update

Reviewed-on: #3

Manifest.toml

@@ -1,8 +1,8 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.11.2"
+julia_version = "1.11.4"
 manifest_format = "2.0"
-project_hash = "b483014657ef9f0fde60d7258585b291d6f0eeca"
+project_hash = "cb7f3c57318e927e8ac4dc2dea9acdcace566ed1"
 
 [[deps.AliasTables]]
 deps = ["PtrArrays", "Random"]
@@ -120,9 +120,9 @@ version = "1.11.0"
 
 [[deps.Distributions]]
 deps = ["AliasTables", "FillArrays", "LinearAlgebra", "PDMats", "Printf", "QuadGK", "Random", "SpecialFunctions", "Statistics", "StatsAPI", "StatsBase", "StatsFuns"]
-git-tree-sha1 = "3101c32aab536e7a27b1763c0797dba151b899ad"
+git-tree-sha1 = "0b4190661e8a4e51a842070e7dd4fae440ddb7f4"
 uuid = "31c24e10-a181-5473-b8eb-7969acd0382f"
-version = "0.25.113"
+version = "0.25.118"
 
     [deps.Distributions.extensions]
     DistributionsChainRulesCoreExt = "ChainRulesCore"
@@ -158,9 +158,9 @@ version = "0.1.10"
 
 [[deps.FileIO]]
 deps = ["Pkg", "Requires", "UUIDs"]
-git-tree-sha1 = "2dd20384bf8c6d411b5c7370865b1e9b26cb2ea3"
+git-tree-sha1 = "b66970a70db13f45b7e57fbda1736e1cf72174ea"
 uuid = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
-version = "1.16.6"
+version = "1.17.0"
 weakdeps = ["HTTP"]
 
     [deps.FileIO.extensions]
@@ -168,9 +168,9 @@ weakdeps = ["HTTP"]
 
 [[deps.FilePathsBase]]
 deps = ["Compat", "Dates"]
-git-tree-sha1 = "7878ff7172a8e6beedd1dea14bd27c3c6340d361"
+git-tree-sha1 = "3bab2c5aa25e7840a4b065805c0cdfc01f3068d2"
 uuid = "48062228-2e41-5def-b9a4-89aafe57970f"
-version = "0.9.22"
+version = "0.9.24"
 weakdeps = ["Mmap", "Test"]
 
     [deps.FilePathsBase.extensions]
@@ -200,11 +200,9 @@ version = "1.11.0"
 
 [[deps.GeneralUtils]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "Distributions", "JSON3", "MQTTClient", "PrettyPrinting", "Random", "SHA", "UUIDs"]
-git-tree-sha1 = "978d9a5c3fc30205dd72d4a2a2ed4fa85ebee5cf"
+path = "/appfolder/app/dev/GeneralUtils"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.1.0"
+version = "0.2.3"
 
 [[deps.HTTP]]
 deps = ["Base64", "CodecZlib", "ConcurrentUtilities", "Dates", "ExceptionUnwrapping", "Logging", "LoggingExtras", "MbedTLS", "NetworkOptions", "OpenSSL", "PrecompileTools", "Random", "SimpleBufferStream", "Sockets", "URIs", "UUIDs"]
@@ -214,9 +212,9 @@ version = "1.10.13"
 
 [[deps.HypergeometricFunctions]]
 deps = ["LinearAlgebra", "OpenLibm_jll", "SpecialFunctions"]
-git-tree-sha1 = "b1c2585431c382e3fe5805874bda6aea90a95de9"
+git-tree-sha1 = "68c173f4f449de5b438ee67ed0c9c748dc31a2ec"
 uuid = "34004b35-14d8-5ef3-9330-4cdb6864b03a"
-version = "0.3.25"
+version = "0.3.28"
 
 [[deps.ICU_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
@@ -260,9 +258,9 @@ uuid = "41ab1584-1d38-5bbf-9106-f11c6c58b48f"
 version = "1.3.0"
 
 [[deps.IrrationalConstants]]
-git-tree-sha1 = "630b497eafcc20001bba38a4651b327dcfc491d2"
+git-tree-sha1 = "e2222959fbc6c19554dc15174c81bf7bf3aa691c"
 uuid = "92d709cd-6900-40b7-9082-c6be49f344b6"
-version = "0.2.2"
+version = "0.2.4"
 
 [[deps.IterTools]]
 git-tree-sha1 = "42d5f897009e7ff2cf88db414a389e5ed1bdd023"
@@ -305,12 +303,10 @@ uuid = "b39eb1a6-c29a-53d7-8c32-632cd16f18da"
 version = "1.19.3+0"
 
 [[deps.LLMMCTS]]
-deps = ["GeneralUtils", "JSON3"]
+deps = ["GeneralUtils", "JSON3", "PrettyPrinting"]
-git-tree-sha1 = "d8c653b8fafbd3757b7332985efaf1fdb8b6fe97"
+path = "/appfolder/app/dev/LLMMCTS"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/LLMMCTS"
 uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
-version = "0.1.2"
+version = "0.1.4"
 
 [[deps.LaTeXStrings]]
 git-tree-sha1 = "dda21b8cbd6a6c40d9d02a73230f9d70fed6918c"
@@ -370,9 +366,9 @@ version = "1.11.0"
 
 [[deps.LogExpFunctions]]
 deps = ["DocStringExtensions", "IrrationalConstants", "LinearAlgebra"]
-git-tree-sha1 = "a2d09619db4e765091ee5c6ffe8872849de0feea"
+git-tree-sha1 = "13ca9e2586b89836fd20cccf56e57e2b9ae7f38f"
 uuid = "2ab3a3ac-af41-5b50-aa03-7779005ae688"
-version = "0.3.28"
+version = "0.3.29"
 
     [deps.LogExpFunctions.extensions]
     LogExpFunctionsChainRulesCoreExt = "ChainRulesCore"
@@ -475,7 +471,7 @@ version = "0.3.27+1"
 [[deps.OpenLibm_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "05823500-19ac-5b8b-9628-191a04bc5112"
-version = "0.8.1+2"
+version = "0.8.1+4"
 
 [[deps.OpenSSL]]
 deps = ["BitFlags", "Dates", "MozillaCACerts_jll", "OpenSSL_jll", "Sockets"]
@@ -493,7 +489,7 @@ version = "3.0.15+1"
 deps = ["Artifacts", "CompilerSupportLibraries_jll", "JLLWrappers", "Libdl", "Pkg"]
 git-tree-sha1 = "13652491f6856acfd2db29360e1bbcd4565d04f1"
 uuid = "efe28fd5-8261-553b-a9e1-b2916fc3738e"
-version = "0.5.5+0"
+version = "0.5.5+2"
 
 [[deps.OrderedCollections]]
 git-tree-sha1 = "12f1439c4f986bb868acda6ea33ebc78e19b95ad"
@@ -502,9 +498,9 @@ version = "1.7.0"
 
 [[deps.PDMats]]
 deps = ["LinearAlgebra", "SparseArrays", "SuiteSparse"]
-git-tree-sha1 = "949347156c25054de2db3b166c52ac4728cbad65"
+git-tree-sha1 = "48566789a6d5f6492688279e22445002d171cf76"
 uuid = "90014a1f-27ba-587c-ab20-58faa44d9150"
-version = "0.11.31"
+version = "0.11.33"
 
 [[deps.Parsers]]
 deps = ["Dates", "PrecompileTools", "UUIDs"]
@@ -556,15 +552,15 @@ uuid = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 version = "1.11.0"
 
 [[deps.PtrArrays]]
-git-tree-sha1 = "77a42d78b6a92df47ab37e177b2deac405e1c88f"
+git-tree-sha1 = "1d36ef11a9aaf1e8b74dacc6a731dd1de8fd493d"
 uuid = "43287f4e-b6f4-7ad1-bb20-aadabca52c3d"
-version = "1.2.1"
+version = "1.3.0"
 
 [[deps.QuadGK]]
 deps = ["DataStructures", "LinearAlgebra"]
-git-tree-sha1 = "cda3b045cf9ef07a08ad46731f5a3165e56cf3da"
+git-tree-sha1 = "9da16da70037ba9d701192e27befedefb91ec284"
 uuid = "1fd47b50-473d-5c70-9696-f719f8f3bcdc"
-version = "2.11.1"
+version = "2.11.2"
 
     [deps.QuadGK.extensions]
     QuadGKEnzymeExt = "Enzyme"
@@ -623,11 +619,9 @@ version = "0.7.0"
 
 [[deps.SQLLLM]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "FileIO", "GeneralUtils", "HTTP", "JSON3", "LLMMCTS", "LibPQ", "PrettyPrinting", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
-git-tree-sha1 = "45e660e44de0950a5e5f92d467298d8b768b6023"
+path = "/appfolder/app/dev/SQLLLM"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/SQLLLM"
 uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
-version = "0.2.0"
+version = "0.2.4"
 
 [[deps.SQLStrings]]
 git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
@@ -672,9 +666,9 @@ version = "1.11.0"
 
 [[deps.SpecialFunctions]]
 deps = ["IrrationalConstants", "LogExpFunctions", "OpenLibm_jll", "OpenSpecFun_jll"]
-git-tree-sha1 = "2f5d4697f21388cbe1ff299430dd169ef97d7e14"
+git-tree-sha1 = "64cca0c26b4f31ba18f13f6c12af7c85f478cfde"
 uuid = "276daf66-3868-5448-9aa4-cd146d93841b"
-version = "2.4.0"
+version = "2.5.0"
 
     [deps.SpecialFunctions.extensions]
     SpecialFunctionsChainRulesCoreExt = "ChainRulesCore"
@@ -699,16 +693,16 @@ uuid = "82ae8749-77ed-4fe6-ae5f-f523153014b0"
 version = "1.7.0"
 
 [[deps.StatsBase]]
-deps = ["DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
+deps = ["AliasTables", "DataAPI", "DataStructures", "LinearAlgebra", "LogExpFunctions", "Missings", "Printf", "Random", "SortingAlgorithms", "SparseArrays", "Statistics", "StatsAPI"]
-git-tree-sha1 = "5cf7606d6cef84b543b483848d4ae08ad9832b21"
+git-tree-sha1 = "29321314c920c26684834965ec2ce0dacc9cf8e5"
 uuid = "2913bbd2-ae8a-5f71-8c99-4fb6c76f3a91"
-version = "0.34.3"
+version = "0.34.4"
 
 [[deps.StatsFuns]]
 deps = ["HypergeometricFunctions", "IrrationalConstants", "LogExpFunctions", "Reexport", "Rmath", "SpecialFunctions"]
-git-tree-sha1 = "b423576adc27097764a90e163157bcfc9acf0f46"
+git-tree-sha1 = "35b09e80be285516e52c9054792c884b9216ae3c"
 uuid = "4c63d2b9-4356-54db-8cca-17b64c39e42c"
-version = "1.3.2"
+version = "1.4.0"
 
     [deps.StatsFuns.extensions]
     StatsFunsChainRulesCoreExt = "ChainRulesCore"

Project.toml

@@ -1,9 +1,10 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.1.1"
+version = "0.2.0"
 
 [deps]
+CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 DataStructures = "864edb3b-99cc-5e75-8d2d-829cb0a9cfe8"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
@@ -21,7 +22,5 @@ URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
 
 [compat]
+CSV = "0.10.15"
 DataFrames = "1.7.0"
-GeneralUtils = "0.1.0"
-LLMMCTS = "0.1.2"
-SQLLLM = "0.2.0"

fast_inference_guideline.txt

@@ -0,0 +1,72 @@
+To make **LLM-driven inference** fast while maintaining its dynamic capabilities, there are a few practices or approaches to avoid, as they could lead to performance bottlenecks or inefficiencies. Here's what *not* to do:
+
+---
+
+### **1. Avoid Using Overly Large Models for Every Query**
+While larger LLMs like GPT-4 provide high accuracy and nuanced responses, they may slow down real-time processing due to their computational complexity. Instead:
+- Use distilled or smaller models (e.g., GPT-3.5 Turbo or fine-tuned versions) for faster inference without compromising much on quality.
+
+---
+
+### **2. Avoid Excessive Entity Preprocessing**
+Don’t rely on overly complicated preprocessing steps (like advanced NER models or regex-heavy pipelines) to extract entities from the query before invoking the LLM. This could add latency. Instead:
+- Design efficient prompts that allow the LLM to extract entities and generate responses simultaneously.
+
+---
+
+### **3. Avoid Asking the LLM Multiple Separate Questions**
+Running the LLM for multiple subtasks—for example, entity extraction first and response generation second—can significantly slow down the pipeline. Instead:
+- Create prompts that combine tasks into one pass, e.g., *"Identify the city name and generate a weather response for this query: 'What's the weather in London?'"*.
+
+---
+
+### **4. Don’t Overload the LLM with Context History**
+Excessively lengthy conversation history or irrelevant context in your prompts can slow down inference times. Instead:
+- Provide only the relevant context for each query, trimming unnecessary parts of the conversation.
+
+---
+
+### **5. Avoid Real-Time Dependence on External APIs**
+Using external APIs to fetch supplementary data (e.g., weather details or location info) during every query can introduce latency. Instead:
+- Pre-fetch API data asynchronously and use the LLM to integrate it dynamically into responses.
+
+---
+
+### **6. Avoid Running LLM on Underpowered Hardware**
+Running inference on CPUs or low-spec GPUs will result in slower response times. Instead:
+- Deploy the LLM on optimized infrastructure (e.g., high-performance GPUs like NVIDIA A100 or cloud platforms like Azure AI) to reduce latency.
+
+---
+
+### **7. Skip Lengthy Generative Prompts**
+Avoid prompts that encourage the LLM to produce overly detailed or verbose responses, as these take longer to process. Instead:
+- Use concise prompts that focus on generating actionable or succinct answers.
+
+---
+
+### **8. Don’t Ignore Optimization Techniques**
+Failing to optimize your LLM setup can drastically impact performance. For example:
+- Avoid skipping techniques like model quantization (reducing numerical precision to speed up inference) or distillation (training smaller models).
+
+---
+
+### **9. Don’t Neglect Response Caching**
+While you may not want a full caching system to avoid sunk costs, dismissing lightweight caching entirely can impact speed. Instead:
+- Use temporary session-based caching for very frequent queries, without committing to a full-fledged cache infrastructure.
+
+---
+
+### **10. Avoid One-Size-Fits-All Solutions**
+Applying the same LLM inference method to all queries—whether simple or complex—will waste processing resources. Instead:
+- Route basic queries to faster, specialized models and use the LLM for nuanced or multi-step queries only.
+
+---
+
+### Summary: Focus on Efficient Design
+By avoiding these pitfalls, you can ensure that LLM-driven inference remains fast and responsive:
+- Optimize prompts.
+- Use smaller models for simpler queries.
+- Run the LLM on high-performance hardware.
+- Trim unnecessary preprocessing or contextual steps.
+
+Would you like me to help refine a prompt or suggest specific tools to complement your implementation? Let me know!

src/llmfunction.jl

@@ -1,7 +1,8 @@
 module llmfunction
 
 export  virtualWineUserChatbox, jsoncorrection, checkinventory, # recommendbox,
-        virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1
+        virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
+        extractWineAttributes_2, paraphrase
 
 using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, Dates
 using GeneralUtils, SQLLLM
@@ -290,20 +291,21 @@ julia> result = checkinventory(agent, input)
 function checkinventory(a::T1, input::T2
   ) where {T1<:agent, T2<:AbstractString}
 
-  println("\n~~~ checkinventory order: $input ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory order: $input ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
   wineattributes_1 = extractWineAttributes_1(a, input)
   wineattributes_2 = extractWineAttributes_2(a, input)
 
   _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
-  inventoryquery = "Retrieves winery, wine_name, vintage, region, country, wine_type, grape, serving_temperature, sweetness, intensity, tannin, acidity, tasting_notes, price and currency of wines that match the following criteria - {$_inventoryquery}"
+  inventoryquery = "Retrieves winery, wine_name, wine_id, vintage, region, country, wine_type, grape, serving_temperature, sweetness, intensity, tannin, acidity, tasting_notes, price and currency of wines that match the following criteria - {$_inventoryquery}"
-  println("~~~ checkinventory input: $inventoryquery ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
   # add suppport for similarSQLVectorDB
   textresult, rawresponse = SQLLLM.query(inventoryquery, a.func[:executeSQL], 
-                        a.func[:text2textInstructLLM],
+                        a.func[:text2textInstructLLM];
                         insertSQLVectorDB=a.func[:insertSQLVectorDB], 
-                        similarSQLVectorDB=a.func[:similarSQLVectorDB])
+                        similarSQLVectorDB=a.func[:similarSQLVectorDB],
+                        llmFormatName="qwen3")
 
-  println("\n~~~ checkinventory result ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory result ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
   println(textresult)
 
   return (result=textresult, rawresponse=rawresponse, success=true, errormsg=nothing)
@@ -325,238 +327,83 @@ julia>
 
 # TODO
   - [] update docstring
-  - [x] implement the function
+  - implement the function
 
 # Signature
 """
-# function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
+function extractWineAttributes_1(a::T1, input::T2; maxattempt=10
-
+  )::String where {T1<:agent, T2<:AbstractString}
-#   systemmsg =
-#   """
-#   As a helpful sommelier, your task is to extract the user information from the user's query as much as possible to fill out user's preference form.
-
-#   At each round of conversation, the user will give you the current situation:
-#   User's query: ...
-
-#   You must follow the following guidelines:
-#     1) If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
-#       Additionally, words like 'any' or 'unlimited' mean no information is available.
-#     2) Do not generate other comments.
-
-#   You should then respond to the user with the following points:
-#   - reasoning: state your understanding of the current situation
-#   - wine_name: name of the wine
-#   - winery: name of the winery
-#   - vintage: the year of the wine
-#   - region: a region in a country where the wine is produced, such as Burgundy, Napa Valley, etc
-#   - country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
-#   - wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
-#   - grape_variety: the name of the primary grape used to make the wine
-#   - tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
-#   - wine_price: price of wine. For example, up to 100, less than 100, 20 to 100, 30-79.95 
-#   - occasion: the occasion the user is having the wine for
-#   - food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
-  
-
-#   You should only respond in the user's preference form as described below:
-#   reasoning: ...
-#   winery: ...
-#   wine_name: ...
-#   vintage: ...
-#   region: ...
-#   country: ...
-#   wine_type: ...
-#   grape_variety: ...
-#   tasting_notes: ...
-#   wine_price: ...
-#   occasion: ...
-#   food_to_be_paired_with_wine: ...
-  
-#   Here are some example:
-#   User's query: red, Chenin Blanc, Riesling, under 20
-#   reasoning: ...
-#   winery: NA
-#   wine_name: NA
-#   vintage: NA
-#   region: NA
-#   country: NA
-#   wine_type: red
-#   grape_variety: Chenin Blanc, Riesling
-#   tasting_notes: NA
-#   wine_price: under 20
-#   occasion: NA
-#   food_to_be_paired_with_wine: NA
-
-#   User's query: Domaine du Collier Saumur Blanc 2019, France, white, Chenin Blanc
-#   reasoning: ...
-#   winery: Domaine du Collier 
-#   wine_name: Saumur Blanc
-#   vintage: 2019
-#   region: Saumur
-#   country: France
-#   wine_type: white
-#   grape_variety: Chenin Blanc
-#   tasting_notes: NA
-#   wine_price: 109
-#   occasion: NA
-#   food_to_be_paired_with_wine: NA
-
-#   Let's begin!
-#   """
-
-#   attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_variety", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
-#   errornote = ""
-#   maxattempt = 5
-#   for attempt in 1:maxattempt
-
-#     usermsg =
-#     """
-#     User's query: $input
-#     $errornote
-#     """
-
-#     _prompt = 
-#     [
-#       Dict(:name=> "system", :text=> systemmsg),
-#       Dict(:name=> "user", :text=> usermsg)
-#     ]
-
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-#     prompt *=
-#       """
-#       <|start_header_id|>assistant<|end_header_id|>
-#       """
-
-#     try
-#       response = a.func[:text2textInstructLLM](prompt)
-#       response = GeneralUtils.remove_french_accents(response)
-
-#       # check wheter all attributes are in the response
-#       for word in attributes
-#         if !occursin(word, response)
-#           error("$word attribute is missing")
-#         end
-#       end
-
-#       responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
-
-#       for i ∈ attributes
-#         if length(JSON3.write(responsedict[Symbol(i)])) == 0
-#           error("$i is empty ", @__LINE__)
-#         end
-#       end
-
-#       #check if the following attributes has more than 1 name
-#       # responsedict[:grape_variety] = split(responsedict[:grape_variety], ',')[1]
-#       # responsedict[:grape_variety] = split(responsedict[:grape_variety], '/')[1]
-
-#       responsedict[:country] = split(responsedict[:country], ',')[1]
-#       responsedict[:country] = split(responsedict[:country], '/')[1]
-      
-#       responsedict[:region] = split(responsedict[:region], ',')[1]
-#       responsedict[:region] = split(responsedict[:region], '/')[1]
-
-#       delete!(responsedict, :reasoning)
-#       delete!(responsedict, :tasting_notes)
-#       delete!(responsedict, :occasion)
-#       delete!(responsedict, :food_to_be_paired_with_wine)
-
-#       # check if winery, wine_name, region, country, wine_type, grape_variety are in the query because sometime AI halucinates
-#       for i in [:grape_variety, :winery, :wine_name, :region]
-#         result = check_key_in_input(input, responsedict, attempt, maxattempt, i)
-#         if result === nothing
-#           # nothing wrong
-#         elseif result == "NA"
-#           responsedict[i] = "NA"
-#         else
-#           errornote = result
-#           error(errornote)
-#         end
-#       end
-
-#       # remove (some text)
-#       for (k, v) in responsedict
-#         _v = replace(v, r"\(.*?\)" => "")
-#         responsedict[k] = _v
-#       end
-
-#       result = ""
-#       for (k, v) in responsedict
-#         # some time LLM generate text with "(some comment)". this line removes it
-#         if !occursin("NA", v) && v != "" && !occursin("none", v) && !occursin("None", v)
-#           result *= "$k: $v, "
-#         end
-#       end
-
-#       #[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_variety: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
-      
-#       result = result[1:end-2]  # remove the ending ", "
-
-#       return result
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("")
-#       println("Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, " ", @__LINE__)
-#       println("")
-#     end
-#   end
-#   error("wineattributes_wordToNumber() failed to get a response")
-# end
-function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
 
   systemmsg =
   """
   As a helpful sommelier, your task is to extract the user information from the user's query as much as possible to fill out user's preference form.
 
-  At each round of conversation, the user will give you the current situation:
+  At each round of conversation, the user will give you the following:
   User's query: ...
 
   You must follow the following guidelines:
-    1) If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
+    - If specific information required in the preference form is not available in the query or there isn't any, mark with "NA" to indicate this.
       Additionally, words like 'any' or 'unlimited' mean no information is available.
-    2) Do not generate other comments.
+    - Do not generate other comments.
 
-  You should then respond to the user with the following points:
+  You should then respond to the user with:
-  - reasoning: state your understanding of the current situation
+    Thought: state your understanding of the current situation
-  - wine_name: name of the wine
+    Wine_name: name of the wine
-  - winery: name of the winery
+    Winery: name of the winery
-  - vintage: the year of the wine
+    Vintage: the year of the wine
-  - region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
+    Region: a region (NOT a country) where the wine is produced, such as Burgundy, Napa Valley, etc
-  - country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
+    Country: a country where the wine is produced. Can be "Austria", "Australia", "France", "Germany", "Italy", "Portugal", "Spain", "United States"
-  - wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
+    Wine_type: can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
-  - grape_variety: the name of the primary grape used to make the wine
+    Grape_varietal: the name of the primary grape used to make the wine
-  - tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
+    Tasting_notes: a brief description of the wine's taste, such as "butter", "oak", "fruity", etc
-  - wine_price: price of wine. For example, up to 100, less than 100, 20 to 100, 30-79.95 
+    Wine_price: price range of wine.
-  - occasion: the occasion the user is having the wine for
+    Occasion: the occasion the user is having the wine for
-  - food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
+    Food_to_be_paired_with_wine: food that the user will be served with the wine such as poultry, fish, steak, etc
 
-
+    You should only respond in format as described below:
-  You should only respond in the user's preference form (JSON) as described below:
+    Thought: ...
-  {"reasoning": ..., "winery": ..., "wine_name": ..., "vintage": ..., "region": ..., "country": ..., "wine_type": ..., "grape_variety": ..., "tasting_notes": ..., "wine_price": ..., "occasion": ..., "food_to_be_paired_with_wine": ...}
+    Wine_name: ...
+    Winery: ...
+    Vintage: ...
+    Region: ...
+    Country: ...
+    Wine_type:
+    Grape_varietal: ...
+    Tasting_notes: ...
+    Wine_price: ...
+    Occasion: ...
+    Food_to_be_paired_with_wine: ...
     
     Here are some example:
-  User's query: red, Chenin Blanc, Riesling, under 20
+    User's query: red, Chenin Blanc, Riesling, 20 USD
-  {"reasoning": ..., "winery": "NA", "wine_name": "NA", "vintage": "NA", "region": "NA", "country": "NA", "wine_type": "red", "grape_variety": "Chenin Blanc, Riesling", "tasting_notes": "NA", "wine_price": "under 20", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
+    {"reasoning": ..., "winery": "NA", "wine_name": "NA", "vintage": "NA", "region": "NA", "country": "NA", "wine_type": "red, white", "grape_varietal": "Chenin Blanc, Riesling", "tasting_notes": "NA", "wine_price": "0-20", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
 
-  User's query: Domaine du Collier Saumur Blanc 2019, France, white, Chenin Blanc
+    User's query: Domaine du Collier Saumur Blanc 2019, France, white, Merlot
-  {"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_variety": "Chenin Blanc", "tasting_notes": "NA", "wine_price": "109", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
+    {"reasoning": ..., "winery": "Domaine du Collier", "wine_name": "Saumur Blanc", "vintage": "2019", "region": "Saumur", "country": "France", "wine_type": "white", "grape_varietal": "Merlot", "tasting_notes": "NA", "wine_price": "NA", "occasion": "NA", "food_to_be_paired_with_wine": "NA"}
 
     Let's begin!
   """
 
-  attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_variety", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
+  header = ["Thought:", "Wine_name:", "Winery:", "Vintage:", "Region:", "Country:", "Wine_type:", "Grape_varietal:", "Tasting_notes:", "Wine_price:", "Occasion:", "Food_to_be_paired_with_wine:"]
-  errornote = ""
+  dictkey = ["thought", "wine_name", "winery", "vintage", "region", "country", "wine_type", "grape_varietal", "tasting_notes", "wine_price", "occasion", "food_to_be_paired_with_wine"]
-  maxattempt = 5
+  errornote = "N/A"
+  
+  llmkwargs=Dict(
+    :num_ctx => 32768,
+    :temperature => 0.5,
+  )
+
   for attempt in 1:maxattempt
+    #[PENDING] I should add generatequestion()
+
+    if attempt > 1
+      println("\nYiemAgent extractWineAttributes_1() attempt $attempt/$maxattempt ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    end
 
     usermsg =
     """
     User's query: $input
-    $errornote
+    P.S. $errornote
     """
 
     _prompt = 
@@ -566,47 +413,100 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
     ]
 
     # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
-    prompt *=
+    response = a.func[:text2textInstructLLM](prompt; 
-      """
+      modelsize="medium", llmkwargs=llmkwargs, senderId=a.id)
-      <|start_header_id|>assistant<|end_header_id|>
-      """
-
-    try
-      response = a.func[:text2textInstructLLM](prompt)
     response = GeneralUtils.remove_french_accents(response)
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
 
     # check wheter all attributes are in the response
-      for word in attributes
+    checkFlag = false
+    for word in header
       if !occursin(word, response)
-          error("$word attribute is missing")
+        errornote = "In your previous attempts, the $word attribute is missing. Please try again."
+        println("\nYiemAgent extractWineAttributes_1() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        checkFlag = true
+        break
       end
     end
+    checkFlag == true ? continue : nothing
     
-      responsedict = copy(JSON3.read(response))
+    # check whether response has all answer's key points
+    detected_kw = GeneralUtils.detect_keyword(header, response)
+    if 0 ∈ values(detected_kw)
+      errornote = "In your previous attempts, the response does not have all answer's key points"
+      println("\nYiemAgent extractWineAttributes_1() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "In your previous attempts, the response has duplicated answer's key points"
+      println("\nYiemAgent extractWineAttributes_1() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+    responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
 
-      delete!(responsedict, :reasoning)
+    delete!(responsedict, :thought)
     delete!(responsedict, :tasting_notes)
     delete!(responsedict, :occasion)
     delete!(responsedict, :food_to_be_paired_with_wine)
 
-      # check if winery, wine_name, region, country, wine_type, grape_variety are in the query because sometime AI halucinates
+    println(@__FILE__, " ", @__LINE__)
-      for i in [:grape_variety, :winery, :wine_name, :region]
+    pprintln(responsedict)
-        content = responsedict[i]
+
-        if occursin(",", content)
+    # check if winery, wine_name, region, country, wine_type, grape_varietal's value are in the query because sometime AI halucinates
+    checkFlag = false
+    for i in dictkey
+      j = Symbol(i)
+      if j ∉ [:thought, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
+        # in case j is wine_price it needs to be checked differently because its value is ranged
+        if j == :wine_price
+          if responsedict[:wine_price] != "NA" 
+            # check whether wine_price is in ranged number
+            if !occursin('-', responsedict[:wine_price]) 
+              errornote = "In your previous attempt, the 'wine_price' was not set to a ranged number. Please adjust it accordingly."
+              println("\nERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+              checkFlag = true
+              break
+            end
+
+            # check whether max wine_price is in the input
+            pricerange = split(responsedict[:wine_price], '-')
+            minprice = pricerange[1]
+            maxprice = pricerange[end]
+            if !occursin(maxprice, input)
+              responsedict[:wine_price] = "NA"
+            end
+            # price range like 100-100 is not good
+            if minprice == maxprice
+              errornote = "In your previous attempt, you inputted 'wine_price' with a 'minimum' value equaling the 'maximum', which is not valid."
+              println("\nERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+              checkFlag = true
+              break
+            end
+          end
+        else
+          content = responsedict[j]
+          if typeof(content) <: AbstractVector
+            content = strip.(content)
+          elseif occursin(',', content)
             content = split(content, ",") # sometime AI generates multiple values e.g. "Chenin Blanc, Riesling"
             content = strip.(content)
           else
             content = [content]
           end
 
-        for x in content
+          # for x in content  #check whether price are mentioned in the input
-          if !occursin("NA", responsedict[i]) && !occursin(x, input)
+          #   if !occursin("NA", responsedict[j]) && !occursin(x, input)
-            errornote = "$x is not mentioned in the user query, you must only use the info from the query."
+          #     errornote = "$x is not mentioned in the user query, you must only use the info from the query."
-            error(errornote)
+          #     println("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+          #     checkFlag == true
+          #     break
+          #   end
+          # end
         end
       end
     end
+    checkFlag == true ? continue : nothing  # skip the rest code if true
 
     # remove (some text)
     for (k, v) in responsedict
@@ -622,20 +522,11 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
       end
     end
 
-      #[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_variety: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
+    #[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_varietal: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
     
     result = result[1:end-2]  # remove the ending ", "
 
     return result
-    catch e
-      io = IOBuffer()
-      showerror(io, e)
-      errorMsg = String(take!(io))
-      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      println("")
-      println("Attempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, " ", @__LINE__)
-      println("")
-    end
   end
   error("wineattributes_wordToNumber() failed to get a response")
 end
@@ -643,12 +534,13 @@ end
 """
 # TODO
  - [PENDING] "French dry white wines with medium bod" the LLM does not recognize sweetness. use LLM self questioning to solve.
+ - [PENDING] French Syrah, Viognier, under 100. LLM extract intensiry of 3-5. why?
 """
 function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
 
   conversiontable =
   """
-  Conversion Table:
+  <Conversion Table>
   Intensity level:
     1 to 2: May correspond to "light-bodied" or a similar description.
     2 to 3: May correspond to "med light bodied", "medium light" or a similar description.
@@ -673,10 +565,9 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
     3 to 4: May correspond to "medium acidity" or a similar description.
     4 to 5: May correspond to "semi high acidity" or a similar description.
     4 to 5: May correspond to "high acidity" or a similar description.
+  </Conversion Table>
   """
 
-  # chathistory = vectorOfDictToText(a.chathistory)
-
   systemmsg =
   """
   As an helpful sommelier, your task is to fill out the user's preference form based on the corresponding words from the user's query.
@@ -693,32 +584,58 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
       Additionally, words like 'any' or 'unlimited' mean no information is available.
     2) Use the conversion table to convert the descriptive word level of sweetness, intensity, tannin, and acidity into a corresponding integer.
     3) Do not generate other comments.
+  You should then respond to the user with:
+    Sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
+    Sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
+    Acidity_keyword: The exact keywords in the user's query describing the acidity level of the wine.
+    Acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
+    Tannin_keyword: The exact keywords in the user's query describing the tannin level of the wine.
+    Tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
+    Intensity_keyword: The exact keywords in the user's query describing the intensity level of the wine.
+    Intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
+  You should only respond in format as described below:
+    Sweetness_keyword: ...
+    Sweetness: ...
+    Acidity_keyword: ...
+    Acidity: ...
+    Tannin_keyword: ...
+    Tannin: ...
+    Intensity_keyword: ...
+    Intensity: ...
     
-  You should then respond to the user with the following points:
+  Here are some examples:
-  - reasoning: State your understanding of the current situation
+    User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
-  - sweetness: ( S ), where ( S ) represents integers indicating the range of sweetness levels. Example: 1-2
+    Sweetness_keyword: NA
-  - acidity: ( A ), where ( A ) represents integers indicating the range of acidity level. Example: 3-5
+    Sweetness: NA
-  - tannin: ( T ), where ( T ) represents integers indicating the range of tannin level. Example: 1-3
+    Acidity_keyword: low acidity
-  - intensity: ( I ), where ( I ) represents integers indicating the range of intensity level. Example: 2-4
+    Acidity: 1-2
-  - notes: Anything you want to add
+    Tannin_keyword: medium tannin
+    Tannin: 3-4
+    Intensity_keyword: medium-bodied
+    Intensity: 3-4
 
-  You should only respond in the form as described below:
+    User's query: German red wine, under 100, pairs with spicy food
-  reasoning: ...
+    Sweetness_keyword: NA
-  sweetness: ...
+    Sweetness: NA
-  acidity: ...
+    Acidity_keyword: NA
-  tannin: ...
+    Acidity: NA
-  intensity: ...
+    Tannin_keyword: NA
-  notes: ...
+    Tannin: NA
+    Intensity_keyword: NA
+    Intensity: NA
 
   Let's begin!
   """
+  header = ["Sweetness_keyword:", "Sweetness:", "Acidity_keyword:", "Acidity:", "Tannin_keyword:", "Tannin:", "Intensity_keyword:", "Intensity:"]
+  dictkey = ["sweetness_keyword", "sweetness", "acidity_keyword", "acidity", "tannin_keyword", "tannin", "intensity_keyword", "intensity"]
+  errornote = ""
 
-  # chathistory = vectorOfDictToText(a.chathistory)
+  for attempt in 1:10
-
     usermsg =
     """
     $conversiontable
     User's query: $input
+    P.S. $errornote
     """
 
     _prompt = 
@@ -728,46 +645,51 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
     ]
 
     # put in model format
-  prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
-  prompt *=
-    """
-    <|start_header_id|>assistant<|end_header_id|>
-    """
 
-  attributes = ["reasoning", "sweetness", "acidity", "tannin", "intensity", "notes"]
-
-  for attempt in 1:5
-    try
     response = a.func[:text2textInstructLLM](prompt)
-      responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
     
-      for i ∈ attributes
+    # check whether response has all answer's key points
-        if length(JSON3.write(responsedict[Symbol(i)])) == 0
+    detected_kw = GeneralUtils.detect_keyword(header, response)
-          error("$i is empty ", @__LINE__)
+    if 0 ∈ values(detected_kw)
-        end
+      errornote = "In your previous attempt does not have all answer's key points"
+      println("\nERROR YiemAgent extractWineAttributes_2() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    elseif sum(values(detected_kw)) > length(header)
+      errornote = "In your previous attempt has duplicated answer's key points"
+      println("\nERROR YiemAgent extractWineAttributes_2() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
     end
 
-      delete!(responsedict, :reasoning)
+    responsedict = GeneralUtils.textToDict(response, header; 
-      delete!(responsedict, :notes) # LLM traps. so it can add useless info here like comments.
+                                            dictKey=dictkey, symbolkey=true)
 
-      # some time LLM think the user mentioning acidity and tannin but actually didn't
+    # check whether each describing keyword is in the input to prevent halucination
-      for (k, v) in responsedict
+    for i in ["sweetness", "acidity", "tannin", "intensity"]
-        if k ∈ [:acidity, :tannin] && !occursin(string(k), input)
+      keyword = Symbol(i * "_keyword")  # e.g. sweetness_keyword
-          responsedict[k] = "NA"
+      value = responsedict[keyword]
-        end
+      if value != "NA" && !occursin(value, input)
+        errornote = "In your previous attempt, keyword $keyword: $value does not appear in the input. You must use information from the input only"
+        println("\nERROR YiemAgent extractWineAttributes_2() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        continue
       end
 
-      # remove (some text)
+      # if value == "NA" then responsedict[i] = "NA" 
-      for (k, v) in responsedict
+      # e.g. if sweetness_keyword == "NA" then sweetness = "NA"
-        _v = replace(v, r"\(.*?\)" => "")
+      if value == "NA"
-        responsedict[k] = _v
+        responsedict[Symbol(i)] = "NA"
+      end
     end
 
     # some time LLM not put integer range
     for (k, v) in responsedict
-        responsedict[k] = v
+      if !occursin("keyword", string(k))
-        if length(v) > 5
+        if v !== "NA" && (!occursin('-', v) || length(v) > 5)
-          error("non-range is not allowed. $k $v")
+          errornote = "WARNING: The non-range value {$k: $v} is not allowed. It should be specified in a range format, i.e. min-max."
+          println("\nERROR YiemAgent extractWineAttributes_2() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+          continue
+        end
       end
     end
 
@@ -786,162 +708,132 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
         result *= "$k: $v, "
       end
     end
-
     result = result[1:end-2]  # remove the ending ", "
 
+    return result
+  end
+  error("wineattributes_wordToNumber() failed to get a response")
+end
+
+
+function paraphrase(text2textInstructLLM::Function, text::String)
+  systemmsg = 
+  """
+    Your name: N/A
+    Your vision: 
+      - You are a helpful assistant who help the user to paraphrase their text.
+    Your mission:
+      - To help paraphrase the user's text
+    Mission's objective includes:
+      - To help paraphrase the user's text
+    Your responsibility includes:
+      1) To help paraphrase the user's text
+    Your responsibility does NOT includes:
+      1) N/A
+    Your profile:
+      - N/A
+    Additional information:
+      - N/A
+
+    At each round of conversation, you will be given the following information:
+    Text: The user's given text 
+
+    You MUST follow the following guidelines:
+    - N/A
+
+    You should follow the following guidelines:
+    - N/A
+
+    You should then respond to the user with:
+    Paraphrase: Paraphrased text
+
+    You should only respond in format as described below:
+    Paraphrase: ...
+
+    Let's begin!
+  """
+
+  header = ["Paraphrase:"]
+  dictkey = ["paraphrase"]
+
+  errornote = ""
+  response = nothing # placeholder for show when error msg show up
+
+
+  for attempt in 1:10
+    usermsg = """
+              Text: $text
+              P.S. $errornote
+              """
+
+    _prompt =
+      [
+        Dict(:name => "system", :text => systemmsg),
+        Dict(:name => "user", :text => usermsg)
+      ]
+
+    # put in model format
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
+
+    try
+      response = text2textInstructLLM(prompt)
+      response = GeneralUtils.deFormatLLMtext(response, "granite3")
+      # sometime the model response like this "here's how I would respond: ..."
+      if occursin("respond:", response)
+        errornote = "You don't need to intro your response"
+        error("\nparaphrase() response contain : ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      end
+      response = GeneralUtils.remove_french_accents(response)
+      response = replace(response, '*'=>"")
+      response = replace(response, '$' => "USD")
+      response = replace(response, '`' => "")
+      response = GeneralUtils.remove_french_accents(response)
+
+      # check whether response has all answer's key points
+      detected_kw = GeneralUtils.detect_keyword(header, response)
+      if 0 ∈ values(detected_kw)
+        errornote = "\nYiemAgent paraphrase() response does not have all answer's key points"
+        continue
+      elseif sum(values(detected_kw)) > length(header)
+        errornote = "\nnYiemAgent paraphrase() response has duplicated answer's key points"
+        continue
+      end
+
+      responsedict = GeneralUtils.textToDict(response, header; 
+                                            dictKey=dictkey, symbolkey=true)
+
+      for i ∈ [:paraphrase]
+        if length(JSON3.write(responsedict[i])) == 0
+          error("$i is empty ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        end
+      end
+
+      # check if there are more than 1 key per categories
+      for i ∈ [:paraphrase]
+        matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
+        if length(matchkeys) > 1
+          error("paraphrase() has more than one key per categories")
+        end
+      end
+
+      println("\nparaphrase() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(Dict(responsedict))
+
+      result = responsedict[:paraphrase]
+
       return result
     catch e
       io = IOBuffer()
       showerror(io, e)
       errorMsg = String(take!(io))
       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-      println("")
+      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      println("Attempt $attempt. Error occurred: $errorMsg\n$st")
-      println("")
     end
   end
-  error("wineattributes_wordToNumber() failed to get a response")
+  error("paraphrase() failed to generate a response")
 end
 
 
-# function recommendbox(a::T1, input::T2)::String where {T1<:agent, T2<:AbstractString}
-#   error("recommendbox")
-#   systemmsg =
-#   """
-#   As an helpful sommelier, your task is to fill out the user's preference form based on the corresponding words from the user's query.
-
-#   At each round of conversation, the user will give you the current situation:
-#   User's query: ...
-
-#   The preference form requires the following information:
-#   wine_type, price, occasion, food_to_be_paired_with_wine, country, grape_variety, flavors, aromas.
-
-#   You must follow the following guidelines:
-#     1) If specific information required in the preference form is not available in the query or there isn't any, mark with 'NA' to indicate this.
-#       Additionally, words like 'any' or 'unlimited' mean no information is available.
-#     2) Use the conversion table to convert the descriptive word level of sweetness, intensity, tannin, and acidity into a corresponding integer.
-#     3) Do not generate other comments.
-
-#   You should then respond to the user with the following points:
-#   - reasoning: State your understanding of the current situation
-#   - wine_type: Can be one of: "red", "white", "sparkling", "rose", "dessert" or "fortified"
-#   - price: Must be an integer representing the cost of the wine.
-#   - occasion: ...
-#   - food_to_be_paired_with_wine: food that the user will be served with wine
-#   - country: wine's country of origin
-#   - region: wine's region of origin such as Burgundy, Napa Valley
-#   - grape variety: a single name of grape used to make wine.
-#   - flavors: Names of items that the wine tastes like.
-#   - aromas: wine's aroma
-
-#   You should only respond in the form as described below:
-#   reasoning: ...
-#   wine_type: ...
-#   price: ...
-#   occasion: ...
-#   food_to_be_paired_with_wine: ...
-#   country: ...
-#   region: ...
-#   grape_variety: ...
-#   flavors: ...
-#   aromas: ...
-
-#   Let's begin!
-#   """
-
-#   attributes = ["reasoning", "wine_type", "price", "occasion", "food_to_be_paired_with_wine", "country", "region", "grape_variety", "flavors", "aromas"]
-#   errornote = ""
-#   for attempt in 1:5
-
-#     usermsg =
-#     """
-#     User's query: $input
-#     $errornote
-#     """
-
-#     _prompt = 
-#     [
-#       Dict(:name=> "system", :text=> systemmsg),
-#       Dict(:name=> "user", :text=> usermsg)
-#     ]
-
-#     # put in model format
-#     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
-#     prompt *=
-#       """
-#       <|start_header_id|>assistant<|end_header_id|>
-#       """
-
-#     try
-#       response = a.func[:text2textInstructLLM](prompt)
-#       responsedict = GeneralUtils.textToDict(response, attributes, rightmarker=":", symbolkey=true)
-
-#       for i ∈ attributes
-#         if length(JSON3.write(responsedict[Symbol(i)])) == 0
-#           error("$i is empty ", @__LINE__)
-#         end
-#       end
-
-#       #[PENDING] check if the following attributes has more than 1 name
-#       x = length(split(responsedict[:grape_variety], ",")) * length(split(responsedict[:grape_variety], "/"))
-#       if x > 1
-#         errornote = "only a single name in grape_variety is allowed"
-#         error("only a single grape_variety name is allowed")
-#       end
-#       x = length(split(responsedict[:country], ",")) * length(split(responsedict[:country], "/"))
-#       if x > 1
-#         errornote = "only a single name in country is allowed"
-#         error("only a single country name is allowed")
-#       end
-#       x = length(split(responsedict[:region], ",")) * length(split(responsedict[:region], "/"))
-#       if x > 1
-#         errornote = "only a single name in region is allowed"
-#         error("only a single region name is allowed")
-#       end
-
-#       # check if grape_variety is mentioned in the input
-#       if responsedict[:grape_variety] != "NA" && !occursin(responsedict[:grape_variety], input)
-#         error("$(responsedict[:grape_variety]) is not mentioned in the input")
-#       end
-
-#       responsedict[:flavors] = replace(responsedict[:flavors], "notes"=>"")
-#       delete!(responsedict, :reasoning)
-#       delete!(responsedict, :tasting_notes)
-#       delete!(responsedict, :flavors)
-#       delete!(responsedict, :aromas)
-
-#       # remove (some text)
-#       for (k, v) in responsedict
-#         _v = replace(v, r"\(.*?\)" => "")
-#         responsedict[k] = _v
-#       end
-
-#       result = ""
-#       for (k, v) in responsedict
-#         # some time LLM generate text with "(some comment)". this line removes it
-#         if !occursin("NA", v) && v != "" && !occursin("none", v) && !occursin("None", v)
-#           result *= "$k: $v, "
-#         end
-#       end
-
-#       #[PENDING] remove halucination. "highend dry white wine" --> "wine_type: white, occasion: special occasion, food_to_be_paired_with_wine: seafood, fish, country: France, Italy, USA, grape_variety: Chardonnay, Sauvignon Blanc, Pinot Grigio\nwine_notes: citrus, green apple, floral"
-      
-#       result = result[1:end-2]  # remove the ending ", "
-
-#       return result
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("")
-#       println("Attempt $attempt. Error occurred: $errorMsg\n$st")
-#       println("")
-#     end
-#   end
-#   error("wineattributes_wordToNumber() failed to get a response")
-# end
-
 
 """ Attemp to correct LLM response's incorrect JSON response.
 
@@ -1101,7 +993,7 @@ end
 #   ]
 
 #   # put in model format
-#   prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+#   prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
 #   prompt *=
 #     """
 #     <|start_header_id|>assistant<|end_header_id|>
@@ -1133,7 +1025,7 @@ end
 #         state[:isterminal] = true
 #         state[:reward] = 1
 #       end
-#       println("--> 5 Evaluator ", @__FILE__, " ", @__LINE__)
+#       println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
 #       pprintln(Dict(responsedict))
 #       return responsedict[:score]
 #     catch e

src/type.jl

@@ -9,11 +9,48 @@ using GeneralUtils
 
 abstract type agent end
 
-
 mutable struct companion <: agent
   name::String  # agent name
   id::String    # agent id
+  systemmsg::String # system message
+  tools::Dict  # tools
   maxHistoryMsg::Integer  # e.g. 21th and earlier messages will get summarized
+  chathistory::Vector{Dict{Symbol, Any}}
+  memory::Dict{Symbol, Any}
+  func::NamedTuple  # NamedTuple of functions 
+  llmFormatName::String
+end
+
+function companion(
+  func::NamedTuple  # NamedTuple of functions
+  ;
+  systemmsg::Union{String, Nothing}= nothing,
+  name::String= "Assistant",
+  id::String= GeneralUtils.uuid4snakecase(), 
+  maxHistoryMsg::Integer= 20,
+  chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
+  llmFormatName::String= "granite3"
+  )
+
+  if systemmsg === nothing
+    systemmsg = 
+      """
+      Your name: $name
+      Your sex: Female
+      Your role: You are a helpful assistant.
+      You should follow the following guidelines:
+      - Focus on the latest conversation.
+      - Your like to be short and concise.
+
+      Let's begin!
+      """
+  end
+
+  tools = Dict( # update input format
+      "CHATBOX"=> Dict(
+        :description => "- CHATBOX which you can use to talk with the user. The input is your intentions for the dialogue. Be specific.",
+      ),
+    )
 
   """ Memory
     Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
@@ -22,38 +59,23 @@ mutable struct companion <: agent
       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=> OrderedDict{Symbol, Any}(),
             :events=> Vector{Dict{Symbol, Any}}(),
-              :state=> Dict{Symbol, Any}(),
+            :state=> Dict{Symbol, Any}(), # state of the agent
+            :recap=> OrderedDict{Symbol, Any}(),  # recap summary of the conversation
           )
 
   newAgent = companion(
     name,
     id,
+    systemmsg,
+    tools,
     maxHistoryMsg,
     chathistory,
     memory,
-    text2textInstructLLM
+    func,
+    llmFormatName
     )
   
   return newAgent
@@ -61,6 +83,7 @@ end
 
 
 
+
 """ A sommelier agent.
 
 # Arguments
@@ -134,20 +157,10 @@ mutable struct sommelier <: agent
   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}
-
   func  # NamedTuple of functions 
+  llmFormatName::String
 end
 
 function sommelier(
@@ -158,6 +171,7 @@ function sommelier(
   retailername::String= "retailer_name",
   maxHistoryMsg::Integer= 20,
   chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
+  llmFormatName::String= "granite3"
   )
 
   tools = Dict( # update input format
@@ -171,21 +185,25 @@ function sommelier(
         :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
+    Ref: Chat prompt format https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/discussions/3
+    NO "system" message in chathistory because I want to add it at the inference time
+    chathistory= [
+      Dict(:name=>"user", :text=> "Wassup!", :timestamp=> Dates.now()),
+      Dict(:name=>"assistant", :text=> "Hi I'm your assistant.", :timestamp=> Dates.now()),
+    ]
+  """
   memory = Dict{Symbol, Any}(
-              :chatbox=> "",
+            :shortmem=> OrderedDict{Symbol, Any}(
-              :shortmem=> OrderedDict{Symbol, Any}(),
+              :available_wine=> [],
+              :found_wine=> [], # used by decisionMaker(). This is to prevent decisionMaker() keep presenting the same wines
+            ),
             :events=> Vector{Dict{Symbol, Any}}(),
             :state=> Dict{Symbol, Any}(
-                :wine_presented_to_user=> "None",
             ),
+            :recap=> OrderedDict{Symbol, Any}(),
           )
 
   newAgent = sommelier(
@@ -196,7 +214,8 @@ function sommelier(
     maxHistoryMsg,
     chathistory,
     memory,
-    func
+    func,
+    llmFormatName
     )
   
   return newAgent

src/util.jl

@@ -1,6 +1,7 @@
 module util
 
-export clearhistory, addNewMessage, vectorOfDictToText, eventdict, noises
+export clearhistory, addNewMessage, chatHistoryToText, eventdict, noises, createTimeline,
+        availableWineToText
 
 using UUIDs, Dates, DataStructures, HTTP, JSON3
 using GeneralUtils
@@ -106,7 +107,7 @@ function addNewMessage(a::T1, name::String, text::T2;
     error("name is not in agent.availableRole $(@__LINE__)")
   end
 
-  #[] summarize the oldest 10 message
+  #[PENDING] summarize the oldest 10 message
   if length(a.chathistory) > maximumMsg
     summarize(a.chathistory)
   else
@@ -121,33 +122,39 @@ This function takes in a vector of dictionaries and outputs a single string wher
 
 # Arguments
   - `vecd::Vector`
-    a vector of dictionaries
+    A vector of dictionaries containing chat messages
   - `withkey::Bool` 
-    whether to include the key in the output text. Default is true
+    Whether to include the name as a prefix in the output text. Default is true
+  - `range::Union{Nothing,UnitRange,Int}` 
+    Optional range of messages to include. If nothing, includes all messages
 
-# Return
+# Returns
-  a string with the formatted dictionaries
+  A formatted string where each line contains either:
+  - If withkey=true: "name> message\n"
+  - If withkey=false: "message\n"
 
 # Example
-```jldoctest
+
 julia> using Revise
 julia> using GeneralUtils
 julia> vecd = [Dict(:name => "John", :text => "Hello"), Dict(:name => "Jane", :text => "Goodbye")]
 julia> GeneralUtils.vectorOfDictToText(vecd, withkey=true)
 "John> Hello\nJane> Goodbye\n"
 ```
-# Signature
 """
-function vectorOfDictToText(vecd::Vector; withkey=true)::String
+function chatHistoryToText(vecd::Vector; withkey=true, range=nothing)::String
   # Initialize an empty string to hold the final text
   text = ""
   
+  # Get the elements within the specified range, or all elements if no range provided
+  elements = isnothing(range) ? vecd : vecd[range]
+  
   # Determine whether to include the key in the output text or not
   if withkey
     # Loop through each dictionary in the input vector
-      for d in vecd
+    for d in elements
         # Extract the 'name' and 'text' keys from the dictionary
-          name = d[:name]
+        name = titlecase(d[:name])
         _text = d[:text]
         
         # Append the formatted string to the text variable
@@ -155,7 +162,7 @@ function vectorOfDictToText(vecd::Vector; withkey=true)::String
       end
   else
     # Loop through each dictionary in the input vector
-      for d in vecd
+    for d in elements
         # Iterate over all key-value pairs in the dictionary
         for (k, v) in d
             # Append the formatted string to the text variable
@@ -169,246 +176,171 @@ function vectorOfDictToText(vecd::Vector; withkey=true)::String
 end
 
 
+function availableWineToText(vecd::Vector)::String
+  # Initialize an empty string to hold the final text
+  rowtext = ""
+  # Loop through each dictionary in the input vector
+  for (i, d) in enumerate(vecd)
+    # Iterate over all key-value pairs in the dictionary
+    temp = []
+    for (k, v) in d
+      # Append the formatted string to the text variable
+      t = "$k:$v"
+      push!(temp, t)
+    end
+    _rowtext = join(temp, ',')
+    rowtext *= "$i) $_rowtext "
+  end
+  
+  return rowtext
+end
+
+
+
+""" Create a dictionary representing an event with optional details.
+
+# Arguments
+  - `event_description::Union{String, Nothing}`
+    A description of the event
+  - `timestamp::Union{DateTime, Nothing}`
+    The time when the event occurred
+  - `subject::Union{String, Nothing}`
+    The subject or entity associated with the event
+  - `thought::Union{AbstractDict, Nothing}`
+    Any associated thoughts or metadata
+  - `actionname::Union{String, Nothing}`
+    The name of the action performed (e.g., "CHAT", "CHECKINVENTORY")
+  - `actioninput::Union{String, Nothing}`
+    Input or parameters for the action
+  - `location::Union{String, Nothing}`
+    Where the event took place
+  - `equipment_used::Union{String, Nothing}`
+    Equipment involved in the event
+  - `material_used::Union{String, Nothing}`
+    Materials used during the event
+  - `outcome::Union{String, Nothing}`
+    The result or consequence of the event after action execution
+  - `note::Union{String, Nothing}`
+    Additional notes or comments
+
+# Returns
+  A dictionary with event details as symbol-keyed key-value pairs
+"""
 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,
+  thought::Union{AbstractDict, Nothing}=nothing,
+  actionname::Union{String, Nothing}=nothing, # "CHAT", "CHECKINVENTORY", "PRESENTBOX", etc
+  actioninput::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}(
+
+  d = Dict{Symbol, Any}(
     :event_description=> event_description,
     :timestamp=> timestamp,
     :subject=> subject,
-      :action_or_dialogue=> action_or_dialogue,
+    :thought=> thought,
+    :actionname=> actionname,
+    :actioninput=> actioninput,
     :location=> location,
     :equipment_used=> equipment_used,
     :material_used=> material_used,
     :outcome=> outcome,
     :note=> note,
   )
+
+  return d
 end
 
 
+""" Create a formatted timeline string from a sequence of events.
 
-# """ Convert a single chat dictionary into LLM model instruct format.
+# Arguments
+  - `events::T1`
+    Vector of event dictionaries containing subject, actioninput and optional outcome fields
+    Each event dictionary should have the following keys:
+    - :subject - The subject or entity performing the action
+    - :actioninput - The action or input performed by the subject
+    - :outcome - (Optional) The result or outcome of the action
 
-# # Llama 3 instruct format example
+# Returns
-#   <|system|>
+  - `timeline::String`
-#   You are a helpful AI assistant.<|end|>
+    A formatted string representing the events with their subjects, actions, and optional outcomes
-#   <|user|>
+    Format: "{index}) {subject}> {actioninput} {outcome}\n" for each event
-#   I am going to Paris, what should I see?<|end|>
+
-#   <|assistant|>
+# Example
-#   Paris, the capital of France, is known for its stunning architecture, art museums."<|end|>
+
-#   <|user|>
+events = [
-#   What is so great about #1?<|end|>
+  Dict(:subject => "User", :actioninput => "Hello", :outcome => nothing),
-#   <|assistant|>
+  Dict(:subject => "Assistant", :actioninput => "Hi there!", :outcome => "with a smile")
+]
+timeline = createTimeline(events)
+# 1) User> Hello
+# 2) Assistant> Hi there! with a smile
+
+"""
+function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothing
+  ) where {T1<:AbstractVector}
+  # Initialize empty timeline string
+  timeline = ""
+  
+  # Determine which indices to use - either provided range or full length
+  ind = 
+    if eventindex !== nothing
+      [eventindex...]
+    else
+      1:length(events)
+    end
+
+  # Iterate through events and format each one
+  for (i, event) in zip(ind, events)
+    # If no outcome exists, format without outcome
+    if event[:outcome] === nothing
+      timeline *= "Event_$i $(event[:subject])> $(event[:actioninput])\n"
+    # If outcome exists, include it in formatting  
+    else
+      timeline *= "Event_$i $(event[:subject])> $(event[:actioninput]) $(event[:outcome])\n"
+    end
+  end
+
+  # Return formatted timeline string
+  return timeline
+end
 
 
-# # Arguments
+# function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothing
-#   - `name::T`
+#   ) where {T1<:AbstractVector}
-#     message owner name e.f. "system", "user" or "assistant"
+#   # Initialize empty timeline string
-#   - `text::T`
+#   timeline = ""
   
-# # Return
+#   # Determine which indices to use - either provided range or full length
-#   - `formattedtext::String`
+#   ind = 
-#     text formatted to model format
+#     if eventindex !== nothing
-
+#       [eventindex...]
-# # 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|>
-#     $text
-#     <|eot_id|>
-#     """
 #     else
-#     """
+#       1:length(events)
-#     <|start_header_id|>$name<|end_header_id|>
-#     $text
-#     <|eot_id|>
-#     """
 #     end
 
-#   return formattedtext
+#   # Iterate through events and format each one
-# end
+#   for (i, event) in zip(ind, events)
+#     # If no outcome exists, format without outcome
+#     subject = titlecase(event[:subject])
+#     if event[:outcome] === nothing
       
-
+#       timeline *= "Event_$i) Who: $subject Action_name: $(event[:actionname]) Action_input: $(event[:actioninput])\n"
-
+#     # If outcome exists, include it in formatting  
-# """ Convert a chat messages in vector of dictionary into LLM model instruct format.
-
-# # Arguments
-#   - `messages::Vector{Dict{Symbol, T}}`
-#     message owner name e.f. "system", "user" or "assistant"
-#   - `formatname::T`
-#     format name to be used
-
-# # Return
-#   - `formattedtext::String`
-#     text formatted to model format
-
-# # Example
-# ```jldoctest
-# julia> using Revise
-# julia> using YiemAgent
-# julia> chatmessage = [
-#           Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",),
-#           Dict(:name=> "user",:text=> "list me all planets in our solar system.",),
-#           Dict(:name=> "assistant",:text=> "I'm sorry. I don't know. You tell me.",),
-#         ]
-# julia> formattedtext = YiemAgent.formatLLMtext(chatmessage, "llama3instruct")
-# "<|begin_of_text|>\n  <|start_header_id|>system<|end_header_id|>\n  You are a helpful, respectful and honest assistant.\n  <|eot_id|>\n  <|start_header_id|>user<|end_header_id|>\n  list me all planets in our solar system.\n  <|eot_id|>\n  <|start_header_id|>assistant<|end_header_id|>\n  I'm sorry. I don't know. You tell me.\n  <|eot_id|>\n"
-# ```
-
-# # Signature
-# """
-# function formatLLMtext(messages::Vector{Dict{Symbol, T}}, 
-#                       formatname::String="llama3instruct") where {T<:Any}
-#   f = if formatname == "llama3instruct"
-#     formatLLMtext_llama3instruct
-#   elseif formatname == "mistral"
-#     # not define yet
-#   elseif formatname == "phi3instruct"
-#     formatLLMtext_phi3instruct
 #     else
-#     error("$formatname template not define yet")
+#       timeline *= "Event_$i) Who: $subject Action_name: $(event[:actionname]) Action_input: $(event[:actioninput]) Action output: $(event[:outcome])\n"
-#   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)
+#   # Return formatted timeline string
+#   return timeline
 # end
 
 
@@ -439,11 +371,6 @@ end
 
 
 
-
-
-
-
-
 
 
 

test/Manifest.toml

@@ -0,0 +1,41 @@
+# This file is machine-generated - editing it directly is not advised
+
+julia_version = "1.11.4"
+manifest_format = "2.0"
+project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
+
+[[deps.Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+version = "1.11.0"
+
+[[deps.InteractiveUtils]]
+deps = ["Markdown"]
+uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
+version = "1.11.0"
+
+[[deps.Logging]]
+uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
+version = "1.11.0"
+
+[[deps.Markdown]]
+deps = ["Base64"]
+uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
+version = "1.11.0"
+
+[[deps.Random]]
+deps = ["SHA"]
+uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
+version = "1.11.0"
+
+[[deps.SHA]]
+uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
+version = "0.7.0"
+
+[[deps.Serialization]]
+uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+version = "1.11.0"
+
+[[deps.Test]]
+deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
+uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+version = "1.11.0"

test/Project.toml

@@ -0,0 +1,2 @@
+[deps]
+Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"

test/config.json

@@ -27,10 +27,14 @@
       "description": "agent role"
   },
   "organization": {
-      "value": "yiem_hq",
+      "value": "yiem_branch_1",
       "description": "organization name"
   },
   "externalservice": {
+    "loadbalancer": {
+      "mqtttopic": "/loadbalancer/requestingservice",
+      "description": "text to text service with instruct LLM"
+    },
     "text2textinstruct": {
         "mqtttopic": "/loadbalancer/requestingservice",
         "description": "text to text service with instruct LLM",
@@ -51,6 +55,22 @@
         "llminfo": {
             "name": "llama3instruct"
         }
+    },
+    "wineDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10201,
+      "dbname": "wineDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
+    },
+    "SQLVectorDB" : {
+      "description": "A wine database connection info for LibPQ client",
+      "host": "192.168.88.12",
+      "port": 10203,
+      "dbname": "SQLVectorDB",
+      "user": "yiemtechnologies",
+      "password": "yiemtechnologies@Postgres_0.0"
     }
   }
 }

test/etc.jl

@@ -1,9 +0,0 @@
-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)
-
-

test/test_1.jl

@@ -8,31 +8,46 @@ using Base.Threads
 
 
 # load config
-config = JSON3.read("./test/config.json")
+config = JSON3.read("/appfolder/app/dev/YiemAgent/test/config.json")
 # config = copy(JSON3.read("../mountvolume/config.json"))
 
 
 function executeSQL(sql::T) where {T<:AbstractString}
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10201 dbname=wineDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
+  host = config[:externalservice][:wineDB][:host]
+  port = config[:externalservice][:wineDB][:port]
+  dbname = config[:externalservice][:wineDB][:dbname]
+  user = config[:externalservice][:wineDB][:user]
+  password = config[:externalservice][:wineDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
   result = LibPQ.execute(DBconnection, sql)
   close(DBconnection)
   return result
 end
 
 function executeSQLVectorDB(sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
+  host = config[:externalservice][:SQLVectorDB][:host]
+  port = config[:externalservice][:SQLVectorDB][:port]
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
+  user = config[:externalservice][:SQLVectorDB][:user]
+  password = config[:externalservice][:SQLVectorDB][:password]
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
   result = LibPQ.execute(DBconnection, sql)
   close(DBconnection)
   return result
 end
 
-function text2textInstructLLM(prompt::String)
+function text2textInstructLLM(prompt::String; maxattempt::Integer=3, modelsize::String="medium",
+    llmkwargs=Dict(
+      :num_ctx => 32768,
+      :temperature => 0.1,
+    )
+  )
   msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+    config[:externalservice][:loadbalancer][:mqtttopic];
     msgPurpose="inference",
     senderName="yiemagent",
-    senderId=string(uuid4()),
+    senderId=sessionId,
-    receiverName="text2textinstruct",
+    receiverName="text2textinstruct_$modelsize",
     mqttBrokerAddress=config[:mqttServerInfo][:broker],
     mqttBrokerPort=config[:mqttServerInfo][:port],
   )
@@ -41,15 +56,24 @@ function text2textInstructLLM(prompt::String)
     :msgMeta => msgMeta,
     :payload => Dict(
       :text => prompt,
-      :kwargs => Dict(
+      :kwargs => llmkwargs
-        :num_ctx => 16384,
-        :temperature => 0.2,
-      )
     )
   )
 
-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
+  response = nothing
+  for attempts in 1:maxattempt
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=180, maxattempt=maxattempt)
+    payload = _response[:response]
+    if _response[:success] && payload[:text] !== nothing
       response = _response[:response][:text]
+      break
+    else
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(outgoingMsg)
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      sleep(3)
+    end
+  end
 
   return response
 end
@@ -57,11 +81,11 @@ end
 # get text embedding from a LLM service
 function getEmbedding(text::T) where {T<:AbstractString}
   msgMeta = GeneralUtils.generate_msgMeta(
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+    config[:externalservice][:loadbalancer][:mqtttopic];
     msgPurpose="embedding",
     senderName="yiemagent",
-    senderId=string(uuid4()),
+    senderId=sessionId,
-    receiverName="text2textinstruct",
+    receiverName="textembedding",
     mqttBrokerAddress=config[:mqttServerInfo][:broker],
     mqttBrokerPort=config[:mqttServerInfo][:port],
   )
@@ -72,7 +96,8 @@ function getEmbedding(text::T) where {T<:AbstractString}
       :text => [text] # must be a vector of string
     )
   )
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
+
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
   embedding = response[:response][:embeddings]
   return embedding
 end
@@ -80,10 +105,8 @@ end
 function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
   vectorDB::Function; limit::Integer=1
   )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-
   # get embedding from LLM service
   embedding = getEmbedding(text)[1]
-
   # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
   sql = """
         SELECT *, $embeddingColumnName <-> '$embedding' as distance
@@ -95,29 +118,29 @@ function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnNam
   return df
 end
 
-
 function similarSQLVectorDB(query; maxdistance::Integer=100)
   tablename = "sqlllm_decision_repository"
   # get embedding of the query
   df = findSimilarTextFromVectorDB(query, tablename, 
                                   "function_input_embedding", executeSQLVectorDB)
+  # println(df[1, [:id, :function_output]])
   row, col = size(df)
   distance = row == 0 ? Inf : df[1, :distance]  
+  # distance = 100  # CHANGE this is for testing only
   if row != 0 && distance < maxdistance
     # if there is usable SQL, return it.
     output_b64 = df[1, :function_output_base64]   # pick the closest match
     output_str = String(base64decode(output_b64))
     rowid = df[1, :id]
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     return (dict=output_str, distance=distance)
   else
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
     return (dict=nothing, distance=nothing)
   end
 end
 
-
+function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
-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]
@@ -134,14 +157,14 @@ function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) where {T1
     sql = """
           INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
           """
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    println(sql)
+    # println(sql)
     _ = executeSQLVectorDB(sql)
   end
 end
 
 
-function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
   )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
   tablename = "sommelier_decision_repository"
   # find similar
@@ -174,7 +197,7 @@ function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::In
   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_embedding = getEmbedding(recentevents)[1]
     recentevents = replace(recentevents, "'" => "")
     decision_json = JSON3.write(decision)
     decision_base64 = base64encode(decision_json)
@@ -214,7 +237,7 @@ a = YiemAgent.sommelier(
 )
 
 while true
-  println("your respond: ")
+  print("\nyour respond: ")
   user_answer = readline()
   response = YiemAgent.conversation(a, Dict(:text=> user_answer))
   println("\n$response")
@@ -224,14 +247,13 @@ end
 # response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
 
 
-
+"""
-
+hello I want to get a bottle of red wine for my boss. I have a budget around 50 dollars. Show me some options.
-
+
-
+I have no idea about his wine taste but he likes spicy food.
-
+
-
+
-
+"""
-