refactor: llm_utils 모듈을 utils/llm으로 이동

engine/query_executor.py

@@ -6,13 +6,13 @@
 """
 
 import logging
-from typing import Dict, Any, Optional, Union
+from typing import Any, Dict, Optional, Union
 
 from langchain_core.messages import HumanMessage
 
-from llm_utils.graph_utils.enriched_graph import builder as enriched_builder
-from llm_utils.graph_utils.basic_graph import builder as basic_builder
-from llm_utils.llm_response_parser import LLMResponseParser
+from utils.llm.graph_utils.basic_graph import builder as basic_builder
+from utils.llm.graph_utils.enriched_graph import builder as enriched_builder
+from utils.llm.llm_response_parser import LLMResponseParser
 
 logger = logging.getLogger(__name__)
 

interface/app_pages/graph_builder.py

@@ -13,7 +13,7 @@
 import streamlit as st
 from langgraph.graph import END, StateGraph
 
-from llm_utils.graph_utils.base import (
+from utils.llm.graph_utils.base import (
     CONTEXT_ENRICHMENT,
     GET_TABLE_INFO,
     PROFILE_EXTRACTION,

interface/core/config/settings.py

@@ -3,19 +3,18 @@
 """
 
 import os
-from typing import Any, Dict, Optional
 from pathlib import Path
+from typing import Any, Dict, Optional
 
 try:
     import streamlit as st  # type: ignore
 except Exception:  # pragma: no cover - streamlit may not be present in non-UI contexts
     st = None  # type: ignore
 
-from llm_utils.tools import set_gms_server
+from utils.llm.tools import set_gms_server
 
 from .models import Config
 
-
 DEFAULT_DATAHUB_SERVER = "http://localhost:8080"
 DEFAULT_VECTORDB_TYPE = os.getenv("VECTORDB_TYPE", "faiss").lower()
 DEFAULT_VECTORDB_LOCATION = os.getenv("VECTORDB_LOCATION", "")
@@ -202,7 +201,7 @@ def update_llm_settings(*, provider: str, values: dict[str, str | None]) -> None
     """Update chat LLM settings from UI into process env and session.
 
     This function mirrors the environment-variable based configuration consumed by
-    llm_utils.llm.factory.get_llm(). Only sets provided keys; missing values are left as-is.
+    utils.llm.core.factory.get_llm(). Only sets provided keys; missing values are left as-is.
     """
     provider_norm = (provider or "").lower()
     if provider_norm not in {
@@ -229,7 +228,7 @@ def update_llm_settings(*, provider: str, values: dict[str, str | None]) -> None
 def update_embedding_settings(*, provider: str, values: dict[str, str | None]) -> None:
     """Update Embeddings settings from UI into process env and session.
 
-    Mirrors env vars consumed by llm_utils.llm.factory.get_embeddings().
+    Mirrors env vars consumed by utils.llm.core.factory.get_embeddings().
     """
     provider_norm = (provider or "").lower()
     if provider_norm not in {

interface/core/result_renderer.py

@@ -11,8 +11,8 @@
 from langchain_core.messages import AIMessage
 
 from infra.observability.token_usage import TokenUtils
-from llm_utils.llm_response_parser import LLMResponseParser
 from utils.databases import DatabaseFactory
+from utils.llm.llm_response_parser import LLMResponseParser
 from viz.display_chart import DisplayChart
 
 

interface/core/session_utils.py

@@ -24,9 +24,9 @@ def init_graph(use_enriched: bool) -> str:
     """
 
     builder_module = (
-        "llm_utils.graph_utils.enriched_graph"
+        "utils.llm.graph_utils.enriched_graph"
         if use_enriched
-        else "llm_utils.graph_utils.basic_graph"
+        else "utils.llm.graph_utils.basic_graph"
     )
 
     builder = __import__(builder_module, fromlist=["builder"]).builder

pyproject.toml

@@ -68,7 +68,6 @@ include = [
 packages = [
   "cli",
   "interface",
-  "llm_utils",
   "engine",
   "infra",
   "viz",

test/test_llm_utils/test_llm_response_parser.py

@@ -9,7 +9,7 @@
 
 import unittest
 
-from llm_utils.llm_response_parser import LLMResponseParser
+from utils.llm.llm_response_parser import LLMResponseParser
 
 
 class TestLLMResponseParser(unittest.TestCase):

llm_utils/README.md → utils/llm/README.md

@@ -1,4 +1,4 @@
-## llm_utils 개요
+## utils.llm 개요
 
 Lang2SQL 파이프라인에서 LLM, 검색(RAG), 그래프 워크플로우, DB 실행, 시각화 등 보조 유틸리티를 모아둔 패키지입니다. 이 문서는 depth(계층)별로 기능과 통합 흐름을 정리합니다.
 

llm_utils/chains.py → utils/llm/chains.py

@@ -8,21 +8,13 @@
 - Question Gate (SQL 적합성 분류)
 """
 
-import os
-from langchain_core.prompts import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-)
+from langchain_core.prompts import ChatPromptTemplate, SystemMessagePromptTemplate
 from pydantic import BaseModel, Field
-from llm_utils.output_parser.question_suitability import QuestionSuitability
-from llm_utils.output_parser.document_suitability import (
-    DocumentSuitabilityList,
-)
-
-from llm_utils.llm import get_llm
 
 from prompt.template_loader import get_prompt_template
-
+from utils.llm.core import get_llm
+from utils.llm.output_parser.document_suitability import DocumentSuitabilityList
+from utils.llm.output_parser.question_suitability import QuestionSuitability
 
 llm = get_llm()
 

llm_utils/llm/__init__.py → utils/llm/core/__init__.py

@@ -1,18 +1,18 @@
-from .factory import (
-    get_llm,
-    get_llm_openai,
-    get_llm_azure,
-    get_llm_bedrock,
-    get_llm_gemini,
-    get_llm_ollama,
-    get_llm_huggingface,
+from utils.llm.core.factory import (
     get_embeddings,
-    get_embeddings_openai,
     get_embeddings_azure,
     get_embeddings_bedrock,
     get_embeddings_gemini,
-    get_embeddings_ollama,
     get_embeddings_huggingface,
+    get_embeddings_ollama,
+    get_embeddings_openai,
+    get_llm,
+    get_llm_azure,
+    get_llm_bedrock,
+    get_llm_gemini,
+    get_llm_huggingface,
+    get_llm_ollama,
+    get_llm_openai,
 )
 
 __all__ = [

llm_utils/llm/factory.py → utils/llm/core/factory.py

@@ -2,7 +2,7 @@
 from typing import Optional
 
 from langchain.llms.base import BaseLanguageModel
-from langchain_aws import ChatBedrockConverse, BedrockEmbeddings
+from langchain_aws import BedrockEmbeddings, ChatBedrockConverse
 from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
 from langchain_huggingface import (
     ChatHuggingFace,
@@ -11,9 +11,9 @@
 )
 from langchain_ollama import ChatOllama, OllamaEmbeddings
 from langchain_openai import (
+    AzureChatOpenAI,
     AzureOpenAIEmbeddings,
     ChatOpenAI,
-    AzureChatOpenAI,
     OpenAIEmbeddings,
 )
 

llm_utils/graph_utils/__init__.py → utils/llm/graph_utils/__init__.py

@@ -4,16 +4,16 @@
 이 패키지는 Lang2SQL의 워크플로우 그래프 구성과 관련된 모듈들을 포함합니다.
 """
 
-from .base import (
-    QueryMakerState,
+from utils.llm.graph_utils.base import (
+    CONTEXT_ENRICHMENT,
     GET_TABLE_INFO,
-    QUERY_MAKER,
     PROFILE_EXTRACTION,
-    CONTEXT_ENRICHMENT,
+    QUERY_MAKER,
+    QueryMakerState,
+    context_enrichment_node,
     get_table_info_node,
-    query_maker_node,
     profile_extraction_node,
-    context_enrichment_node,
+    query_maker_node,
 )
 
 from .basic_graph import builder as basic_builder

llm_utils/graph_utils/base.py → utils/llm/graph_utils/base.py

@@ -1,18 +1,16 @@
 import json
 
-from typing_extensions import TypedDict, Annotated
 from langgraph.graph.message import add_messages
+from typing_extensions import Annotated, TypedDict
 
-
-from llm_utils.chains import (
-    query_maker_chain,
+from utils.llm.chains import (
+    document_suitability_chain,
     profile_extraction_chain,
     query_enrichment_chain,
+    query_maker_chain,
     question_gate_chain,
-    document_suitability_chain,
 )
-
-from llm_utils.retrieval import search_tables
+from utils.llm.retrieval import search_tables
 
 # 노드 식별자 정의
 QUESTION_GATE = "question_gate"

llm_utils/graph_utils/basic_graph.py → utils/llm/graph_utils/basic_graph.py

@@ -1,23 +1,22 @@
-import json
+"""
+기본 워크플로우를 위한 StateGraph 구성입니다.
+GET_TABLE_INFO -> QUERY_MAKER 순서로 실행됩니다.
+"""
 
-from langgraph.graph import StateGraph, END
-from llm_utils.graph_utils.base import (
-    QueryMakerState,
-    QUESTION_GATE,
-    GET_TABLE_INFO,
+from langgraph.graph import END, StateGraph
+
+from utils.llm.graph_utils.base import (
     EVALUATE_DOCUMENT_SUITABILITY,
+    GET_TABLE_INFO,
     QUERY_MAKER,
-    question_gate_node,
-    get_table_info_node,
+    QUESTION_GATE,
+    QueryMakerState,
     document_suitability_node,
+    get_table_info_node,
     query_maker_node,
+    question_gate_node,
 )
 
-"""
-기본 워크플로우를 위한 StateGraph 구성입니다.
-GET_TABLE_INFO -> QUERY_MAKER 순서로 실행됩니다.
-"""
-
 # StateGraph 생성 및 구성
 builder = StateGraph(QueryMakerState)
 builder.set_entry_point(QUESTION_GATE)

llm_utils/graph_utils/enriched_graph.py → utils/llm/graph_utils/enriched_graph.py

@@ -1,27 +1,26 @@
-import json
+"""
+기본 워크플로우에 '프로파일 추출(PROFILE_EXTRACTION)'과 '컨텍스트 보강(CONTEXT_ENRICHMENT)'를
+추가한 확장된 그래프입니다.
+"""
 
-from langgraph.graph import StateGraph, END
-from llm_utils.graph_utils.base import (
-    QueryMakerState,
-    QUESTION_GATE,
-    GET_TABLE_INFO,
+from langgraph.graph import END, StateGraph
+
+from utils.llm.graph_utils.base import (
+    CONTEXT_ENRICHMENT,
     EVALUATE_DOCUMENT_SUITABILITY,
+    GET_TABLE_INFO,
     PROFILE_EXTRACTION,
-    CONTEXT_ENRICHMENT,
     QUERY_MAKER,
-    question_gate_node,
-    get_table_info_node,
+    QUESTION_GATE,
+    QueryMakerState,
+    context_enrichment_node,
     document_suitability_node,
+    get_table_info_node,
     profile_extraction_node,
-    context_enrichment_node,
     query_maker_node,
+    question_gate_node,
 )
 
-"""
-기본 워크플로우에 '프로파일 추출(PROFILE_EXTRACTION)'과 '컨텍스트 보강(CONTEXT_ENRICHMENT)'를 
-추가한 확장된 그래프입니다.
-"""
-
 # StateGraph 생성 및 구성
 builder = StateGraph(QueryMakerState)
 builder.set_entry_point(QUESTION_GATE)

llm_utils/output_parser/document_suitability.py → utils/llm/output_parser/document_suitability.py

@@ -5,7 +5,8 @@
 최상위는 테이블명(string) -> 평가 객체 매핑을 담는 Root 모델입니다.
 """
 
-from typing import Dict, List
+from typing import List
+
 from pydantic import BaseModel, Field
 
 

llm_utils/retrieval.py → utils/llm/retrieval.py

@@ -1,12 +1,11 @@
 import os
-from langchain_community.vectorstores import FAISS
-from langchain_openai import OpenAIEmbeddings
+
 from langchain.retrievers import ContextualCompressionRetriever
 from langchain.retrievers.document_compressors import CrossEncoderReranker
 from langchain_community.cross_encoders import HuggingFaceCrossEncoder
 from transformers import AutoModelForSequenceClassification, AutoTokenizer
 
-from llm_utils.vectordb import get_vector_db
+from utils.llm.vectordb import get_vector_db
 
 
 def load_reranker_model(device: str = "cpu"):

llm_utils/tools/__init__.py → utils/llm/tools/__init__.py

@@ -1,7 +1,7 @@
-from .datahub import (
-    set_gms_server,
+from utils.llm.tools.datahub import (
     get_info_from_db,
     get_metadata_from_db,
+    set_gms_server,
 )
 
 __all__ = [

llm_utils/vectordb/__init__.py → utils/llm/vectordb/__init__.py

@@ -2,6 +2,6 @@
 VectorDB 모듈 - FAISS와 pgvector를 지원하는 벡터 데이터베이스 추상화
 """
 
-from .factory import get_vector_db
+from utils.llm.vectordb.factory import get_vector_db
 
 __all__ = ["get_vector_db"]

llm_utils/vectordb/factory.py → utils/llm/vectordb/factory.py

@@ -5,8 +5,8 @@
 import os
 from typing import Optional
 
-from llm_utils.vectordb.faiss_db import get_faiss_vector_db
-from llm_utils.vectordb.pgvector_db import get_pgvector_db
+from utils.llm.vectordb.faiss_db import get_faiss_vector_db
+from utils.llm.vectordb.pgvector_db import get_pgvector_db
 
 
 def get_vector_db(

llm_utils/vectordb/faiss_db.py → utils/llm/vectordb/faiss_db.py

@@ -7,8 +7,8 @@
 
 from langchain_community.vectorstores import FAISS
 
-from llm_utils.llm import get_embeddings
-from llm_utils.tools import get_info_from_db
+from utils.llm.core import get_embeddings
+from utils.llm.tools import get_info_from_db
 
 
 def get_faiss_vector_db(vectordb_path: Optional[str] = None):

llm_utils/vectordb/pgvector_db.py → utils/llm/vectordb/pgvector_db.py

@@ -4,12 +4,12 @@
 
 import os
 from typing import Optional
+
 import psycopg2
-from sqlalchemy.orm import Session
 from langchain_postgres.vectorstores import PGVector
 
-from llm_utils.tools import get_info_from_db
-from llm_utils.llm import get_embeddings
+from utils.llm.core import get_embeddings
+from utils.llm.tools import get_info_from_db
 
 
 def _check_collection_exists(connection_string: str, collection_name: str) -> bool: