update

Project.toml

@@ -1,6 +1,6 @@
 name = "GeneralUtils"
 uuid = "c6c72f09-b708-4ac8-ac7c-2084d70108fe"
-version = "0.5.10"
+version = "0.5.11"
 authors = ["tonaerospace <tonaerospace.etc@gmail.com>"]
 
 [deps]

src/dbUtil.jl

@@ -1,7 +1,7 @@
 module dbUtil
   
 export dictToPostgresKeyValueString, generateInsertSQL, generateUpdateSQL, find_text_vector_similarity,
-        execute_postgres_sql, get_embedding_nats
+        execute_postgres_sql
 
 using JSON, DataStructures, Distributions, Random, Dates, UUIDs, DataFrames,
       SHA, NATS, LibPQ
@@ -39,60 +39,6 @@ function execute_postgres_sql(pg_conn_str::T, sql::T) where {T<:AbstractString}
   return result
 end
 
-"""
-get_embedding_nats(nats_server_url::String, texts::Vector{String}, sendto_topic::String, fileserver_url::String)
-
-Fetch embeddings for a list of texts from a NATS-based embedding service.
-
-# Arguments
-- `nats_server_url::String`: NATS server connection URL
-- `texts::Vector{String}`: List of text strings to generate embeddings for
-- `sendto_topic::String`: NATS topic to send the embedding request to
-- `fileserver_url::String`: URL of the file server for additional data
-
-# Returns
-- `Vector` of embeddings, where each embedding is a vector of floats (Vector{Float32} or Vector{Float64})
-
-# Example
-```julia
-nats_server_url = "nats://localhost:4222"
-texts = ["Hello world", "Another text"]
-sendto_topic = "embedding.service"
-fileserver_url = "http://localhost:8080"
-embeddings = get_embedding_nats(nats_server_url, texts, sendto_topic, fileserver_url)
-```
-"""
-function get_embedding_nats(nats_server_url::String, texts::Vector{String}, sendto_topic::String, 
-  fileserver_url::String)
-  nats_conn = NATS.connect(nats_server_url)
-  return get_embedding_nats(nats_conn, texts, sendto_topic, fileserver_url)
-end
-
-function get_embedding_nats(nats_conn::NATS.Connection, texts::Vector{String}, sendto_topic::String, 
-  fileserver_url::String)
-  println("Generating embeddings for $(length(texts)) texts...")
-  documents_dict = Dict("documents" => texts)
-  payloads = [("documents", documents_dict, "dictionary")]
-  _, msg_envelope_json_str = msghandler.smartpack(
-    sendto_topic, 
-    payloads;   
-    msg_purpose="embedding",
-    fileserver_url=fileserver_url)
-
-  reply = NATS.request(nats_conn, 
-                      sendto_topic, 
-                      msg_envelope_json_str, timeout=300)
-  incoming_env_json_str = String(reply.payload)
-  incoming_env = msghandler.smartunpack(incoming_env_json_str)
-  embedding_response = incoming_env["payloads"][1][2]
-
-  result = []
-  for i in embedding_response["data"]
-    embedding_vector = i["embedding"]
-    push!(result, embedding_vector)
-  end
-  return result
-end
 
 """ find_text_vector_similarity