update

2025-05-01 12:05:59 +07:00 · 2025-05-01 08:04:01 +07:00 · 2025-05-01 07:59:37 +07:00 · 2025-04-30 12:58:32 +07:00 · 2025-04-29 18:45:52 +07:00 · 2025-04-29 11:01:36 +07:00
13 changed files with 2238 additions and 1694 deletions
--- a/Manifest.toml
+++ b/Manifest.toml
@@ -1,8 +1,8 @@
 # This file is machine-generated - editing it directly is not advised
-julia_version = "1.11.2"
+julia_version = "1.11.4"
 manifest_format = "2.0"
-project_hash = "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"
--- a/Project.toml
+++ b/Project.toml
@@ -1,9 +1,10 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
-version = "0.1.2"
+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, 0.2"
 LLMMCTS = "0.1.2"
 SQLLLM = "0.2.0"
--- a/fast_inference_guideline.txt
+++ b/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!
--- a/src/interface.jl
+++ b/src/interface.jl
--- a/src/llmfunction.jl
+++ b/src/llmfunction.jl
@@ -2,7 +2,7 @@ module llmfunction
 export  virtualWineUserChatbox, jsoncorrection, checkinventory, # recommendbox,
        virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
-        extractWineAttributes_2
+        extractWineAttributes_2, paraphrase
 using HTTP, JSON3, URIs, Random, PrettyPrinting, UUIDs, Dates
 using GeneralUtils, SQLLLM
@@ -291,24 +291,23 @@ julia> result = checkinventory(agent, input)
 function checkinventory(a::T1, input::T2
  ) where {T1<:agent, T2<:AbstractString}
-  println("\n~~~ checkinventory order: $input ", Dates.now(), " ", @__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 ", Dates.now(), " ", @__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 ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+  println("\ncheckinventory result ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
  println(textresult)
  #[WORKING] when rawresponse is nothing, AI get errors
  return (result=textresult, rawresponse=rawresponse, success=true, errormsg=nothing)
 end
@@ -328,60 +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:
+  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_varietal: 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 range of wine.
+    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 the user's preference form (JSON) as described below:
+    You should only respond in format as described below:
-  {"reasoning": ..., "winery": ..., "wine_name": ..., "vintage": ..., "region": ..., "country": ..., "wine_type": ..., "grape_varietal": ..., "tasting_notes": ..., "wine_price": ..., "occasion": ..., "food_to_be_paired_with_wine": ...}
+    Thought: ...
-  
+    Wine_name: ...
-  Here are some example:
+    Winery: ...
-  User's query: red, Chenin Blanc, Riesling, 20 USD
+    Vintage: ...
-  {"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"}
+    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, 20 USD
    {"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_varietal": "Chenin Blanc", "tasting_notes": "NA", "wine_price": "NA", "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!
+    Let's begin!
  """
-  attributes = ["reasoning", "winery", "wine_name", "vintage", "region", "country", "wine_type", "grape_varietal", "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"]
  errornote = "N/A"
-  for attempt in 1:5
+  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. $errornote
    """
    _prompt = 
@@ -391,32 +413,39 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
    ]
    # put in model format
-    prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
-    prompt *=
+    response = a.func[:text2textInstructLLM](prompt; 
-      """
+      modelsize="medium", llmkwargs=llmkwargs, senderId=a.id)
      <|start_header_id|>assistant<|end_header_id|>
      """
    response = a.func[:text2textInstructLLM](prompt)
    response = GeneralUtils.remove_french_accents(response)
    response = GeneralUtils.deFormatLLMtext(response, "granite3")
    # check wheter all attributes are in the response
    checkFlag = false
-    for word in attributes
+    for word in header
      if !occursin(word, response)
-        errornote = "$word attribute is missing in previous attempts"
+        errornote = "In your previous attempts, the $word attribute is missing. Please try again."
-        println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        println("\nYiemAgent extractWineAttributes_1() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        checkFlag = true
        break
      end
    end
    checkFlag == true ? continue : nothing
    # 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)
-    responsedict = copy(JSON3.read(response))
+    delete!(responsedict, :thought)
    # convert 
    delete!(responsedict, :reasoning)
    delete!(responsedict, :tasting_notes)
    delete!(responsedict, :occasion)
    delete!(responsedict, :food_to_be_paired_with_wine)
@@ -426,25 +455,34 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
    # 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 attributes
+    for i in dictkey
      j = Symbol(i)
-      if j ∉ [:reasoning, :tasting_notes, :occasion, :food_to_be_paired_with_wine]
+      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 = "wine_price must be a range number"
+              errornote = "In your previous attempt, the 'wine_price' was not set to a ranged number. Please adjust it accordingly."
-              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+              println("\nERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
              checkFlag = true
              break
            end
            # check whether max wine_price is in the input
-            maxprice = split(responsedict[:wine_price], '-')[end]
+            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__, " $(Dates.now())")
              checkFlag = true
              break
            end
          end
        else
          content = responsedict[j]
@@ -457,18 +495,18 @@ function extractWineAttributes_1(a::T1, input::T2)::String where {T1<:agent, T2<
            content = [content]
          end
-          for x in content  #BUG why x is "0-1500"
+          # for x in content  #check whether price are mentioned in the input
-            if !occursin("NA", responsedict[j]) && !occursin(x, input)
+          #   if !occursin("NA", responsedict[j]) && !occursin(x, input)
-              errornote = "$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."
-              println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+          #     println("ERROR YiemAgent extractWineAttributes_1() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-              checkFlag == true
+          #     checkFlag == true
-              break
+          #     break
-            end
+          #   end
-          end
+          # end
        end
      end
    end
-    checkFlag == true ? continue : nothing
+    checkFlag == true ? continue : nothing  # skip the rest code if true
    # remove (some text)
    for (k, v) in responsedict
@@ -502,7 +540,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
  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.
@@ -527,6 +565,7 @@ 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>
  """
  systemmsg =
@@ -545,67 +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:
-  You should then respond to the user with the following points:
+    Sweetness_keyword: The exact keywords in the user's query describing the sweetness level of the wine.
-  - 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
-  - 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_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
-  - 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_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
-  - 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_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
-  - 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: ...
-  You should only respond in the form (JSON) as described below:
+    Sweetness: ...
-  {
+    Acidity_keyword: ...
-    "sweetness_keyword": ...,
+    Acidity: ...
-    "sweetness": ...,
+    Tannin_keyword: ...
-    "acidity_keyword": ...,
+    Tannin: ...
-    "acidity": ...,
+    Intensity_keyword: ...
-    "tannin_keyword": ...,
+    Intensity: ...
-    "tannin": ...,
+    
    "intensity_keyword": ...,
    "intensity": ...
  }
  Here are some examples:
-  User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
+    User's query: I want a wine with a medium-bodied, low acidity, medium tannin.
-  {
+    Sweetness_keyword: NA
-    "sweetness_keyword": "NA",
+    Sweetness: NA
-    "sweetness": "NA",
+    Acidity_keyword: low acidity
-    "acidity_keyword": "low acidity",
+    Acidity: 1-2
-    "acidity": "1-2",
+    Tannin_keyword: medium tannin
-    "tannin_keyword": "medium tannin",
+    Tannin: 3-4
-    "tannin": "3-4",
+    Intensity_keyword: medium-bodied
-    "intensity_keyword": "medium-bodied",
+    Intensity: 3-4
    "intensity": "3-4"
  }
  User's query: German red wine, under 100, pairs with spicy food
  {
    "sweetness_keyword": "NA",
    "sweetness": "NA",
    "acidity_keyword": "NA",
    "acidity": "NA",
    "tannin_keyword": "NA",
    "tannin": "NA",
    "intensity_keyword": "NA",
    "intensity": "NA"
  }
    User's query: German red wine, under 100, pairs with spicy food
    Sweetness_keyword: NA
    Sweetness: NA
    Acidity_keyword: NA
    Acidity: NA
    Tannin_keyword: NA
    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 = ""
-  for attempt in 1:5
+  for attempt in 1:10
    usermsg =
    """
    $conversiontable
    User's query: $input
-    $errornote
+    P.S. $errornote
    """
    _prompt = 
@@ -615,22 +645,33 @@ 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|>
      """
    response = a.func[:text2textInstructLLM](prompt)
-    responsedict = copy(JSON3.read(response))
+    response = GeneralUtils.deFormatLLMtext(response, "granite3")
    # 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 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
    responsedict = GeneralUtils.textToDict(response, header; 
                                            dictKey=dictkey, symbolkey=true)
    # check whether each describing keyword is in the input to prevent halucination
    for i in ["sweetness", "acidity", "tannin", "intensity"]
      keyword = Symbol(i * "_keyword")  # e.g. sweetness_keyword
      value = responsedict[keyword]
      if value != "NA" && !occursin(value, input)
-        errornote = "WARNING. Keyword $keyword: $value does not appear in the input. You must use information from the input only"
+        errornote = "In your previous attempt, keyword $keyword: $value does not appear in the input. You must use information from the input only"
-        println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        println("\nERROR YiemAgent extractWineAttributes_2() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
        continue
      end
@@ -646,7 +687,7 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
      if !occursin("keyword", string(k))
        if v !== "NA" && (!occursin('-', v) || length(v) > 5)
          errornote = "WARNING: The non-range value {$k: $v} is not allowed. It should be specified in a range format, i.e. min-max."
-          println("Attempt $attempt $errornote ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+          println("\nERROR YiemAgent extractWineAttributes_2() Attempt $attempt $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
          continue
        end
      end
@@ -675,168 +716,122 @@ function extractWineAttributes_2(a::T1, input::T2)::String where {T1<:agent, T2<
 end
-# function concept(a::sommelier, thoughtDict)
+function paraphrase(text2textInstructLLM::Function, text::String)
-#   systemmsg = 
+  systemmsg = 
-#   """
+  """
-#     Your name: N/A
+    Your name: N/A
-#     Situation:
+    Your vision: 
-#       - You are a helpful assistant
+      - You are a helpful assistant who help the user to paraphrase their text.
-#     Your vision: 
+    Your mission:
-#       - This is a good opportunity to help the user
+      - To help paraphrase the user's text
-#     Your mission:
+    Mission's objective includes:
-#       - To describe the concept of a conversation
+      - To help paraphrase the user's text
-#     Mission's objective includes:
+    Your responsibility includes:
-#       - To 
+      1) To help paraphrase the user's text
-#     Your responsibility includes:
+    Your responsibility does NOT includes:
-#       1) Given the situation, convey your thoughts to the user.
+      1) N/A
-#     Your responsibility excludes:
+    Your profile:
-#       1) Asking or guiding the user to make a purchase
+      - N/A
-#       2) Processing sales orders or engaging in any other sales-related activities
+    Additional information:
-#       3) Answering questions and offering additional services beyond just recommendations, such as delivery, box, gift wrapping, personalized messages. Customers can reach out to our sales at the store.
+      - N/A
 #     Your profile:
 #       - You are a young professional in a big company.
 #       - You are avid party goer
 #       - You like beer.
 #       - You know nothing about wine.
 #       - You have a budget of 1500usd.
 #     Additional information:
 #       - your boss like spicy food.
 #       - your boss is a middle-aged man.
-#     At each round of conversation, you will be given the following information:
+    At each round of conversation, you will be given the following information:
-#     Your ongoing conversation with the user: ...
+    Text: The user's given text 
 #     Context: ...
 #     Your thoughts: Your current thoughts in your mind
-#     You MUST follow the following guidelines:
+    You MUST follow the following guidelines:
-#     - Do not offer additional services you didn't thought.
+    - N/A
-#     You should follow the following guidelines:
+    You should follow the following guidelines:
-#     - Focus on the latest conversation.
+    - N/A
 #     - If the user interrupts, prioritize the user
 #     - Medium and full-bodied red wines should not be paired with spicy foods.
-#     You should then respond to the user with:
+    You should then respond to the user with:
-#     1) Chat: Given the situation, How would you respond to the user to express your thoughts honestly and keep the conversation going smoothly?
+    Paraphrase: Paraphrased text
-#     You should only respond in format as described below:
+    You should only respond in format as described below:
-#     Chat: ...
+    Paraphrase: ...
-#     Here are some examples of response format:
+    Let's begin!
-#     Chat: "I see. Let me think about it. I'll get back to you with my recommendation."
+  """
-#     Let's begin!
+  header = ["Paraphrase:"]
-#   """
+  dictkey = ["paraphrase"]
-#   # a.memory[:shortmem][:available_wine] is a dataframe.
+  errornote = ""
-#   context =
+  response = nothing # placeholder for show when error msg show up
 #     if haskey(a.memory[:shortmem], :available_wine)
 #       "Available wines $(GeneralUtils.dfToString(a.memory[:shortmem][:available_wine]))"
 #     else
 #       "None"
 #     end
 #   chathistory = vectorOfDictToText(a.chathistory)
 #   errornote = ""
 #   response = nothing # placeholder for show when error msg show up
-#   for attempt in 1:10
+  for attempt in 1:10
-#     usermsg = """
+    usermsg = """
-#               Your ongoing conversation with the user: $chathistory
+              Text: $text
-#               Contex: $context
+              P.S. $errornote
-#               Your thoughts: $(thoughtDict[:understanding]) $(thoughtDict[:reasoning]) $(thoughtDict[:plan]) 
+              """
 #               $errornote
 #               """
-#     _prompt =
+    _prompt =
-#       [
+      [
-#         Dict(:name => "system", :text => systemmsg),
+        Dict(:name => "system", :text => systemmsg),
-#         Dict(:name => "user", :text => usermsg)
+        Dict(:name => "user", :text => usermsg)
-#       ]
+      ]
-#     # put in model format
+    # put in model format
-#     prompt = GeneralUtils.formatLLMtext(_prompt; formatname="llama3instruct")
+    prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
 #     prompt *= """
 #               <|start_header_id|>assistant<|end_header_id|>
 #               """
-#     try
+    try
-#       response = a.func[:text2textInstructLLM](prompt)
+      response = text2textInstructLLM(prompt)
-#       # sometime the model response like this "here's how I would respond: ..."
+      response = GeneralUtils.deFormatLLMtext(response, "granite3")
-#       if occursin("respond:", response)
+      # sometime the model response like this "here's how I would respond: ..."
-#         errornote = "You don't need to intro your response"
+      if occursin("respond:", response)
-#         error("generatechat() response contain : ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+        errornote = "You don't need to intro your response"
-#       end
+        error("\nparaphrase() response contain : ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       response = GeneralUtils.remove_french_accents(response)
+      end
-#       response = replace(response, '*'=>"")
+      response = GeneralUtils.remove_french_accents(response)
-#       response = replace(response, '$' => "USD")
+      response = replace(response, '*'=>"")
-#       response = replace(response, '`' => "")
+      response = replace(response, '$' => "USD")
-#       response = GeneralUtils.remove_french_accents(response)
+      response = replace(response, '`' => "")
-#       responsedict = GeneralUtils.textToDict(response, ["Chat"],
+      response = GeneralUtils.remove_french_accents(response)
 #         rightmarker=":", symbolkey=true, lowercasekey=true)
-#       for i ∈ [:chat]
+      # check whether response has all answer's key points
-#         if length(JSON3.write(responsedict[i])) == 0
+      detected_kw = GeneralUtils.detect_keyword(header, response)
-#           error("$i is empty ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+      if 0 ∈ values(detected_kw)
-#         end
+        errornote = "\nYiemAgent paraphrase() response does not have all answer's key points"
-#       end
+        continue
      elseif sum(values(detected_kw)) > length(header)
        errornote = "\nnYiemAgent paraphrase() response has duplicated answer's key points"
        continue
      end
-#       # check if there are more than 1 key per categories
+      responsedict = GeneralUtils.textToDict(response, header; 
-#       for i ∈ [:chat]
+                                            dictKey=dictkey, symbolkey=true)
 #         matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
 #         if length(matchkeys) > 1
 #           error("generatechat has more than one key per categories")
 #         end
 #       end
-#       # check if Context: is in chat
+      for i ∈ [:paraphrase]
-#       if occursin("Context:", responsedict[:chat])
+        if length(JSON3.write(responsedict[i])) == 0
-#         error("Context: is in text. This is not allowed")
+          error("$i is empty ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       end
+        end
      end
-#       println("\n~~~ generatechat() ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+      # check if there are more than 1 key per categories
-#       pprintln(Dict(responsedict))
+      for i ∈ [:paraphrase]
        matchkeys = GeneralUtils.findMatchingDictKey(responsedict, i)
        if length(matchkeys) > 1
          error("paraphrase() has more than one key per categories")
        end
      end
-#       # check whether an agent recommend wines before checking inventory or recommend wines 
+      println("\nparaphrase() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       # outside its inventory
+      pprintln(Dict(responsedict))
 #       # ask LLM whether there are any winery mentioned in the response
 #       mentioned_winery = detectWineryName(a, responsedict[:chat])
 #       if mentioned_winery != "None"
 #         mentioned_winery = String.(strip.(split(mentioned_winery, ",")))
-#         # check whether the wine is in event
+      result = responsedict[:paraphrase]
 #         isWineInEvent = false
 #         for winename in mentioned_winery
 #           for event in a.memory[:events]
 #             if event[:outcome] !== nothing && occursin(winename, event[:outcome])
 #               isWineInEvent = true
 #               break
 #             end
 #           end
 #         end
-#         # if wine is mentioned but not in timeline or shortmem,
+      return result
-#         # then the agent is not supposed to recommend the wine
+    catch e
-#         if isWineInEvent == false
+      io = IOBuffer()
-
+      showerror(io, e)
-#           errornote = "Previously: You recommend a wine that is not in your inventory which is not allowed."
+      errorMsg = String(take!(io))
-#           error("Previously: You recommend a wine that is not in your inventory which is not allowed.")
+      st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#         end
+      println("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       end
+    end
-
+  end
-#       result = responsedict[:chat]
+  error("paraphrase() failed to generate a response")
-
+end
 #       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("\nAttempt $attempt. Error occurred: $errorMsg\n$st ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
 #     end
 #   end
 #   error("generatechat failed to generate a response")
 # end
@@ -998,7 +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|>
@@ -1030,7 +1025,7 @@ end
 #         state[:isterminal] = true
 #         state[:reward] = 1
 #       end
-#       println("--> 5 Evaluator ", Dates.now(), " ", @__FILE__, " ", @__LINE__)
+#       println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
 #       pprintln(Dict(responsedict))
 #       return responsedict[:score]
 #     catch e
--- a/src/type.jl
+++ b/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::Union{String, Nothing}
+  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
@@ -22,45 +59,31 @@ 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
  ;
  id::String= string(uuid4()),
  systemmsg::Union{String, Nothing}= nothing,
  maxHistoryMsg::Integer= 20,
  chathistory::Vector{Dict{Symbol, String}} = Vector{Dict{Symbol, String}}(),
  )
  memory = Dict{Symbol, Any}(
-              :chatbox=> "",
+            :events=> Vector{Dict{Symbol, Any}}(),
-              :shortmem=> OrderedDict{Symbol, Any}(),
+            :state=> Dict{Symbol, Any}(), # state of the agent
-              :events=> Vector{Dict{Symbol, Any}}(),
+            :recap=> OrderedDict{Symbol, Any}(),  # recap summary of the conversation
-              :state=> Dict{Symbol, Any}(),
+          )
            )
  newAgent = companion(
-                        id,
+    name,
-                        systemmsg,
+    id,
-                        maxHistoryMsg,
+    systemmsg,
-                        chathistory,
+    tools,
-                        memory,
+    maxHistoryMsg,
-                        text2textInstructLLM
+    chathistory,
-                      )
+    memory,
    func,
    llmFormatName
    )
  return newAgent
 end
 """ A sommelier agent.
 # Arguments
@@ -134,19 +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(
@@ -157,6 +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
@@ -170,16 +185,17 @@ 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}(
              :available_wine=> [],
              :found_wine=> [], # used by decisionMaker(). This is to prevent decisionMaker() keep presenting the same wines
@@ -198,7 +214,8 @@ function sommelier(
    maxHistoryMsg,
    chathistory,
    memory,
-    func
+    func,
    llmFormatName
    )
  return newAgent
--- a/src/util.jl
+++ b/src/util.jl
@@ -107,7 +107,7 @@ function addNewMessage(a::T1, name::String, text::T2;
    error("name is not in agent.availableRole $(@__LINE__)")
  end
-  #[WORKING] summarize the oldest 10 message
+  #[PENDING] summarize the oldest 10 message
  if length(a.chathistory) > maximumMsg
    summarize(a.chathistory)
  else
@@ -122,47 +122,53 @@ 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 chatHistoryToText(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
+    # 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
+        # Extract the 'name' and 'text' keys from the dictionary
-          name = d[:name]
+        name = titlecase(d[:name])
-          _text = d[:text]
+        _text = d[:text]
-          
+        
-          # Append the formatted string to the text variable
+        # Append the formatted string to the text variable
-          text *= "$name> $_text \n"
+        text *= "$name> $_text \n"
      end
  else
-      # Loop through each dictionary in the input vector
+    # Loop through each dictionary in the input vector
-      for d in vecd
+    for d in elements
-          # Iterate over all key-value pairs in the dictionary
+        # Iterate over all key-value pairs in the dictionary
-          for (k, v) in d
+        for (k, v) in d
-              # Append the formatted string to the text variable
+            # Append the formatted string to the text variable
-              text *= "$v \n"
+            text *= "$v \n"
-          end
+        end
-      end
+    end
  end
  # Return the final text
@@ -191,6 +197,35 @@ 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,
@@ -204,254 +239,108 @@ function eventdict(;
  outcome::Union{String, Nothing}=nothing,
  note::Union{String, Nothing}=nothing,
  )
-  return Dict{Symbol, Any}(
+
-      :event_description=> event_description,
+  d = Dict{Symbol, Any}(
-      :timestamp=> timestamp,
+    :event_description=> event_description,
-      :subject=> subject,
+    :timestamp=> timestamp,
-      :thought=> thought,
+    :subject=> subject,
-      :actionname=> actionname,
+    :thought=> thought,
-      :actioninput=> actioninput,
+    :actionname=> actionname,
-      :location=> location,
+    :actioninput=> actioninput,
-      :equipment_used=> equipment_used,
+    :location=> location,
-      :material_used=> material_used,
+    :equipment_used=> equipment_used,
-      :outcome=> outcome,
+    :material_used=> material_used,
-      :note=> note,
+    :outcome=> outcome,
-    )
+    :note=> note,
  )
  return d
 end
-function createTimeline(memory::T1; skiprecent::Integer=0) where {T1<:AbstractVector}
+""" Create a formatted timeline string from a sequence of events.
  events = memory[1:end-skiprecent]
 # 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
 # Returns
  - `timeline::String`
    A formatted string representing the events with their subjects, actions, and optional outcomes
    Format: "{index}) {subject}> {actioninput} {outcome}\n" for each event
 # Example
 events = [
  Dict(:subject => "User", :actioninput => "Hello", :outcome => nothing),
  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 = ""
-  for (i, event) in enumerate(events)
+  
-    if event[:outcome] === nothing
+  # Determine which indices to use - either provided range or full length
-      timeline *= "$i) $(event[:subject])> $(event[:actioninput])\n"
+  ind = 
    if eventindex !== nothing
      [eventindex...]
    else
-      timeline *= "$i) $(event[:subject])> $(event[:actioninput]) $(event[:outcome])\n"
+      1:length(events)
    end
  # Iterate through events and format each one
  for (i, event) in zip(ind, events)
    # If no outcome exists, format without outcome
    if event[:outcome] === nothing
      timeline *= "Event_$i $(event[:subject])> $(event[:actioninput])\n"
    # If outcome exists, include it in formatting  
    else
      timeline *= "Event_$i $(event[:subject])> $(event[:actioninput]) $(event[:outcome])\n"
    end
  end
  # Return formatted timeline string
  return timeline
 end
 # function createTimeline(events::T1; eventindex::Union{UnitRange, Nothing}=nothing
 #   ) where {T1<:AbstractVector}
 #   # Initialize empty timeline string
 #   timeline = ""
 #   # Determine which indices to use - either provided range or full length
 #   ind = 
 #     if eventindex !== nothing
 #       [eventindex...]
 #     else
 #       1:length(events)
 #     end
-
+#   # Iterate through events and format each one
-# """ Convert a single chat dictionary into LLM model instruct format.
+#   for (i, event) in zip(ind, events)
-
+#     # If no outcome exists, format without outcome
-# # Llama 3 instruct format example
+#     subject = titlecase(event[:subject])
-#   <|system|>
+#     if event[:outcome] === nothing
-#   You are a helpful AI assistant.<|end|>
+      
-#   <|user|>
+#       timeline *= "Event_$i) Who: $subject Action_name: $(event[:actionname]) Action_input: $(event[:actioninput])\n"
-#   I am going to Paris, what should I see?<|end|>
+#     # If outcome exists, include it in formatting  
-#   <|assistant|>
+#     else
-#   Paris, the capital of France, is known for its stunning architecture, art museums."<|end|>
+#       timeline *= "Event_$i) Who: $subject Action_name: $(event[:actionname]) Action_input: $(event[:actioninput]) Action output: $(event[:outcome])\n"
 #   <|user|>
 #   What is so great about #1?<|end|>
 #   <|assistant|>
 # # Arguments
 #   - `name::T`
 #     message owner name e.f. "system", "user" or "assistant"
 #   - `text::T`
 # # Return
 #   - `formattedtext::String`
 #     text formatted to model format
 # # Example
 # ```jldoctest
 # julia> using Revise
 # julia> using YiemAgent
 # julia> d = Dict(:name=> "system",:text=> "You are a helpful, respectful and honest assistant.",)
 # julia> formattedtext = YiemAgent.formatLLMtext_phi3instruct(d[:name], d[:text])
 # ```
 # Signature
 # """
 # function formatLLMtext_phi3instruct(name::T, text::T) where {T<:AbstractString}
 #   formattedtext = 
 #   """
 #   <|$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)
+#   # Return formatted timeline string
 #   return timeline
 # end
@@ -482,11 +371,6 @@ end
--- a/test/Manifest.toml
+++ b/test/Manifest.toml
@@ -0,0 +1,41 @@
 # This file is machine-generated - editing it directly is not advised
 julia_version = "1.11.4"
 manifest_format = "2.0"
 project_hash = "71d91126b5a1fb1020e1098d9d492de2a4438fd2"
 [[deps.Base64]]
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 [[deps.InteractiveUtils]]
 deps = ["Markdown"]
 uuid = "b77e0a4c-d291-57a0-90e8-8db25a27a240"
 version = "1.11.0"
 [[deps.Logging]]
 uuid = "56ddb016-857b-54e1-b83d-db4d58db5568"
 version = "1.11.0"
 [[deps.Markdown]]
 deps = ["Base64"]
 uuid = "d6f4376e-aef5-505a-96c1-9c027394607a"
 version = "1.11.0"
 [[deps.Random]]
 deps = ["SHA"]
 uuid = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
 version = "1.11.0"
 [[deps.SHA]]
 uuid = "ea8e919c-243c-51af-8825-aaa63cd721ce"
 version = "0.7.0"
 [[deps.Serialization]]
 uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 version = "1.11.0"
 [[deps.Test]]
 deps = ["InteractiveUtils", "Logging", "Random", "Serialization"]
 uuid = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
 version = "1.11.0"
--- a/test/Project.toml
+++ b/test/Project.toml
@@ -0,0 +1,2 @@
 [deps]
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
--- a/test/config.json
+++ b/test/config.json
@@ -27,30 +27,50 @@
      "description": "agent role"
  },
  "organization": {
-      "value": "yiem_hq",
+      "value": "yiem_branch_1",
      "description": "organization name"
  },
  "externalservice": {
-      "text2textinstruct": {
+    "loadbalancer": {
-          "mqtttopic": "/loadbalancer/requestingservice",
+      "mqtttopic": "/loadbalancer/requestingservice",
-          "description": "text to text service with instruct LLM",
+      "description": "text to text service with instruct LLM"
-          "llminfo": {
+    },
-              "name": "llama3instruct"
+    "text2textinstruct": {
-          }
+        "mqtttopic": "/loadbalancer/requestingservice",
-      },
+        "description": "text to text service with instruct LLM",
-      "virtualWineCustomer_1": {
+        "llminfo": {
-          "mqtttopic": "/virtualenvironment/winecustomer",
+            "name": "llama3instruct"
-          "description": "text to text service with instruct LLM that act as wine customer",
+        }
-          "llminfo": {
+    },
-              "name": "llama3instruct"
+    "virtualWineCustomer_1": {
-          }
+        "mqtttopic": "/virtualenvironment/winecustomer",
-      },
+        "description": "text to text service with instruct LLM that act as wine customer",
-      "text2textchat": {
+        "llminfo": {
-          "mqtttopic": "/loadbalancer/requestingservice",
+            "name": "llama3instruct"
-          "description": "text to text service with instruct LLM",
+        }
-          "llminfo": {
+    },
-              "name": "llama3instruct"
+    "text2textchat": {
-          }
+        "mqtttopic": "/loadbalancer/requestingservice",
-      }
+        "description": "text to text service with instruct LLM",
        "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"
    }
  }
 }
--- a/test/etc.jl
+++ b/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)
--- a/test/runtests.jl
+++ b/test/runtests.jl
--- a/test/runtest.jl
+++ b/test/runtest.jl
@@ -1,272 +1,294 @@
-using Revise
+using Revise
-using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
+using JSON, JSON3, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames
-using YiemAgent, GeneralUtils
+using YiemAgent, GeneralUtils
-using Base.Threads
+using Base.Threads
-
+
-# ---------------------------------------------- 100 --------------------------------------------- #
+# ---------------------------------------------- 100 --------------------------------------------- #
-
+
-
+
-
+
-# load config
+# 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"))
+# config = copy(JSON3.read("../mountvolume/config.json"))
-
+
-
+
-function executeSQL(sql::T) where {T<:AbstractString}
+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]
-  result = LibPQ.execute(DBconnection, sql)
+  port = config[:externalservice][:wineDB][:port]
-  close(DBconnection)
+  dbname = config[:externalservice][:wineDB][:dbname]
-  return result
+  user = config[:externalservice][:wineDB][:user]
-end
+  password = config[:externalservice][:wineDB][:password]
-
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-function executeSQLVectorDB(sql)
+  result = LibPQ.execute(DBconnection, sql)
-  DBconnection = LibPQ.Connection("host=192.168.88.12 port=10203 dbname=SQLVectorDB user=yiemtechnologies password=yiemtechnologies@Postgres_0.0")
+  close(DBconnection)
-  result = LibPQ.execute(DBconnection, sql)
+  return result
-  close(DBconnection)
+end
-  return result
+
-end
+function executeSQLVectorDB(sql)
-
+  host = config[:externalservice][:SQLVectorDB][:host]
-function text2textInstructLLM(prompt::String)
+  port = config[:externalservice][:SQLVectorDB][:port]
-  msgMeta = GeneralUtils.generate_msgMeta(
+  dbname = config[:externalservice][:SQLVectorDB][:dbname]
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+  user = config[:externalservice][:SQLVectorDB][:user]
-    msgPurpose="inference",
+  password = config[:externalservice][:SQLVectorDB][:password]
-    senderName="yiemagent",
+  DBconnection = LibPQ.Connection("host=$host port=$port dbname=$dbname user=$user password=$password")
-    senderId=string(uuid4()),
+  result = LibPQ.execute(DBconnection, sql)
-    receiverName="text2textinstruct",
+  close(DBconnection)
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+  return result
-    mqttBrokerPort=config[:mqttServerInfo][:port],
+end
-  )
+
-
+function text2textInstructLLM(prompt::String; maxattempt::Integer=3, modelsize::String="medium",
-  outgoingMsg = Dict(
+    llmkwargs=Dict(
-    :msgMeta => msgMeta,
+      :num_ctx => 32768,
-    :payload => Dict(
+      :temperature => 0.1,
-      :text => prompt,
+    )
-      :kwargs => Dict(
+  )
-        :num_ctx => 16384,
+  msgMeta = GeneralUtils.generate_msgMeta(
-        :temperature => 0.2,
+    config[:externalservice][:loadbalancer][:mqtttopic];
-      )
+    msgPurpose="inference",
-    )
+    senderName="yiemagent",
-  )
+    senderId=sessionId,
-
+    receiverName="text2textinstruct_$modelsize",
-  _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-  response = _response[:response][:text]
+    mqttBrokerPort=config[:mqttServerInfo][:port],
-
+  )
-  return response
+
-end
+  outgoingMsg = Dict(
-
+    :msgMeta => msgMeta,
-# get text embedding from a LLM service
+    :payload => Dict(
-function getEmbedding(text::T) where {T<:AbstractString}
+      :text => prompt,
-  msgMeta = GeneralUtils.generate_msgMeta(
+      :kwargs => llmkwargs
-    config[:externalservice][:text2textinstruct][:mqtttopic];
+    )
-    msgPurpose="embedding",
+  )
-    senderName="yiemagent",
+
-    senderId=string(uuid4()),
+  response = nothing
-    receiverName="text2textinstruct",
+  for attempts in 1:maxattempt
-    mqttBrokerAddress=config[:mqttServerInfo][:broker],
+    _response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=180, maxattempt=maxattempt)
-    mqttBrokerPort=config[:mqttServerInfo][:port],
+    payload = _response[:response]
-  )
+    if _response[:success] && payload[:text] !== nothing
-
+      response = _response[:response][:text]
-  outgoingMsg = Dict(
+      break
-    :msgMeta => msgMeta,
+    else
-    :payload => Dict(
+      println("\n<text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-      :text => [text] # must be a vector of string
+      pprintln(outgoingMsg)
-    )
+      println("</text2textInstructLLM()> attempt $attempts/$maxattempt failed ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
-  )
+      sleep(3)
-  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=6000)
+    end
-  embedding = response[:response][:embeddings]
+  end
-  return embedding
+
-end
+  return response
-
+end
-function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
+
-   vectorDB::Function; limit::Integer=1
+# get text embedding from a LLM service
-   )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+function getEmbedding(text::T) where {T<:AbstractString}
-
+  msgMeta = GeneralUtils.generate_msgMeta(
-  # get embedding from LLM service
+    config[:externalservice][:loadbalancer][:mqtttopic];
-  embedding = getEmbedding(text)[1] 
+    msgPurpose="embedding",
-
+    senderName="yiemagent",
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
+    senderId=sessionId,
-  sql = """
+    receiverName="textembedding",
-        SELECT *, $embeddingColumnName <-> '$embedding' as distance
+    mqttBrokerAddress=config[:mqttServerInfo][:broker],
-        FROM $tablename
+    mqttBrokerPort=config[:mqttServerInfo][:port],
-        ORDER BY distance LIMIT $limit;
+  )
-        """
+
-  response = vectorDB(sql)
+  outgoingMsg = Dict(
-  df = DataFrame(response)
+    :msgMeta => msgMeta,
-  return df
+    :payload => Dict(
-end
+      :text => [text] # must be a vector of string
-
+    )
-
+  )
-function similarSQLVectorDB(query; maxdistance::Integer=100)
+
-  tablename = "sqlllm_decision_repository"
+  response = GeneralUtils.sendReceiveMqttMsg(outgoingMsg; timeout=120, maxattempt=3)
-  # get embedding of the query
+  embedding = response[:response][:embeddings]
-  df = findSimilarTextFromVectorDB(query, tablename, 
+  return embedding
-                                  "function_input_embedding", executeSQLVectorDB)
+end
-  row, col = size(df)
+
-  distance = row == 0 ? Inf : df[1, :distance]  
+function findSimilarTextFromVectorDB(text::T1, tablename::T2, embeddingColumnName::T3,
-  if row != 0 && distance < maxdistance
+  vectorDB::Function; limit::Integer=1
-    # if there is usable SQL, return it.
+  )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
+  # get embedding from LLM service
-    output_str = String(base64decode(output_b64))
+  embedding = getEmbedding(text)[1]
-    rowid = df[1, :id]
+  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+  sql = """
-    return (dict=output_str, distance=distance)
+        SELECT *, $embeddingColumnName <-> '$embedding' as distance
-  else
+        FROM $tablename
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+        ORDER BY distance LIMIT $limit;
-    return (dict=nothing, distance=nothing)
+        """
-  end
+  response = vectorDB(sql)
-end
+  df = DataFrame(response)
-
+  return df
-
+end
-function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=1) where {T1<:AbstractString, T2<:AbstractString}
+
-  tablename = "sqlllm_decision_repository"
+function similarSQLVectorDB(query; maxdistance::Integer=100)
-  # get embedding of the query
+  tablename = "sqlllm_decision_repository"
-  # query = state[:thoughtHistory][:question]
+  # get embedding of the query
-  df = findSimilarTextFromVectorDB(query, tablename, 
+  df = findSimilarTextFromVectorDB(query, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
+                                  "function_input_embedding", executeSQLVectorDB)
-  row, col = size(df)
+  # println(df[1, [:id, :function_output]])
-  distance = row == 0 ? Inf : df[1, :distance]
+  row, col = size(df)
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+  distance = row == 0 ? Inf : df[1, :distance]  
-    query_embedding = getEmbedding(query)[1]
+  # distance = 100  # CHANGE this is for testing only
-    query = replace(query, "'" => "")
+  if row != 0 && distance < maxdistance
-    sql_base64 = base64encode(SQL)
+    # if there is usable SQL, return it.
-    sql_ = replace(SQL, "'" => "")
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
-
+    output_str = String(base64decode(output_b64))
-    sql = """
+    rowid = df[1, :id]
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-          """
+    return (dict=output_str, distance=distance)
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+  else
-    println(sql)
+    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    _ = executeSQLVectorDB(sql)
+    return (dict=nothing, distance=nothing)
-  end
+  end
-end
+end
-
+
-
+function insertSQLVectorDB(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
-function similarSommelierDecision(recentevents::T1; maxdistance::Integer=5
+  tablename = "sqlllm_decision_repository"
-  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+  # get embedding of the query
-  tablename = "sommelier_decision_repository"
+  # query = state[:thoughtHistory][:question]
-  # find similar
+  df = findSimilarTextFromVectorDB(query, tablename, 
-  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
+                                  "function_input_embedding", executeSQLVectorDB)
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+  row, col = size(df)
-                                  "function_input_embedding", executeSQLVectorDB)
+  distance = row == 0 ? Inf : df[1, :distance]
-  row, col = size(df)
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-  distance = row == 0 ? Inf : df[1, :distance]
+    query_embedding = getEmbedding(query)[1]
-  if row != 0 && distance < maxdistance
+    query = replace(query, "'" => "")
-    # if there is usable decision, return it.
+    sql_base64 = base64encode(SQL)
-    rowid = df[1, :id]
+    sql_ = replace(SQL, "'" => "")
-    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
+    sql = """
-    _output_str = String(base64decode(output_b64))
+          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-    output = copy(JSON3.read(_output_str))
+          """
-    return output
+    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  else
+    # println(sql)
-    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+    _ = executeSQLVectorDB(sql)
-    return nothing
+  end
-  end
+end
-end
+
-
+
-
+function similarSommelierDecision(recentevents::T1; maxdistance::Integer=3
-function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
+  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-  ) where {T1<:AbstractString, T2<:AbstractDict}
+  tablename = "sommelier_decision_repository"
-  tablename = "sommelier_decision_repository"
+  # find similar
-  # find similar
+  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
-  df = findSimilarTextFromVectorDB(recentevents, tablename, 
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-                                  "function_input_embedding", executeSQLVectorDB)
+                                  "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 > maxdistance # no close enough SQL stored in the database
+  if row != 0 && distance < maxdistance
-    recentevents_embedding = a.func[:getEmbedding](recentevents)[1]
+    # if there is usable decision, return it.
-    recentevents = replace(recentevents, "'" => "")
+    rowid = df[1, :id]
-    decision_json = JSON3.write(decision)
+    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    decision_base64 = base64encode(decision_json)
+    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    decision = replace(decision_json, "'" => "")
+    _output_str = String(base64decode(output_b64))
-    
+    output = copy(JSON3.read(_output_str))
-    sql = """
+    return output
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+  else
-          """
+    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+    return nothing
-    println(sql)
+  end
-    _ = executeSQLVectorDB(sql)
+end
-  else
+
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+
-  end
+function insertSommelierDecision(recentevents::T1, decision::T2; maxdistance::Integer=5
-end
+  ) where {T1<:AbstractString, T2<:AbstractDict}
-
+  tablename = "sommelier_decision_repository"
-
+  # find similar
-sessionId = "12345"
+  df = findSimilarTextFromVectorDB(recentevents, tablename, 
-
+                                  "function_input_embedding", executeSQLVectorDB)
-externalFunction = (
+  row, col = size(df)
-    getEmbedding=getEmbedding,
+  distance = row == 0 ? Inf : df[1, :distance]
-    text2textInstructLLM=text2textInstructLLM,
+  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    executeSQL=executeSQL,
+    recentevents_embedding = getEmbedding(recentevents)[1]
-    similarSQLVectorDB=similarSQLVectorDB,
+    recentevents = replace(recentevents, "'" => "")
-    insertSQLVectorDB=insertSQLVectorDB,
+    decision_json = JSON3.write(decision)
-    similarSommelierDecision=similarSommelierDecision,
+    decision_base64 = base64encode(decision_json)
-    insertSommelierDecision=insertSommelierDecision,
+    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');
-
+          """
-a = YiemAgent.sommelier(
+    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-  externalFunction;
+    println(sql)
-  name="Ton",
+    _ = executeSQLVectorDB(sql)
-  id=sessionId,  # agent instance id
+  else
-  retailername="Yiem",
+    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
-)
+  end
-
+end
-while true
+
-  println("your respond: ")
+
-  user_answer = readline()
+sessionId = "12345"
-  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
+
-  println("\n$response")
+externalFunction = (
-end
+    getEmbedding=getEmbedding,
-
+    text2textInstructLLM=text2textInstructLLM,
-
+    executeSQL=executeSQL,
-# response = YiemAgent.conversation(a, Dict(:text=> "I want to get a French red wine under 100."))
+    similarSQLVectorDB=similarSQLVectorDB,
-
+    insertSQLVectorDB=insertSQLVectorDB,
-
+    similarSommelierDecision=similarSommelierDecision,
-
+    insertSommelierDecision=insertSommelierDecision,
-
+  )
-
+
-
+
-
+
-
+a = YiemAgent.sommelier(
-
+  externalFunction;
-
+  name="Ton",
-
+  id=sessionId,  # agent instance id
-
+  retailername="Yiem",
-
+)
-
+
-
+while true
-
+  print("\nyour respond: ")
-
+  user_answer = readline()
-
+  response = YiemAgent.conversation(a, Dict(:text=> user_answer))
-
+  println("\n$response")
-
+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.
-
+
-
+
-
+"""
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
-
+
Author	SHA1	Message	Date
narawat lamaiin	a01a91e7b9	update	2025-05-01 12:05:59 +07:00
narawat lamaiin	aa8436c0ed	update	2025-05-01 08:04:01 +07:00
narawat lamaiin	cccad676db	update	2025-05-01 07:59:37 +07:00
narawat lamaiin	03de659c9b	update companion	2025-04-30 12:58:32 +07:00
narawat lamaiin	affb96f0cf	update	2025-04-29 18:45:52 +07:00
narawat lamaiin	f19f302bd9	update	2025-04-29 11:01:36 +07:00
narawat lamaiin	7ca4f5276d	update	2025-04-26 06:20:09 +07:00
narawat lamaiin	44804041a3	update	2025-04-25 21:12:27 +07:00
narawat lamaiin	48a3704f6d	update	2025-04-13 21:46:54 +07:00
tonaerospace	8321a13afc	update	2025-04-04 15:23:34 +07:00
tonaerospace	b26ae31d4c	mark new version	2025-04-04 15:23:11 +07:00
ton	b397bf7bdb	Merge pull request 'v0.1.4' (#3 ) from v0.1.4 into main Reviewed-on: #3	2025-04-04 08:14:57 +00:00
narawat lamaiin	c0edf7dadf	update	2025-04-04 15:04:02 +07:00
narawat lamaiin	c21f943b12	update	2025-04-01 21:17:15 +07:00
narawat lamaiin	b8fd772a28	update	2025-03-31 21:30:14 +07:00
narawat lamaiin	883f581b2a	update	2025-03-22 15:34:00 +07:00
narawat lamaiin	5a890860a6	update	2025-03-22 09:42:51 +07:00
tonaerospace	7d5bc14a09	mark new version	2025-03-21 10:13:53 +07:00
ton	37ba3a9d31	Merge pull request 'v0.1.3-dev' (#2 ) from v0.1.3-dev into main Reviewed-on: #2	2025-03-21 03:09:16 +00:00
tonaerospace	bfadd53033	update	2025-03-21 10:03:08 +07:00
tonaerospace	8fc3afe348	update	2025-03-20 16:15:38 +07:00
tonaerospace	c60037226a	update	2025-03-13 19:11:20 +07:00
narawat lamaiin	db6c9c5f2b	update	2025-03-07 13:34:15 +07:00
narawat lamaiin	6504099959	update	2025-01-31 09:50:44 +07:00
tonaerospace	724b092bdb	update	2025-01-30 21:28:49 +07:00
tonaerospace	c56c3d02b0	update	2025-01-29 12:16:01 +07:00
ton	a7f3e29e9c	Merge pull request 'WIP v0.1.2-dev' (#1 ) from v0.1.2-dev into main Reviewed-on: #1	2025-01-25 07:30:18 +00:00
		`@@ -0,0 +1,2 @@`
							`[deps]`
							`Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"`