1. 랭체인 이미지 인식 후 처리 마무리

2. fastAPI로 프로젝트 구조 실습

1. 랭체인 이미지 인식 후 처리 마무리
2. fastAPI로 프로젝트 구조 실습

project/rag_app/backend/ai/embedding.py

@@ -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}",
+)

project/rag_app/backend/ai/llm.py

@@ -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
+    }
+)

project/rag_app/backend/config/settings.py

@@ -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()

project/rag_app/backend/main.py

@@ -0,0 +1,16 @@
+from fastapi import FastAPI
+from starlette.staticfiles import StaticFiles
+from backend.routers.page_router import router as page_router
+from backend.routers.api_router import router as api_router
+app = FastAPI()
+
+app.include_router(page_router)
+app.include_router(api_router)
+
+# static 폴더 지정
+app.mount("/static", StaticFiles(directory="backend/static"), name="static")
+
+# http://127.0.0.1:8000/item/1 + GET
+# @app.get("/item/{item_id}")
+# async def read_item(item_id):
+#     return {"item_id" : item_id}

project/rag_app/backend/routers/api_router.py

@@ -0,0 +1,25 @@
+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
+router = APIRouter(prefix="/api")
+
+# http://127.0.0.1:8000/api/question
+@router.post("/question")
+async def question(req:QuestionRequest):
+    answer = question_and_answer(req.question)
+
+    return {"message" : answer}
+
+# http://127.0.0.1:8000/api/rag/upload
+@router.post("/rag/upload")
+async def fileUpload(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

project/rag_app/backend/routers/page_router.py

@@ -0,0 +1,15 @@
+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("/rag")
+async def rag(request : Request):
+    return templates.TemplateResponse(request = request, name="rag.html")

project/rag_app/backend/schemas/basic_schema.py

@@ -0,0 +1,5 @@
+from pydantic import BaseModel
+
+class QuestionRequest(BaseModel):
+    question : str
+

project/rag_app/backend/services/llm_service.py

@@ -0,0 +1,8 @@
+from backend.ai.llm import watson_llm
+
+# LLM 모델 통신
+# 데이터베이스 통신
+
+def question_and_answer(question):
+    response = watson_llm.invoke(question)
+    return response.content

project/rag_app/backend/services/rag_service.py

@@ -0,0 +1,6 @@
+# pdf 업로드 => 분할 => 인덱스 생성
+def upload_document(file):
+    pass
+
+
+# 질문 => 유사도 검색 => 문서 => llm 답변 생성

project/rag_app/backend/static/js/index.js

@@ -0,0 +1,44 @@
+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/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').textContent = answer.message
+}
+
+// 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 선택해주세요.");
+        return;
+    }
+
+    // form 만들어 전송
+    const formData = new FormData()
+    formData.append("file", file)
+
+    const response = await fetch("/api/reg/question", {
+        method: "POST",
+        body: formData
+    })
+    // 전송 후 answer 도착 시 answer 화면에 보여주기
+    const answer = await response.json()
+    document.querySelector('#answer').textContent = answer.message
+}

project/rag_app/backend/templates/index.html

@@ -0,0 +1,19 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <h1>HOME</h1>
+    <ul>
+        <a href="/rag">RAG</a>
+    </ul>
+    <div>
+        <input type="text" name="question" id="question">
+        <button type="submit">질문</button>
+    </div>
+    <div id="answer"></div>
+    <script src="{{ url_for('static', path='js/index.js')}}"></script>
+</body>
+</html>

project/rag_app/backend/templates/rag.html

@@ -0,0 +1,17 @@
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <title>Title</title>
+</head>
+<body>
+    <h2>RAG</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/index.js')}}"></script>
+</body>
+</html>