Merge pull request 'v0.5.0-tool_role' (#9) from v0.5.0-tool_role into v0.5.0

Reviewed-on: #9

Manifest.toml

@@ -2,7 +2,7 @@
 
 julia_version = "1.12.6"
 manifest_format = "2.0"
-project_hash = "5b5e1c071ff66b72aeed7d8a4316829e2407ae1a"
+project_hash = "a2c996ffe370e277cbff80af974d2701698f1b6c"
 
 [[deps.Accessors]]
 deps = ["CompositionsBase", "ConstructionBase", "Dates", "InverseFunctions", "MacroTools"]
@@ -38,12 +38,6 @@ version = "1.1.3"
 uuid = "0dad84c5-d112-42e6-8d28-ef12dabb789f"
 version = "1.1.2"
 
-[[deps.Arrow]]
-deps = ["ArrowTypes", "BitIntegers", "CodecLz4", "CodecZstd", "ConcurrentUtilities", "DataAPI", "Dates", "EnumX", "Mmap", "PooledArrays", "SentinelArrays", "StringViews", "Tables", "TimeZones", "TranscodingStreams", "UUIDs"]
-git-tree-sha1 = "4a69a3eadc1f7da78d950d1ef270c3a62c1f7e01"
-uuid = "69666777-d1a9-59fb-9406-91d4454c9d45"
-version = "2.8.1"
-
 [[deps.ArrowTypes]]
 deps = ["Sockets", "UUIDs"]
 git-tree-sha1 = "404265cd8128a2515a81d5eae16de90fdef05101"
@@ -58,12 +52,6 @@ version = "1.11.0"
 uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
 version = "1.11.0"
 
-[[deps.BitIntegers]]
-deps = ["Random"]
-git-tree-sha1 = "091d591a060e43df1dd35faab3ca284925c48e46"
-uuid = "c3b6d118-76ef-56ca-8cc7-ebb389d030a1"
-version = "0.3.7"
-
 [[deps.BufferedStreams]]
 git-tree-sha1 = "6863c5b7fc997eadcabdbaf6c5f201dc30032643"
 uuid = "e1450e63-4bb3-523b-b2a4-4ffa8c0fd77d"
@@ -96,24 +84,12 @@ git-tree-sha1 = "40956acdbef3d8c7cc38cba42b56034af8f8581a"
 uuid = "6c391c72-fb7b-5838-ba82-7cfb1bcfecbf"
 version = "0.3.4"
 
-[[deps.CodecLz4]]
-deps = ["Lz4_jll", "TranscodingStreams"]
-git-tree-sha1 = "d58afcd2833601636b48ee8cbeb2edcb086522c2"
-uuid = "5ba52731-8f18-5e0d-9241-30f10d1ec561"
-version = "0.4.6"
-
 [[deps.CodecZlib]]
 deps = ["TranscodingStreams", "Zlib_jll"]
 git-tree-sha1 = "962834c22b66e32aa10f7611c08c8ca4e20749a9"
 uuid = "944b1d66-785c-5afd-91f1-9de20f533193"
 version = "0.7.8"
 
-[[deps.CodecZstd]]
-deps = ["TranscodingStreams", "Zstd_jll"]
-git-tree-sha1 = "da54a6cd93c54950c15adf1d336cfd7d71f51a56"
-uuid = "6b39b394-51ab-5f42-8807-6242bab2b4c2"
-version = "0.8.7"
-
 [[deps.CommonSolve]]
 git-tree-sha1 = "99ee296f88c12485402e37c2fd025f95ae097637"
 uuid = "38540f10-b2f7-11e9-35d8-d573e4eb0ff2"
@@ -148,12 +124,6 @@ weakdeps = ["InverseFunctions"]
     [deps.CompositionsBase.extensions]
     CompositionsBaseInverseFunctionsExt = "InverseFunctions"
 
-[[deps.ConcurrentUtilities]]
-deps = ["Serialization", "Sockets"]
-git-tree-sha1 = "21d088c496ea22914fe80906eb5bce65755e5ec8"
-uuid = "f0e56b4a-5159-44fe-b623-3e5288b988bb"
-version = "2.5.1"
-
 [[deps.ConstructionBase]]
 git-tree-sha1 = "b4b092499347b18a015186eae3042f72267106cb"
 uuid = "187b0558-2788-49d3-abe0-74a17ed4e7c9"
@@ -436,7 +406,9 @@ version = "1.21.3+0"
 
 [[deps.LLMMCTS]]
 deps = ["GeneralUtils", "JSON", "PrettyPrinting"]
-path = "../LLMMCTS"
+git-tree-sha1 = "3dff98131dfa79be8c9bd84fc51cb0ba1832c472"
+repo-rev = "main"
+repo-url = "https://git.yiem.cc/ton/LLMMCTS"
 uuid = "d76c5a4d-449e-4835-8cc4-dd86ec44f241"
 version = "0.1.5"
 
@@ -522,12 +494,6 @@ git-tree-sha1 = "3733419e9a71156b389f3e331672d2e95436783f"
 uuid = "6f1432cf-f94c-5a45-995e-cdbf5db27b0b"
 version = "3.6.2"
 
-[[deps.Lz4_jll]]
-deps = ["Artifacts", "JLLWrappers", "Libdl"]
-git-tree-sha1 = "191686b1ac1ea9c89fc52e996ad15d1d241d1e33"
-uuid = "5ced341a-0733-55b8-9ab6-a4889d929147"
-version = "1.10.1+0"
-
 [[deps.MacroTools]]
 git-tree-sha1 = "1e0228a030642014fe5cfe68c2c0a818f9e3f522"
 uuid = "1914dd2f-81c6-5fcd-8719-6d5c9610ff09"
@@ -794,11 +760,11 @@ version = "0.7.0"
 
 [[deps.SQLLLM]]
 deps = ["CSV", "DataFrames", "DataStructures", "Dates", "FileIO", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "PrettyPrinting", "Random", "Revise", "StatsBase", "Tables", "URIs", "UUIDs"]
-git-tree-sha1 = "997602ed56a285ac29d74c91bb57bc5faeadfad6"
+git-tree-sha1 = "2807a768907f59308d8d71ece599037b0a66b0a5"
 repo-rev = "main"
 repo-url = "https://git.yiem.cc/ton/SQLLLM"
 uuid = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
-version = "0.2.5"
+version = "0.2.7"
 
 [[deps.SQLStrings]]
 git-tree-sha1 = "55de0530689832b1d3d43491ee6b67bd54d3323c"
@@ -902,11 +868,6 @@ git-tree-sha1 = "d05693d339e37d6ab134c5ab53c29fce5ee5d7d5"
 uuid = "892a3eda-7b42-436c-8928-eab12a02cf0e"
 version = "0.4.4"
 
-[[deps.StringViews]]
-git-tree-sha1 = "f2dcb92855b31ad92fe8f079d4f75ac57c93e4b8"
-uuid = "354b36f9-a18e-4713-926e-db85100087ba"
-version = "1.3.7"
-
 [[deps.StructTypes]]
 deps = ["Dates", "UUIDs"]
 git-tree-sha1 = "159331b30e94d7b11379037feeb9b690950cace8"
@@ -1022,10 +983,10 @@ uuid = "76eceee3-57b5-4d4a-8e66-0e911cebbf60"
 version = "1.6.1"
 
 [[deps.YiemAgent]]
-deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "SQLLLM", "Serialization", "URIs", "UUIDs", "msghandler"]
+deps = ["CSV", "DataFrames", "DataStructures", "Dates", "GeneralUtils", "HTTP", "JSON", "LLMMCTS", "LibPQ", "NATS", "PrettyPrinting", "Random", "Revise", "Serialization", "URIs", "UUIDs"]
 path = "."
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.5.0"
 
 [[deps.Zlib_jll]]
 deps = ["Libdl"]
@@ -1049,14 +1010,6 @@ git-tree-sha1 = "011b0a7331b41c25524b64dc42afc9683ee89026"
 uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
 version = "1.0.21+0"
 
-[[deps.msghandler]]
-deps = ["Arrow", "Base64", "DataFrames", "Dates", "GeneralUtils", "HTTP", "JSON", "NATS", "PrettyPrinting", "Revise", "UUIDs"]
-git-tree-sha1 = "db16f76f72bd4fa2a87e34ef47bb787204e1f888"
-repo-rev = "main"
-repo-url = "https://git.yiem.cc/ton/msghandler"
-uuid = "f2724d33-f338-4a57-b9f8-1be882570d10"
-version = "0.5.7"
-
 [[deps.nghttp2_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"

Project.toml

@@ -1,6 +1,6 @@
 name = "YiemAgent"
 uuid = "e012c34b-7f78-48e0-971c-7abb83b6f0a2"
-version = "0.4.0"
+version = "0.5.0"
 authors = ["narawat lamaiin <narawat@outlook.com>"]
 
 [deps]
@@ -21,7 +21,6 @@ SQLLLM = "2ebc79c7-cc10-4a3a-9665-d2e1d61e63d3"
 Serialization = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
 URIs = "5c2747f8-b7ea-4ff2-ba2e-563bfd36b1d4"
 UUIDs = "cf7118a7-6976-5b1a-9a39-7adc72f591a4"
-msghandler = "f2724d33-f338-4a57-b9f8-1be882570d10"
 
 [compat]
 CSV = "0.10.15"
@@ -29,6 +28,6 @@ DataFrames = "1.7.0"
 GeneralUtils = "0.4.9"
 HTTP = "2.4.0"
 JSON = "1.6.1"
+LLMMCTS = "0.1.5"
 NATS = "0.1.0"
-SQLLLM = "0.2.5"
-msghandler = "0.5.6"
+SQLLLM = "0.2.7"

etc.jl

@@ -1,93 +1,13 @@
-using DataStructures
-
-function dictify2(x; keytype::Type=Any, sort_order::Union{Nothing, Vector}=nothing)
-    # Dict-like objects
-    if x isa AbstractDict
-        out = OrderedDict{keytype, Any}()
-        
-        # 1. Process and normalize all keys from the input dictionary
-        processed_dict = OrderedDict{keytype, Any}()
-        for (k, v) in x
-            if keytype === String
-                newk = string(k)
-            elseif keytype === Symbol
-                newk = Symbol(string(k))
-            else
-                newk = k
-            end
-            processed_dict[newk] = dictify(v; keytype=keytype, sort_order=sort_order)
-        end
-
-        # 2. If a sort order is specified, apply it
-        if !isnothing(sort_order)
-            # Normalize the sort_order elements to match the requested keytype
-            normalized_order = map(sort_order) do tk
-                if keytype === String
-                    return string(tk)
-                elseif keytype === Symbol
-                    return Symbol(string(tk))
-                else
-                    return tk
-                end
-            end
-
-            # First, insert keys that match the requested order
-            for target_key in normalized_order
-                if haskey(processed_dict, target_key)
-                    out[target_key] = processed_dict[target_key]
-                end
-            end
-
-            # Then, append any remaining keys that weren't in the sort_order
-            for (k, v) in processed_dict
-                if !haskey(out, k)
-                    out[k] = v
-                end
-            end
-        else
-            # If no sort order is given, just use the processed dict
-            out = processed_dict
-        end
-
-        return out
-
-    # Arrays / vectors: map elements recursively
-    elseif x isa AbstractArray
-        return [dictify(element; keytype=keytype, sort_order=sort_order) for element in x]
-    
-    # Everything else: return as-is
-    else
-        return x
-    end
-end
-
-
-
-
-function dict_to_string_html2(d::AbstractDict; indent_level=1, indent_str=" ")
-  lines = String[]
-  padding = indent_str ^ indent_level
-  
-  # Sort keys for predictable, clean output
-  for k in keys(d)
-      v = d[k]
-      
-      if v isa AbstractDict
-          # Open tag, recurse for children, then close tag
-          push!(lines, "$padding<$k>")
-          ind_level = indent_level + 1
-          push!(lines, dict_to_string_html(v; indent_level=ind_level, indent_str=indent_str))
-          push!(lines, "$padding</$k>")
-      else
-          # Leaf node: put key and value on a single line
-          push!(lines, "$padding<$k>$v</$k>")
-      end
-  end
-  return join(lines, "\n")
-end
-
 
 
 
+d = Dict(
+  "hello"=> 555,
+  "world"=> Dict(
+    "name"=> "ton"
+  )
+)
 
+x = 55
 
+@info "YiemAgent think() 1 " d x @__LINE__

src/llmfunction.jl

@@ -1,10 +1,10 @@
 module llmfunction
 
-export  virtualWineUserChatbox, jsoncorrection, checkwine, # recommendbox,
+export  virtualWineUserChatbox, jsoncorrection, checkwine!, # recommendbox,
         virtualWineUserRecommendbox, userChatbox, userRecommendbox, extractWineAttributes_1,
         extractWineAttributes_2, paraphrase
 
-using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames
+using HTTP, JSON, URIs, Random, PrettyPrinting, UUIDs, Dates, DataFrames, DataStructures
 using GeneralUtils, SQLLLM
 using ..type, ..util
 
@@ -269,7 +269,7 @@ end
 # Arguments
   - `a::T1` 
     one of ChatAgent's agent.
-  - `input::T2`
+  - `thoughtdict::AbstractDict`
 # Return
   A JSON string of available wine
 
@@ -281,71 +281,354 @@ julia> input = "{\"food\": \"pizza\", \"occasion\": \"anniversary\"}"
 julia> result = checkinventory(agent, input)
 "{"wine 1": {\"Winery\": \"Pichon Baron\", \"wine name\": \"Pauillac (Grand Cru Classé)\", \"grape variety\": \"Cabernet Sauvignon\", \"year\": 2010, \"price\": \"125 USD\", \"stock ID\": \"ar-17\"}, }"
 ```
-""" 
-function checkwine(a::T1, input::T2; maxattempt::Int=3
-  ) where {T1<:agent, T2<:AbstractString}
+"""
+function checkwine!(a::T, thoughtdict::AbstractDict; useSQLLLM::Bool=false
+  )::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
 
-  println("\ncheckinventory order: $input ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  wineattributes_1 = extractWineAttributes_1(a, input)
-  wineattributes_2 = extractWineAttributes_2(a, input)
+  println("\ncheckinventory order: $(thoughtdict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+  wineattributes_1 = extractWineAttributes_1(a, thoughtdict["action_input"])
+  wineattributes_2 = extractWineAttributes_2(a, thoughtdict["action_input"])
 
-  # placeholder
-  # textresult = nothing
-  # rawresponse = nothing
-  
-  # for i in 1:maxattempt
-
-  #   #CHANGE if you want to add retailer name
-  #   # _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
-  #   _inventoryquery = "$wineattributes_1, $wineattributes_2"
-    
-  #   retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency"]
-  #   inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
-  #   println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  #   # add suppport for similarSQLVectorDB
-  #   textresult, result_raw = SQLLLM.query(
-  #                         inventoryquery, 
-  #                         a.context.executeSQL, 
-  #                         a.context.text2textInstructLLM;
-  #                         insertSQLVectorDB=a.context.insertSQLVectorDB, 
-  #                         similarSQLVectorDB=a.context.similarSQLVectorDB,
-  #                         llmFormatName="qwen3")
-
-  #   # check if all of retrieve_attributes appears in textresult
-  #   isin = [occursin(x, textresult) for x in retrieve_attributes]
-  #   # check if rawresponse type is DataFrame so that I can check for column
-  #   if typeof(result_raw) == DataFrame && 
-  #     !occursin("The resulting table has 0 row", textresult) &&
-  #     !all(isin)
-      
-  #     errornote = "Not all of $retrieve_attributes appear in search result"
-  #     println("\nERROR YiemAgent checkwine() $errornote ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  #     continue
-  #   else
-  #     break
-  #   end
-  # end
-
-  #CHANGE if you want to add retailer name
-    # _inventoryquery = "retailer name: $(a.retailername), $wineattributes_1, $wineattributes_2"
   retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency"]
   _inventoryquery = "$wineattributes_1, $wineattributes_2"
   inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
   println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  # add suppport for similarSQLVectorDB
-  textresult, result_raw = SQLLLM.query(
-                        inventoryquery, 
-                        a.context.executeSQL, 
-                        a.context.text2textInstructLLM;
-                        insertSQLVectorDB=a.context.insertSQLVectorDB, 
-                        similarSQLVectorDB=a.context.similarSQLVectorDB,
-                        llmFormatName="qwen3")
-  # println("\n--- YiemAgent checkwine() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-  # println(textresult)
-  # println(result_raw)
-  # println("---")
 
-  return (result_str=textresult, result_raw=result_raw, success=true, errormsg=nothing)
+  if useSQLLLM
+    # add suppport for similarSQLVectorDB
+    textresult, result_raw = SQLLLM.query(
+                          inventoryquery, 
+                          a.context.executeSQL, 
+                          a.context.text2textInstructLLM;
+                          insertSQLVectorDB=a.context.insertSQLVectorDB, 
+                          similarSQLVectorDB=a.context.similarSQLVectorDB,
+                          llmFormatName="qwen3")
+    thoughtdict["action_result"] = textresult
+  else
+
+    # direct query with possible sql instead of SQLLLM. 
+    sql = generatesql(a, inventoryquery)
+    textresult, result_raw, _, _ = SQLexecution(a.context.executeSQL, sql)
+    thoughtdict["action_result"] = textresult
+  end
+
+  return (thoughtdict=thoughtdict, result_raw=result_raw)
+end
+
+
+function generatesql(a::T, searchterm::String,
+  ; maxattempt=10
+  )::String where {T<:agent}
+
+  systemmsg = 
+    """
+    # database_search_guidelines
+      - Keep SQL queries focused only on the provided information.
+      - Do not create any table in the database
+      - A junction table can be used to link tables together. Another use case is for filtering data.
+      - If you can't find a single table that can be used to answer the user's search term, try joining multiple tables to see if you can obtain the answer.
+      - Text information in the database usually stored in lower case. If your search returns empty, try using lower case to search.
+      - If there is no search result from the database, remove the restrictive criteria until a search result is available, and proceed from there.
+
+    # situation
+      At each round of conversation, you will be given the following:
+      - user search term
+
+    # objective
+      Consult the database_search_guidelines. Then find the data from a database to satisfy the user's search term.
+
+    # your responsibility includes
+      Fulfill the objective.
+    
+    # you should then respond to the user with interleaving plan, action_name, action_input
+      1) "plan, Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) "action_name, Must be "RUNSQL"
+      3) "action_input, The input to the action you are about to perform according to your plan.
+      After the action is executed you gets "action_result". It is the output from the action you selected.
+
+    # you should only respond in JSON format as described below
+      "plan": "...",
+      "action_name": "...",
+      "action_input": "..."
+
+     # available_actions
+      "RUNSQL", which you can use to execute SQL against the database. 
+        The input must be a single SQL query to be executed against the database.
+        For more effective text search, it's necessary to use case-insensitivity and the ILIKE operator.
+        Do not wrap the SQL as it will be executed against the database directly and SQL must be ended with ';'.
+    """
+
+  table_schema =
+    """
+    create table customer (
+      customer_id uuid primary key default gen_random_uuid (),
+      customer_firstname varchar(128),
+      customer_lastname varchar(128),
+      customer_displayname varchar(128) not null,
+      customer_username varchar(128),
+      customer_password varchar(128),
+      customer_gender varchar(128),
+      country	varchar(128),
+      telephone varchar(128),
+      email varchar(128) not null,
+      customer_birthdate varchar(128),
+      note text,
+      
+      other_attributes jsonb,
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp,
+      description	text
+    );
+
+    create table retailer (
+      retailer_id uuid primary key default gen_random_uuid (),
+      retailer_name varchar(128) not null,
+      retailer_username varchar(128) not null,
+      retailer_password varchar(128) not null,
+      retailer_address	text not null,
+      country	varchar(128) not null,
+      contact_person varchar(128) not null,
+      telephone	varchar(128) not null,
+      email	varchar(128) not null,
+      note text,
+      
+      other_attributes jsonb,
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp,
+      description	text
+    ); 
+
+    create table food (
+      food_id uuid primary key default gen_random_uuid (),
+      food_name varchar(128) not null,
+      country	varchar(128),
+      spiciness	integer,
+      sweetness	integer,
+      sourness	integer,
+      savoriness	integer, 
+      bitterness	integer,
+      serving_temperature	integer,
+      image_url jsonb,
+      note text,
+      other_attributes jsonb,
+
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp,
+      description	text
+    );
+
+    create table wine (
+      wine_id uuid primary key default gen_random_uuid (),
+      seo_name varchar(128) not null,
+      wine_name varchar(128) not null,
+      winery varchar(128) not null,
+      vintage integer not null,
+      region varchar(128) not null,
+      country	varchar(128) not null,
+      wine_type	varchar(128) not null,
+      grape	varchar(128) not null,
+      serving_temperature	varchar(128) not null,
+      intensity	integer,
+      sweetness	integer,
+      tannin integer,
+      acidity integer,
+      fizziness	integer,
+      tasting_notes text,
+      image_url jsonb,
+      manufacturer_sku text,
+      note text,
+      other_attributes jsonb,
+      
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp,
+      description	text
+    );
+
+    create table wine_food (
+      wine_id uuid references wine(wine_id),
+      food_id uuid references food(food_id),
+      constraint wine_food_id primary key (wine_id, food_id),
+
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp
+    );
+
+    CREATE TABLE retailer_wine (
+      retailer_id uuid references retailer(retailer_id),
+      wine_id uuid references wine(wine_id),
+      constraint retailer_wine_id primary key (retailer_id, wine_id),
+      price NUMERIC(10, 2),
+      currency varchar(3) not null,
+      
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp
+    );
+
+    CREATE TABLE retailer_food (
+      retailer_id uuid references retailer(retailer_id),
+      food_id uuid references food(food_id),
+      constraint retailer_food_id primary key (retailer_id, food_id),
+      price NUMERIC(10, 2),
+      currency varchar(3) not null,
+      
+      created_time timestamptz default current_timestamp,
+      updated_time timestamptz default current_timestamp
+    );
+    """
+
+  requiredKeys = ["plan", "action_name", "action_input"]
+  errornote = ""
+  # provide similar sql only for the first attempt
+  sql, distance = a.context.similarSQLVectorDB(searchterm)
+
+  similarSQL_ = sql !== nothing ? sql : "None"
+  # if sql is really close, just use it
+  if similarSQL_ != "None" && distance <= 0.1
+    return similarSQL_
+  end
+
+  context = 
+    """
+    <internal_context_for_assistant>
+      <database_table_schema>
+        $table_schema
+      </database_table_schema>
+      <possible SQL for user's search term>
+        $similarSQL_
+      </possible SQL for user's search term>
+      <error_note>
+        $errornote
+      <error_note>
+    </internal_context_for_assistant>
+    """
+  input = context * searchterm
+
+  msg = Dict(
+    "model" => "gemma-4-E4B-it-UD-Q4_K_XL",
+    "messages" => [
+      Dict(
+        "role" => "system",
+        "content" => [
+          Dict("type" => "text", "text" => systemmsg),
+        ]
+      ),
+      Dict(
+        "role" => "user",
+        "content" => [
+          Dict("type" => "text", "text" => input),
+        ]
+      ),
+    ],
+    "temperature" => 0.7
+  )
+
+  for attempt in 1:maxattempt
+    response = a.context.text2textInstructLLM("random_id", msg)
+
+    response = GeneralUtils.clean_json_response(response)
+
+    think, response = GeneralUtils.extractthink(response)
+    responsedict = nothing
+     try
+      _responsedict = JSON.parse(response)
+      responsedict = GeneralUtils.dictify(_responsedict, keytype=String, sort_order=requiredKeys)
+    catch
+      println("\nERROR decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    # check whether all answer's key points are in responsedict
+    ispass, errormsg = GeneralUtils.checkAgentResponse_JSON(responsedict, requiredKeys)
+    if !ispass
+      errornote = errormsg
+      println("\nERROR YiemAgent decisionMaker() $errornote --(not qualify response)> $responsedict ", @__FILE__, ":", @__LINE__, " $(Dates.now())\n")
+      continue
+    end
+
+    # remove backticks Error occurred: MethodError: no method matching occursin(::String, ::Vector{String})
+    if occursin("```", responsedict["action_input"])
+      sql = GeneralUtils.extract_triple_backtick_text(responsedict["action_input"])[1]
+      if sql[1:4] == "sql\n"
+        sql = sql[5:end]
+      end
+      sql = split(sql, ';')   # some time there are comments in the sql
+      sql = sql[1] * ';'
+
+      responsedict["action_input"] = sql
+    end
+
+    toollist = ["RUNSQL"]
+    if responsedict["action_name"] ∉ toollist
+      errornote = "Your previous attempt has action_name that is not in the tool list"
+      println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote --(not qualify response)--> $(responsedict["action_name"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      continue
+    end
+
+    for i in toollist
+      if occursin(i, responsedict["action_input"])
+        errornote = "Your previous attempt has action_name in action_input which is not allowed"
+        println("\nERROR SQLLLM decisionMaker(). Attempt $attempt/$maxattempt. $errornote --(not qualify response)--> $(responsedict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+        continue
+      end
+    end
+    
+    # println("\nSQLLLM decisionMaker() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+    # pprintln(responsedict)
+    # println("---")
+    
+    return responsedict["action_input"]
+  end
+  error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
+end
+
+function SQLexecution(executeSQL::Function, sql::T
+  )::NamedTuple where {T<:AbstractString}
+
+  try
+    # add LIMIT to the SQL to prevent loading large data
+    sql = strip(sql)
+
+    # remove DISTINCT keyword because it is incompatible with RANDOM()
+    sql = replace(sql, "DISTINCT" => "")  
+
+    if sql[end] == ';'
+      if !occursin("LIMIT", sql)
+        sql = sql[1:end-1] * " ORDER BY RANDOM() LIMIT 2;"
+      end
+    else
+      sql = sql * ";"
+    end
+    result = executeSQL(sql)
+    df = DataFrame(result)
+    tablesize = size(df)
+    row, column = tablesize
+    if row == 0
+      return (result_str="No records found.", result_raw=df, success=true, errormsg=nothing)
+    elseif column > 30
+      return (result_str="There are more than 30 columns. Please be more specific.", result_raw=df, success=true, errormsg=nothing)
+    else
+      df1 =
+        if row > 2
+          # ramdom row to pick
+          df[sample(1:nrow(df), 2, replace=false), :] # random select 2 rows from df
+        else
+          df
+        end
+      result = GeneralUtils.dfToString(df1)
+      # println("\n~~~ SQLexecution() result: ", @__FILE__, " ", @__LINE__)
+      # println(sql)
+      # println(df1)
+      # println("\n")
+      return (result_str=result, result_raw=df1, success=true, errormsg=nothing)
+    end
+  catch e
+    io = IOBuffer()
+    showerror(io, e)
+    errorMsg = String(take!(io))
+    st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
+    println(errorMsg)
+    return (result_str=nothing, result_raw=nothing, success=false, errormsg=errorMsg)
+  end
 end
 
 
@@ -888,7 +1171,7 @@ function jsoncorrection(config::T1, input::T2, correctJsonExample::T3;
       """
 
       # apply LLM specific instruct format
-externalService = config["externalservice"]["text2textinstruct"]
+      externalService = config["externalservice"]["text2textinstruct"]
       llminfo = externalService["llminfo"]
       prompt = 
       if llminfo["name"] == "llama3instruct"
@@ -924,126 +1207,6 @@ externalService = config["externalservice"]["text2textinstruct"]
 end
 
 
-# function isrecommend(state::T1, text2textInstructLLM::Function
-#   ) where {T1<:AbstractDict}
-
-#   systemmsg = 
-#   """
-#   You are a helpful assistant that analyzes agent's trajectories to find solutions and observations (i.e., the results of actions) to answer the user's questions.
-
-#   Definitions:
-#   "question" is the user's question.
-#   "thought" is step-by-step reasoning about the current situation.
-#   "plan" is what to do to complete the task from the current situation.
-#   “action_name” is the name of the action taken, which can be one of the following functions:
-#       1) CHAT_BOX[text], which you can use to talk with the user. "text" is in verbal English. 
-#       2) WINESTOCK[query], which you can use to find info about wine in your inventory. "query" is a search term in verbal English. The best query must includes "budget", "type of wine", "characteristics of wine" and "food pairing".
-#   "action_input" is the input to the action
-#   "observation" is result of the preceding immediate action.
-
-#   At each round of conversation, the user will give you:
-#   Context: ...
-#   Trajectories: ...
-    
-#   You should then respond to the user with:
-#   1) trajectory_evaluation:
-#     - Analyze the trajectories of a solution to answer the user's original question. 
-#       Then given a question and a trajectory, evaluate its correctness and provide your reasoning and
-#       analysis in detail. Focus on the latest thought, action, and observation. 
-#       Incomplete trajectories can be correct if the thoughts and actions so far are correct, 
-#       even if the answer is not found yet. Do not generate additional thoughts or actions.
-#   2) answer_evaluation: Focus only on the matter mentioned in the question and analyze how the latest observation addresses the question.
-#   3) accepted_as_answer: Decide whether the latest observation's content answers the question. The possible responses are either 'Yes' or 'No.'
-#     Bad example (The observation didn't answers the question):
-#       question: Find cars with 4 wheels.
-#       observation: There are 2 cars in the table.
-#     Good example (The observation answers the question): 
-#       question: Find cars with a stereo.
-#       observation: There are 1 cars in the table. 1) brand: Toyota, model: yaris, color: black.
-#   4) score: Correctness score s where s is a single integer between 0 to 9. 
-#       - 0 means the trajectories are incorrect.
-#       - 9 means the trajectories are correct, and the observation's content directly answers the question.
-#   5) suggestion: if accepted_as_answer is "No", provide suggestion.
-
-#   You should only respond in format as described below:
-#   trajectory_evaluation: ...
-#   answer_evaluation: ...
-#   accepted_as_answer: ...
-#   score: ...
-#   suggestion: ...
-
-#   Let's begin!
-#   """
-
-#   thoughthistory = ""
-#   for (k, v) in state[:thoughtHistory]
-#     thoughthistory *= "$k: $v\n" 
-#   end
-
-#   usermsg =
-#   """
-#   Context: None
-#   Trajectories: $thoughthistory
-#   """
-
-#   _prompt = 
-#   [
-#     Dict(:name=> "system", :text=> systemmsg),
-#     Dict(:name=> "user", :text=> usermsg)
-#   ]
-
-#   # put in model format
-#   prompt = GeneralUtils.formatLLMtext(_prompt, "granite3")
-#   prompt *=
-#     """
-#     <|start_header_id|>assistant<|end_header_id|>
-#     """
-
-#   for attempt in 1:5
-#     try
-#       response = text2textInstructLLM(prompt)
-#       responsedict = GeneralUtils.textToDict(response,
-#             ["trajectory_evaluation", "answer_evaluation", "accepted_as_answer", "score", "suggestion"], 
-#             rightmarker=":", symbolkey=true)
-
-#       # check if dict has all required value
-#       trajectoryevaluation_text::AbstractString = responsedict[:trajectory_evaluation]
-#       answerevaluation_text::AbstractString = responsedict[:answer_evaluation]
-#       responsedict[:score] = parse(Int, responsedict[:score]) # convert string "5" into integer 5
-#       score::Integer = responsedict[:score]
-#       accepted_as_answer::AbstractString = responsedict[:accepted_as_answer]
-#       suggestion::AbstractString = responsedict[:suggestion]
-
-#       # add to state here instead to in transition() because the latter causes julia extension crash (a bug in julia extension)
-#       state[:evaluation] = "$(responsedict[:trajectory_evaluation]) $(responsedict[:answer_evaluation])" 
-#       state[:evaluationscore] = responsedict[:score] 
-#       state[:accepted_as_answer] = responsedict[:accepted_as_answer]
-#       state[:suggestion] = responsedict[:suggestion]
-
-#       # mark as terminal state when the answer is achieved
-#       if accepted_as_answer == "Yes"
-#         state[:isterminal] = true
-#         state[:reward] = 1
-#       end
-#       println("--> 5 Evaluator ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-#       pprintln(Dict(responsedict))
-#       return responsedict[:score]
-#     catch e
-#       io = IOBuffer()
-#       showerror(io, e)
-#       errorMsg = String(take!(io))
-#       st = sprint((io, v) -> show(io, "text/plain", v), stacktrace(catch_backtrace()))
-#       println("")
-#       println("Attempt $attempt. Error occurred: $errorMsg\n$st")
-#       println("")
-#     end
-#   end
-#   error("evaluator failed to generate an evaluation")
-# end
-
-
-
-
 
 
 

src/type.jl

@@ -223,10 +223,9 @@ function sommelier(
     context,
     llmFormatName
     )
-
   systemmsg = 
     """
-    <store_policy>
+    # store_policy
       - Generally speaking, the store inventory has some wines from France, the United States, Australia, Spain, and Italy, but you won't know exactly until you check your inventory.
       - If you found wines in the store's database, they are in stock.
       - You can only recommend wines that are currently in our inventory
@@ -238,8 +237,8 @@ function sommelier(
       - Spicy foods should be paired only with light red wines.
       - We do not sell organic, sustainable, gluten-free, and sulfite-free wine. Inform the user imediately if they are looking for these types of wines. Do not sell our wines as such.
       - Gift box, gift card, and custom messages are available. Inform the user to contact our sales team.
-    </store_policy>
-    <store_guidelines>
+
+    # store_guidelines
       - Greeting the customer warmly by ask them how could you help. Do not ask any other questions during this greeting.
       - Customer may provide images for you to look up.
       - Encourage the customer to explore different options and try new things.
@@ -247,47 +246,45 @@ function sommelier(
       - Your store carries only wine.
       - Vintage 0 means non-vintage.
       - Start searching the database as broadly as possible within the given information boundary to maximize the chances of finding. Avoid unnecessary parameters unless specified by the user. Refine the search subsequently.
-    </store_guidelines>
-    <situation>
-      Your customer is coming into the store
-    </situation>
-    <your role>
-      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store. You are working under your mentor supervision.
-    </your role>
-    <objective>
-      1) Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
-      2) Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
-    </objective>
-    <your responsibility includes>
-      1) According to the store's policy and guidelines, make an informed decision about what you need to do to achieve the objective
-      2) Keep the conversation with the customer going smoothly
-      2) Obey your mentor's suggestions.
-    </your responsibility includes>
-    <your responsibility does NOT includes>
-      1) Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
-      2) Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
-      3) Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
-    </your responsibility does NOT includes>
-    <you should then respond to the user with interleaving plan, action_name, action_input>
-      1) plan: Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
-      2) action_name: (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
-      3) action_input: The input to the action you are about to perform according to your plan.
+
+    # situation
+      You are having conversation with a customer.
+
+    # your role
+      Your name is $(newAgent.name). You are a helpful sommelier for website-based $(newAgent.retailername)'s wine store.
+
+    # objective
+      - Establish a connection with the customer by talking to them politely and showing your enthusiasm for their wine preferences.
+      - Provide relevant information and guide them to select the best wines only from your store's inventory that align with their preferences.
+
+    # your responsibility includes
+      - According to the store's policy and guidelines, and make an informed decision about what available_actions you need to use to achieve the objective.
+      - Keep the conversation with the customer going smoothly
+
+    # your responsibility does NOT includes
+      - Requesting the user to place an order, make a purchase, or confirm the order. These are the job of our sales team at the store.
+      - Processing sales orders or engaging in any other sales-related activities. These are the job of our sales team at the store.
+      - Answering questions or offering additional services beyond those related to your store's wine recommendations such as discounts, quantity, rewards programs, promotions, delivery options, shipping, boxes, gift wrapping, packaging, personalized messages or something similar. These are the job of our sales team at the store.
+
+    # you should then respond to the user with interleaving plan, action_name, action_input
+      1) "plan", Based on the current situation, state a complete action plan to complete the task and rationale. Be specific.
+      2) "action_name", (Typically corresponds to the execution of the first step in your plan) Can be one of the available_actions name
+      3) "action_input", The input to the action you are about to perform according to your plan.
       After the action is executed you gets "action_result". It is the output from the action you selected.
-    </you should then respond to the user with interleaving plan, action_name, action_input>
-    <you should only respond in JSON format as described below>
+
+    # you should only respond in JSON format as described below (not Markdown format)
       "plan": "...",
       "action_name": "...",
       "action_input": "..."
-    </you should only respond in JSON format as described below>
-    <available_actions>
-      - CHAT_BOX which you can use to talk with the user.
-      - CHECK_WINE allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
-          Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
-          Example query 2: "Red or white wine, medium tannin, price under 700 USD"
-          Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
-      - PRESENT_WINE_GUIDELINE which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-      - END_CONVER_GUIDELINE which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
-    </available_actions>
+
+    # available actions
+      "CHAT_BOX", which you can use to talk with the user. The input is dialogue you want to chat with the user according to your plan.
+      "CHECK_WINE", allows you to check information about wines you want in your inventory's database. The input is text that specify supported search criteria includeing: retailer_name, wine price, winery, name, vintage, region, country, type, grape varietal, tasting notes, occasion, food pairing, intensity, tannin, sweetness, and acidity.
+        Example query 1: "Dry, full-bodied red wine from 1) region: Burgundy, country: France or 2) region: Tuscany, country: Italy. Grape varietal: Merlot or Syrah. price 100 to 1000 USD."
+        Example query 2: "Red or white wine, medium tannin, price under 700 USD"
+        Example query 3: "white wine, region: Tuscany or Bordeaux, country: Italy or France
+      "WINE_PRESENTATION_GUIDELINE", which you can use to check the store guidelines about how to present wines you have found to the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
+      "END_CONVER_GUIDELINE", which you can use to check the store guidelines about how to end the conversation with the user. The input is "nothing" keyword. The output is the guidelines that you can follow.
     """
     
   system_msg = Dict(

src/util.jl

@@ -95,15 +95,15 @@ end
 """
 function addNewMessage(a::T1, name::String, userinput::T2; 
                       maximumMsg::Integer=30) where {T1<:agent, T2<:AbstractDict}
-  if name ∉ ["system", "user", "assistant"]   # guard against typo
-    error("name is not in agent.availableRole $(@__LINE__)")
-  end
+  # if name ∉ ["system", "user", "assistant"]   # guard against typo
+  #   error("name is not in agent.availableRole $(@__LINE__)")
+  # end
 
   #TODO summarize the oldest 10 message
   if length(a.chathistory) > maximumMsg
     summarize(a.chathistory)
   else
-    userinput["timestamp"] = Dates.now()
+    # userinput["timestamp"] = Dates.now()
     push!(a.chathistory, userinput)
   end
 end

test/test1.jl

@@ -2,200 +2,236 @@ using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructu
       NATS, Base.Threads
 using YiemAgent, GeneralUtils, msghandler
 
+  function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
+    payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
+    _, msg_envelope_json_str = msghandler.smartpack(
+          config["externalservice"]["servicesloadbalancer"]["nats"], 
+          payloads;
+          sender_id=sender_id,
+          msg_purpose="text2text",  
+          broker_url=config["nats_server_info"]["url"],
+          fileserver_url=config["externalservice"]["fileserver"]["url"])
 
-function text2text_instruct_llm(sender_id::String, openai_msg::Dict{String, Any})
-  payloads = [("msg", openai_msg, "dictionary")]  # List of tuples
-  _, msg_envelope_json_str = msghandler.smartpack(
-        config["externalService"]["servicesloadbalancer"]["nats"], 
-        payloads;
-        sender_id=sender_id,
-        msg_purpose="text2text",  
-        broker_url=config["nats_server_info"]["url"],
-        fileserver_url=config["externalService"]["fileserver"]["url"])
+    reply = NATS.request(agent_conn, 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
+                        msg_envelope_json_str, timeout=120)
 
-  reply = NATS.request(agent_conn, 
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
-                      msg_envelope_json_str, timeout=120)
-
-  incoming_env_json_str = String(reply.payload)
-  incoming_env = msghandler.smartunpack(incoming_env_json_str)
-  _llm_response = incoming_env["payloads"][1][2]
-  llm_response = _llm_response["choices"][1]["message"]["content"]
-  return llm_response
-end
-
-#TESTING get text embedding from a LLM service
-function get_embedding(text::AbstractArray{String})
-  documents_dict = Dict("documents" => text)
-  payloads = [("documents", documents_dict, "dictionary")]
-  _, msg_envelope_json_str = msghandler.smartpack(
-    config["externalService"]["servicesloadbalancer"]["nats"], 
-    payloads;   
-    msg_purpose="embedding",  
-    broker_url=config["nats_server_info"]["url"],
-    fileserver_url=config["externalService"]["fileserver"]["url"])
-
-  reply = NATS.request(agent_conn, 
-                      config["externalService"]["servicesloadbalancer"]["nats"], 
-                      msg_envelope_json_str, timeout=120)
-  incoming_env_json_str = String(reply.payload)
-  incoming_env = msghandler.smartunpack(incoming_env_json_str)
-  embedding_response = incoming_env["payloads"][1][2]
-
-  return embedding_response
-end
-
-#TESTING
-function execute_sql_winedb(config::JSON.Object, sql::T) where {T<:AbstractString}
-  host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
-  port = parse(Int, _port)
-  dbname = "winedb"
-  user = config["externalservice"]["sommpanion_db"]["user"]
-  password = config["externalservice"]["sommpanion_db"]["password"]
-  db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-  result = LibPQ.execute(db_connection, sql)
-  LibPQ.close(db_connection)
-  return result
-end
-
-#TESTING
-function similar_sql_vectordb(query; maxdistance::Integer=100)
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  df = find_similar_text_from_vectordb(query, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  # println(df[1, [:id, :function_output]])
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]  
-  # distance = 100  # CHANGE this is for testing only
-  if row != 0 && distance < maxdistance
-    # if there is usable SQL, return it.
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    output_str = String(base64decode(output_b64))
-    rowid = df[1, :id]
-    println("\n~~~ found similar sql. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    return (dict=output_str, distance=distance)
-  else
-    println("\n~~~ similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    return (dict=nothing, distance=nothing)
+    incoming_env_json_str = String(reply.payload)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
+    _llm_response = incoming_env["payloads"][1][2]
+    llm_response = _llm_response["choices"][1]["message"]["content"]
+    return llm_response
   end
-end
 
-#TESTING
-function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Integer=3) where {T1<:AbstractString, T2<:AbstractString}
-  tablename = "sqlllm_decision_repository"
-  # get embedding of the query
-  # query = state[:thoughtHistory][:question]
-  df = find_similar_text_from_vectordb(query, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    _query_embedding = get_embedding([query])[1]
-    query_embedding = _query_embedding["data"][1]["embedding"]
-    query = replace(query, "'" => "")
-    sql_base64 = base64encode(SQL)
-    sql_ = replace(SQL, "'" => "")
+  """ get a single text embedding from a LLM service
+  Example
+  text = ["hello"]
+  embedding = get_embedding(text)
+  """
+  function get_embedding(text::AbstractArray{String})
+    documents_dict = Dict("documents" => text)
+    payloads = [("documents", documents_dict, "dictionary")]
+    _, msg_envelope_json_str = msghandler.smartpack(
+      config["externalservice"]["servicesloadbalancer"]["nats"], 
+      payloads;   
+      msg_purpose="embedding",  
+      broker_url=config["nats_server_info"]["url"],
+      fileserver_url=config["externalservice"]["fileserver"]["url"])
 
-    sql = """
-          INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
-          """
-    # println("\n~~~ added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
-    # println(sql)
-    _ = execute_sql_vectordb(sql)
+    reply = NATS.request(agent_conn, 
+                        config["externalservice"]["servicesloadbalancer"]["nats"], 
+                        msg_envelope_json_str, timeout=120)
+    incoming_env_json_str = String(reply.payload)
+    incoming_env = msghandler.smartunpack(incoming_env_json_str)
+    embedding_response = incoming_env["payloads"][1][2]
+
+    return embedding_response
   end
-end
 
-#TESTING
-function execute_sql_vectordb(sql::T) where {T<:AbstractString}
-  host_url, _port = split(config["SQLVectorDB"]["url"], ':')
-  port = parse(Int, _port)
-  dbname = config[:externalservice][:SQLVectorDB][:dbname]
-  user = config[:externalservice][:SQLVectorDB][:user]
-  password = config[:externalservice][:SQLVectorDB][:password]
-  DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
-  result = LibPQ.execute(DBconnection, sql)
-  close(DBconnection)
-  return result
-end
-
-
-function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
-  )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  println("\n~~~ search vectorDB for this: $recentevents ", @__FILE__, " ", @__LINE__)
-  df = find_similar_text_from_vectordb(recentevents, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row != 0 && distance < maxdistance
-    # if there is usable decision, return it.
-    rowid = df[1, :id]
-    println("\n~~~ found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
-    output_b64 = df[1, :function_output_base64]   # pick the closest match
-    _output_str = String(base64decode(output_b64))
-    output = copy(JSON.read(_output_str))
-    return output
-  else
-    println("\n~~~ similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
-    return nothing
-  end
-end
-
-#TESTING
-function find_similar_text_from_vectordb(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 = get_embedding([text])[1]
-  embedding = _embedding["data"][1]["embedding"]
-  # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
-  sql = """
-        SELECT *, $embeddingColumnName <-> '$embedding' as distance
-        FROM $tablename
-        ORDER BY distance LIMIT $limit;
-        """
-  response = vectorDB(sql)
-  df = DataFrame(response)
-  return df
-end
-
-
-function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
-  ) where {T1<:AbstractString, T2<:AbstractDict}
-  tablename = "sommelier_decision_repository"
-  # find similar
-  df = find_similar_text_from_vectordb(recentevents, tablename, 
-                                  "function_input_embedding", execute_sql_vectordb)
-  row, col = size(df)
-  distance = row == 0 ? Inf : df[1, :distance]
-  if row == 0 || distance > maxdistance # no close enough SQL stored in the database
-    _embedding = get_embedding([recentevents])[1]
-    recentevents_embedding = _embedding["data"][1]["embedding"]
-    recentevents = replace(recentevents, "'" => "")
-    decision_json = JSON.json(decision)
-    decision_base64 = base64encode(decision_json)
-    decision = replace(decision_json, "'" => "")
+  """ sql = "SELECT * FROM wine;"
+  result = execute_sql_winedb(sql)
+  """
+  function execute_sql_winedb(sql::T) where {T<:AbstractString}
+    host_url, _port = split(config["externalservice"]["sommpanion_db"]["url"], ':')
+    port = parse(Int, _port)
+    dbname = "winedb"
+    user = config["externalservice"]["sommpanion_db"]["user"]
+    password = config["externalservice"]["sommpanion_db"]["password"]
+    db_connection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = nothing
+    try
+      result = LibPQ.execute(db_connection, sql)  
+    catch e
+      LibPQ.close(db_connection)
+    end
     
-    sql = 
-    """
-    INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
-    """
-    println("\n~~~ added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
-    println(sql)
-    _ = execute_sql_vectordb(sql)
-  else
-    println("~~~ similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+    LibPQ.close(db_connection)
+    return result
   end
-end
 
+  """ find similar sql from vector database
+  sql = "SELECT * FROM wine;"
+  result, distance = similar_sql_vectordb(sql)
+  """
+  function similar_sql_vectordb(sql::T; maxdistance::Number=0.2) where {T<:AbstractString}
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    df = find_similar_text_from_vectordb(sql, tablename, 
+          "function_input_embedding", execute_sql_vectordb)
+    # println(df[1, [:id, :function_output]])
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]  
+    if row != 0 && distance < maxdistance
+      # if there is usable SQL, return it.
+      output_b64 = df[1, :function_output_base64]   # pick the closest match
+      output_str = String(base64decode(output_b64))
+      rowid = df[1, :id]
+      println("\n--| similar sql found. row id $rowid, distance $distance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      pprintln(output_str)
+      return (result=output_str, distance=distance)
+    else
+      println("\n--| similar sql not found, max distance $maxdistance ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      return (result=nothing, distance=nothing)
+    end
+  end
+
+  """ insert query and sql into vector database
+  query = "get all wines from wine table"
+  sql = "SELECT * FROM wine;"
+  insert_sql_vectordb(query, sql)
+  """
+  function insert_sql_vectordb(query::T1, SQL::T2; maxdistance::Number=3
+    ) where {T1<:AbstractString, T2<:AbstractString}
+    
+    tablename = "sqlllm_decision_repository"
+    # get embedding of the query
+    # query = state[:thoughtHistory][:question]
+    df = find_similar_text_from_vectordb(query, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      _query_embedding = get_embedding([query])
+      _query_embedding = GeneralUtils.dictify(_query_embedding)
+      # println("\n--- _query_embedding() ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      # println(_query_embedding)
+      # println("---\n")
+      query_embedding = _query_embedding["data"][1]["embedding"]
+      query = replace(query, "'" => "")
+      sql_base64 = base64encode(SQL)
+      sql_ = replace(SQL, "'" => "")
+
+      sql = 
+        """
+        INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$query', '$sql_', '$sql_base64', '$query_embedding');
+        """
+      # println("\n--| added new decision to vectorDB ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
+      # println(sql)
+      _ = execute_sql_vectordb(sql)
+    end
+  end
+
+  """ execute sql against vectordb
+  sql = "SELECT * FROM wine;"
+  result = execute_sql_vectordb(sql)
+  """
+  function execute_sql_vectordb(sql::T) where {T<:AbstractString}
+    host_url, _port = split(config["externalservice"]["sommpanion_vectordb"]["url"], ':')
+    port = parse(Int, _port)
+    dbname = config["externalservice"]["sommpanion_vectordb"]["dbname"]
+    user = config["externalservice"]["sommpanion_vectordb"]["user"]
+    password = config["externalservice"]["sommpanion_vectordb"]["password"]
+    DBconnection = LibPQ.Connection("host=$host_url port=$port dbname=$dbname user=$user password=$password")
+    result = LibPQ.execute(DBconnection, sql)
+    close(DBconnection)
+    return result
+  end
+
+  """ search similar decision llm made from vectordb
+  """
+  function similar_sommelier_decision(recentevents::T1; maxdistance::Integer=3
+    )::Union{AbstractDict, Nothing} where {T1<:AbstractString}
+
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = find_similar_text_from_vectordb(recentevents, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row != 0 && distance < maxdistance
+      # if there is usable decision, return it.
+      rowid = df[1, :id]
+      println("\n--| found similar decision. row id $rowid, distance $distance ", @__FILE__, " ", @__LINE__)
+      output_b64 = df[1, :function_output_base64]   # pick the closest match
+      _output_str = String(base64decode(output_b64))
+      output = copy(JSON.read(_output_str))
+      return output
+    else
+      println("\n--| similar decision not found, max distance $maxdistance ", @__FILE__, " ", @__LINE__)
+      return nothing
+    end
+  end
+
+  """ search similar text from vectordb
+  """
+  function find_similar_text_from_vectordb(text::T1, tablename::T2, embeddingColumnName::T3,
+    vectorDB::Function; limit::Integer=1
+    )::DataFrame where {T1<:AbstractString, T2<:AbstractString, T3<:AbstractString}
+    # get embedding from LLM service
+    _embedding = get_embedding([text])
+    _embedding = _embedding["data"][1]["embedding"]
+    _embedding = "$_embedding"
+
+    embedding = _embedding[4:end]
+
+    # check whether there is close enough vector already store in vectorDB. if no, add, else skip 
+    sql = """
+          SELECT *, $embeddingColumnName <-> '$embedding' as distance
+          FROM $tablename
+          ORDER BY distance LIMIT $limit;
+          """
+    response = vectorDB(sql)
+    df = DataFrame(response)
+
+    return df
+  end
+
+  """ insert decision llm made to vectordb
+  """
+  function insert_sommelier_decision(recentevents::T1, decision::T2; maxdistance::Integer=5
+    ) where {T1<:AbstractString, T2<:AbstractDict}
+    tablename = "sommelier_decision_repository"
+    # find similar
+    df = find_similar_text_from_vectordb(recentevents, tablename, 
+                                    "function_input_embedding", execute_sql_vectordb)
+    row, col = size(df)
+    distance = row == 0 ? Inf : df[1, :distance]
+    if row == 0 || distance > maxdistance # no close enough SQL stored in the database
+      _embedding = get_embedding([recentevents])[1]
+      recentevents_embedding = _embedding["data"][1]["embedding"]
+      recentevents = replace(recentevents, "'" => "")
+      decision_json = JSON.json(decision)
+      decision_base64 = base64encode(decision_json)
+      decision = replace(decision_json, "'" => "")
+      
+      sql = 
+      """
+      INSERT INTO $tablename (function_input, function_output, function_output_base64, function_input_embedding) VALUES ('$recentevents', '$decision', '$decision_base64', '$recentevents_embedding');
+      """
+      println("\n--| added new decision to vectorDB ", @__FILE__, " ", @__LINE__)
+      println(sql)
+      _ = execute_sql_vectordb(sql)
+    else
+      println("--| similar decision previously cached, distance $distance ", @__FILE__, " ", @__LINE__)
+    end
+  end
+
+config = JSON.parsefile("./appconfig.json")
 sessionId = "0"
-backend_session_topic = "sommpanion.backend.agentbackend.v1.inbox.$sessionId"
-
-config = JSON.parsefile("./dummy_config.json")
+backend_session_topic = "sommpanion.testsubject"
 agent_ch = Channel(8)
 agent_conn  = NATS.connect(config["nats_server_info"]["url"])
+
 sub2 = NATS.subscribe(agent_conn, backend_session_topic) do msg
   put!(agent_ch, msg)
 end
@@ -210,32 +246,34 @@ agent_context = YiemAgent.agentcontext(
     insert_sommelier_decision
   )
 
-# can't instantiate
-agent = YiemAgent.sommelier(
-  agent_context;
-  name="Janie",
-  id=sessionId,  # agent instance id
-  retailername="Yiem",
-  llmFormatName=""
-)
+  # can't instantiate
+  agent = YiemAgent.sommelier(
+    agent_context;
+    name="Janie",
+    id=sessionId,  # agent instance id
+    retailername="Yiem Wine Ltd.",
+    llmFormatName=""
+  )
 
 
-
-
-# 1. Read local file and encode to base64 string
 image1_path = "test/large_image.png"
 image1_bytes = read(image1_path)
 image1_base64_string = base64encode(image1_bytes)
-
-# 2. Match the MIME type according to your file extension (e.g., png, jpeg)
 mime_type = "image/png" 
 data1_uri = "data:$(mime_type);base64,$(image1_base64_string)"
 
+# 1. Read local file and encode to base64 string
+image2_path = "test/small_image.png"
+image2_bytes = read(image2_path)
+image2_base64_string = base64encode(image2_bytes)
+mime_type = "image/png" 
+data2_uri = "data:$(mime_type);base64,$(image2_base64_string)"
+
 # 3. Construct payload with the Data URI
-usermsg = Dict{String, Any}(
+message = Dict(
     "role" => "user",
     "content" => [
-      Dict("type" => "text", "text" => "รู้จักไวน์ที่อยู่ในรูปมั้ย"),
+      Dict("type" => "text", "text" => "Do you know type of wine in the image?"),
       Dict(
         "type" => "image_url",
         "image_url" => Dict("url" => data1_uri)
@@ -243,8 +281,80 @@ usermsg = Dict{String, Any}(
     ]
   )
 
-result = YiemAgent.conversation(agent; userinput=usermsg)
-println(result)
+result = YiemAgent.conversation(agent; userinput=message)
+println("\n$result")
+
+
+
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => 
+#       "
+#       เป็นงานเลี้ยงทั่วไป
+#       "),
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => "no thanks. that's all"),
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+# message = Dict(
+#     "role" => "user",
+#     "content" => [
+#       Dict("type" => "text", "text" => "What about this wine?"),
+#       Dict(
+#         "type" => "image_url",
+#         "image_url" => Dict("url" => data2_uri)
+#       )
+#     ]
+#   )
+
+# result = YiemAgent.conversation(agent; userinput=message)
+# println("\n$result")
+
+
+
+
+
+
+
+
+
+
+
+
+
+