Initial commit

.dockerignore

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+# Git 관련 파일
+.git
+.gitignore
+
+# 가상환경
+.venv
+__pycache__
+
+# 캐시 파일
+*.pyc
+*.pyo
+*.log
+*.tmp
+
+# 빌드 결과물
+dist/
+build/
+rag/
+*.egg-info/
+
+# 시스템 파일
+.DS_Store
+Thumbs.db
+
+# IDE 설정 파일
+.idea/
+.vscode/
+*.code-workspace

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+*.egg
+*.egg-info/
+dist/
+build/
+*.wheel
+.venv/
+fastapi/
+rag/
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+*.coverage
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+pytest_cache/
+cover/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# PyCharm
+.idea/
+
+# VSCode
+.vscode/
+*.code-workspace
+
+# MyPy
+.mypy_cache/
+
+# Pyre
+.pyre/
+
+# Pytype
+.pytype/
+
+# Celery beat schedule file
+celerybeat-schedule
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# dotenv
+.env
+*.env
+
+# IDEs and editors
+*.swp
+*~
+.vimrc
+.history
+.idea/
+*.sublime-project
+*.sublime-workspace
+
+# macOS
+.DS_Store
+
+# Windows
+Thumbs.db
+desktop.ini
+
+# Logs
+*.log
+*.out
+*.err

Dockerfile

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+# Build Stage
+FROM python:3.11-slim as builder
+
+# 사용자 환경 변수 설정
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# 사용자 생성 및 권한 설정
+RUN useradd -m -d $HOME -s /bin/bash user
+
+# 작업 디렉터리 설정
+WORKDIR $HOME/app
+
+# 시스템 패키지 업데이트 및 필수 패키지 설치
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libmariadb-dev \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Python 패키지 관리
+RUN pip install --upgrade pip
+
+# 의존성 파일 복사
+COPY --chown=user:user requirements.txt ./
+
+# Python 패키지 설치
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Runtime Stage
+FROM python:3.11-slim
+
+# 사용자 환경 변수 설정
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH
+
+# 사용자 생성 및 권한 설정
+RUN useradd -m -d $HOME -s /bin/bash user
+
+# 작업 디렉터리 설정
+WORKDIR $HOME/app
+
+# 필요한 시스템 패키지 설치
+RUN apt-get update && apt-get install -y \
+    libmariadb-dev \
+    wget \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+# Build Stage에서 설치된 Python 패키지 복사
+COPY --from=builder /usr/local/lib/python3.11/site-packages /usr/local/lib/python3.11/site-packages
+COPY --from=builder /usr/local/bin /usr/local/bin
+
+# 애플리케이션 코드 복사
+COPY --chown=user:user . .
+
+# 포트 노출 (FastAPI 기본 포트: 8000)
+EXPOSE 8000
+
+# 업로드 디렉터리 생성 및 권한 설정
+RUN mkdir -p $HOME/app/uploaded_files && \
+    chown -R user:user $HOME/app/uploaded_files && \
+    chmod -R 777 $HOME/app/uploaded_files
+
+# 애플리케이션 실행을 사용자 권한으로 실행
+USER user
+
+# FastAPI 실행 명령
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

Dockerfile copy

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+# Python 3.11 이미지 사용
+FROM python:3.11-slim
+
+# 작업 디렉터리 설정
+WORKDIR /app
+
+# 시스템 패키지 업데이트 및 필수 패키지 설치
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    libmariadb-dev \
+    && rm -rf /var/lib/apt/lists/*
+
+# 의존성 파일 복사
+COPY requirements.txt .
+
+# Python 패키지 설치
+RUN pip install --upgrade pip \
+    && pip install --no-cache-dir -r requirements.txt
+
+# 애플리케이션 코드 복사
+COPY . .
+
+# 포트 노출 (FastAPI 기본 포트: 8000)
+EXPOSE 8000
+
+# FastAPI 실행 명령
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

README.md

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+---
+title: Rag
+emoji: 📚
+colorFrom: yellow
+colorTo: yellow
+sdk: docker
+pinned: false
+license: apache-2.0
+short_description: rag
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app/main.py

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+from fastapi import FastAPI
+from app.routers import upload,search,user
+#from config.database import engine
+# from models.user_info import Base
+
+app = FastAPI()
+
+# 라우터 추가
+app.include_router(upload.router)
+app.include_router(search.router)
+# app.include_router(user.router)
+
+
+
+# 데이터베이스 테이블 생성
+#Base.metadata.create_all(bind=engine)

app/modules/embedding.py

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+import os
+import asyncio
+from langchain.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+
+async def process_and_store_file(file_path, user_id, websocket=None, upload_directory="./uploaded_files"):
+    try:
+        # 1. PDF 파일 로드
+        if websocket:
+            await websocket.send_text("1. PDF 파일 로드 중...")
+        loader = PyPDFLoader(file_path)
+        documents = loader.load()
+        if websocket:
+            await websocket.send_text(f"PDF 파일 로드 완료: {len(documents)} 문서")
+    except Exception as e:
+        if websocket:
+            await websocket.send_text(f"PDF 파일 로드 오류: {e}")
+        return
+
+    try:
+        # 2. 텍스트 분할
+        if websocket:
+            await websocket.send_text("2. 텍스트 분할 중...")
+        text_splitter = RecursiveCharacterTextSplitter(chunk_size=3000, chunk_overlap=500)
+        docs = text_splitter.split_documents(documents)
+        if websocket:
+            await websocket.send_text(f"텍스트 분할 완료: {len(docs)} 청크")
+    except Exception as e:
+        if websocket:
+            await websocket.send_text(f"텍스트 분할 오류: {e}")
+        return
+
+    try:
+        # 3. 임베딩 생성 및 벡터화
+        if websocket:
+            await websocket.send_text("3. 임베딩 생성 및 벡터화 중...")
+        embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+        vectors = FAISS.from_documents(docs, embeddings)
+
+        # 4. 벡터 저장
+        db_path = os.path.join(upload_directory, "faiss_index")
+        vectors.save_local(db_path)
+        if websocket:
+            await websocket.send_text(f"FAISS 인덱스 저장 완료: {db_path}")
+    except Exception as e:
+        if websocket:
+            await websocket.send_text(f"벡터화 오류: {e}")
+        return
+    finally:
+        # 5. 파일 삭제
+        try:
+            if os.path.exists(file_path):
+                os.remove(file_path)
+                if websocket:
+                    await websocket.send_text(f"파일 삭제 완료: {file_path}")
+        except Exception as e:
+            if websocket:
+                await websocket.send_text(f"파일 삭제 오류: {e}")

app/modules/evaluation.py

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+import os
+from huggingface_hub import InferenceClient
+
+class Evaluation:
+    def __init__(self, model: str = "Qwen/Qwen2.5-72B-Instruct"):
+        """
+        Args:
+            model (str): 사용할 Hugging Face 모델 이름 (기본값: Qwen/Qwen2.5-72B-Instruct).
+        """
+        self.api_key = os.getenv("HF_API_KEY")
+        if not self.api_key:
+            raise ValueError("HF_API_KEY 환경변수가 설정되지 않았습니다.")
+        self.client = InferenceClient(api_key=self.api_key)
+        self.model = model
+
+    def evaluate(self, instruction: str, answer: str) -> str:
+        """
+        사용자의 답변과 평가 기준을 기반으로 AI 모델 평가를 수행합니다.
+
+        Args:
+            instruction (str): 평가 기준이 포함된 지침.
+            answer (str): 사용자 답변.
+
+        Returns:
+            str: 평가 결과.
+        """
+        messages = [
+            {"role": "system", "content": "수업도구 구성 마법사입니다. 퀴즈, 과제, 토론을 생성할 수 있습니다."},
+            {"role": "user", "content": instruction},
+        ]
+
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                temperature=0.2,
+                max_tokens=2048,
+                top_p=0.7,
+                stream=True,
+            )
+
+            result = ""
+            for chunk in stream:
+                if "delta" in chunk.choices[0]:
+                    result += chunk.choices[0].delta.content
+                    print(f"Intermediate result: {result}")  # 디버깅용 출력
+
+            return result.strip()
+
+        except Exception as e:
+            error_message = f"An error occurred during evaluation: {e}"
+            print(error_message)  # 디버깅용 출력
+            return error_message
+
+    async def evaluate_stream(self, instruction: str):
+        """
+        비동기 방식으로 평가 결과를 스트리밍 처리합니다.
+
+        Args:
+            instruction (str): 평가 기준이 포함된 지침.
+
+        Yields:
+            str: 실시간 평가 결과.
+        """
+        messages = [
+            {"role": "system", "content": "선생님에게 꼭 필요한 수업도구 구성 마법사입니다."},
+            {"role": "user", "content": instruction},
+        ]
+
+        try:
+            stream = self.client.chat.completions.create(
+                model=self.model,
+                messages=messages,
+                temperature=0.2,
+                max_tokens=2048,
+                top_p=0.7,
+                stream=True,
+            )
+
+            for chunk in stream:
+                if "delta" in chunk.choices[0]:
+                    content = chunk.choices[0].delta.content
+                    print(f"Streaming result: {content}")  # 디버깅용 출력
+                    yield content
+
+        except Exception as e:
+            error_message = f"Error: {e}"
+            print(error_message)  # 디버깅용 출력
+            yield error_message

app/routers/search.py

@@ -0,0 +1,58 @@
+from fastapi import APIRouter, Request, Form
+from fastapi.responses import HTMLResponse, StreamingResponse
+from fastapi.templating import Jinja2Templates
+from langchain.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from app.modules.evaluation import Evaluation  # 평가 모듈 임포트
+import os
+
+router = APIRouter()
+
+# 로컬 모델 로드 (HuggingFace T5 사용)
+# tokenizer = AutoTokenizer.from_pretrained("gogamza/kobart-base-v2")
+# model = AutoModelForSeq2SeqLM.from_pretrained("gogamza/kobart-base-v2")
+
+# def summarize_text(text: str, max_length=150):
+#     inputs = tokenizer.encode("summarize: " + text, return_tensors="pt", max_length=512, truncation=True)
+#     outputs = model.generate(inputs, max_length=512, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)
+#     return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# 템플릿 설정
+templates = Jinja2Templates(directory="app/templates")
+
+# 업로드된 파일로부터 생성된 벡터 데이터베이스 경로 설정
+UPLOAD_DIRECTORY = "./uploaded_files"
+db_path = os.path.join(UPLOAD_DIRECTORY, "faiss_index")
+
+# 벡터 데이터베이스 로드
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2")
+
+@router.get("/search", response_class=HTMLResponse)
+async def search_page(request: Request):
+    return templates.TemplateResponse("search.html", {"request": request})
+
+
+@router.post("/search/stream")
+async def search_stream(query: str = Form(...), model: str = Form(...)):
+     # 벡터 데이터베이스 로드
+    vector_db = FAISS.load_local(db_path, embeddings, allow_dangerous_deserialization=True)
+    results = vector_db.similarity_search(query, k=5)
+    
+    # 검색된 결과 텍스트를 연결
+    full_text = "\n\n".join([result.page_content for result in results])
+    
+    # 평가 모듈 초기화
+    evaluator = Evaluation(model=model)
+    
+    # 평가 기준
+    instruction = (
+        f"다음 텍스트를 기반으로 {query}\n\n{full_text}"
+    )
+    
+
+    async def stream():
+        async for data in evaluator.evaluate_stream(instruction):
+            yield data
+
+    return StreamingResponse(stream(), media_type="text/plain")

app/routers/upload.py

@@ -0,0 +1,60 @@
+from fastapi import APIRouter, File, UploadFile, Request, WebSocket, WebSocketDisconnect
+from fastapi.responses import HTMLResponse, RedirectResponse
+from fastapi.templating import Jinja2Templates
+from app.modules.embedding import process_and_store_file
+import shutil
+import os
+import uuid
+import asyncio
+
+router = APIRouter()
+
+# 업로드된 파일을 저장할 디렉토리 설정
+UPLOAD_DIRECTORY = "./uploaded_files"
+if not os.path.exists(UPLOAD_DIRECTORY):
+    os.makedirs(UPLOAD_DIRECTORY, exist_ok=True)
+    os.chmod(UPLOAD_DIRECTORY, 0o777)
+
+# 템플릿 디렉토리 설정
+templates = Jinja2Templates(directory="app/templates")
+
+# WebSocket 연결 관리
+connections = {}
+
+@router.get("/", response_class=HTMLResponse)
+async def main(request: Request):
+    return templates.TemplateResponse("upload.html", {"request": request})
+
+@router.post("/uploadfile/{user_id}/")
+async def upload_file(user_id: str, file: UploadFile = File(...)):
+    # 고유 파일 이름 생성
+    unique_filename = f"{uuid.uuid4()}_{file.filename}"
+    file_location = os.path.join(UPLOAD_DIRECTORY, unique_filename)
+
+    # 파일 저장
+    try:
+        with open(file_location, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+        print(f"File saved at {file_location}")
+    except Exception as e:
+        return {"error": f"파일 저장 오류: {e}"}
+
+    # 비동기 작업 실행
+    asyncio.create_task(process_and_store_file(file_location, user_id, connections.get(user_id), UPLOAD_DIRECTORY))
+
+    return {"info": f"File '{file.filename}' uploaded successfully."}
+
+@router.websocket("/ws/{user_id}")
+async def websocket_endpoint(websocket: WebSocket, user_id: str):
+    await websocket.accept()
+    connections[user_id] = websocket  # WebSocket 연결 저장
+    try:
+        while True:
+            data = await websocket.receive_text()
+            print(f"Received from {user_id}: {data}")
+    except WebSocketDisconnect:
+        print(f"User {user_id} disconnected")
+        del connections[user_id]  # 연결 제거
+    except Exception as e:
+        print(f"Error with user {user_id}: {e}")
+        del connections[user_id]  # 연결 제거

app/routers/user.py

@@ -0,0 +1,127 @@
+# from fastapi import APIRouter, Depends, HTTPException
+# from sqlalchemy.orm import Session
+# from models.user_info import UserInfo
+# from config.database import get_db
+# from typing import Optional
+# from datetime import datetime
+
+# router = APIRouter()
+
+# # 사용자 조회
+# @router.get("/users/{user_id}")
+# async def get_user(user_id: str, db: Session = Depends(get_db)):
+#     user = db.query(UserInfo).filter(UserInfo.id == user_id).first()
+#     if not user:
+#         raise HTTPException(status_code=404, detail="User not found")
+#     return user
+
+# # 사용자 생성
+# @router.post("/users")
+# async def create_user(
+#     id: str,
+#     preferred_username: Optional[str] = None,
+#     mobile: Optional[str] = None,
+#     business_cd: Optional[str] = None,
+#     role_seq: Optional[int] = None,
+#     name: Optional[str] = None,
+#     email: Optional[str] = None,
+#     profile: Optional[str] = None,
+#     school_code: Optional[str] = None,
+#     grade: Optional[int] = None,
+#     class_name: Optional[str] = None,
+#     student_no: Optional[int] = None,
+#     creator_id: Optional[str] = None,
+#     creator_ip: Optional[str] = None,
+#     db: Session = Depends(get_db)
+# ):
+#     new_user = UserInfo(
+#         id=id,
+#         preferred_username=preferred_username,
+#         mobile=mobile,
+#         business_cd=business_cd,
+#         role_seq=role_seq,
+#         name=name,
+#         email=email,
+#         profile=profile,
+#         school_code=school_code,
+#         grade=grade,
+#         class_name=class_name,
+#         student_no=student_no,
+#         created_at=datetime.now(),
+#         creator_id=creator_id,
+#         creator_ip=creator_ip,
+#     )
+#     db.add(new_user)
+#     db.commit()
+#     db.refresh(new_user)
+#     return new_user
+
+# # 사용자 목록 조회
+# @router.get("/users")
+# async def list_users(db: Session = Depends(get_db)):
+#     return db.query(UserInfo).all()
+
+# # 사용자 업데이트
+# @router.put("/users/{user_id}")
+# async def update_user(
+#     user_id: str,
+#     preferred_username: Optional[str] = None,
+#     mobile: Optional[str] = None,
+#     business_cd: Optional[str] = None,
+#     role_seq: Optional[int] = None,
+#     name: Optional[str] = None,
+#     email: Optional[str] = None,
+#     profile: Optional[str] = None,
+#     school_code: Optional[str] = None,
+#     grade: Optional[int] = None,
+#     class_name: Optional[str] = None,
+#     student_no: Optional[int] = None,
+#     updater_id: Optional[str] = None,
+#     updater_ip: Optional[str] = None,
+#     db: Session = Depends(get_db)
+# ):
+#     user = db.query(UserInfo).filter(UserInfo.id == user_id).first()
+#     if not user:
+#         raise HTTPException(status_code=404, detail="User not found")
+    
+#     # 필드 업데이트
+#     if preferred_username is not None:
+#         user.preferred_username = preferred_username
+#     if mobile is not None:
+#         user.mobile = mobile
+#     if business_cd is not None:
+#         user.business_cd = business_cd
+#     if role_seq is not None:
+#         user.role_seq = role_seq
+#     if name is not None:
+#         user.name = name
+#     if email is not None:
+#         user.email = email
+#     if profile is not None:
+#         user.profile = profile
+#     if school_code is not None:
+#         user.school_code = school_code
+#     if grade is not None:
+#         user.grade = grade
+#     if class_name is not None:
+#         user.class_name = class_name
+#     if student_no is not None:
+#         user.student_no = student_no
+    
+#     user.updated_at = datetime.now()
+#     user.updater_id = updater_id
+#     user.updater_ip = updater_ip
+
+#     db.commit()
+#     db.refresh(user)
+#     return user
+
+# # 사용자 삭제
+# @router.delete("/users/{user_id}")
+# async def delete_user(user_id: str, db: Session = Depends(get_db)):
+#     user = db.query(UserInfo).filter(UserInfo.id == user_id).first()
+#     if not user:
+#         raise HTTPException(status_code=404, detail="User not found")
+#     db.delete(user)
+#     db.commit()
+#     return {"message": "User deleted successfully"}

app/templates/search.html

@@ -0,0 +1,168 @@
+<!DOCTYPE html>
+<html lang="ko">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>문서 검색</title>
+    <style>
+        .container {
+            width: 50%;
+            margin: 0 auto;
+            padding: 20px;
+            text-align: center;
+            border: 1px solid #ccc;
+            border-radius: 8px;
+            background-color: #f9f9f9;
+        }
+        .search-bar {
+            display: flex;
+            justify-content: center;
+            align-items: center;
+            gap: 10px;
+        }
+        input[type="text"], select {
+            padding: 10px;
+            border: 1px solid #ccc;
+            border-radius: 4px;
+            font-size: 14px;
+        }
+        input[type="text"] {
+            flex: 1;
+        }
+        select {
+            flex: 0.5;
+        }
+        button {
+            padding: 10px 20px;
+            background-color: #007bff;
+            color: white;
+            border: none;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 14px;
+        }
+        button:hover {
+            background-color: #0056b3;
+        }
+        .results, .evaluation, .stream-output {
+            margin-top: 5px;
+        }
+        .stream-output {
+            text-align: left;
+            white-space: pre-wrap;
+            border: 1px solid #ccc;
+            padding: 10px;
+            height: 500px;
+            overflow-y: scroll;
+        }
+        nav {
+            background-color: #333;
+            padding: 10px;
+            text-align: center;
+        }
+        nav ul {
+            list-style-type: none;
+            padding: 0;
+            margin: 0;
+        }
+        nav ul li {
+            display: inline;
+            margin: 0 15px;
+        }
+        nav ul li a {
+            color: white;
+            text-decoration: none;
+            font-weight: bold;
+        }
+        nav ul li a:hover {
+            text-decoration: underline;
+        }
+    </style>
+</head>
+<body>
+    <header>
+        <nav>
+            <ul>
+                <li><a href="/search">문서 검색</a></li>
+                <li><a href="/">파일 업로드</a></li>
+            </ul>
+        </nav>
+    </header>
+    <div class="container">
+        <h2>문서 검색</h2>
+        <form id="search-form" class="search-bar">
+            <input type="text" id="query" name="query" placeholder="검색어를 입력하세요..." required>
+            <select id="model" name="model">
+                <option value="Qwen/Qwen2.5-72B-Instruct">Qwen/Qwen2.5-72B-Instruct</option>
+                <option value="meta-llama/Llama-3.3-70B-Instruct">meta-llama/Llama-3.3-70B-Instruct</option>
+                <option value="CohereForAI/c4ai-command-r-plus-08-2024">CohereForAI/c4ai-command-r-plus-08-2024</option>
+                <option value="Qwen/QwQ-32B-Preview">Qwen/QwQ-32B-Preview</option>
+                <option value="nvidia/Llama-3.1-Nemotron-70B-Instruct-HF">nvidia/Llama-3.1-Nemotron-70B-Instruct-HF</option>
+                <option value="Qwen/Qwen2.5-Coder-32B-Instruct">Qwen/Qwen2.5-Coder-32B-Instruct</option>
+                <option value="meta-llama/Llama-3.2-11B-Vision-Instruct">meta-llama/Llama-3.2-11B-Vision-Instruct</option>
+                <option value="NousResearch/Hermes-3-Llama-3.1-8B">NousResearch/Hermes-3-Llama-3.1-8B</option>
+                <option value="mistralai/Mistral-Nemo-Instruct-2407">mistralai/Mistral-Nemo-Instruct-2407</option>
+                <option value="microsoft/Phi-3.5-mini-instruct">microsoft/Phi-3.5-mini-instruct</option>
+            </select>
+            <button type="submit">검색</button>
+        </form>
+        <h3>실시간 결과</h3>
+        <div id="stream-output" class="stream-output">
+            
+            <div id="stream-content">
+                <!-- 실시간 데이터가 여기에 표시됩니다 -->
+            </div>
+        </div>
+    </div>
+
+    <script>
+        let currentController = null; // 현재 요청을 제어할 AbortController
+
+        document.getElementById("search-form").addEventListener("submit", async (event) => {
+            event.preventDefault(); // 기본 폼 제출 방지
+            const query = document.getElementById("query").value;
+            const model = document.getElementById("model").value;
+            const outputContainer = document.getElementById("stream-content");
+
+            // 이전 요청 중단
+            if (currentController) {
+                currentController.abort();
+            }
+            currentController = new AbortController();
+            const signal = currentController.signal;
+
+            // 출력 초기화
+            outputContainer.textContent = "검색 중...\n";
+
+            try {
+                const response = await fetch("/search/stream", {
+                    method: "POST",
+                    headers: {
+                        "Content-Type": "application/x-www-form-urlencoded"
+                    },
+                    body: new URLSearchParams({ query, model }),
+                    signal, // AbortController의 signal 전달
+                });
+
+                const reader = response.body.getReader();
+                const decoder = new TextDecoder("utf-8");
+
+                while (true) {
+                    const { value, done } = await reader.read();
+                    if (done) break;
+                    outputContainer.textContent += decoder.decode(value);
+                    outputContainer.scrollTop = outputContainer.scrollHeight; // 스크롤 자동 이동
+                }
+            } catch (error) {
+                if (error.name === "AbortError") {
+                    outputContainer.textContent += "\n요청이 취소되었습니다.\n";
+                } else {
+                    outputContainer.textContent = `오류 발생: ${error.message}`;
+                }
+            } finally {
+                currentController = null; // 현재 요청 완료 후 초기화
+            }
+        });
+    </script>
+</body>
+</html>

app/templates/upload.html

@@ -0,0 +1,142 @@
+<!DOCTYPE html>
+<html lang="ko">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>파일 업로드</title>
+    <style>
+        body {
+            font-family: Arial, sans-serif;
+            background-color: #f4f4f4;
+            margin: 0;
+            padding: 0;
+        }
+        .container {
+            width: 50%;
+            margin: 50px auto;
+            padding: 20px;
+            text-align: center;
+            border: 1px solid #ccc;
+            border-radius: 8px;
+            background-color: #ffffff;
+            box-shadow: 0 4px 8px rgba(0, 0, 0, 0.1);
+        }
+        input[type="file"] {
+            margin: 10px 0;
+            padding: 10px;
+        }
+        button {
+            padding: 10px 20px;
+            background-color: #007bff;
+            color: white;
+            border: none;
+            border-radius: 4px;
+            cursor: pointer;
+            font-size: 16px;
+        }
+        button:hover {
+            background-color: #0056b3;
+        }
+        .status {
+            margin-top: 20px;
+            text-align: left;
+            font-size: 14px;
+        }
+        .status p {
+            background-color: #e9f4ff;
+            border-left: 4px solid #007bff;
+            padding: 10px;
+            margin: 5px 0;
+        }
+        nav {
+            background-color: #333;
+            padding: 10px;
+            text-align: center;
+        }
+        nav ul {
+            list-style-type: none;
+            padding: 0;
+            margin: 0;
+        }
+        nav ul li {
+            display: inline;
+            margin: 0 15px;
+        }
+        nav ul li a {
+            color: white;
+            text-decoration: none;
+            font-weight: bold;
+        }
+        nav ul li a:hover {
+            text-decoration: underline;
+        }
+    </style>
+</head>
+<body>
+    <header>
+        <nav>
+            <ul>
+                <li><a href="/search">문서 검색</a></li>
+                <li><a href="/">파일 업로드</a></li>
+            </ul>
+        </nav>
+    </header>
+    <div class="container">
+        <h2>파일 업로드</h2>
+        <form id="upload-form" enctype="multipart/form-data">
+            <input type="file" name="file" required>
+            <button type="submit">업로드</button>
+        </form>
+        <div class="status" id="status">
+            <h3>업로드 상태</h3>
+        </div>
+    </div>
+
+    <script>
+        const form = document.getElementById('upload-form');
+        const statusDiv = document.getElementById('status');
+        const userId = "user123"; // 고유 사용자 ID (동적으로 설정 가능)
+
+        // WebSocket 설정
+        const socket = new WebSocket(`ws://127.0.0.1:8000/ws/${userId}`);
+
+        socket.onmessage = (event) => {
+            const message = event.data;
+            const p = document.createElement('p');
+            p.textContent = message;
+            statusDiv.appendChild(p);
+        };
+
+        socket.onerror = () => {
+            const p = document.createElement('p');
+            p.textContent = "WebSocket 연결 오류.";
+            p.style.color = "red";
+            statusDiv.appendChild(p);
+        };
+
+        form.addEventListener('submit', async (e) => {
+            e.preventDefault();
+            statusDiv.innerHTML = '<h3>업로드 상태</h3>';
+            const formData = new FormData(form);
+            
+
+            // 파일 업로드 요청
+            const response = await fetch(`/uploadfile/${userId}/`, {
+                method: "POST",
+                body: formData,
+            });
+
+            if (response.ok) {
+                const p = document.createElement('p');
+                p.textContent = "파일 업로드 성공!";
+                statusDiv.appendChild(p);
+            } else {
+                const p = document.createElement('p');
+                p.textContent = "파일 업로드 실패.";
+                p.style.color = "red";
+                statusDiv.appendChild(p);
+            }
+        });
+    </script>
+</body>
+</html>

config/database.py

@@ -0,0 +1,39 @@
+# import os
+# from sqlalchemy import create_engine
+# from sqlalchemy.ext.declarative import declarative_base
+# from sqlalchemy.orm import sessionmaker
+# from sqlalchemy.exc import SQLAlchemyError
+
+# # MySQL 데이터베이스 설정
+# DATABASE_URL = os.getenv("DATABASE_URL", "mysql+mysqlconnector://root:root@10.10.10.180:3306/chathess")
+
+# # SQLAlchemy 엔진 생성 (예외 처리 추가)
+# try:
+#     engine = create_engine(DATABASE_URL)
+#     print("Database engine created successfully.")
+# except SQLAlchemyError as e:
+#     print("Failed to create database engine.")
+#     print("Error:", e)
+#     engine = None
+
+# # 세션 생성 (엔진이 None이면 세션 초기화 안 함)
+# if engine:
+#     SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+# else:
+#     SessionLocal = None
+
+# # Base 클래스 생성
+# Base = declarative_base()
+
+# # 데이터베이스 세션 의존성
+# def get_db():
+#     if not SessionLocal:
+#         print("Database session is not available.")
+#         raise RuntimeError("Database is not initialized.")
+    
+#     db = SessionLocal()
+#     try:
+#         yield db
+#     finally:
+#         db.close()
+

gradio/app.py

@@ -0,0 +1,23 @@
+# import gradio as gr
+# from transformers import pipeline
+
+# # Hugging Face 이미지 생성 모델 로드
+# image_generator = pipeline("image-generation", model="CompVis/stable-diffusion-v1-4")
+
+# # 함수 정의
+# def generate_image(prompt):
+#     result = image_generator(prompt)
+#     return result[0]["image"]
+
+# # Gradio 인터페이스
+# demo = gr.Interface(
+#     fn=generate_image,                      # 함수 연결
+#     inputs=gr.Textbox(label="Image Prompt"), # 입력 텍스트 박스
+#     outputs="image",                         # 출력 이미지
+#     title="Image Generation with Stable Diffusion",
+#     description="Enter a prompt, and the Stable Diffusion model will generate an image.",
+# )
+
+# # 인터페이스 실행
+# if __name__ == "__main__":
+#     demo.launch()

models/user_info.py

@@ -0,0 +1,25 @@
+# from sqlalchemy import Column, String, BigInteger, DateTime, ForeignKey
+# from config.database import Base
+# from sqlalchemy.dialects.mysql import TINYINT  # MySQL TINYINT 타입
+
+# class UserInfo(Base):
+#     __tablename__ = "USER_INFO"
+    
+#     id = Column(String(36), primary_key=True, comment="ID")
+#     preferred_username = Column(String(50), nullable=True, comment="로그인아이디")
+#     mobile = Column(String(15), nullable=True, comment="모바일")
+#     business_cd = Column(String(8), nullable=True, comment="소속기관일련번")
+#     role_seq = Column(BigInteger, ForeignKey("ROLE.SEQ"), nullable=True, comment="역할일련번호")
+#     name = Column(String(255), nullable=True, comment="사용자이름")
+#     email = Column(String(255), nullable=True, comment="이메일")
+#     profile = Column(String(255), nullable=True, comment="프로필")
+#     school_code = Column(String(50), nullable=True, comment="학교코드")
+#     grade = Column(TINYINT, nullable=True, comment="학년")
+#     class_name = Column(String(50), nullable=True, comment="학급")
+#     student_no = Column(TINYINT, nullable=True, comment="학생번호")
+#     created_at = Column(DateTime, nullable=False, comment="생성일")
+#     creator_id = Column(String(36), nullable=True, comment="생성자아이디")
+#     creator_ip = Column(String(50), nullable=True, comment="생성자IP")
+#     updated_at = Column(DateTime, nullable=True, comment="수정일")
+#     updater_id = Column(String(36), nullable=True, comment="수정자아이디")
+#     updater_ip = Column(String(50), nullable=True, comment="수정자IP")

readme.txt

@@ -0,0 +1,63 @@
+# 프로젝트 구성 (Windows 환경)
+
+# 1. 프로젝트 폴더 생성 및 파일 구성 (Windows 명령어)
+# - 프로젝트 폴더: fastapi_upload_project
+# - 폴더 구조:
+RAG/
+├── .venv/                     # 가상환경 폴더
+├── .vscode/                   # VSCode 설정 폴더
+├── app/                       # FastAPI 애플리케이션 폴더
+│   ├── __pycache__/           # 캐시 파일 (자동 생성)
+│   ├── resources/             # 추가적인 리소스 (XML 등)
+│   ├── routers/               # 라우터 폴더
+│   │   ├── __init__.py        # 라우터 초기화 파일
+│   │   ├── user.py            # 사용자 관련 라우터
+│   │   ├── search.py          # 검색 관련 라우터
+│   │   ├── upload.py          # 업로드 관련 라우터
+│   ├── templates/             # HTML 템플릿 폴더
+│   │   ├── upload.html        # 업로드 페이지
+│   │   ├── search.html        # 검색 페이지
+│   ├── main.py                # FastAPI 애플리케이션 진입점
+├── config/                    # 설정 관련 폴더
+│   ├── __pycache__/           # 캐시 파일 (자동 생성)
+│   ├── database.py            # SQLAlchemy 데이터베이스 설정
+│   ├── mybatis_manager.py     # XML 매퍼 및 SQL 실행 관리
+├── mapper/                    # MyBatis XML 매퍼 폴더
+│   ├── user_mapper.xml        # 사용자 매퍼 XML
+│   ├── order_mapper.xml       # 주문 매퍼 XML
+│   ├── product_mapper.xml     # 상품 매퍼 XML
+├── models/                    # 데이터베이스 모델 폴더
+│   ├── __pycache__/           # 캐시 파일 (자동 생성)
+│   ├── user_info.py           # 사용자 정보 모델
+├── uploaded_files/            # 업로드된 파일 저장소
+├── .gitignore                 # Git 무시 파일
+├── requirements.txt           # Python 패키지 의존성
+├── readme.txt                 # 프로젝트 설명 파일
+
+
+# 라우터 추가
+# main.py 파일에 라우터 추가
+app.include_router(upload.router)
+
+# FastAPI 라우터 파일 작성
+# app/routers/upload.py 파일에 파일 업로드 관련 라우터 작성
+
+-------------------------------------------------------------------------
+# 가상 환경 생성 및 패키지 설치 (Windows)
+# Python 가상 환경을 생성하고 필요한 패키지를 설치하세요.
+# 
+# python -m venv rag
+# rag\Scripts\activate
+# ./rag/scripts/activate.ps1(powershell evn switching)
+# pip install -r requirements.txt
+
+# FastAPI 서버 실행
+# FastAPI 서버를 실행하세요.
+# uvicorn app.main:app --reload
+# pytorch 설치를 위해 파이썬 버전 3.11로 낮춤
+pip install torch --extra-index-url https://download.pytorch.org/whl/cpu
+
+-------------------------------------------------------------------------
+# 7. Docker 이미지 빌드&실행
+# docker build -t app .
+# docker run -d -p 8000:8000 app

requirements.txt

@@ -0,0 +1,48 @@
+# Web framework
+fastapi[all]
+
+# Database interaction
+#sqlalchemy
+#mysql-connector-python
+
+# ASGI server
+uvicorn
+
+# File upload support
+python-multipart
+
+# Template engine
+Jinja2
+
+# LangChain for LLMs and AI workflow management
+langchain
+
+# LangChain HuggingFace integration
+langchain-huggingface>=0.1.2
+
+# Vector search and similarity
+faiss-cpu
+
+# OpenAI API (uncomment if needed)
+#openai
+
+# PDF processing
+pypdf2
+pypdf
+
+# Community package for LangChain
+langchain-community>=0.0.1
+
+# Hugging Face integration
+huggingface-hub>=0.23.0
+
+protobuf
+
+# Sentence Transformers for embeddings
+sentence-transformers>=2.2.0, <4.0.0
+
+# PyTorch installation from CPU-specific index
+torch --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Gradio for building UI
+#gradio