From b14a396b91e261c964cf1360bd024a710d9869c9 Mon Sep 17 00:00:00 2001
From: ehddnr301 <dy950328@gmail.com>
Date: Sat, 5 Apr 2025 06:05:30 +0000
Subject: [PATCH 1/5] Implement QueryRefinedAgainChain for enhanced query
 refinement process

---
 llm_utils/chains.py | 57 +++++++++++++++++++++++++++++++++++++++++++++
 llm_utils/graph.py  | 25 ++++++++++++++++++--
 2 files changed, 80 insertions(+), 2 deletions(-)

diff --git a/llm_utils/chains.py b/llm_utils/chains.py
index d9e5e6c..271dc29 100644
--- a/llm_utils/chains.py
+++ b/llm_utils/chains.py
@@ -72,6 +72,61 @@ def create_query_refiner_chain(llm):
     return tool_choice_prompt | llm
 
 
+# QueryRefinedAgainChain
+def create_query_redefined_again_chain(llm):
+    query_redefined_again_prompt = ChatPromptTemplate.from_messages(
+        [
+            (
+                "system",
+                """
+                당신은 데이터 분석 전문가(데이터 분석가 페르소나)입니다.
+                사용자의 질문과 이미 구체화된 질문을 바탕으로, 실제 사용 가능한 테이블과 컬럼 정보를 검토하여
+                더욱 정교하게 질문을 재정의해 주세요.
+                
+                주의사항:
+                - 이전에 구체화된 질문을 기반으로 하되, 실제 DB 환경에서 사용 가능한 테이블/컬럼을 고려해 현실적인 분석 방향을 제시하세요.
+                - 불필요한 재질문 없이, 주어진 데이터로 최대한 분석 가능한 형태로 질문을 구체화하세요.
+                - 테이블 구조에 맞게 분석 질문을 조정하고, 필요한 가정을 추가하세요.
+                - 최종 출력 형식은 반드시 아래와 같아야 합니다.
+                
+                최종 형태 예시:
+                
+                <최종 구체화된 질문>
+                ```
+                최근 30일간 결제 금액이 10만원 이상인 사용자들의 서비스 이용 패턴과 이탈율을 분석하여, 
+                어떤 활동 패턴을 보이는 고액 결제자가 이탈하는지 파악
+                ```
+                
+                <분석 접근 방향>
+                ```
+                1. subscription_activities와 contract_activities 테이블을 조인하여 고액 결제자 식별
+                2. 해당 사용자들의 activity_type 분포 확인
+                3. 이탈 사용자(30일 이상 미접속)와 활성 사용자의 행동 패턴 비교 분석
+                4. 주요 이탈 지점 식별
+                ```
+                """,
+            ),
+            (
+                "system",
+                "아래는 사용자의 원래 질문 및 1차 구체화된 질문입니다:",
+            ),
+            MessagesPlaceholder(variable_name="user_input"),
+            MessagesPlaceholder(variable_name="refined_input"),
+            (
+                "system",
+                "다음은 사용자의 DB 환경정보와 실제 사용 가능한 테이블 및 컬럼 정보입니다:",
+            ),
+            MessagesPlaceholder(variable_name="user_database_env"),
+            MessagesPlaceholder(variable_name="searched_tables"),
+            (
+                "system",
+                "위 정보를 바탕으로 DB 구조에 맞게 더욱 구체화된 최종 질문과 분석 접근 방향을 최종 형태 예시와 같은 형식으로 작성해주세요.",
+            ),
+        ]
+    )
+    return query_redefined_again_prompt | llm
+
+
 # QueryMakerChain
 def create_query_maker_chain(llm):
     query_maker_prompt = ChatPromptTemplate.from_messages(
@@ -110,6 +165,7 @@ def create_query_maker_chain(llm):
             ),
             MessagesPlaceholder(variable_name="user_input"),
             MessagesPlaceholder(variable_name="refined_input"),
+            MessagesPlaceholder(variable_name="refined_input_again"),
             (
                 "system",
                 "다음은 사용자의 db 환경정보와 사용 가능한 테이블 및 컬럼 정보입니다:",
@@ -126,4 +182,5 @@ def create_query_maker_chain(llm):
 
 
 query_refiner_chain = create_query_refiner_chain(llm)
+query_redefined_again_chain = create_query_redefined_again_chain(llm)
 query_maker_chain = create_query_maker_chain(llm)
diff --git a/llm_utils/graph.py b/llm_utils/graph.py
index 0aef51d..c65e75f 100644
--- a/llm_utils/graph.py
+++ b/llm_utils/graph.py
@@ -10,6 +10,7 @@
 
 from llm_utils.chains import (
     query_refiner_chain,
+    query_redefined_again_chain,
     query_maker_chain,
 )
 
@@ -17,6 +18,7 @@
 
 # 노드 식별자 정의
 QUERY_REFINER = "query_refiner"
+QUERY_REFINED_AGAIN = "query_redefined_again"
 GET_TABLE_INFO = "get_table_info"
 TOOL = "tool"
 TABLE_FILTER = "table_filter"
@@ -30,6 +32,7 @@ class QueryMakerState(TypedDict):
     searched_tables: dict[str, dict[str, str]]
     best_practice_query: str
     refined_input: str
+    refined_input_again: str
     generated_query: str
 
 
@@ -90,6 +93,20 @@ def get_table_info_node(state: QueryMakerState):
     return state
 
 
+def query_redefined_again_node(state: QueryMakerState):
+    res = query_redefined_again_chain.invoke(
+        input={
+            "user_input": [state["messages"][0].content],
+            "refined_input": [state["refined_input"]],
+            "user_database_env": [state["user_database_env"]],
+            "searched_tables": [json.dumps(state["searched_tables"])],
+        }
+    )
+    state["refined_input_again"] = res
+    print(state["refined_input_again"])
+    return state
+
+
 # 노드 함수: QUERY_MAKER 노드
 def query_maker_node(state: QueryMakerState):
     res = query_maker_chain.invoke(
@@ -121,7 +138,9 @@ def query_maker_node_with_db_guide(state: QueryMakerState):
     res = chain.invoke(
         input={
             "input": "\n\n---\n\n".join(
-                [state["messages"][0].content] + [state["refined_input"].content]
+                [state["messages"][0].content]
+                # + [state["refined_input"].content]
+                + [state["refined_input_again"].content]
             ),
             "table_info": [json.dumps(state["searched_tables"])],
             "top_k": 10,
@@ -143,10 +162,12 @@ def query_maker_node_with_db_guide(state: QueryMakerState):
 builder.add_node(
     QUERY_MAKER, query_maker_node_with_db_guide
 )  #  query_maker_node_with_db_guide
+builder.add_node(QUERY_REFINED_AGAIN, query_redefined_again_node)
 
 # 기본 엣지 설정
 builder.add_edge(QUERY_REFINER, GET_TABLE_INFO)
-builder.add_edge(GET_TABLE_INFO, QUERY_MAKER)
+builder.add_edge(GET_TABLE_INFO, QUERY_REFINED_AGAIN)
+builder.add_edge(QUERY_REFINED_AGAIN, QUERY_MAKER)
 
 # QUERY_MAKER 노드 후 종료
 builder.add_edge(QUERY_MAKER, END)

From fa9652eae8aa4d3c313ae71508454175d3ff8104 Mon Sep 17 00:00:00 2001
From: ehddnr301 <dy950328@gmail.com>
Date: Sat, 5 Apr 2025 06:06:16 +0000
Subject: [PATCH 2/5] Remove obsolete binary files

---
 table_info_db/index.faiss | Bin 30765 -> 0 bytes
 table_info_db/index.pkl   | Bin 2920 -> 0 bytes
 2 files changed, 0 insertions(+), 0 deletions(-)
 delete mode 100644 table_info_db/index.faiss
 delete mode 100644 table_info_db/index.pkl

diff --git a/table_info_db/index.faiss b/table_info_db/index.faiss
deleted file mode 100644
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GIT binary patch
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z7uR)yS6=wn`kY@&<CrC0I88!}Ve%}OBbOs@3YP7MGtUj@b0@g^@Q!;L*XK({c@^P2
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From c59211fbc7f936778526b76cae97f33a4e9ca689 Mon Sep 17 00:00:00 2001
From: ehddnr301 <dy950328@gmail.com>
Date: Mon, 7 Apr 2025 09:55:31 +0000
Subject: [PATCH 3/5] Remove debug print statement

---
 llm_utils/graph.py | 1 -
 1 file changed, 1 deletion(-)

diff --git a/llm_utils/graph.py b/llm_utils/graph.py
index c65e75f..9b6e503 100644
--- a/llm_utils/graph.py
+++ b/llm_utils/graph.py
@@ -103,7 +103,6 @@ def query_redefined_again_node(state: QueryMakerState):
         }
     )
     state["refined_input_again"] = res
-    print(state["refined_input_again"])
     return state
 
 

From 335fd9d81c8df6dfd85ae3c6dea02feb2119a89b Mon Sep 17 00:00:00 2001
From: ehddnr301 <dy950328@gmail.com>
Date: Mon, 14 Apr 2025 07:15:27 +0900
Subject: [PATCH 4/5] Refactor query handling in llm_utils

- Add Reranker
- Added new dependencies: langchain-huggingface==0.1.2 and transformers==4.51.2 to requirements.txt.
- Removed the QueryRefinedAgainChain and its associated logic from chains.py and graph.py to streamline the query refinement process.
---
 llm_utils/chains.py | 68 +++++------------------------------------
 llm_utils/graph.py  | 73 ++++++++++++++++++++++++++-------------------
 requirements.txt    |  2 ++
 3 files changed, 52 insertions(+), 91 deletions(-)

diff --git a/llm_utils/chains.py b/llm_utils/chains.py
index 271dc29..9806721 100644
--- a/llm_utils/chains.py
+++ b/llm_utils/chains.py
@@ -40,23 +40,28 @@ def create_query_refiner_chain(llm):
                 예시:
                 사용자가 "유저 이탈 원인이 궁금해요"라고 했다면,
                 재질문 형식이 아니라
-                "최근 1개월 간의 접속·결제 로그를 기준으로,
+                "접속·결제 로그를 기준으로,
                 주로 어떤 사용자가 어떤 과정을 거쳐 이탈하는지를 분석해야 한다"처럼
                 분석 방향이 명확해진 질문 한 문장(또는 한 문단)으로 정리해 주세요.
 
                 최종 출력 형식 예시:
                 ------------------------------
                 구체화된 질문:
-                "최근 1개월 동안 고액 결제 경험이 있는 유저가 
+                "고액 결제 경험이 있는 유저가 
                 행동 로그에서 이탈 전 어떤 패턴을 보였는지 분석"
 
                 가정한 조건:
-                - 최근 1개월치 행동 로그와 결제 로그 중심
+                - 행동 로그와 결제 로그 중심
                 - 고액 결제자(월 결제액 10만 원 이상) 그룹 대상으로 한정
                 ------------------------------
                 """,
             ),
             MessagesPlaceholder(variable_name="user_input"),
+            (
+                "system",
+                "다음은 사용자의 실제 사용 가능한 테이블 및 컬럼 정보입니다:",
+            ),
+            MessagesPlaceholder(variable_name="searched_tables"),
             (
                 "system",
                 """
@@ -72,61 +77,6 @@ def create_query_refiner_chain(llm):
     return tool_choice_prompt | llm
 
 
-# QueryRefinedAgainChain
-def create_query_redefined_again_chain(llm):
-    query_redefined_again_prompt = ChatPromptTemplate.from_messages(
-        [
-            (
-                "system",
-                """
-                당신은 데이터 분석 전문가(데이터 분석가 페르소나)입니다.
-                사용자의 질문과 이미 구체화된 질문을 바탕으로, 실제 사용 가능한 테이블과 컬럼 정보를 검토하여
-                더욱 정교하게 질문을 재정의해 주세요.
-                
-                주의사항:
-                - 이전에 구체화된 질문을 기반으로 하되, 실제 DB 환경에서 사용 가능한 테이블/컬럼을 고려해 현실적인 분석 방향을 제시하세요.
-                - 불필요한 재질문 없이, 주어진 데이터로 최대한 분석 가능한 형태로 질문을 구체화하세요.
-                - 테이블 구조에 맞게 분석 질문을 조정하고, 필요한 가정을 추가하세요.
-                - 최종 출력 형식은 반드시 아래와 같아야 합니다.
-                
-                최종 형태 예시:
-                
-                <최종 구체화된 질문>
-                ```
-                최근 30일간 결제 금액이 10만원 이상인 사용자들의 서비스 이용 패턴과 이탈율을 분석하여, 
-                어떤 활동 패턴을 보이는 고액 결제자가 이탈하는지 파악
-                ```
-                
-                <분석 접근 방향>
-                ```
-                1. subscription_activities와 contract_activities 테이블을 조인하여 고액 결제자 식별
-                2. 해당 사용자들의 activity_type 분포 확인
-                3. 이탈 사용자(30일 이상 미접속)와 활성 사용자의 행동 패턴 비교 분석
-                4. 주요 이탈 지점 식별
-                ```
-                """,
-            ),
-            (
-                "system",
-                "아래는 사용자의 원래 질문 및 1차 구체화된 질문입니다:",
-            ),
-            MessagesPlaceholder(variable_name="user_input"),
-            MessagesPlaceholder(variable_name="refined_input"),
-            (
-                "system",
-                "다음은 사용자의 DB 환경정보와 실제 사용 가능한 테이블 및 컬럼 정보입니다:",
-            ),
-            MessagesPlaceholder(variable_name="user_database_env"),
-            MessagesPlaceholder(variable_name="searched_tables"),
-            (
-                "system",
-                "위 정보를 바탕으로 DB 구조에 맞게 더욱 구체화된 최종 질문과 분석 접근 방향을 최종 형태 예시와 같은 형식으로 작성해주세요.",
-            ),
-        ]
-    )
-    return query_redefined_again_prompt | llm
-
-
 # QueryMakerChain
 def create_query_maker_chain(llm):
     query_maker_prompt = ChatPromptTemplate.from_messages(
@@ -165,7 +115,6 @@ def create_query_maker_chain(llm):
             ),
             MessagesPlaceholder(variable_name="user_input"),
             MessagesPlaceholder(variable_name="refined_input"),
-            MessagesPlaceholder(variable_name="refined_input_again"),
             (
                 "system",
                 "다음은 사용자의 db 환경정보와 사용 가능한 테이블 및 컬럼 정보입니다:",
@@ -182,5 +131,4 @@ def create_query_maker_chain(llm):
 
 
 query_refiner_chain = create_query_refiner_chain(llm)
-query_redefined_again_chain = create_query_redefined_again_chain(llm)
 query_maker_chain = create_query_maker_chain(llm)
diff --git a/llm_utils/graph.py b/llm_utils/graph.py
index 9b6e503..37e1d1a 100644
--- a/llm_utils/graph.py
+++ b/llm_utils/graph.py
@@ -10,7 +10,6 @@
 
 from llm_utils.chains import (
     query_refiner_chain,
-    query_redefined_again_chain,
     query_maker_chain,
 )
 
@@ -18,7 +17,6 @@
 
 # 노드 식별자 정의
 QUERY_REFINER = "query_refiner"
-QUERY_REFINED_AGAIN = "query_redefined_again"
 GET_TABLE_INFO = "get_table_info"
 TOOL = "tool"
 TABLE_FILTER = "table_filter"
@@ -32,7 +30,6 @@ class QueryMakerState(TypedDict):
     searched_tables: dict[str, dict[str, str]]
     best_practice_query: str
     refined_input: str
-    refined_input_again: str
     generated_query: str
 
 
@@ -43,6 +40,7 @@ def query_refiner_node(state: QueryMakerState):
             "user_input": [state["messages"][0].content],
             "user_database_env": [state["user_database_env"]],
             "best_practice_query": [state["best_practice_query"]],
+            "searched_tables": [json.dumps(state["searched_tables"])],
         }
     )
     state["messages"].append(res)
@@ -66,9 +64,42 @@ def get_table_info_node(state: QueryMakerState):
         db = FAISS.from_documents(documents, embeddings)
         db.save_local(os.getcwd() + "/table_info_db")
         print("table_info_db not found")
-    doc_res = db.similarity_search(state["messages"][-1].content)
-    documents_dict = {}
 
+    retriever = db.as_retriever(search_kwargs={"k": 10})
+
+    from langchain.retrievers import ContextualCompressionRetriever
+    from langchain.retrievers.document_compressors import CrossEncoderReranker
+    from langchain_community.cross_encoders import HuggingFaceCrossEncoder
+    from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
+    # Reranking 적용 여부 설정
+    use_rerank = True  # 필요에 따라 True 또는 False로 설정
+
+    if use_rerank:
+        local_model_path = os.path.join(os.getcwd(), "ko_reranker_local")
+
+        # 로컬에 저장된 모델이 있으면 불러오고, 없으면 다운로드 후 저장
+        if os.path.exists(local_model_path) and os.path.isdir(local_model_path):
+            print("🔄 ko-reranker 모델 로컬에서 로드 중...")
+        else:
+            print("⬇️ ko-reranker 모델 다운로드 및 저장 중...")
+            model = AutoModelForSequenceClassification.from_pretrained(
+                "Dongjin-kr/ko-reranker"
+            )
+            tokenizer = AutoTokenizer.from_pretrained("Dongjin-kr/ko-reranker")
+            model.save_pretrained(local_model_path)
+            tokenizer.save_pretrained(local_model_path)
+        model = HuggingFaceCrossEncoder(model_name=local_model_path)
+        compressor = CrossEncoderReranker(model=model, top_n=3)
+        retriever = db.as_retriever(search_kwargs={"k": 10})
+        compression_retriever = ContextualCompressionRetriever(
+            base_compressor=compressor, base_retriever=retriever
+        )
+
+        doc_res = compression_retriever.invoke(state["messages"][0].content)
+    else:  # Reranking 미적용
+        doc_res = db.similarity_search(state["messages"][0].content, k=10)
+    documents_dict = {}
     for doc in doc_res:
         lines = doc.page_content.split("\n")
 
@@ -93,19 +124,6 @@ def get_table_info_node(state: QueryMakerState):
     return state
 
 
-def query_redefined_again_node(state: QueryMakerState):
-    res = query_redefined_again_chain.invoke(
-        input={
-            "user_input": [state["messages"][0].content],
-            "refined_input": [state["refined_input"]],
-            "user_database_env": [state["user_database_env"]],
-            "searched_tables": [json.dumps(state["searched_tables"])],
-        }
-    )
-    state["refined_input_again"] = res
-    return state
-
-
 # 노드 함수: QUERY_MAKER 노드
 def query_maker_node(state: QueryMakerState):
     res = query_maker_chain.invoke(
@@ -137,9 +155,7 @@ def query_maker_node_with_db_guide(state: QueryMakerState):
     res = chain.invoke(
         input={
             "input": "\n\n---\n\n".join(
-                [state["messages"][0].content]
-                # + [state["refined_input"].content]
-                + [state["refined_input_again"].content]
+                [state["messages"][0].content] + [state["refined_input"].content]
             ),
             "table_info": [json.dumps(state["searched_tables"])],
             "top_k": 10,
@@ -152,21 +168,16 @@ def query_maker_node_with_db_guide(state: QueryMakerState):
 
 # StateGraph 생성 및 구성
 builder = StateGraph(QueryMakerState)
-builder.set_entry_point(QUERY_REFINER)
+builder.set_entry_point(GET_TABLE_INFO)
 
 # 노드 추가
-builder.add_node(QUERY_REFINER, query_refiner_node)
 builder.add_node(GET_TABLE_INFO, get_table_info_node)
-# builder.add_node(QUERY_MAKER, query_maker_node)  #  query_maker_node_with_db_guide
-builder.add_node(
-    QUERY_MAKER, query_maker_node_with_db_guide
-)  #  query_maker_node_with_db_guide
-builder.add_node(QUERY_REFINED_AGAIN, query_redefined_again_node)
+builder.add_node(QUERY_REFINER, query_refiner_node)
+builder.add_node(QUERY_MAKER, query_maker_node_with_db_guide)
 
 # 기본 엣지 설정
-builder.add_edge(QUERY_REFINER, GET_TABLE_INFO)
-builder.add_edge(GET_TABLE_INFO, QUERY_REFINED_AGAIN)
-builder.add_edge(QUERY_REFINED_AGAIN, QUERY_MAKER)
+builder.add_edge(GET_TABLE_INFO, QUERY_REFINER)
+builder.add_edge(QUERY_REFINER, QUERY_MAKER)
 
 # QUERY_MAKER 노드 후 종료
 builder.add_edge(QUERY_MAKER, END)
diff --git a/requirements.txt b/requirements.txt
index 2c506a8..2998e38 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -11,3 +11,5 @@ pre_commit==4.1.0
 setuptools
 wheel
 twine
+langchain-huggingface==0.1.2
+transformers==4.51.2
\ No newline at end of file

From 5244fda33ce642a524f6d0a5e26b18ebba5ea530 Mon Sep 17 00:00:00 2001
From: ehddnr301 <dy950328@gmail.com>
Date: Fri, 18 Apr 2025 08:49:24 +0000
Subject: [PATCH 5/5] Add reranking functionality and new dependencies

- add reranking feature in the Streamlit app to enhance search result accuracy.
- Added new dependencies: transformers==4.51.2 and langchain-huggingface==0.1.2 to setup.py.
- Created a new retrieval module to handle vector database interactions and reranking logic.
---
 interface/streamlit_app.py |  7 +++
 llm_utils/graph.py         | 76 +++---------------------------
 llm_utils/retrieval.py     | 94 ++++++++++++++++++++++++++++++++++++++
 setup.py                   |  2 +
 4 files changed, 109 insertions(+), 70 deletions(-)
 create mode 100644 llm_utils/retrieval.py

diff --git a/interface/streamlit_app.py b/interface/streamlit_app.py
index 395b2b7..7846eed 100644
--- a/interface/streamlit_app.py
+++ b/interface/streamlit_app.py
@@ -28,6 +28,12 @@ def summarize_total_tokens(data):
     return total_tokens
 
 
+use_reranker = st.checkbox(
+    "리랭킹(Reranking) 기능 사용",
+    value=False,
+    help="검색 결과의 정확도를 높이기 위한 리랭킹 기능을 사용합니다.",
+)
+
 # 버튼 클릭 시 실행
 if st.button("쿼리 실행"):
     # 그래프 컴파일 및 쿼리 실행
@@ -38,6 +44,7 @@ def summarize_total_tokens(data):
             "messages": [HumanMessage(content=user_query)],
             "user_database_env": user_database_env,
             "best_practice_query": "",
+            "use_rerank": use_reranker,
         }
     )
     total_tokens = summarize_total_tokens(res["messages"])
diff --git a/llm_utils/graph.py b/llm_utils/graph.py
index 37e1d1a..112b25f 100644
--- a/llm_utils/graph.py
+++ b/llm_utils/graph.py
@@ -14,6 +14,7 @@
 )
 
 from llm_utils.tools import get_info_from_db
+from llm_utils.retrieval import search_tables
 
 # 노드 식별자 정의
 QUERY_REFINER = "query_refiner"
@@ -31,6 +32,7 @@ class QueryMakerState(TypedDict):
     best_practice_query: str
     refined_input: str
     generated_query: str
+    use_rerank: bool
 
 
 # 노드 함수: QUERY_REFINER 노드
@@ -49,76 +51,10 @@ def query_refiner_node(state: QueryMakerState):
 
 
 def get_table_info_node(state: QueryMakerState):
-    from langchain_community.vectorstores import FAISS
-    from langchain_openai import OpenAIEmbeddings
-
-    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
-    try:
-        db = FAISS.load_local(
-            os.getcwd() + "/table_info_db",
-            embeddings,
-            allow_dangerous_deserialization=True,
-        )
-    except:
-        documents = get_info_from_db()
-        db = FAISS.from_documents(documents, embeddings)
-        db.save_local(os.getcwd() + "/table_info_db")
-        print("table_info_db not found")
-
-    retriever = db.as_retriever(search_kwargs={"k": 10})
-
-    from langchain.retrievers import ContextualCompressionRetriever
-    from langchain.retrievers.document_compressors import CrossEncoderReranker
-    from langchain_community.cross_encoders import HuggingFaceCrossEncoder
-    from transformers import AutoModelForSequenceClassification, AutoTokenizer
-
-    # Reranking 적용 여부 설정
-    use_rerank = True  # 필요에 따라 True 또는 False로 설정
-
-    if use_rerank:
-        local_model_path = os.path.join(os.getcwd(), "ko_reranker_local")
-
-        # 로컬에 저장된 모델이 있으면 불러오고, 없으면 다운로드 후 저장
-        if os.path.exists(local_model_path) and os.path.isdir(local_model_path):
-            print("🔄 ko-reranker 모델 로컬에서 로드 중...")
-        else:
-            print("⬇️ ko-reranker 모델 다운로드 및 저장 중...")
-            model = AutoModelForSequenceClassification.from_pretrained(
-                "Dongjin-kr/ko-reranker"
-            )
-            tokenizer = AutoTokenizer.from_pretrained("Dongjin-kr/ko-reranker")
-            model.save_pretrained(local_model_path)
-            tokenizer.save_pretrained(local_model_path)
-        model = HuggingFaceCrossEncoder(model_name=local_model_path)
-        compressor = CrossEncoderReranker(model=model, top_n=3)
-        retriever = db.as_retriever(search_kwargs={"k": 10})
-        compression_retriever = ContextualCompressionRetriever(
-            base_compressor=compressor, base_retriever=retriever
-        )
-
-        doc_res = compression_retriever.invoke(state["messages"][0].content)
-    else:  # Reranking 미적용
-        doc_res = db.similarity_search(state["messages"][0].content, k=10)
-    documents_dict = {}
-    for doc in doc_res:
-        lines = doc.page_content.split("\n")
-
-        # 테이블명 및 설명 추출
-        table_name, table_desc = lines[0].split(": ", 1)
-
-        # 컬럼 정보 추출
-        columns = {}
-        if len(lines) > 2 and lines[1].strip() == "Columns:":
-            for line in lines[2:]:
-                if ": " in line:
-                    col_name, col_desc = line.split(": ", 1)
-                    columns[col_name.strip()] = col_desc.strip()
-
-        # 딕셔너리 저장
-        documents_dict[table_name] = {
-            "table_description": table_desc.strip(),
-            **columns,  # 컬럼 정보 추가
-        }
+    # state의 use_rerank 값을 이용하여 검색 수행
+    documents_dict = search_tables(
+        state["messages"][0].content, use_rerank=state["use_rerank"]
+    )
     state["searched_tables"] = documents_dict
 
     return state
diff --git a/llm_utils/retrieval.py b/llm_utils/retrieval.py
new file mode 100644
index 0000000..dbd1d2b
--- /dev/null
+++ b/llm_utils/retrieval.py
@@ -0,0 +1,94 @@
+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 .tools import get_info_from_db
+
+
+def get_vector_db():
+    """벡터 데이터베이스를 로드하거나 생성합니다."""
+    embeddings = OpenAIEmbeddings(model="text-embedding-3-small")
+    try:
+        db = FAISS.load_local(
+            os.getcwd() + "/table_info_db",
+            embeddings,
+            allow_dangerous_deserialization=True,
+        )
+    except:
+        documents = get_info_from_db()
+        db = FAISS.from_documents(documents, embeddings)
+        db.save_local(os.getcwd() + "/table_info_db")
+        print("table_info_db not found")
+    return db
+
+
+def load_reranker_model():
+    """한국어 reranker 모델을 로드하거나 다운로드합니다."""
+    local_model_path = os.path.join(os.getcwd(), "ko_reranker_local")
+
+    # 로컬에 저장된 모델이 있으면 불러오고, 없으면 다운로드 후 저장
+    if os.path.exists(local_model_path) and os.path.isdir(local_model_path):
+        print("🔄 ko-reranker 모델 로컬에서 로드 중...")
+    else:
+        print("⬇️ ko-reranker 모델 다운로드 및 저장 중...")
+        model = AutoModelForSequenceClassification.from_pretrained(
+            "Dongjin-kr/ko-reranker"
+        )
+        tokenizer = AutoTokenizer.from_pretrained("Dongjin-kr/ko-reranker")
+        model.save_pretrained(local_model_path)
+        tokenizer.save_pretrained(local_model_path)
+
+    return HuggingFaceCrossEncoder(model_name=local_model_path)
+
+
+def get_retriever(use_rerank=False):
+    """검색기를 생성합니다. use_rerank가 True이면 reranking을 적용합니다."""
+    db = get_vector_db()
+    retriever = db.as_retriever(search_kwargs={"k": 10})
+
+    if use_rerank:
+        model = load_reranker_model()
+        compressor = CrossEncoderReranker(model=model, top_n=3)
+        return ContextualCompressionRetriever(
+            base_compressor=compressor, base_retriever=retriever
+        )
+    else:
+        return retriever
+
+
+def search_tables(query, use_rerank=False):
+    """쿼리에 맞는 테이블 정보를 검색합니다."""
+    if use_rerank:
+        retriever = get_retriever(use_rerank=True)
+        doc_res = retriever.invoke(query)
+    else:
+        db = get_vector_db()
+        doc_res = db.similarity_search(query, k=10)
+
+    # 결과를 사전 형태로 변환
+    documents_dict = {}
+    for doc in doc_res:
+        lines = doc.page_content.split("\n")
+
+        # 테이블명 및 설명 추출
+        table_name, table_desc = lines[0].split(": ", 1)
+
+        # 컬럼 정보 추출
+        columns = {}
+        if len(lines) > 2 and lines[1].strip() == "Columns:":
+            for line in lines[2:]:
+                if ": " in line:
+                    col_name, col_desc = line.split(": ", 1)
+                    columns[col_name.strip()] = col_desc.strip()
+
+        # 딕셔너리 저장
+        documents_dict[table_name] = {
+            "table_description": table_desc.strip(),
+            **columns,  # 컬럼 정보 추가
+        }
+
+    return documents_dict
diff --git a/setup.py b/setup.py
index d5e4805..d02bc0f 100644
--- a/setup.py
+++ b/setup.py
@@ -24,6 +24,8 @@
         "streamlit==1.41.1",
         "python-dotenv==1.0.1",
         "faiss-cpu==1.10.0",
+        "transformers==4.51.2",
+        "langchain-huggingface==0.1.2",
     ],
     entry_points={
         "console_scripts": [