what food the user will have with wine
3) Get to know how much the user willing to spend 3) Get to know how much the user willing to spend
4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified 4) Get to know type of wine the user is looking for e.g. Red, White, Sparkling, Rose, Dessert, Fortified
5) Get to know what characteristics of wine the user is looking for 5) Get to know what characteristics of wine the user is looking for
e.g. tannin, sweetness, intensity, acidity e.g. tannin, sweetness, intensity, acidity
6) Check your inventory for the best wine that match the user preference 6) Check your inventory for the best wine that match the user preference
7) Recommend wine to the user 7) Recommend wine to the user
You should only respond with interleaving Thought, Action, Observation steps. You should only respond with interleaving Thought, Action, Observation steps.
Thought can reason about the current situation, and Action can be three types: 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. 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. 2) chatbox[text], which you can use to interact with the user.
3) recommendation[answer], which returns your wine reccommendation to the user. 3) recommendation[answer], which returns your wine reccommendation to the user.
You should only respond in JSON format as describe below: You should only respond in JSON format as describe below:
{ {
"Thought": "your reasoning", "Thought": "your reasoning",
"Action": {"name": "action to take", "input": "Action input"}, "Action": {"name": "action to take", "input": "Action input"},
"Observation": "result of the action" "Observation": "result of the action"
} }
Here are some examples: Here are some examples:
{ {
"Question": "I would like to buy a sedan with 8 seats.", "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.", "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."}, "Action_1": {"name": "inventory", "input": "sedan with 8 seats."},
"Observation_1": "Several model has 8 seats. Available color are black, red green" "Observation_1": "Several model has 8 seats. Available color are black, red green"
} }
{ {
"Thought_2": "I have to ask the user what color he likes.", "Thought_2": "I have to ask the user what color he likes.",
"Action_2": {"name": "chatbox", "input": "Which color do you like?"} "Action_2": {"name": "chatbox", "input": "Which color do you like?"}
"Observation_2": "I'll take black." "Observation_2": "I'll take black."
} }
{ {
"Thought_3": "There is only one model that fits the user preference. It's Yiem model A", "Thought_3": "There is only one model that fits the user preference. It's Yiem model A",
"Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"} "Action_3": {"name": "recommendation", "input": "I recommend a Yiem model A"}
} }
Let's begin! Let's begin!
$(JSON3.write(thoughtDict)) $(JSON3.write(thoughtDict))
{Thought_$nextThoughtIndice {Thought_$nextThoughtIndice
""" """
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt) prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt @show prompt
msgMeta = Dict(:requestResponse => nothing, msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing, :msgPurpose => nothing,
:receiverId => nothing, :receiverId => nothing,
:getPost => nothing, :getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be", :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing, :acknowledgestatus => nothing,
:replyToMsgId => nothing, :replyToMsgId => nothing,
:msgFormatVersion => nothing, :msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"), :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice", :sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct", :receiverName => "text2textinstruct",
:replyTopic => nothing, :replyTopic => nothing,
:senderName => "decisionMaker", :senderName => "decisionMaker",
:senderSelfnote => nothing, :senderSelfnote => nothing,
:senderId => "testingSessionID", :senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561" :timeStamp => "2024-05-04T08:06:23.561"
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> prompt, :text=> prompt,
) )
) )
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
thoughtJsonStr = _response[:response][:text] thoughtJsonStr = _response[:response][:text]

174
test/prompttest_1.jl
View File

@@ -1,87 +1,87 @@
using Revise # remove when this package is completed using Revise # remove when this package is completed
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, DataStructures
using Base.Threads using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json")) config = copy(JSON3.read("config.json"))
instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1" instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
client, connection = MakeConnection(config[:mqttServerInfo][:broker], client, connection = MakeConnection(config[:mqttServerInfo][:broker],
config[:mqttServerInfo][:port]) config[:mqttServerInfo][:port])
receiveUserMsgChannel = Channel{Dict}(4) receiveUserMsgChannel = Channel{Dict}(4)
receiveInternalMsgChannel = Channel{Dict}(4) receiveInternalMsgChannel = Channel{Dict}(4)
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( agentConfig = 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, :internal=> instanceInternalTopic,
), ),
:externalservice=> config[:externalservice], :externalservice=> config[:externalservice],
) )
# Instantiate an agent # Instantiate an agent
tools=Dict( # update input format tools=Dict( # update input format
"askbox"=> Dict( "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>", :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>""", :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 => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
# "winestock"=> Dict( # "winestock"=> Dict(
# :description => "<winestock tool description>A handy tool for searching wine in your inventory that match the user preferences.</winestock tool description>", # :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>""", # :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.""", # :output => """<output>Output are wines that match the search query in JSON format.""",
# :func => ChatAgent.winestock, # :func => ChatAgent.winestock,
# ), # ),
"finalanswer"=> Dict( "finalanswer"=> Dict(
:description => "<tool description>Useful for when you are ready to recommend wines to the user.</tool description>", :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>""", :input => """<input format>{\"finalanswer\": \"some text\"}.</input format><input example>{\"finalanswer\": \"I recommend Zena Crown Vista\"}</input example>""",
:output => "" , :output => "" ,
:func => nothing, :func => nothing,
), ),
) )
a = YiemAgent.sommelier( a = YiemAgent.sommelier(
receiveUserMsgChannel, receiveUserMsgChannel,
receiveInternalMsgChannel, receiveInternalMsgChannel,
agentConfig, agentConfig,
name= "assistant", name= "assistant",
id= "testingSessionID", # agent instance id id= "testingSessionID", # agent instance id
tools=tools, tools=tools,
) )
input = input =
OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.") OrderedDict{Symbol, Any}(:question => "Hello, I would like a get a bottle of wine", :thought_1 => "It's great that the user is looking for a bottle of wine. To give them a personalized recommendation, I need to know more about their preferences.", :action_1 => Dict{Symbol, Any}(:name => "chatbox", :input => "What occasion are you planning to use this wine for?"), :observation_1 => "We are holding a wedding party", :thought_2 => "A wedding party is a great occasion for a special bottle of wine. I need to know what type of food will be served, and how much the user is willing to spend.", :action_2 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of food will you be serving at the wedding?"), :observation_2 => "It will be Thai dishes.", :thought_3 => "The type of wine that pairs well with Thai dishes is usually a crisp and refreshing white wine, but I also need to consider the budget and personal preferences.", :action_3 => Dict{Symbol, Any}(:name => "chatbox", :input => "How much are you willing to spend on this bottle of wine?"), :observation_3 => "I would spend up to 50 bucks.", :thought_4 => "I have a good idea of the occasion, food, and budget. Now I need to know what type of wine the user is looking for.", :action_4 => Dict{Symbol, Any}(:name => "chatbox", :input => "What type of wine are you usually looking for? Red, White, Sparkling, Rose, Dessert or Fortified?"), :observation_4 => "I like full-bodied Red wine with low tannin.", :thought_5 => "Now that I have all the necessary information, I can start searching for a suitable wine in our inventory.", :action_5 => Dict{Symbol, Any}(:name => "winestock", :input => "red wine with low tannins"), :observation_5 => "I found the following wines in our stock: \n{\n 1: El Enemigo Cabernet Franc 2019\n2: Tantara Chardonnay 2017\n\n}\n", :thought_6 => "Now that I have the information about the wine, it's time to make a recommendation.", :action_6 => Dict{Symbol, Any}(:name => "recommendbox", :input => "El Enemigo Cabernet Franc 2019"), :observation_6 => "I don't like the one you recommend. I want dry wine.")
result = YiemAgent.jsoncorrection(a, input) result = YiemAgent.jsoncorrection(a, input)

238
test/prompttest_2.jl
View File

@@ -1,119 +1,119 @@
using Revise using Revise
using YiemAgent, GeneralUtils, JSON3, DataStructures, LibPQ using YiemAgent, GeneralUtils, JSON3, DataStructures, LibPQ
using SQLLLM using SQLLLM
# _prompt = # _prompt =
# """ # """
# You are a helpful assistant. # You are a helpful assistant.
# answer the following question: # answer the following question:
# From the following CSV text: # From the following CSV text:
# "{\"tabledescription\":[\"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.\",\"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.\",\"The wine_food table represents the association between wines and food items. It estab" ⋯ 477 bytes ⋯ "ed to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description.\",\"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.\",\"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.\"],\"tablename\":[\"customer\",\"wine\",\"wine_food\",\"food\",\"retailer\",\"retailer_wine\",\"retailer_food\"]}" # "{\"tabledescription\":[\"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.\",\"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.\",\"The wine_food table represents the association between wines and food items. It estab" ⋯ 477 bytes ⋯ "ed to retailer names, usernames, passwords, addresses, contact persons, telephone numbers, email addresses, additional_search_term, other attributes (in JSON format) and a description.\",\"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.\",\"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.\"],\"tablename\":[\"customer\",\"wine\",\"wine_food\",\"food\",\"retailer\",\"retailer_wine\",\"retailer_food\"]}"
# What is the description of table wine? # What is the description of table wine?
# """ # """
# prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt) # prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
# @show prompt # @show prompt
# msgMeta = Dict(:requestResponse => nothing, # msgMeta = Dict(:requestResponse => nothing,
# :msgPurpose => nothing, # :msgPurpose => nothing,
# :receiverId => nothing, # :receiverId => nothing,
# :getPost => nothing, # :getPost => nothing,
# :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be", # :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
# :acknowledgestatus => nothing, # :acknowledgestatus => nothing,
# :replyToMsgId => nothing, # :replyToMsgId => nothing,
# :msgFormatVersion => nothing, # :msgFormatVersion => nothing,
# :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"), # :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
# :sendTopic => "/loadbalancer/requestingservice", # :sendTopic => "/loadbalancer/requestingservice",
# :receiverName => "text2textinstruct", # :receiverName => "text2textinstruct",
# :replyTopic => nothing, # :replyTopic => nothing,
# :senderName => "decisionMaker", # :senderName => "decisionMaker",
# :senderSelfnote => nothing, # :senderSelfnote => nothing,
# :senderId => "testingSessionID", # :senderId => "testingSessionID",
# :timeStamp => "2024-05-04T08:06:23.561" # :timeStamp => "2024-05-04T08:06:23.561"
# ) # )
# outgoingMsg = Dict( # outgoingMsg = Dict(
# :msgMeta=> msgMeta, # :msgMeta=> msgMeta,
# :payload=> Dict( # :payload=> Dict(
# :text=> prompt, # :text=> prompt,
# ) # )
# ) # )
# _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) # _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
# result = _response[:response][:text] # result = _response[:response][:text]
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0") DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0")
tableinfo, df1, df2, df3 = SQLLLM.tableinfo(DBconnection, "wine") tableinfo, df1, df2, df3 = SQLLLM.tableinfo(DBconnection, "wine")
_prompt = _prompt =
""" """
You are a helpful assistant helping to answer user question from a database table. You are a helpful assistant helping to answer user question from a database table.
$tableinfo $tableinfo
Are there any chardonnay? Are there any chardonnay?
""" """
prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt) prompt = YiemAgent.formatLLMtext_llama3instruct("system", _prompt)
@show prompt @show prompt
msgMeta = Dict(:requestResponse => nothing, msgMeta = Dict(:requestResponse => nothing,
:msgPurpose => nothing, :msgPurpose => nothing,
:receiverId => nothing, :receiverId => nothing,
:getPost => nothing, :getPost => nothing,
:msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be", :msgId => "4c7111e0-c30e-44c3-8f85-1c8b3f03a8be",
:acknowledgestatus => nothing, :acknowledgestatus => nothing,
:replyToMsgId => nothing, :replyToMsgId => nothing,
:msgFormatVersion => nothing, :msgFormatVersion => nothing,
:mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"), :mqttServerInfo => Dict(:port => 1883, :broker => "mqtt.yiem.cc"),
:sendTopic => "/loadbalancer/requestingservice", :sendTopic => "/loadbalancer/requestingservice",
:receiverName => "text2textinstruct", :receiverName => "text2textinstruct",
:replyTopic => nothing, :replyTopic => nothing,
:senderName => "decisionMaker", :senderName => "decisionMaker",
:senderSelfnote => nothing, :senderSelfnote => nothing,
:senderId => "testingSessionID", :senderId => "testingSessionID",
:timeStamp => "2024-05-04T08:06:23.561" :timeStamp => "2024-05-04T08:06:23.561"
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta=> msgMeta,
:payload=> Dict( :payload=> Dict(
:text=> prompt, :text=> prompt,
) )
) )
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
result2 = _response[:response][:text] result2 = _response[:response][:text]

412
test/runtest.jl
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@@ -1,227 +1,255 @@
using Revise # remove when this package is completed using Revise
using YiemAgent, GeneralUtils, JSON3, MQTTClient, Dates, UUIDs, LibPQ, Base64, DataFrames using JSON, JSON3, MQTTClient, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
using YiemAgent, GeneralUtils
using Base.Threads using Base.Threads
# ---------------------------------------------- 100 --------------------------------------------- # # ---------------------------------------------- 100 --------------------------------------------- #
config = copy(JSON3.read("config.json"))
# instanceInternalTopic = config[:serviceInternalTopic][:mqtttopic] * "/1"
# client, connection = MakeConnection(config[:mqttServerInfo][:broker], # load config
# config[:mqttServerInfo][:port]) config = copy(JSON3.read("../mountvolume/config.json"))
receiveUserMsgChannel = Channel{Dict}(4)
# receiveInternalMsgChannel = Channel{Dict}(4)
# println(typeof(connection))
# msgMeta = GeneralUtils.generate_msgMeta(
# "N/A",
# replyTopic = config[:servicetopic][:mqtttopic] # ask frontend reply to this instance_chat_topic
# )
function executeSQL(sql::T) where {T<:AbstractString} function executeSQL(sql::T) where {T<:AbstractString}
DBconnection = LibPQ.Connection("host=192.168.88.12 port=5432 dbname=yiem_wine_assistant user=yiem password=yiem@Postgres_0.0") DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
result = LibPQ.execute(DBconnection, sql) result = LibPQ.execute(DBconnection, sql)
close(DBconnection) close(DBconnection)
return result return result
end 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 text2textInstructLLM(prompt::String) function text2textInstructLLM(prompt::String)
msgMeta = GeneralUtils.generate_msgMeta( msgMeta = GeneralUtils.generate_msgMeta(
config[:externalservice][:text2textinstruct][:mqtttopic]; config[:externalservice][:text2textinstruct][:mqtttopic];
msgPurpose= "inference", msgPurpose="inference",
senderName= "yiemagent", senderName="yiemagent",
senderId= string(uuid4()), senderId=string(uuid4()),
receiverName= "text2textinstruct", receiverName="text2textinstruct",
mqttBrokerAddress= config[:mqttServerInfo][:broker], mqttBrokerAddress=config[:mqttServerInfo][:broker],
mqttBrokerPort= config[:mqttServerInfo][:port], mqttBrokerPort=config[:mqttServerInfo][:port],
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta => msgMeta,
:payload=> Dict( :payload => Dict(
:text=> prompt, :text => prompt,
:kwargs=> Dict( :kwargs => Dict(
:max_tokens=> 2048, :num_ctx => 16384,
:stop=> ["<|eot_id|>"], :temperature => 0.2,
:temperature=> 0.2,
)
) )
) )
)
_response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120) _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
response = _response[:response][:text] response = _response[:response][:text]
return response return response
end end
function executeSQLVectorDB(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
function addSQLVectorDB(state) # get text embedding from a LLM service
# get embedding of the query function getEmbedding(text::T) where {T<:AbstractString}
query = [state[:thoughtHistory][:question]]
msgMeta = GeneralUtils.generate_msgMeta( msgMeta = GeneralUtils.generate_msgMeta(
config[:externalservice][:text2textinstruct][:mqtttopic]; config[:externalservice][:text2textinstruct][:mqtttopic];
msgPurpose= "embedding", msgPurpose="embedding",
senderName= "yiemagent", senderName="yiemagent",
senderId= string(uuid4()), senderId=string(uuid4()),
receiverName= "text2textinstruct", receiverName="text2textinstruct",
mqttBrokerAddress= config[:mqttServerInfo][:broker], mqttBrokerAddress=config[:mqttServerInfo][:broker],
mqttBrokerPort= config[:mqttServerInfo][:port], mqttBrokerPort=config[:mqttServerInfo][:port],
) )
outgoingMsg = Dict( outgoingMsg = Dict(
:msgMeta=> msgMeta, :msgMeta => msgMeta,
:payload=> Dict( :payload => Dict(
:text=> query :text => [text] # must be a vector of string
)
) )
)
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg) response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
embedding = response[:response][:embeddings][1] 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 # check whether there is close enough vector already store in vectorDB. if no, add, else skip
sql = sql = """
""" SELECT *, $embeddingColumnName <-> '$embedding' as distance
SELECT *, embedding <-> '$embedding' as distance FROM $tablename
FROM sql_statement_repository ORDER BY distance LIMIT $limit;
ORDER BY distance LIMIT 1; """
""" response = vectorDB(sql)
response = executeSQLVectorDB(sql)
df = DataFrame(response) 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)
row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance]
if row != 0 && distance < maxdistance
# if there is usable SQL, return it.
output_b64 = df[1, :function_output_base64] # pick the closest match
output_str = String(base64decode(output_b64))
rowid = df[1, :id]
println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
return (dict=output_str, distance=distance)
else
println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
return (dict=nothing, distance=nothing)
end
end
function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) 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) row, col = size(df)
distance = row == 0 ? Inf : df[1, :distance] distance = row == 0 ? Inf : df[1, :distance]
if row == 0 || distance > 10 # no close enough SQL stored in the database if row == 0 || distance > maxdistance # no close enough SQL stored in the database
latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input) query_embedding = getEmbedding(query)[1]
_sqlStatement = state[:thoughtHistory][latestKey] query = replace(query, "'" => "")
if occursin("SELECT", _sqlStatement) # make sure it is an SQL statement before adding into DB sql_base64 = base64encode(SQL)
sqlStatementBase64 = base64encode(_sqlStatement) sql_ = replace(SQL, "'" => "")
sqlStatement = replace(_sqlStatement, "'"=>"")
sql = sql = """
""" INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
INSERT INTO sql_statement_repository (question, sql_statement, sql_statement_base64, embedding) VALUES ('$query', '$sqlStatement', '$sqlStatementBase64', '$embedding'); """
""" println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
_ = executeSQLVectorDB(sql) println(sql)
println("--> added new SQL statement to vectorDB ", @__FILE__, " ", @__LINE__) _ = executeSQLVectorDB(sql)
println(sqlStatement)
end
end end
end end
function querySQLVectorDB(state)
# provide similarSQL at the first time thinking only function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
latestKey, _ = GeneralUtils.findHighestIndexKey(state[:thoughtHistory], :action_input) )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
if latestKey === nothing tablename = "sommelier_decision_repository"
# get embedding of the query # find similar
query = [state[:thoughtHistory][:question]] println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
msgMeta = GeneralUtils.generate_msgMeta( df = findSimilarTextFromVectorDB(recentevents, tablename,
config[:externalservice][:text2textinstruct][:mqtttopic]; "function_input_embedding", executeSQLVectorDB)
msgPurpose= "embedding", row, col = size(df)
senderName= "yiemagent", distance = row == 0 ? Inf : df[1, :distance]
senderId= string(uuid4()), if row != 0 && distance < maxdistance
receiverName= "text2textinstruct", # if there is usable decision, return it.
mqttBrokerAddress= config[:mqttServerInfo][:broker], rowid = df[1, :id]
mqttBrokerPort= config[:mqttServerInfo][:port], 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))
outgoingMsg = Dict( output = copy(JSON3.read(_output_str))
:msgMeta=> msgMeta, return output
:payload=> Dict( else
:text=> query println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
) return nothing
)
response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg)
embedding = response[:response][:embeddings][1]
# check whether there is close enough vector already store in vectorDB. if no, add, else skip
sql =
"""
SELECT *, embedding <-> '$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))
println("--> getting SQL statement from vectorDB ", @__FILE__, " ", @__LINE__)
println(sqlStatement)
return sqlStatement
else
return nothing
end
end end
return nothing
end end
# Instantiate an agent 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 = a.func[: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 $distance ", @__FILE__, " ", @__LINE__)
end
end
sessionId = "12345"
externalFunction = (
getEmbedding=getEmbedding,
text2textInstructLLM=text2textInstructLLM,
executeSQL=executeSQL,
similarSQLVectorDB=similarSQLVectorDB,
insertSQLVectorDB=insertSQLVectorDB,
similarSommelierDecision=similarSommelierDecision,
insertSommelierDecision=insertSommelierDecision,
)
a = YiemAgent.sommelier( a = YiemAgent.sommelier(
text2textInstructLLM, externalFunction;
executeSQL, name="Ton",
querySQLVectorDB, id=sessionId, # agent instance id
addSQLVectorDB; retailername="Yiem",
name= "Jene",
id= "tempId", # agent instance id
) )
function main() while true
for i in 1:10 println("your respond: ")
userinput = "" user_answer = readline()
for i in 1:3 response = YiemAgent.conversation(a, Dict(:text=> user_answer))
if userinput == "" println("\n$response")
println("")
println("--> user input:")
userinput = readline()
else
break
end
end
response = YiemAgent.conversation(a, Dict(:text=> userinput))
println("")
println("--> assistant response: \n", response)
end
end end
main()
""" # response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
I'm joining a graduation party this evening. I want to get a bottle of white wine from the US to celebrate. I'm ok with any price range.
Well, the party is small casual with close friends and no food serving.
I'm open to suggestion since I have no specific idea.
I'm ok with any region.
The input is instructions on how you want the presentation to be conducted.
"""
wines =
"""
Summary: This table contains two wine records, both from the United States, with white wine types, moderate sweetness (2), and high intensity (5).
More details: 1) wine_id: add9824f-81b0-47da-a08a-ee20498bc6c8, wine_name: Belle Cote Chardonnay, brand: Peter Michael, manufacturer: Peter Michael, region: Californian, country: United States, wine_type: white, grape_variety: Chardonnay, serving_temperature: 11 to 13 Celsius, intensity: 5, sweetness: 2, tannin: missing, acidity: 3, fizziness: missing, tasting_notes: oak, butter, vanilla, cream, oil, lemon curd, pear, peach, apple
2) wine_id: ff9a494c-e916-44c4-9385-1c18b23aa825, wine_name: Ma Belle-Fille Chardonnay, brand: Peter Michael, manufacturer: Peter Michael, region: Californian, country: United States, wine_type: white, grape_variety: Chardonnay, serving_temperature: 11 to 13 Celsius, intensity: 5, sweetness: 2, tannin: missing, acidity: 3, fizziness: missing, tasting_notes: oak, butter, vanilla, cream, banana, cheese, apricot, peach, apple
"""
# response = YiemAgent.conversation(a, Dict(:text=> "newtopic",) )
# response = YiemAgent.conversation(a, Dict(:text=> "Hello, I would like a get a bottle of wine."))
# println("---> YiemAgent: ", response)
# response = YiemAgent.conversation(a, Dict(:text=> "I'm having a graduation party this evening. I'll pay at most 30 bucks."))
@@ -229,60 +257,16 @@ More details: 1) wine_id: add9824f-81b0-47da-a08a-ee20498bc6c8, wine_name: Belle
# # input = "query=\"off dry, medium tannin, French Rosé\""
# input = "Search the database for wine type: white, country: France, sweetness level: 1"
# YiemAgent.winestock(a, input)
# input = "French dry white wines with medium body"
# input = "query=\"medium-bodied dry white wine\""
# # input = "the customer is looking for a medium-bodied, dry white wine."
# result = YiemAgent.checkinventory(a, input)
error("test done")
function run_with_timeout(f, args...; timeout=5)
result = Ref{Any}()
task = Threads.@spawn try
result[] = f(args...)
catch e
println("Task interrupted: ", e)
end
Timer(timeout) do _
if !istaskdone(task)
schedule(task, InterruptException())
println("Task did not complete in time. Aborting.")
else
println("Task completed within the timeout.")
end
end
return result[]
end
# Example function that takes arguments and returns a value
function example_function(x, y)
sleep(10) # Simulate a long-running task
return x + y
end
# Example usage
result = run_with_timeout(example_function, 3, 4; timeout=5)
println("Result: ", result)
a = `$"hello\nworld"`
a = $"hello\nworld"
a = """$("hello\nworld")"""