fastAPI 심화

- Chart.js - pdf, csv 파일 업로드 후 데이터 정제하여 llm으로 처리 후 결과 도출 - sqlite로 데이터 저장 - ORM - SQLAlchemy
2026-06-16 18:03:02 +09:00
parent ccfdac1286
commit 06eb3c57ab
43 changed files with 1912 additions and 39 deletions
@@ -0,0 +1,10 @@
+# 디폴트 무시된 파일
+/shelf/
+/workspace.xml
+# 에디터 기반 HTTP 클라이언트 요청
+/httpRequests/
+# 쿼리 파일을 포함한 무시된 디폴트 폴더
+/queries/
+# Datasource local storage ignored files
+/dataSources/
+/dataSources.local.xml
@@ -0,0 +1,11 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$">
+      <sourceFolder url="file://$MODULE_DIR$" isTestSource="false" />
+      <excludeFolder url="file://$MODULE_DIR$/.venv" />
+    </content>
+    <orderEntry type="jdk" jdkName="~/Source/project/CREDIT_APP/.venv" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
@@ -0,0 +1,16 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="CsvFileAttributes">
+    <option name="attributeMap">
+      <map>
+        <entry key="/transactions.csv">
+          <value>
+            <Attribute>
+              <option name="separator" value="," />
+            </Attribute>
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
+</project>
@@ -0,0 +1,19 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="DataSourceManagerImpl" format="xml" multifile-model="true">
+    <data-source source="LOCAL" name="history" uuid="d34d9830-06d9-4e92-bd10-84567e5a4809">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$PROJECT_DIR$/db/history.db</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+    <data-source source="LOCAL" name="users" uuid="2d5d9d87-b30f-47d4-9d9d-61542cb64ea0">
+      <driver-ref>sqlite.xerial</driver-ref>
+      <synchronize>true</synchronize>
+      <jdbc-driver>org.sqlite.JDBC</jdbc-driver>
+      <jdbc-url>jdbc:sqlite:$PROJECT_DIR$/db/users.db</jdbc-url>
+      <working-dir>$ProjectFileDir$</working-dir>
+    </data-source>
+  </component>
+</project>
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="db-forest-configuration">
+    <data version="2">.
+	----------------------------------------
+	1:0:d34d9830-06d9-4e92-bd10-84567e5a4809
+	2:0:2d5d9d87-b30f-47d4-9d9d-61542cb64ea0
+		.</data>
+  </component>
+</project>
@@ -0,0 +1,6 @@
+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/CREDIT_APP.iml" filepath="$PROJECT_DIR$/.idea/CREDIT_APP.iml" />
+    </modules>
+  </component>
+</project>
@@ -0,0 +1,34 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PyToolsState">
+    <option name="tools">
+      <map>
+        <entry key="black">
+          <value>
+            <ToolEntry />
+          </value>
+        </entry>
+        <entry key="pyrefly">
+          <value>
+            <ToolEntry />
+          </value>
+        </entry>
+        <entry key="pyright">
+          <value>
+            <ToolEntry />
+          </value>
+        </entry>
+        <entry key="ruff">
+          <value>
+            <ToolEntry />
+          </value>
+        </entry>
+        <entry key="ty">
+          <value>
+            <ToolEntry />
+          </value>
+        </entry>
+      </map>
+    </option>
+  </component>
+</project>
@@ -0,0 +1,6 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="VcsDirectoryMappings">
+    <mapping directory="$PROJECT_DIR$/../.." vcs="Git" />
+  </component>
+</project>
@@ -0,0 +1,9 @@
+from langchain_ibm import WatsonxEmbeddings
+from backend.config.settings import settings
+
+watson_embedding = WatsonxEmbeddings(
+    model_id="ibm/granite-embedding-278m-multilingual",
+    url=f"{settings.watsonx_url}",
+    api_key=f"{settings.watsonx_api_key}",
+    project_id=f"{settings.watsonx_project_id}",
+)
@@ -0,0 +1,13 @@
+from langchain_ibm import ChatWatsonx
+from backend.config.settings import settings
+
+watson_llm = ChatWatsonx(
+    model_id="ibm/granite-4-h-small",
+    url=f"{settings.watsonx_url}",
+    api_key=f"{settings.watsonx_api_key}",
+    project_id=f"{settings.watsonx_project_id}",
+    max_tokens=2000,
+    params={
+        "temperature": 0
+    }
+)
@@ -0,0 +1,11 @@
+from pydantic import Field
+from pydantic_settings import BaseSettings, SettingsConfigDict
+
+class Settings(BaseSettings):
+    model_config = SettingsConfigDict(env_file="backend/.env", extra="ignore")
+    # 사용할 모델
+    watsonx_api_key: str = Field(alias="WATSONX_API_KEY")
+    watsonx_project_id: str = Field(alias="WATSONX_PROJECT_ID")
+    watsonx_url: str = Field(alias="WATSONX_URL")
+
+settings = Settings()
@@ -0,0 +1,25 @@
+from fastapi import APIRouter, Request, UploadFile
+from backend.schemas.card_schema import AnalysisRequest
+from backend.services.card_service import upload_csv, card_history, get_dashboard, card_analysis
+from fastapi.templating import Jinja2Templates
+
+router = APIRouter(prefix="/api/card")
+templates = Jinja2Templates(directory="backend/templates")
+
+@router.post("/upload")
+async def upload_file(file: UploadFile):
+    return await upload_csv(file)
+
+@router.get("/history")
+async def history(request : Request):
+    card_infos = card_history()
+
+    return templates.TemplateResponse(request, name = "history.html", context = {"history": card_infos})
+
+@router.get("/dashboard")
+async def dashboard():
+    return get_dashboard()
+
+@router.post("/analysis")
+async def sql_llm_analysis(request : AnalysisRequest):
+    return card_analysis(request.question)
@@ -0,0 +1,22 @@
+from fastapi import APIRouter, Request
+from fastapi.templating import Jinja2Templates
+
+router = APIRouter()
+templates = Jinja2Templates(directory="backend/templates")
+
+# http://127.0.0.1:8000
+@router.get("/")
+async def home(request : Request):
+    return templates.TemplateResponse(request = request, name="index.html")
+
+@router.get("/card/upload")
+async def rag(request : Request):
+    return templates.TemplateResponse(request = request, name="card.html")
+
+@router.get("/card/dashboard")
+async def dashboard(request : Request):
+    return templates.TemplateResponse(request = request, name="dashboard.html")
+
+@router.get("/card/analysis")
+async def analysis(request : Request):
+    return templates.TemplateResponse(request = request, name="analysis.html")
@@ -0,0 +1,8 @@
+from pydantic import BaseModel
+
+class AnalysisRequest(BaseModel):
+    question: str
+
+class AnalysisResponse(BaseModel):
+    message: str
+
@@ -0,0 +1,140 @@
+from fastapi import UploadFile
+from langchain_core.runnables import RunnablePassthrough
+
+from backend.services.db_service import get_connection
+from backend.services.db_service import get_table_columns
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.output_parsers import StrOutputParser
+from backend.ai.llm import watson_llm
+import csv
+
+async def upload_csv(file :UploadFile):
+    """
+    csv 파일을 테이블에 저장
+    """
+
+    conn =get_connection()
+    cursor = conn.cursor()
+
+    # file.read() : 비동기 함수임
+    contents = await file.read()
+    csv_text = contents.decode('utf-8')
+    reader = csv.DictReader(csv_text.splitlines())
+    count = 0
+
+    for row in reader:
+        cursor.execute("""
+                        INSERT INTO transactions (date, category, merchant, amount) 
+                        VALUES (?, ?,?, ?)"""
+                        ,
+                        (row['date'], row['category'], row['merchant'], row['amount'])
+                    )
+        count += 1
+
+    conn.commit()
+    conn.close()
+    return {"message": f"{count} 건 저장 완료"}
+
+def card_history():
+    """
+    db에서 카드 정보 조회
+    """
+    conn =get_connection()
+    cursor = conn.cursor()
+
+    try :
+        cursor.execute("""
+                       SELECT * FROM transactions ORDER BY date DESC
+                       """)
+        rows = cursor.fetchall()
+        query_result = [dict(row) for row in rows]
+
+        conn.close()
+    except Exception as e:
+        query_result = [f"SQL 실행 오류 : {e}"]
+
+    return query_result
+
+def get_dashboard():
+    """
+    db에서 대시보드 정보 조회
+    """
+    conn = get_connection()
+    cursor = conn.cursor()
+
+    # 카테고리 별 사용금액
+    cursor.execute(
+        """
+        SELECT category, SUM(amount)
+        FROM transactions
+        GROUP BY category
+        ORDER BY SUM(amount) DESC
+        """
+    )
+
+    category_rows = cursor.fetchall()
+
+    # 월별 사용금액
+    cursor.execute(
+        """
+        SELECT strftime('%Y-%m', date), SUM(amount)
+        FROM transactions
+        GROUP BY strftime('%Y-%m', date)
+        ORDER BY strftime('%Y-%m', date)
+        """
+    )
+    month_rows = cursor.fetchall()
+    conn.close()
+
+    return  {
+        "category": [{"category":row[0], "amount":row[1]} for row in category_rows],
+        "monthly": [{"month":row[0], "amount":row[1]} for row in month_rows],
+    }
+
+def sql_generate_llm(question):
+    """자연어 -> SQL"""
+    sql_prompt = ChatPromptTemplate.from_template("""
+    당신은 SQLite 전문가입니다.
+    
+    테이블명 : transactions
+    
+    컬럼 : {columns}
+    
+    질문 : {question}
+    
+    SQL 만 출력하세요
+    """)
+
+    columns = get_table_columns("transactions")
+    sql_chain = sql_prompt | watson_llm | StrOutputParser()
+
+    sql = sql_chain.invoke({"columns": columns, "question": question})
+    print(f"sql: {sql}")
+
+    sql = sql.replace("```sql","").replace("```", "").strip()
+
+    # sql 문 실제 실행
+    conn = get_connection()
+    cursor = conn.cursor()
+    cursor.execute(sql)
+    rows = cursor.fetchall()
+    query_result = [dict(row) for row in rows]
+    conn.close()
+
+    return query_result
+
+def card_analysis(question):
+    query_result = sql_generate_llm(question)
+
+    analysis_prompt = ChatPromptTemplate.from_template("""
+    사용자 질문 : {question}
+
+    SQL 결과
+    {result}
+
+    결과를 설명하고 소비 습관을 분석하고 절약 팁을 제시해 주세요.
+    """)
+    analysis_chain = analysis_prompt | watson_llm | StrOutputParser()
+    answer = analysis_chain.invoke({"question": question, "result": query_result})
+
+    return {"message": answer}
@@ -0,0 +1,44 @@
+import sqlite3
+import os
+
+DB_PATH = "db/history.db"
+
+"""
+cursor.execute('select id, name for users')
+cursor.fetchone() => (1, 'alice', '010-1234-5678')
+
+row['id']
+"""
+
+def get_connection():
+    coon = sqlite3.connect(DB_PATH, isolation_level=None)
+    # 조회 결과를 어떻게 반활할지 설정
+    coon.row_factory = sqlite3.Row
+    return coon
+
+def init_db():
+    # 디렉토리 생성
+    os.makedirs("db", exist_ok=True)
+    conn = get_connection()
+    cursor = conn.cursor()
+    cursor.execute("""create table if not exists transactions (
+                    id integer primary key AUTOINCREMENT,
+                    date text, 
+                    category text,
+                    merchant text,
+                    amount integer
+                    )
+""")
+    conn.commit()
+    conn.close()
+
+    print("DB 초기화")
+
+def get_table_columns(table_name):
+    conn = get_connection()
+    cursor = conn.cursor()
+    cursor.execute("PRAGMA table_info({})".format(table_name))
+    columns = cursor.fetchall()
+    conn.close()
+    return [column[1] for column in columns]
+
@@ -0,0 +1,17 @@
+document.querySelector("button").addEventListener("click", ask)
+
+async function ask() {
+    // 사용자가 질문 입력 시 질문을 서버로 전송
+    const question = document.querySelector('#question').value
+
+    const response = await fetch("/api/card/analysis", {
+        method: "POST",
+        headers: {
+            "Content-Type": "application/json"
+        },
+        body: JSON.stringify({question : question})
+    })
+    // 전송 후 answer 도착 시 answer 화면에 보여주기
+    const answer = await response.json()
+    document.querySelector('#answer').textContent = answer.message
+}
@@ -0,0 +1,24 @@
+// 파일 업로드
+document.querySelector("#uploadBtn").addEventListener("click",uploadFile)
+async function uploadFile() {
+    const fileInput = document.querySelector("#file")
+    // 첨부파일 정보 가져오기
+    const file = fileInput.files[0]
+
+    if(!file){
+        alert('파일을 선택하세요');
+        return;
+    }
+
+    // form 만들어 전송
+    const formData = new FormData();
+    formData.append("file",file);
+
+    const response = await fetch("/api/card/upload",{
+        method:"POST",
+        body:formData
+    })
+    // 전송 후 answer 도착 시 answer 화면에 보여주기
+    const answer = await response.json()
+    document.querySelector('#result').textContent = answer.message
+}
@@ -0,0 +1,65 @@
+
+const drawCategoryChart = (data) => {
+    new Chart(document.querySelector("#categoryChart"), {
+        type: 'pie',
+        data: {
+            labels : data.map((x) => x.category),
+            datasets:[
+                {
+                    data:data.map((x)=>x.amount)
+                }
+            ]
+        },
+        options: {
+            responsive: true,
+            plugins: {
+                legend: {
+                    position: 'top',
+                },
+                title: {
+                    display: true,
+                    text: '카테고리별 소비내역'
+                }
+            }
+        },
+    })
+}
+
+const drawMonthlyChart = (data) => {
+        new Chart(document.querySelector("#monthlyChart"), {
+        type: 'line',
+        data: {
+            labels : data.map((x) => x.month),
+            datasets:[
+                {
+                    label:"월별 소비",
+                    data:data.map((x)=>x.amount)
+                }
+            ]
+        },
+        options: {
+            responsive: true,
+            plugins: {
+                legend: {
+                    position: 'top',
+                },
+                title: {
+                    display: true,
+                    text: '월별 소비내역'
+                }
+            }
+        },
+    })
+}
+
+async function loadDashboard() {
+    const response = await fetch("/api/card/dashboard");
+
+    const data = await response.json();
+    console.log(data);
+
+    drawCategoryChart(data.category);
+    drawMonthlyChart(data.monthly)
+}
+
+window.onload = loadDashboard;
@@ -0,0 +1,17 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <H2>신용카드 사용내역 분석</H2>
+    <div>
+        <input type = "text" name = "question" id = "question" size = "50">
+        <button type="button">질문하기</button>
+    </div>
+    <hr>
+    <div id="answer"></div>
+    <script src="{{ url_for('static', path='js/analysis.js')}}"></script>
+</body>
+</html>
@@ -0,0 +1,17 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <h2>카드 정보 추가</h2>
+    <div>
+        <input type="file" name="file" id="file">
+        <button id="uploadBtn">업로드</button>
+    </div>
+    <hr>
+    <div id="result"></div>
+    <script src="{{ url_for('static', path='js/card.js')}}"></script>
+</body>
+</html>
@@ -0,0 +1,18 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <h2>소비내역 대시보드</h2>
+    <div style ="width: 700px; height: 300px">
+        <canvas id="categoryChart"></canvas>
+    </div>
+        <div style ="width: 700px; height: 300px">
+        <canvas id="monthlyChart"></canvas>
+    </div>
+    <script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
+    <script src="{{ url_for('static', path='js/dashboard.js')}}"></script>
+</body>
+</html>
@@ -0,0 +1,28 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <h2>카드 소비 내역</h2>
+    <table border = "1">
+        <tr>
+            <th>날짜</th>
+            <th>카테고리</th>
+            <th>가맹점</th>
+            <th>금액</th>
+        </tr>
+        <tbody>
+            {% for row in history %}
+            <tr>
+                <td>{{ row.date }}</td>
+                <td>{{ row.category }}</td>
+                <td>{{ row.merchant }}</td>
+                <td>{{ row.amount }}</td>
+            </tr>
+            {% endfor %}
+        </tbody>
+    </table>
+</body>
+</html>
@@ -0,0 +1,24 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <h1>신용카드 분석</h1>
+    <ul>
+        <li>
+            <a href="/card/upload">1. 카드 사용 정보 업로드</a>
+        </li>
+        <li>
+            <a href="/api/card/history">2. 카드 사용 내역 확인</a>
+        </li>
+        <li>
+            <a href="/card/dashboard">3. 카드 소비 내역 대시보드</a>
+        </li>
+        <li>
+            <a href="/card/analysis">4. 카드 소비 내역 분석 및 조언</a>
+        </li>
+    </ul>
+</body>
+</html>
@@ -0,0 +1,23 @@
+from fastapi import FastAPI
+from starlette.staticfiles import StaticFiles
+from contextlib import asynccontextmanager
+from backend.services.db_service import init_db
+from backend.routers.api_router import router as api_router
+from backend.routers.page_router import router as page_router
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    print("서버 시작")
+    init_db()
+    yield
+    print("DB 초기화")
+
+app = FastAPI(lifespan=lifespan, title="Insights Advisor", version="1.0")
+# app = FastAPI()
+
+# static 폴더 지정
+app.mount("/static", StaticFiles(directory="backend/static"), name="static")
+
+# 라우터 등록
+app.include_router(page_router)
+app.include_router(api_router)
@@ -0,0 +1,105 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "markdown",
+   "source": [
+    "### ORM(Object Relational Mapping)\n",
+    "- 데이터베이스 테이블을 파이썬의 클래스로 매핑\n",
+    "- 컬럼 == 속성\n",
+    "- SQLALchemy 라이브러리가 ORM 지원"
+   ],
+   "id": "11f3431eb6bf7a12"
+  },
+  {
+   "cell_type": "code",
+   "id": "initial_id",
+   "metadata": {
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2026-06-16T08:48:53.375278031Z",
+     "start_time": "2026-06-16T08:48:51.509377417Z"
+    }
+   },
+   "source": "!pip install sqlalchemy",
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Requirement already satisfied: sqlalchemy in ./.venv/lib/python3.12/site-packages (2.0.51)\n",
+      "Requirement already satisfied: greenlet>=1 in ./.venv/lib/python3.12/site-packages (from sqlalchemy) (3.5.1)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.0 in ./.venv/lib/python3.12/site-packages (from sqlalchemy) (4.15.0)\n"
+     ]
+    }
+   ],
+   "execution_count": 1
+  },
+  {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2026-06-16T08:57:27.860045705Z",
+     "start_time": "2026-06-16T08:57:27.561065535Z"
+    }
+   },
+   "cell_type": "code",
+   "source": [
+    "# Users 테이블 (id, name, email)\n",
+    "# create table users()\n",
+    "from sqlalchemy import Column, Integer, String, create_engine\n",
+    "from sqlalchemy.orm import declarative_base, sessionmaker\n",
+    "\n",
+    "# 데이터베이스 연결\n",
+    "engine = create_engine('sqlite:///db/users.db')\n",
+    "# engine = create_engine('sqlite:///users.db')\n",
+    "\n",
+    "# 모든 모델 크래스의 부모 클래스가 될 Base 객체 생성\n",
+    "Base = declarative_base()\n",
+    "class User(Base):\n",
+    "    __tablename__ = 'users'\n",
+    "\n",
+    "    id = Column(Integer, primary_key=True)\n",
+    "    name = Column(String)\n",
+    "    email = Column(String, unique=True)\n",
+    "\n",
+    "    def __str__(self):\n",
+    "        return f\"<User(name='{self.name}', email='{self.email}')>\"\n",
+    "\n",
+    "# 테이블 생성\n",
+    "Base.metadata.create_all(engine)"
+   ],
+   "id": "7a41f863de1efd48",
+   "outputs": [],
+   "execution_count": 2
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": "",
+   "id": "de1ebcd8f162fd42"
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
@@ -0,0 +1,24 @@
+date,category,merchant,amount
+2023-12-05,Online Shopping,E-Store,120
+2023-12-02,Utilities,Internet Bill,80
+2023-11-29,Dining,Restaurant A,85
+2023-11-22,Groceries,Supermarket,60
+2023-11-15,Travel,Airline Ticket,350
+2023-11-12,Utilities,Electricity Bill,75
+2023-11-08,Online Shopping,Online Marketplace,45
+2023-11-05,Entertainment,Cinema,30
+2023-10-20,Dining,Cafe,20
+2023-10-15,Travel,Hotel Booking,200
+2023-10-10,Groceries,Local Market,50
+2023-10-06,Utilities,Water Bill,40
+2023-09-25,Online Shopping,Tech Store,150
+2023-09-15,Travel,Taxi Service,25
+2023-09-22,Dining,Restaurant B,90
+2023-09-10,Utilities,Electricity Bill,100
+2023-09-05,Groceries,Grocery Store,80
+2023-08-18,Utilities,Gas Bill,60
+2023-08-12,Entertainment,Streaming Service,15
+2023-08-06,Online Shopping,E-Store,300
+2023-07-30,Travel,Train Ticket,40
+2023-07-10,Dining,Fast Food,25
+2023-07-03,Utilities,Water Bill,50
@@ -1,13 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="PortForwardingSettings">
-    <ports>
-      <entry key="8000">
-        <ForwardedPortInfo>
-          <option name="hostPort" value="8000" />
-          <option name="readOnly" value="false" />
-        </ForwardedPortInfo>
-      </entry>
-    </ports>
-  </component>
-</project>
@@ -2,7 +2,7 @@
 <project version="4">
  <component name="ProjectModuleManager">
    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/rag_app.iml" filepath="$PROJECT_DIR$/.idea/rag_app.iml" />
+      <module fileurl="file://$PROJECT_DIR$/.idea/rag-app.iml" filepath="$PROJECT_DIR$/.idea/rag-app.iml" />
    </modules>
  </component>
 </project>
@@ -0,0 +1,10 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="PySourceRootDetectionService">
+    <option name="sourcePathsSet">
+      <set>
+        <option value="$PROJECT_DIR$" />
+      </set>
+    </option>
+  </component>
+</project>
@@ -1,5 +1,5 @@
 <?xml version="1.0" encoding="UTF-8"?>
-<module type="PYTHON_MODULE" version="4">
+<module external.system.id="pyproject.toml" type="PYTHON_MODULE" version="4">
  <component name="NewModuleRootManager">
    <content url="file://$MODULE_DIR$">
      <sourceFolder url="file://$MODULE_DIR$" isTestSource="false" />
@@ -1,7 +1,8 @@
 from fastapi import APIRouter, Request, UploadFile
 from backend.services.llm_service import question_and_answer
 from backend.schemas.basic_schema import QuestionRequest
-from backend.services.rag_service import upload_document
+from backend.services.rag_service import upload_document, rag_chat, rag_chat_stream
+from fastapi.responses import StreamingResponse
 router = APIRouter(prefix="/api")

 # http://127.0.0.1:8000/api/question
@@ -13,13 +14,20 @@ async def question(req:QuestionRequest):

 # http://127.0.0.1:8000/api/rag/upload
@router.post("/rag/upload")
-async def fileUpload(file:UploadFile):
+async def file_Upload(file: UploadFile):
    # 서비스 호출
    return upload_document(file)


 # http://127.0.0.1:8000/api/rag/question
-
@router.post("/rag/question")
-async def question():
-    pass
+async def question(req: QuestionRequest):
+    answer = rag_chat(req.question)
+
+    return {"message" : answer}
+
+@router.post("/rag/question/stream")
+async def question_stream(req: QuestionRequest):
+    answer = rag_chat_stream(req.question)
+
+    return StreamingResponse(rag_chat_stream(req.question), media_type="text/plain")
@@ -1,6 +1,90 @@
-# pdf 업로드 => 분할 => 인덱스 생성
-def upload_document(file):
-    pass
+import os

+from langchain_community.document_loaders import PyPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_community.vectorstores import FAISS
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+
+from backend.ai.embedding import watson_embedding
+from backend.ai.llm import watson_llm
+
+UPLOAD_PATH = "uploads"
+
+def upload_document(file):
+    # file 저장
+    file_path = os.path.join(UPLOAD_PATH, file.filename)
+
+    with open(file_path, "wb") as f:
+        f.write(file.file.read())
+
+    # pdf 업로드 => 분할 => 인덱스 생성
+    # pdf 로드
+    loader = PyPDFLoader(file_path)
+    docs = loader.load()
+
+    splitter = RecursiveCharacterTextSplitter(chunk_size=300, chunk_overlap=30)
+    chunks = splitter.split_documents(docs)
+
+    faiss_store = FAISS.from_documents(documents=chunks, embedding=watson_embedding)
+    faiss_store.save_local("./db/vectorstore")
+
+    return {"message": "업로드 성공"}

 # 질문 => 유사도 검색 => 문서 => llm 답변 생성
+def rag_chat(question: str):
+
+    faiss_store = FAISS.load_local("./db/vectorstore", watson_embedding, allow_dangerous_deserialization=True)
+
+    retriever = faiss_store.as_retriever(search_kwargs={"k": 3})
+
+    ### LLM
+    # 1. prompt
+    message = """\
+    당신은 PDF 기반 RAG AI 입니다.
+    다음 문서를 참고해서 질문에 답변하세요.
+
+    문서:
+    {context}
+
+    질문:
+    {question}
+    """
+
+    rag_prompt = ChatPromptTemplate.from_template(message)
+    # 2. chain
+    chain = {"context": retriever, "question": RunnablePassthrough()} | rag_prompt | watson_llm | StrOutputParser()
+
+    # 3. answer
+    answer = chain.invoke(question)
+    return answer
+
+async def rag_chat_stream(question: str):
+
+    faiss_store = FAISS.load_local("./db/vectorstore", watson_embedding, allow_dangerous_deserialization=True)
+
+    retriever = faiss_store.as_retriever(search_kwargs={"k": 3})
+
+    ### LLM
+    # 1. prompt
+    message = """\
+    당신은 PDF 기반 RAG AI 입니다.
+    다음 문서를 참고해서 질문에 답변하세요.
+
+    문서:
+    {context}
+
+    질문:
+    {question}
+    """
+
+    rag_prompt = ChatPromptTemplate.from_template(message)
+    # 2. chain
+    chain = {"context": retriever, "question": RunnablePassthrough()} | rag_prompt | watson_llm | StrOutputParser()
+
+    # 3. answer
+    # chain.stream() : 동기방식(요청 => 응답을 할 떄까지 기다리는 방식)
+    # chain.astream() : 비동기방식(다른 일 처리 가능)
+    async for chunk in chain.astream(question):
+        yield chunk
@@ -18,27 +18,57 @@ async function ask() {

 // 파일 업로드
 document.querySelector("#uploadBtn").addEventListener("click",uploadFile)
-async function uploadFile()
-{
-    const fileInput = document.querySelector("#file");
+async function uploadFile() {
+    const fileInput = document.querySelector("#file")
    // 첨부파일 정보 가져오기
-    const file = fileInput.files[0];
+    const file = fileInput.files[0]

-    if(!file)
-    {
-        alert("파일을 선택해주세요.");
+    if(!file){
+        alert('파일을 선택하세요');
        return;
    }

    // form 만들어 전송
-    const formData = new FormData()
-    formData.append("file", file)
+    const formData = new FormData();
+    formData.append("file",file);

-    const response = await fetch("/api/reg/question", {
+    const response = await fetch("/api/rag/upload",{
        method:"POST",
        body:formData
    })
    // 전송 후 answer 도착 시 answer 화면에 보여주기
    const answer = await response.json()
-    document.querySelector('#answer').textContent = answer.message
+    document.querySelector('#result').textContent = answer.message
+}
+
+document.querySelector("#askBtn").addEventListener("click", rag_ask)
+async function rag_ask() {
+    // 사용자가 질문 입력 시 질문을 서버로 전송
+    const question = document.querySelector('#question').value
+
+    const response = await fetch("/api/rag/question", {
+        method: "POST",
+        headers: {
+            "Content-Type": "application/json"
+        },
+        body: JSON.stringify({question : question})
+    })
+    // 전송 후 answer 도착 시 answer 화면에 보여주기
+    // const answer = await response.json()
+    // document.querySelector('#answer_result').textContent = answer.message
+
+    // stream 방식
+    const reader = response.body.getReader();
+    const decoder = new TextDecoder();
+    let answer = "";
+
+    while(true){
+        const { value, done } = await reader.read();
+
+        if (done) {
+            break;
+        }
+        answer += decoder.decode(value);
+        document.querySelector('#answer_result').textContent = answer;
+    }
 }
@@ -12,6 +12,12 @@
    </div>
    <hr>
    <div id="result"></div>
+    <hr>
+    <div>
+        <input type="text" name="question" id="question">
+        <button id="askBtn">질문</button>
+    </div>
+    <div id="answer_result"></div>
    <script src="{{ url_for('static', path='js/index.js')}}"></script>
 </body>
 </html>
@@ -2,4 +2,6 @@
 name = "rag-app"
 version = "0.1.0"
 requires-python = ">=3.12"
-dependencies = []
+dependencies = [
+    "recursivecharactertextsplitter>=0.1.0",
+]