using JSON, Dates, UUIDs, PrettyPrinting, LibPQ, Base64, DataFrames, DataStructures, HTTP, Base64, 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"]) 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) 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, "'" => "") 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__, " $(Dates.now())") # println(sql) _ = execute_sql_vectordb(sql) end end #TESTING function execute_sql_vectordb(sql::T) where {T<:AbstractString} host_url, _port = split(config["SQLVectorDB"]["url"], ':') port = parse(Int, _port) dbname = config[:externalservice][:SQLVectorDB][:dbname] user = config[:externalservice][:SQLVectorDB][:user] password = config[:externalservice][:SQLVectorDB][:password] DBconnection = LibPQ.Connection("host=$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 = """ 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 $distance ", @__FILE__, " ", @__LINE__) end end sessionId = "0" backend_session_topic = "sommpanion.backend.agentbackend.v1.inbox.$sessionId" config = JSON.parsefile("./dummy_config.json") 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 agent_context = YiemAgent.agentcontext( text2text_instruct_llm, get_embedding, execute_sql_winedb, similar_sql_vectordb, insert_sql_vectordb, similar_sommelier_decision, insert_sommelier_decision ) # can't instantiate agent = YiemAgent.sommelier( agent_context; name="Janie", id=sessionId, # agent instance id retailername="Yiem", 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)" # 3. Construct payload with the Data URI usermsg = Dict{String, Any}( "role" => "user", "content" => [ Dict("type" => "text", "text" => "รู้จักไวน์ที่อยู่ในรูปมั้ย"), Dict( "type" => "image_url", "image_url" => Dict("url" => data1_uri) ) ] ) result = YiemAgent.conversation(agent; userinput=usermsg) println(result)