141 기능 self rag 적용

interface/lang2sql.py

@@ -13,7 +13,6 @@
 
 from db_utils import get_db_connector
 from db_utils.base_connector import BaseConnector
-from infra.db.connect_db import ConnectDB
 from viz.display_chart import DisplayChart
 from engine.query_executor import execute_query as execute_query_common
 from llm_utils.llm_response_parser import LLMResponseParser
@@ -30,6 +29,8 @@
     "show_sql": "Show SQL",
     "show_question_reinterpreted_by_ai": "Show User Question Reinterpreted by AI",
     "show_referenced_tables": "Show List of Referenced Tables",
+    "show_question_gate_result": "Show Question Gate Result",
+    "show_document_suitability": "Show Document Suitability",
     "show_table": "Show Table",
     "show_chart": "Show Chart",
 }
@@ -103,8 +104,55 @@ def should_show(_key: str) -> bool:
     show_sql_section = has_query and should_show("show_sql")
     show_result_desc = has_query and should_show("show_result_description")
     show_reinterpreted = has_query and should_show("show_question_reinterpreted_by_ai")
+    show_gate_result = should_show("show_question_gate_result")
+    show_doc_suitability = should_show("show_document_suitability")
     show_table_section = has_query and should_show("show_table")
     show_chart_section = has_query and should_show("show_chart")
+    if show_gate_result and ("question_gate_result" in res):
+        st.markdown("---")
+        st.markdown("**Question Gate 결과:**")
+        details = res.get("question_gate_result")
+        if details:
+            try:
+                import json as _json
+
+                st.code(
+                    _json.dumps(details, ensure_ascii=False, indent=2), language="json"
+                )
+            except Exception:
+                st.write(details)
+
+    if show_doc_suitability and ("document_suitability" in res):
+        st.markdown("---")
+        st.markdown("**문서 적합성 평가:**")
+        ds = res.get("document_suitability")
+        if not isinstance(ds, dict):
+            st.write(ds)
+        else:
+
+            def _as_float(value):
+                try:
+                    return float(value)
+                except Exception:
+                    return -1.0
+
+            rows = [
+                {
+                    "table": table_name,
+                    "score": _as_float(info.get("score", -1)),
+                    "matched_columns": ", ".join(info.get("matched_columns", [])),
+                    "missing_entities": ", ".join(info.get("missing_entities", [])),
+                    "reason": info.get("reason", ""),
+                }
+                for table_name, info in ds.items()
+                if isinstance(info, dict)
+            ]
+
+            rows.sort(key=lambda r: r["score"], reverse=True)
+            if rows:
+                st.dataframe(rows, use_container_width=True)
+            else:
+                st.info("문서 적합성 평가 결과가 비어 있습니다.")
 
     if should_show("show_token_usage"):
         st.markdown("---")

llm_utils/chains.py

@@ -1,10 +1,23 @@
+"""
+LLM 체인 생성 모듈.
+
+이 모듈은 Lang2SQL에서 사용하는 다양한 LangChain 기반 체인을 정의합니다.
+- Query Maker
+- Query Enrichment
+- Profile Extraction
+- Question Gate (SQL 적합성 분류)
+"""
+
 import os
 from langchain_core.prompts import (
     ChatPromptTemplate,
-    MessagesPlaceholder,
     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
 
@@ -15,6 +28,12 @@
 
 
 class QuestionProfile(BaseModel):
+    """
+    자연어 질문의 특징을 구조화해 표현하는 프로파일 모델.
+
+    이 프로파일은 이후 컨텍스트 보강 및 SQL 생성 시 힌트로 사용됩니다.
+    """
+
     is_timeseries: bool = Field(description="시계열 분석 필요 여부")
     is_aggregation: bool = Field(description="집계 함수 필요 여부")
     has_filter: bool = Field(description="조건 필터 필요 여부")
@@ -26,6 +45,15 @@ class QuestionProfile(BaseModel):
 
 # QueryMakerChain
 def create_query_maker_chain(llm):
+    """
+    SQL 쿼리 생성을 위한 체인을 생성합니다.
+
+    Args:
+        llm: LangChain 호환 LLM 인스턴스
+
+    Returns:
+        Runnable: 입력 프롬프트를 받아 SQL을 생성하는 체인
+    """
     prompt = get_prompt_template("query_maker_prompt")
     query_maker_prompt = ChatPromptTemplate.from_messages(
         [
@@ -36,6 +64,15 @@ def create_query_maker_chain(llm):
 
 
 def create_query_enrichment_chain(llm):
+    """
+    사용자 질문을 메타데이터로 보강하기 위한 체인을 생성합니다.
+
+    Args:
+        llm: LangChain 호환 LLM 인스턴스
+
+    Returns:
+        Runnable: 보강된 질문 텍스트를 반환하는 체인
+    """
     prompt = get_prompt_template("query_enrichment_prompt")
 
     enrichment_prompt = ChatPromptTemplate.from_messages(
@@ -49,6 +86,15 @@ def create_query_enrichment_chain(llm):
 
 
 def create_profile_extraction_chain(llm):
+    """
+    질문으로부터 `QuestionProfile`을 추출하는 체인을 생성합니다.
+
+    Args:
+        llm: LangChain 호환 LLM 인스턴스
+
+    Returns:
+        Runnable: `QuestionProfile` 구조화 출력을 반환하는 체인
+    """
     prompt = get_prompt_template("profile_extraction_prompt")
 
     profile_prompt = ChatPromptTemplate.from_messages(
@@ -61,9 +107,47 @@ def create_profile_extraction_chain(llm):
     return chain
 
 
+def create_question_gate_chain(llm):
+    """
+    질문 적합성(Question Gate) 체인을 생성합니다.
+
+    ChatPromptTemplate(SystemMessage) + LLM 구조화 출력으로
+    `QuestionSuitability`를 반환합니다.
+
+    Args:
+        llm: LangChain 호환 LLM 인스턴스
+
+    Returns:
+        Runnable: invoke({"question": str}) -> QuestionSuitability
+    """
+
+    prompt = get_prompt_template("question_gate_prompt")
+    gate_prompt = ChatPromptTemplate.from_messages(
+        [SystemMessagePromptTemplate.from_template(prompt)]
+    )
+    return gate_prompt | llm.with_structured_output(QuestionSuitability)
+
+
+def create_document_suitability_chain(llm):
+    """
+    문서 적합성 평가 체인을 생성합니다.
+
+    질문(question)과 검색 결과(tables)를 입력으로 받아
+    테이블별 적합도 점수를 포함한 JSON 딕셔너리를 반환합니다.
+
+    Returns:
+        Runnable: invoke({"question": str, "tables": dict}) -> {"results": DocumentSuitability[]}
+    """
+
+    prompt = get_prompt_template("document_suitability_prompt")
+    doc_prompt = ChatPromptTemplate.from_messages(
+        [SystemMessagePromptTemplate.from_template(prompt)]
+    )
+    return doc_prompt | llm.with_structured_output(DocumentSuitabilityList)
+
+
 query_maker_chain = create_query_maker_chain(llm)
 profile_extraction_chain = create_profile_extraction_chain(llm)
 query_enrichment_chain = create_query_enrichment_chain(llm)
-
-if __name__ == "__main__":
-    pass
+question_gate_chain = create_question_gate_chain(llm)
+document_suitability_chain = create_document_suitability_chain(llm)

llm_utils/graph_utils/base.py

@@ -1,22 +1,22 @@
-import os
 import json
 
 from typing_extensions import TypedDict, Annotated
-from langgraph.graph import END, StateGraph
 from langgraph.graph.message import add_messages
 
 
 from llm_utils.chains import (
     query_maker_chain,
     profile_extraction_chain,
     query_enrichment_chain,
+    question_gate_chain,
+    document_suitability_chain,
 )
 
-from llm_utils.tools import get_info_from_db
 from llm_utils.retrieval import search_tables
-from llm_utils.graph_utils.profile_utils import profile_to_text
 
 # 노드 식별자 정의
+QUESTION_GATE = "question_gate"
+EVALUATE_DOCUMENT_SUITABILITY = "evaluate_document_suitability"
 GET_TABLE_INFO = "get_table_info"
 TOOL = "tool"
 TABLE_FILTER = "table_filter"
@@ -30,12 +30,39 @@ class QueryMakerState(TypedDict):
     messages: Annotated[list, add_messages]
     user_database_env: str
     searched_tables: dict[str, dict[str, str]]
+    document_suitability: dict
     best_practice_query: str
     question_profile: dict
     generated_query: str
     retriever_name: str
     top_n: int
     device: str
+    question_gate_result: dict
+
+
+# 노드 함수: QUESTION_GATE 노드
+def question_gate_node(state: QueryMakerState):
+    """
+    사용자의 질문이 SQL로 답변 가능한지 판별하고, 구조화된 결과를 반환하는 게이트 노드입니다.
+
+    - question_gate_chain 으로 적합성을 판정하여
+      `question_gate_result`를 설정합니다.
+
+    Args:
+        state (QueryMakerState): 그래프 상태
+
+    Returns:
+        QueryMakerState: 게이트 판정 결과가 반영된 상태
+    """
+
+    question_text = state["messages"][0].content
+    suitability = question_gate_chain.invoke({"question": question_text})
+    state["question_gate_result"] = {
+        "reason": getattr(suitability, "reason", ""),
+        "missing_entities": getattr(suitability, "missing_entities", []),
+        "requires_data_science": getattr(suitability, "requires_data_science", False),
+    }
+    return state
 
 
 # 노드 함수: PROFILE_EXTRACTION 노드
@@ -132,6 +159,70 @@ def get_table_info_node(state: QueryMakerState):
     return state
 
 
+# 노드 함수: DOCUMENT_SUITABILITY 노드
+def document_suitability_node(state: QueryMakerState):
+    """
+    GET_TABLE_INFO에서 수집된 테이블 후보들에 대해 문서 적합성 점수를 계산하는 노드입니다.
+
+    질문(`messages[0].content`)과 `searched_tables`(테이블→칼럼 설명 맵)를 입력으로
+    프롬프트 체인(`document_suitability_chain`)을 호출하고, 결과 딕셔너리를
+    `document_suitability` 상태 키에 저장합니다.
+
+    Returns:
+        QueryMakerState: 문서 적합성 평가 결과가 포함된 상태
+    """
+
+    # 관련 테이블이 없으면 즉시 반환
+    if not state.get("searched_tables"):
+        state["document_suitability"] = {}
+        return state
+
+    res = document_suitability_chain.invoke(
+        {
+            "question": state["messages"][0].content,
+            "tables": state["searched_tables"],
+        }
+    )
+
+    items = (
+        res.get("results", [])
+        if isinstance(res, dict)
+        else getattr(res, "results", None)
+        or (res.model_dump().get("results", []) if hasattr(res, "model_dump") else [])
+    )
+
+    normalized = {}
+    for x in items:
+        d = (
+            x.model_dump()
+            if hasattr(x, "model_dump")
+            else (
+                x
+                if isinstance(x, dict)
+                else {
+                    "table_name": getattr(x, "table_name", ""),
+                    "score": getattr(x, "score", 0),
+                    "reason": getattr(x, "reason", ""),
+                    "matched_columns": getattr(x, "matched_columns", []),
+                    "missing_entities": getattr(x, "missing_entities", []),
+                }
+            )
+        )
+        t = d.get("table_name")
+        if not t:
+            continue
+        normalized[t] = {
+            "score": float(d.get("score", 0)),
+            "reason": d.get("reason", ""),
+            "matched_columns": d.get("matched_columns", []),
+            "missing_entities": d.get("missing_entities", []),
+        }
+
+    state["document_suitability"] = normalized
+
+    return state
+
+
 # 노드 함수: QUERY_MAKER 노드
 def query_maker_node(state: QueryMakerState):
     # 사용자 원 질문 + (있다면) 컨텍스트 보강 결과를 하나의 문자열로 결합

llm_utils/graph_utils/basic_graph.py

@@ -3,9 +3,13 @@
 from langgraph.graph import StateGraph, END
 from llm_utils.graph_utils.base import (
     QueryMakerState,
+    QUESTION_GATE,
     GET_TABLE_INFO,
+    EVALUATE_DOCUMENT_SUITABILITY,
     QUERY_MAKER,
+    question_gate_node,
     get_table_info_node,
+    document_suitability_node,
     query_maker_node,
 )
 
@@ -16,14 +20,31 @@
 
 # StateGraph 생성 및 구성
 builder = StateGraph(QueryMakerState)
-builder.set_entry_point(GET_TABLE_INFO)
+builder.set_entry_point(QUESTION_GATE)
 
 # 노드 추가
+builder.add_node(QUESTION_GATE, question_gate_node)
 builder.add_node(GET_TABLE_INFO, get_table_info_node)
+builder.add_node(EVALUATE_DOCUMENT_SUITABILITY, document_suitability_node)
 builder.add_node(QUERY_MAKER, query_maker_node)
 
+
+def _route_after_gate(state: QueryMakerState):
+    return GET_TABLE_INFO
+
+
+builder.add_conditional_edges(
+    QUESTION_GATE,
+    _route_after_gate,
+    {
+        GET_TABLE_INFO: GET_TABLE_INFO,
+        END: END,
+    },
+)
+
 # 기본 엣지 설정
-builder.add_edge(GET_TABLE_INFO, QUERY_MAKER)
+builder.add_edge(GET_TABLE_INFO, EVALUATE_DOCUMENT_SUITABILITY)
+builder.add_edge(EVALUATE_DOCUMENT_SUITABILITY, QUERY_MAKER)
 
 # QUERY_MAKER 노드 후 종료
 builder.add_edge(QUERY_MAKER, END)