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v0.7.4-predefine_wine_search #30

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ton merged 2 commits from v0.7.4-predefine_wine_search into v0.7.4 2026-07-21 13:17:25 +00:00
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+251 -155
3 changed files with 251 additions and 155 deletions
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Showing only changes of commit 8fed0b5e8f - Show all commits
etc.jl
+89
View File
@@ -109,3 +109,92 @@ end
function generate_wine_retail_sql(conditions::Dict{String, Any})::String
# 1. Base SQL structure
base_query = """
SELECT
w.winery,
w.wine_name,
w.wine_id,
w.vintage,
w.region,
w.country,
w.wine_type,
w.grape,
w.serving_temperature,
w.sweetness,
w.intensity,
w.tannin,
w.acidity,
w.tasting_notes,
rw.price,
rw.currency,
w.image_url,
NULL AS retailer_name,
rw.retailer_id
FROM wine AS w
JOIN retailer_wine AS rw
ON w.wine_id = rw.wine_id
"""
# 2. Dynamic WHERE Clause Builder
where_clauses = String[]
# Iterate over each table condition provided
for (table_name, table_conditions) in conditions
# Determine table alias
alias = if table_name == "wine"
"w"
elseif table_name == "retailer_wine"
"rw"
else
continue # Skip unsupported tables
end
# Process condition dictionaries
if isa(table_conditions, Dict) && !isempty(table_conditions)
for (column_name, filter_details) in table_conditions
if isa(filter_details, Dict) && haskey(filter_details, "operator")
op = filter_details["operator"]
raw_val = filter_details["value"]
# --- Value Type Handling ---
# Use tryparse instead of try/catch for cleaner, faster parsing
final_val = raw_val
if op in ("=", "<", ">", "<=", ">=")
str_val = string(raw_val)
num_val = tryparse(Float64, str_val)
if !isnothing(num_val)
final_val = isinteger(num_val) ? round(Int, num_val) : num_val
end
end
# --- SQL Formatting ---
if isa(final_val, Number)
clause = "$(alias).$(column_name) $(op) $(final_val)"
else
# Escape single quotes within string values
escaped_val = replace(string(final_val), "'" => "''")
clause = "$(alias).$(column_name) $(op) '$(escaped_val)'"
end
push!(where_clauses, clause)
end
end
end
end
# 3. Assemble Final Query
where_sql = isempty(where_clauses) ? "" : "WHERE " * join(where_clauses, " AND ")
return string(base_query, where_sql, ";")
end
src/llmfunction.jl
+162 -82
View File
@@ -288,18 +288,7 @@ julia> thoughtdict =
function search_wine_database!(a::T, thoughtdict::AbstractDict; useSQLLLM::Bool=false
)::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
# WORKING
# look_for_wine_in_wine_database(a, thoughtdict["action_input"])
println("\ncheckinventory order: $(thoughtdict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
wineattributes_1 = extractWineAttributes_1(a, thoughtdict["action_input"])
wineattributes_2 = extractWineAttributes_2(a, thoughtdict["action_input"])
retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency", "image_url", "retailer_name", "retailer_id"]
_inventoryquery = "$(thoughtdict["action_input"]), $wineattributes_1, $wineattributes_2, retailer_name: $(a.retailername), retailerid: $(a.retailerid)"
inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
if useSQLLLM
# add suppport for similarSQLVectorDB
textresult, result_raw = SQLLLM.query(
@@ -313,7 +302,8 @@ function search_wine_database!(a::T, thoughtdict::AbstractDict; useSQLLLM::Bool=
else
# direct query with possible sql instead of SQLLLM.
sql = generatesql(a, inventoryquery)
sql = predefined_wine_search_sql(a, thoughtdict["action_input"])
# sql = generatesql(a, inventoryquery)
println("\nSQL: $sql ", @__FILE__, ":", @__LINE__, " $(Dates.now()) \n")
textresult, sql_result_df, success, _ = SQLexecution(a.context.executeSQL, sql)
@@ -627,9 +617,9 @@ julia> thoughtdict =
"action_name" => "SEARCH_WINE_DATABASE",
"action_input" => "Brunello di Montalcino from Tenuta CastelGiocondo")
```
julia> look_for_wine_in_wine_database(agent, thoughtdict["action_input"])
julia> predefined_wine_search_sql(agent, thoughtdict["action_input"])
"""
function look_for_wine_in_wine_database(a::T, searchterm::String,
function predefined_wine_search_sql(a::T, searchterm::String,
; maxattempt=10
)::String where {T<:agent}
@@ -731,6 +721,10 @@ function look_for_wine_in_wine_database(a::T, searchterm::String,
response = a.context.text2textInstructLLM("random_id", msg)
responsedict = Serde.parse_yaml(response)
# println("\n ", table_schema)
println("\n ", responsedict)
@info "before BM25 " @__LINE__
"""
responsedict = Dict(
"wine" => Dict(
@@ -758,83 +752,26 @@ function look_for_wine_in_wine_database(a::T, searchterm::String,
for (column_name, v) in table_info_dict
#
do_not_resolve_BM25_list = ["tasting_notes", "seo_name", "vintage", "grape"]
if column_name ∉ do_not_resolve_list
do_not_resolve_BM25_column = ["tasting_notes", "seo_name", "vintage", "grape", "price"]
if column_name ∉ do_not_resolve_BM25_column
words_catalog = GeneralUtils.harvest_entity_catalog(a.context.pg_conn_str, table_name, column_name)
resolved_word = GeneralUtils.resolve_entity(v["value"], words_catalog; threshold=0.9)
table_info_dict[column_name] = resolved_word
table_info_dict[column_name]["value"] = resolved_word
else
delete!(responsedict[table_name], column_name)
if length(responsedict[table_name]) == 0
delete!(responsedict, table_name)
end
end
end
end
# check each attributes against each column in a database table with BM25 and get the closest
# word match because there is a typo sometimes.
for (k, v) in responsedict
if k ∉ ["tasting_notes"]
words_catalog = GeneralUtils.harvest_entity_catalog(a.context.pg_conn_str, "wine", k)
resolved_word = GeneralUtils.resolve_entity(v, words_catalog; threshold=0.9)
responsedict[k] = resolved_word
end
end
#WORKING
println("\n", responsedict)
@info "test done " @__LINE__
error(9999)
@info "after BM25 " @__LINE__
think, response = GeneralUtils.extractthink(response)
responsedict = nothing
try
_responsedict = JSON.parse(response)
responsedict = GeneralUtils.dictify(_responsedict, keytype=String)
catch
println("\nERROR decisionMaker() failed to parse response: $response ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
continue
end
# check each attributes against each column in a database table with BM25 and get the closest
# word match because there is a typo sometimes.
for (k, v) in responsedict
if k ∉ ["tasting_notes"]
words_catalog = GeneralUtils.harvest_entity_catalog(a.context.pg_conn_str, "wine", k)
resolved_word = GeneralUtils.resolve_entity(v, words_catalog; threshold=0.9)
responsedict[k] = resolved_word
end
end
# LLM already extract user search term against tables schema
# Ex. responsedict = Dict(
# "wine_type"=> "red", # hard constraint
# "region"=> "bordeaux", # hard constraint
# "price_max"=> "100", # hard constraint
# "tasting_notes"=> "fruity, oak" # semantic search)
sql = predefined_wine_search_sql(responsedict)
return items
return sql
end
error("SQLLLM DecisionMaker() failed to generate a thought \n", response)
end
@@ -889,6 +826,64 @@ function SQLexecution(executeSQL::Function, sql::T
end
end
function DEPRECIATED_search_wine_database!(a::T, thoughtdict::AbstractDict; useSQLLLM::Bool=false
)::NamedTuple{(:thoughtdict, :result_raw), Tuple{OrderedDict, Any}} where {T<:agent}
# XXX
predefined_wine_search_sql(a, thoughtdict["action_input"])
println("\ncheckinventory order: $(thoughtdict["action_input"]) ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
wineattributes_1 = extractWineAttributes_1(a, thoughtdict["action_input"])
wineattributes_2 = extractWineAttributes_2(a, thoughtdict["action_input"])
retrieve_attributes = ["winery", "wine_name", "wine_id", "vintage", "region", "country", "wine_type", "grape", "serving_temperature", "sweetness", "intensity", "tannin", "acidity", "tasting_notes", "price", "currency", "image_url", "retailer_name", "retailer_id"]
_inventoryquery = "$(thoughtdict["action_input"]), $wineattributes_1, $wineattributes_2, retailer_name: $(a.retailername), retailerid: $(a.retailerid)"
inventoryquery = "Retrieves $retrieve_attributes of wines that match the following criteria - {$_inventoryquery}"
println("\ncheckinventory input: $inventoryquery ", @__FILE__, ":", @__LINE__, " $(Dates.now())")
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)
println("\nSQL: $sql ", @__FILE__, ":", @__LINE__, " $(Dates.now()) \n")
textresult, sql_result_df, success, _ = SQLexecution(a.context.executeSQL, sql)
items = nothing
if sql_result_df !== nothing
result_vec = GeneralUtils.dfToVectorDict(sql_result_df)
# get image
for d in result_vec
image_url_json_str = d["image_url"]
image_url_json_obj = JSON.parse(image_url_json_str)
base_url = "http://192.168.88.106:8080/"
if haskey(image_url_json_obj, "bottle")
url = base_url * image_url_json_obj["bottle"]
image_data = HTTP.get(url) # vector{int} data
image_base64_string = base64encode(image_data.body)
d["image"] = image_base64_string
else
d["image"] = nothing
end
end
items = result_vec # image is added to each item
end
thoughtdict["action_result"] = textresult
end
return (thoughtdict=thoughtdict, result_raw=items)
end
"""
@@ -1475,7 +1470,92 @@ function jsoncorrection(config::T1, input::T2, correctJsonExample::T3;
end
function predefined_wine_search_sql(conditions::Dict{String, Any})::String
# 1. Base SQL structure
base_query =
"""
SELECT
w.winery,
w.wine_name,
w.wine_id,
w.vintage,
w.region,
w.country,
w.wine_type,
w.grape,
w.serving_temperature,
w.sweetness,
w.intensity,
w.tannin,
w.acidity,
w.tasting_notes,
rw.price,
rw.currency,
w.image_url,
NULL AS retailer_name,66
rw.retailer_id
FROM wine AS w
JOIN retailer_wine AS rw
ON w.wine_id = rw.wine_id
"""
# 2. Dynamic WHERE Clause Builder
where_clauses = String[]
# Iterate over each table condition provided
for (table_name, table_conditions) in conditions
# Determine table alias
alias = if table_name == "wine"
"w"
elseif table_name == "retailer_wine"
"rw"
else
continue # Skip unsupported tables
end
# Process condition dictionaries
if isa(table_conditions, Dict) && !isempty(table_conditions)
for (column_name, filter_details) in table_conditions
if isa(filter_details, Dict) && haskey(filter_details, "operator")
op = filter_details["operator"]
raw_val = filter_details["value"]
# --- Value Type Handling ---
# Use tryparse instead of try/catch for cleaner, faster parsing
final_val = raw_val
if op in ("=", "<", ">", "<=", ">=")
str_val = string(raw_val)
num_val = tryparse(Float64, str_val)
if !isnothing(num_val)
final_val = isinteger(num_val) ? round(Int, num_val) : num_val
end
end
# --- SQL Formatting ---
if isa(final_val, Number)
clause = "$(alias).$(column_name) $(op) $(final_val)"
else
# Escape single quotes within string values
escaped_val = replace(string(final_val), "'" => "''")
clause = "$(alias).$(column_name) $(op) '$(escaped_val)'"
end
push!(where_clauses, clause)
end
end
end
end
# 3. Assemble Final Query
where_sql = isempty(where_clauses) ? "" : "WHERE " * join(where_clauses, " AND ")
return string(base_query, where_sql, ";")
end
src/type.jl
-73
View File
@@ -23,75 +23,6 @@ end
abstract type agent end
mutable struct companion <: agent
name::String # agent name
id::String # agent id
systemmsg::String # system message
tools::Dict # tools
maxHistoryMsg::Integer # e.g. 21th and earlier messages will get summarized
chathistory::Vector{Dict{String, Any}}
memory::Dict{String, Any}
context::NamedTuple # NamedTuple of functions
llmFormatName::String
end
function companion(
context::agentcontext # NamedTuple of functions
;
name::String= "Assistant",
id::String= GeneralUtils.uuid4snakecase(),
maxHistoryMsg::Integer= 20,
chathistory::Vector{Dict{String, String}} = Vector{Dict{String, String}}(),
llmFormatName::String= "granite3",
systemmsg::String=
"""
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!
""",
)
tools = Dict( # update input format
"CHAT_BOX"=> Dict(
"description" => "- CHAT_BOX 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
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{String, Any}(
"events"=> Vector{Dict{String, Any}}(),
"state"=> Dict{String, Any}(), # state of the agent
"recap"=> OrderedDict{String, Any}(), # recap summary of the conversation
)
newAgent = companion(
name,
id,
systemmsg,
tools,
maxHistoryMsg,
chathistory,
memory,
context,
llmFormatName
)
return newAgent
end
mutable struct sommelier <: agent
name::String # agent name
id::String # agent id
@@ -210,11 +141,7 @@ function sommelier(
memory = Dict{String, Any}(
"shortmem"=> OrderedDict{String, Any}(),
"scratchpad"=> "",
"events"=> Vector{Dict{String, Any}}(),
"state"=> Dict{String, Any}(
),
"recap"=> OrderedDict{String, Any}(),
)
newAgent = sommelier(