diff --git a/.gitignore b/.gitignore
index e9eb0a6..f12775e 100644
--- a/.gitignore
+++ b/.gitignore
@@ -17,6 +17,7 @@ app/model/model_save/*
 #data
 app/services/graph_service/data/indices/paper_ivf.index
 app/services/graph_service/data/indices
+app/services/papers_service/data
 
 app/indices
 app/routers/previous/output/summaries_dir
@@ -25,11 +26,17 @@ app/data
 app/routers/output/summaries_dir/*
 
 
+
+
 #logs
 app/scripts/logs
 app/scripts/pyrouge_root
 app/scripts/running_logs
 
+app/services/old
+
+
+
 #etc
 app/etc/*
 
@@ -58,6 +65,11 @@ share/python-wheels/
 *.egg
 MANIFEST
 
+
+app/data/
+app/model
+app/tools/
+
 # 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.
@@ -206,3 +218,4 @@ cython_debug/
 .pypirc
 app/data/inductive/train.jsonl
 app/routers/output/summaries_dir/
+app/services/papers_service/data/paper_db.json
diff --git a/app/services/papers_service/data/__init__.py b/app/NoneType
similarity index 100%
rename from app/services/papers_service/data/__init__.py
rename to app/NoneType
diff --git a/app/runtime/Dockerfile b/app/runtime/Dockerfile
index f1c9f86..da9a191 100644
--- a/app/runtime/Dockerfile
+++ b/app/runtime/Dockerfile
@@ -5,21 +5,23 @@ ENV DEBIAN_FRONTEND=noninteractive \
     PYTHONUNBUFFERED=1 \
     NVIDIA_VISIBLE_DEVICES=all
 
+WORKDIR /app
+
 # 필수 패키지
 RUN apt-get update && apt-get install -y \
         python3-pip python3-dev git && \
     rm -rf /var/lib/apt/lists/*
 
-# 프로젝트 소스 복사
-WORKDIR /workspace
-COPY .. /workspace      
+# 1) requirements만 먼저 복사 → 레이어 캐시
+COPY runtime/requirements.txt ./requirements.txt
+RUN pip install --no-cache-dir --upgrade pip \
+ && pip install --no-cache-dir -r requirements.txt
 
-# 의존성(인퍼런스 전용)
-RUN pip3 install --upgrade pip \
- && pip3 install -r app/runtime/requirements.txt
+# 2) 실제 런타임 코드 복사
+COPY runtime/ ./runtime/
 
 # 지표·인덱스(volume 으로 붙여도 OK)
 # COPY indices/ /workspace/indices
 
 EXPOSE 8004
-CMD ["uvicorn", "app.runtime.api:app", "--host", "0.0.0.0", "--port", "8004"]
\ No newline at end of file
+CMD ["uvicorn", "runtime.api:app", "--host", "0.0.0.0", "--port", "8004"]
diff --git a/app/services/papers_service/data/ids.json b/app/runtime/__init__.py
similarity index 100%
rename from app/services/papers_service/data/ids.json
rename to app/runtime/__init__.py
diff --git a/app/runtime/api.py b/app/runtime/api.py
index 355c838..47b7cd1 100644
--- a/app/runtime/api.py
+++ b/app/runtime/api.py
@@ -6,6 +6,7 @@
 from runtime.cluster_searcher import search_clusters, cluster2pids, meta
 from runtime.graph_builder    import build_tree
 
+
 app = FastAPI(title="SearchForest-AI Recommend API")
 
 
@@ -32,7 +33,7 @@ class RecResponse(BaseModel):
 @app.get("/inference", response_model=RecResponse)
 def recommend(
     query: str = Query(..., description="검색 쿼리"),
-    top_k: int = Query(5, gt=1, le=10)          # default 5
+    top_k: int = Query(10, gt=1, le=10)          # default 10
 ):
     # 1) 쿼리 기준 top-k 클러스터
     hits = search_clusters(query, top_k)
@@ -46,11 +47,6 @@ def recommend(
         root["children"].append(cluster_node)
     return {"results": root }
 
-    
-
-
-
-
 # 로컬 실행용
 if __name__ == "__main__":
     uvicorn.run(app, host="0.0.0.0", port=8004)
diff --git a/app/runtime/graph_builder.py b/app/runtime/graph_builder.py
index d6bc7df..ec7e99e 100644
--- a/app/runtime/graph_builder.py
+++ b/app/runtime/graph_builder.py
@@ -9,7 +9,7 @@
 import torch
 from runtime.cluster_searcher import meta, cluster2pids   # ← meta 와 함께 추가로 import
 
-
+kw2pids: dict[str, list[str]] = {}     # keyword → [paper_ids]
 
 
 # ── 전역 설정 ───────────────────────────────────────────
@@ -121,6 +121,19 @@ def contains_kw(abs_txt: str, kw: str) -> bool:
     max_features=40_000,
 )
 
+# from sklearn.feature_extraction.text import TfidfVectorizer
+
+# # 전처리한 phrase 문서를 그대로 feeding
+# vectorizer = TfidfVectorizer(
+#     tokenizer=lambda s: s, preprocessor=lambda s: s, lowercase=False,
+#     ngram_range=(1,3), min_df=5, max_df=0.8
+# )
+# tfidf_mat = vectorizer.fit_transform(docs)           # shape (N_docs, N_terms)
+# idf   = vectorizer.idf_
+# vocab = vectorizer.vocabulary_                       # dict{phrase: idx}
+
+
+
 def top_keywords(pids, n=8):
     docs = [_as_text(G.nodes[p].get("abstract", "")).lower()
             for p in pids if G.has_node(p)]
@@ -156,7 +169,17 @@ def build_tree(root_kw: str, cid: int, depth: int = 1):
     ))
     pids_lvl0 = cluster2pids[cid]
 
-    tree = {"id": root_kw, "value": 1.0, "children": []}
+
+    # ① root 에도 pids 부여
+    tree = {
+            "id":    root_kw,
+            "value": 1.0,
+            "pids":  pids_lvl0,     # ★ 추가
+            "children": []
+        }
+
+    # kw2pids 전역 캐시에도 root 등록
+    kw2pids[root_kw] = pids_lvl0
 
     # ── depth-1  (최대 3개) ──────────────────────
     for kw1, sc1 in select_kw_scored(root_kw, cand, tfidf_dict, k=3):
@@ -172,16 +195,36 @@ def build_tree(root_kw: str, cid: int, depth: int = 1):
             "id":      kw1,
             "value":   round(sc1, 4),
             "pids":    hop1,          # 필요 없으면 제거
+            "children": []
         }
 
+        kw2pids[kw1] = hop1
+
         # ── depth-2 : parent=kw1, 최대 3개 ───────
         if depth > 1:
             for kw2, sc2 in select_kw_scored(kw1, cand, tfidf_dict, k=3):
+                # node1["children"].append({
+                #     "id":    kw2,
+                #     "value": round(sc2, 4),
+                # })
+                # hop-2 pids (root → kw1 → kw2)
+                kw2_emb = model.encode([kw2],
+                                        normalize_embeddings=True)[0]
+                hop2 = [
+                    p for p in hop1
+                    if (emb := get_abs_emb(p)) is not None
+                    and util.cos_sim(kw2_emb, emb).item() > COS_TH2
+                ]
+
                 node1["children"].append({
                     "id":    kw2,
                     "value": round(sc2, 4),
+                    "pids":  hop2,
                 })
 
+                # kw2pids에 2-depth 저장
+                kw2pids[kw2] = hop2
+
         tree["children"].append(node1)
 
     return tree
diff --git a/app/runtime/requirements.txt b/app/runtime/requirements.txt
index 087a22d..4be3dea 100644
--- a/app/runtime/requirements.txt
+++ b/app/runtime/requirements.txt
@@ -4,4 +4,8 @@ torch
 sentence-transformers
 faiss-gpu
 networkx
-tqdm
\ No newline at end of file
+tqdm
+fastapi
+uvicorn
+pydantic
+spacy
\ No newline at end of file
diff --git a/app/runtime/run_uvicorn.sh b/app/runtime/run_uvicorn.sh
index bce9f44..015e7b2 100755
--- a/app/runtime/run_uvicorn.sh
+++ b/app/runtime/run_uvicorn.sh
@@ -1,3 +1,3 @@
 #!/bin/bash
 
-uvicorn runtime.api:app --reload --port 8004
\ No newline at end of file
+uvicorn runtime.api:app --host 0.0.0.0 --port 8004 --reload
diff --git a/app/services/get_meta.py b/app/services/get_meta.py
new file mode 100644
index 0000000..5c8f2e1
--- /dev/null
+++ b/app/services/get_meta.py
@@ -0,0 +1,17 @@
+import aiohttp, asyncio
+
+S2_URL = "https://api.semanticscholar.org/graph/v1/paper/{}"
+FIELDS = "title,abstract,year,venue,referenceCount,citationCount," \
+         "influentialCitationCount,fieldsOfStudy,authors,tldr"
+
+async def fetch_paper(session, pid):
+    url = S2_URL.format(pid)
+    params = {"fields": FIELDS}
+    async with session.get(url, params=params, timeout=10) as r:
+        r.raise_for_status()
+        return await r.json()
+
+async def fetch_many(pids):
+    async with aiohttp.ClientSession() as session:
+        tasks = [fetch_paper(session, pid) for pid in pids]
+        return await asyncio.gather(*tasks)
\ No newline at end of file
diff --git a/app/services/graph_service/Dockerfile b/app/services/graph_service/Dockerfile
index 6cf9703..4ebe574 100644
--- a/app/services/graph_service/Dockerfile
+++ b/app/services/graph_service/Dockerfile
@@ -2,10 +2,9 @@
 FROM python:3.9-slim
 
 WORKDIR /app
-COPY requirements.txt .
+COPY . .
 RUN pip install --no-cache-dir -r requirements.txt
 
-COPY . .
 
 
 # FastAPI Uvicorn 실행
diff --git a/app/services/graph_service/dummy_data.py b/app/services/graph_service/dummy_data.py
deleted file mode 100644
index 478db51..0000000
--- a/app/services/graph_service/dummy_data.py
+++ /dev/null
@@ -1,115 +0,0 @@
-def get_dummy_tree(root: str, top1: int = 5, top2: int = 3):
-    """
-    root 키워드 + top1, top2 파라미터를 반영한 더미 radial-tree 반환
-    depth1: top1 개수, depth2: top2 개수로 구성됩니다.
-    """
-    children = []
-
-    # depth1: top1개의 자식 노드 생성
-    for i in range(top1):
-        # 라벨은 A, B, C, ... 또는 번호로 생성
-        label = chr(ord('A') + i) if i < 26 else str(i+1)
-        # base_sim: 0.9에서 일정 간격으로 감소
-        base_sim = round(1.0 - (i + 1) * (0.1), 4)
-
-        # depth2: 각 depth1 노드에 top2 개수만큼 자식 생성
-        grandchildren = []
-        for j in range(top2):
-            child_label = f"{root}-{label}-{j+1}"
-            # sim value: base_sim에서 0.05씩 감소
-            child_sim = round(base_sim - (j + 1) * 0.05, 4)
-            grandchildren.append({
-                "id": child_label,
-                "value": child_sim,
-                "children": []
-            })
-
-        children.append({
-            "id": f"{root}-{label}",
-            "value": base_sim,
-            "children": grandchildren
-        })
-
-    # 루트 노드 반환
-    return {
-        "id": root,
-        "value": 1.0,
-        "children": children
-    }
-
-# --- 기본 더미 트리 생성기 (context 포함) ---
-def get_dummy_tree_with_context(root: str, top1: int = 5, top2: int = 3):
-    children = []
-    for i in range(top1):
-        label = chr(ord('A') + i) if i < 26 else str(i+1)
-        base_sim = round(1.0 - (i + 1) * 0.1, 4)
-        grandchildren = []
-        for j in range(top2):
-            child_label = f"{label}-{j+1}"
-            full_context = f"{root}-{label}-{j+1}"
-            child_sim = round(base_sim - (j + 1) * 0.05, 4)
-            grandchildren.append({
-                "id": child_label,
-                "context": full_context,
-                "value": child_sim,
-                "children": []
-            })
-        children.append({
-            "id": label,
-            "context": f"{root}-{label}",
-            "value": base_sim,
-            "children": grandchildren
-        })
-    return {
-        "id": root,
-        "context": root,
-        "value": 1.0,
-        "children": children
-    }
-
-def get_dummy_tree_with_context_and_example(root: str, top1: int = 5, top2: int = 3):
-    """
-    root 키워드를 중심으로 top1개의 1-depth와 각 1-depth마다 top2개의 2-depth를 생성한 더미 트리를 반환.
-    각 노드에는 context, value, example 필드가 포함됨.
-    """
-    children = []
-
-    for i in range(top1):
-        label = chr(ord('A') + i) if i < 26 else str(i + 1)
-        base_sim = round(1.0 - (i + 1) * 0.1, 4)
-        lvl1_id = label
-        lvl1_context = f"{root}-{label}"
-        lvl1_example = f"{root} 분야의 하위 주제 {label}에 대한 간단한 설명입니다."
-
-        grandchildren = []
-        for j in range(top2):
-            lvl2_id = f"{label}-{j + 1}"
-            lvl2_context = f"{root}-{label}-{j + 1}"
-            lvl2_value = round(base_sim - (j + 1) * 0.05, 4)
-            lvl2_example = f"{label} 세부 주제 {j + 1}에 대한 예시 설명입니다."
-
-            grandchildren.append({
-                "id": lvl2_id,
-                "context": lvl2_context,
-                "value": lvl2_value,
-                "example": lvl2_example,
-                "children": []
-            })
-
-        children.append({
-            "id": lvl1_id,
-            "context": lvl1_context,
-            "value": base_sim,
-            "example": lvl1_example,
-            "children": grandchildren
-        })
-
-    return {
-        "id": root,
-        "context": root,
-        "value": 1.0,
-        "example": f"{root}라는 주제를 중심으로 확장된 키워드 구조입니다.",
-        "children": children
-    }
-
-
diff --git a/app/services/graph_service/graph_service.py b/app/services/graph_service/graph_service.py
index 0f19ede..63eb2b4 100644
--- a/app/services/graph_service/graph_service.py
+++ b/app/services/graph_service/graph_service.py
@@ -1,152 +1,113 @@
-import os
-import json, hashlib
-from typing import List, Dict, Optional, Tuple, Union
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import aioredis
-from data_util.logging import logger
-
-from data_util.config import Config
-from collections import defaultdict
-import requests
-from fastapi import Query
-from tree_mapping import extract_tree_mapping
-
-# ────────────────────────────────────────────────────────────────
-app = FastAPI(title="Graph Service with AI Inference")
-
-# Redis 초기화용 글로벌
-REDIS_URL = os.getenv("REDIS_URL", "redis://localhost:6379")
-redis: Optional[aioredis.Redis] = None
-
-# 요청 모델
-class GraphRequest(BaseModel):
-    root: str
-    top1: int = 5
-    top2: int = 3
-
-# 응답 트리 노드 구조
-class KeywordNode(BaseModel):
-    id: str
-    value: float
-    children: List["KeywordNode"]
-KeywordNode.update_forward_refs()
-
-# 전체 응답 구조
-class GraphResponse(BaseModel):
-    keyword_tree: KeywordNode
-    
-# Redis 연결
-@app.on_event("startup")
-async def startup_event():
-    global redis
-    # modern aioredis uses from_url
-    try:
-        redis = await aioredis.from_url(
-            REDIS_URL,
-            encoding="utf-8",
-            decode_responses=True,
-            max_connections=10
-        )
-        logger.info(f"✅ Connected to Redis at {REDIS_URL}")
-    except Exception as e:
-        logger.warning(f"⚠️ Redis 연결 실패, 캐시 미사용: {e}")
-        redis = None
-
-@app.on_event("shutdown")
-async def shutdown_event():
-    await redis.close()
-
-# 캐시 키 생성 함수
-def make_cache_key( root: str, top1: int, top2: int) -> str:
-    # 파라미터 조합으로 고유 키 생성
-    key_str = f"{root}|{top1}|{top2}"
-    return "graph:" + hashlib.sha256(key_str.encode()).hexdigest()
-
-
-# AI 서버 호출 함수
-def fetch_keywords(query: str) -> list[str]:
-    try:
-        response = requests.get(
-            "http://searchforest-ai:8004/inference",
-            params={"query": query, "top_k": 5}
-        )
-        response.raise_for_status()
-        data = response.json()
-        keywords = [child["kw"] for child in data["results"]["children"]]
-        return keywords
-    except Exception as e:
-        print(f"[ERROR] AI 서버 호출 실패: {e}")
-        return []
-
-# AI 서버 호출 + 결과 캐싱
-async def fetch_from_ai_and_cache(root: str, top1: int, top2: int):
-    try:
-        # response = requests.get("http://searchforest-ai:8004/inference", params={"query": root, "top_k": top1})
-        response = requests.get("http://localhost:8004/inference", params={"query": root, "top_k": top1})
-
-        response.raise_for_status()
-        data = response.json()
-
-        # 트리 구성
-        keyword_tree = {
-            "id": root,
-            "value": 1.0,
-            "children": []
-        }
-        kw2pids = {}
-
-        for cluster in data["results"]["children"]:
-            cluster_kw = cluster["kw"]
-            subnodes = cluster.get("children", [])
-            child_node = {
-                "id": cluster_kw,
-                "value": cluster["sim"],
-                "children": []
-            }
-            for sub in subnodes:
-                child_node["children"].append({"id": sub["kw"], "value": 0.8, "children": []})
-                kw2pids[sub["kw"]] = sub["pids"]
-
-            keyword_tree["children"].append(child_node)
-
-        cache_key = make_cache_key(root, top1, top2)
-        if redis:
-            await redis.set(cache_key, json.dumps({"tree": keyword_tree, "kw2pids": kw2pids}), ex=3600)
-
-        return keyword_tree, kw2pids
-
-    except Exception as e:
-        print(f"[ERROR] AI 호출 실패: {e}")
-        raise
-
-# /graph 엔드포인트
-@app.post("/graph", response_model=GraphResponse)
-async def build_graph(req: GraphRequest):
-
-    cache_key = make_cache_key(req.root, req.top1, req.top2)
-    if redis:
-        cached = await redis.get(cache_key)
-        if cached:
-            obj = json.loads(cached)
-            return {"keyword_tree": obj["tree"], "kw2pids": obj["kw2pids"]}
-
-    tree = await fetch_from_ai_and_cache(req.root, req.top1, req.top2)
-    
-    root, mapping = extract_tree_mapping(original_json)
-    tree = manual_tree_with_full_values(root, mapping)
-    tree_parsed = manual_tree_with_full_values(tree)
-
-    return {"keyword_tree": tree_parsed, "kw2pids": kw2pids}
-
-
-# /kw2pids 엔드포인트 (핑퐁용)
-@app.get("/kw2pids")
-async def get_kw2pids(query: str = Query(...), top1: int = 5, top2: int = 3):
-    cache_key = make_cache_key(query, top1, top2)
-    if redis:
-        cached = await redis.get(cache_key)
-        if cached:
-            obj = json.loads(cached)
-            return obj["kw2pids"]
-    return {"message": "No cached kw2pids available."}
+import os, json, hashlib
+from typing import List, Dict, Optional
+from fastapi import FastAPI, Query
+from pydantic import BaseModel
+import aioredis, requests
+import httpx
+
+from json_to_tree_and_kw2pid import manual_tree_with_full_values
+
+# ─────────────────────────────
+app = FastAPI(title="Graph Service with AI Inference")
+REDIS_URL = os.getenv("REDIS_URL", "redis://redis:6379")
+redis: Optional[aioredis.Redis] = None
+
+# ───────── Models ────────────
+class GraphRequest(BaseModel):
+    root: str
+    top1: int = 5
+    top2: int = 3
+
+class KeywordNode(BaseModel):
+    id: str
+    value: float
+    children: List["KeywordNode"]
+KeywordNode.update_forward_refs()
+
+class GraphResponse(BaseModel):          # ✨
+    keyword_tree: KeywordNode
+    kw2pids: Dict[str, List[str]]
+
+# ───────── Redis Events ──────
+@app.on_event("startup")
+async def startup_event():
+    global redis
+    try:
+        redis = await aioredis.from_url(
+            REDIS_URL, encoding="utf-8", decode_responses=True, max_connections=10
+        )
+        print(f"✅ Connected to Redis at {REDIS_URL}")
+    except Exception as e:
+        print(f"⚠️ Redis 연결 실패, 캐시 미사용: {e}")
+        redis = None
+
+@app.on_event("shutdown")
+async def shutdown_event():
+    if redis:                            # ✨
+        await redis.close()
+
+# ───────── Utils ────────────
+def make_cache_key(root: str, top1: int, top2: int) -> str:
+    return "graph:" + hashlib.sha256(f"{root}|{top1}|{top2}".encode()).hexdigest()
+
+async def fetch_from_ai_and_cache(root: str, top1: int, top2: int):
+    url = "https://58b9-165-194-104-91.ngrok-free.app/inference"
+    params = {"query": root, "top_k": top1, "top2": top2}
+
+    async with httpx.AsyncClient() as client:
+        resp = await client.get(url, params=params, timeout=15)
+        resp.raise_for_status()
+        data = resp.json()
+
+    # 2-1) keyword_tree
+    mapping = { n["id"]:{"value": n.get("sim",0.8),"children": n.get("children",[])}
+                for n in data["results"]["children"][:top1] }
+    keyword_tree = manual_tree_with_full_values(root, mapping)
+
+    # 2-2) kw2pids  ☑ root + 1-depth + 2-depth
+    kw2pids = {}
+
+    # root → 모든 1-depth pids 합집합
+    root_pids = []
+    for n in data["results"]["children"][:top1]:
+        root_pids.extend(n.get("pids", []))
+    kw2pids[root] = root_pids
+
+    # 1-depth
+    for n in data["results"]["children"][:top1]:
+        if "pids" in n:
+            kw2pids[n["id"]] = n["pids"]
+
+        # 2-depth
+        for child in n.get("children", []):
+            if "pids" in child:
+                kw2pids[child["id"]] = child["pids"]
+
+    if redis:
+        await redis.set(
+            make_cache_key(root, top1, top2),
+            json.dumps({"keyword_tree": keyword_tree, "kw2pids": kw2pids}),
+            ex=3600,
+        )
+    return keyword_tree, kw2pids
+
+# ───────── API ────────────
+@app.post("/graph", response_model=GraphResponse)
+async def build_graph(req: GraphRequest):
+    cache_key = make_cache_key(req.root, req.top1, req.top2)
+    if redis and (cached := await redis.get(cache_key)):
+        obj = json.loads(cached)
+        return obj                                  # FastAPI가 모델로 자동 직렬화
+
+    keyword_tree, kw2pids = await fetch_from_ai_and_cache(
+        req.root, req.top1, req.top2
+    )
+
+    
+    return {"keyword_tree": keyword_tree, "kw2pids": kw2pids}   # ✨
+
+@app.get("/kw2pids")
+async def get_kw2pids(query: str = Query(...), top1: int = 5, top2: int = 3):
+    if redis and (cached := await redis.get(make_cache_key(query, top1, top2))):
+        return json.loads(cached)["kw2pids"]
+    return {"message": "No cached kw2pids available."}
\ No newline at end of file
diff --git a/app/services/graph_service/json_to_tree_and_kw2pid.py b/app/services/graph_service/json_to_tree_and_kw2pid.py
new file mode 100644
index 0000000..5c22db8
--- /dev/null
+++ b/app/services/graph_service/json_to_tree_and_kw2pid.py
@@ -0,0 +1,108 @@
+#!/usr/bin/env python3
+"""
+json_to_tree_and_kw2pid.py
+
+– 입력:  ① your_raw_json : 처음 보내주신 {"results": …} 구조
+– 출력: ② tree_dict      : manual_tree_with_full_values() 로 만든 2-depth 그래프
+        ③ kw2pid_dict    : {키워드(str): [pid, …]} 형태
+"""
+import json
+from typing import Dict, List, Union, Tuple
+
+# ---------- 타입 & util ---------- #
+TreeMapping = Dict[str, Dict[str, Union[float, List[Dict[str, Union[str, float]]]]]]
+
+def manual_tree_with_full_values(root: str, mapping: TreeMapping):
+    children = []
+    for lvl1_label, data in mapping.items():
+        lvl1_value = data["value"]
+        lvl2_items = data["children"]
+
+        grandchildren = []
+        for lvl2 in lvl2_items:
+            lvl2_label = lvl2["id"]
+            lvl2_value = lvl2["value"]
+
+            grandchildren.append({
+                "id": lvl2_label,
+                "context": f"{root}-{lvl1_label}-{lvl2_label}",
+                "value": lvl2_value,
+                "children": []
+            })
+
+        children.append({
+            "id": lvl1_label,
+            "context": f"{root}-{lvl1_label}",
+            "value": lvl1_value,
+            "children": grandchildren
+        })
+
+    return {
+        "id": root,
+        "context": root,
+        "value": 1.0,
+        "children": children
+    }
+
+# ---------- 파싱 핵심 ---------- #
+def split_json(
+    raw_json: str
+) -> Tuple[dict, dict]:
+    """
+    1) 2-depth 트리(dict)
+    2) kw2pid 매핑(dict)  반환
+    """
+    data = json.loads(raw_json)
+    res = data["results"]
+
+    root = res["root"]
+    lvl1_nodes = res["children"]
+
+    # 1-depth → 2-depth 변환용 임시 mapping
+    mapping: TreeMapping = {}
+    # kw2pid 누적
+    kw2pid: Dict[str, List[str]] = {}
+
+    for lvl1 in lvl1_nodes:
+        lvl1_label = lvl1["id"]
+        lvl1_value = lvl1["value"]
+        mapping[lvl1_label] = {"value": lvl1_value, "children": []}
+
+        # 이 예시 JSON에선 pids 가 2-depth 키워드마다 달려있음
+        for lvl2 in lvl1["children"]:
+            lvl2_label = lvl2["id"]
+            lvl2_value = lvl2["value"]
+            mapping[lvl1_label]["children"].append(
+                {"id": lvl2_label, "value": lvl2_value}
+            )
+
+            # kw2pid 추가
+            if "pids" in lvl2 and lvl2["pids"]:
+                kw2pid[lvl2_label] = lvl2["pids"]
+
+        # (선택) 1-depth 키워드에도 동일 pids 부여하고 싶다면 주석 해제
+        # if mapping[lvl1_label]["children"]:
+        #     kw2pid[lvl1_label] = mapping[lvl1_label]["children"][0]["pids"]
+
+    tree_dict = manual_tree_with_full_values(root, mapping)
+    return tree_dict, kw2pid
+
+# ---------- 사용 예시 ---------- #
+if __name__ == "__main__":
+    with open("first_result.json", "r", encoding="utf-8") as f:
+        raw = f.read()
+
+    tree, kw2pid = split_json(raw)
+
+    # 필요에 따라 파일로 저장
+    with open("tree_2depth.json", "w", encoding="utf-8") as f:
+        json.dump(tree, f, ensure_ascii=False, indent=2)
+
+    with open("kw2pid.json", "w", encoding="utf-8") as f:
+        json.dump(kw2pid, f, ensure_ascii=False, indent=2)
+
+    # 콘솔 확인
+    print("=== 2-depth tree ===")
+    print(json.dumps(tree, ensure_ascii=False, indent=2)[:800], "...\n")
+    print("=== kw2pid ===")
+    print(json.dumps(kw2pid, ensure_ascii=False, indent=2)[:800], "...")
\ No newline at end of file
diff --git a/app/services/graph_service/requirements.txt b/app/services/graph_service/requirements.txt
index 087a22d..2708847 100644
--- a/app/services/graph_service/requirements.txt
+++ b/app/services/graph_service/requirements.txt
@@ -1,7 +1,6 @@
-numpy
-scikit-learn
-torch
-sentence-transformers
-faiss-gpu
-networkx
-tqdm
\ No newline at end of file
+fastapi
+uvicorn[standard]
+pydantic
+aioredis
+requests
+httpx>=0.26
\ No newline at end of file
diff --git a/app/services/graph_service/test.json b/app/services/graph_service/test.json
new file mode 100644
index 0000000..3197678
--- /dev/null
+++ b/app/services/graph_service/test.json
@@ -0,0 +1 @@
+{"results":{"root":"test","children":[{"id":"dark matter density","value":1.0,"children":[{"id":"dark matter density","value":0.8,"pids":["59396448","119215569","119234393","118493497","118683283","41660913","118557040","56387919","17578958","118365709","116981343","118607320","73631352","118865190","119393943","14073020","119414536","59065822","15089339","202740","13418816","119111724","119298498","15832449","119241489","14563896","119187336","66122070","30687560","119244797","53009603","119377295","195750878","119442219","118884237","52076653","55808663","119269304"]},{"id":"such dark 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\ No newline at end of file
diff --git a/app/services/main.py b/app/services/main.py
index fe07af2..e3edcd7 100644
--- a/app/services/main.py
+++ b/app/services/main.py
@@ -75,4 +75,23 @@ def api_search(
     """
     TODO: api_graph + api_papers 조합해서 한번에 반환
     """
-    raise NotImplementedError
\ No newline at end of file
+    raise NotImplementedError
+
+
+    import aiohttp, asyncio
+
+    S2_URL = "https://api.semanticscholar.org/graph/v1/paper/{}"
+    FIELDS = "title,abstract,year,venue,referenceCount,citationCount," \
+            "influentialCitationCount,fieldsOfStudy,authors,tldr"
+
+    async def fetch_paper(session, pid):
+        url = S2_URL.format(pid)
+        params = {"fields": FIELDS}
+        async with session.get(url, params=params, timeout=10) as r:
+            r.raise_for_status()
+            return await r.json()
+
+    async def fetch_many(pids):
+        async with aiohttp.ClientSession() as session:
+            tasks = [fetch_paper(session, pid) for pid in pids]
+            return await asyncio.gather(*tasks)
\ No newline at end of file
diff --git a/app/services/papers_service/Dockerfile b/app/services/papers_service/Dockerfile
index 0bdfd6d..03d2255 100644
--- a/app/services/papers_service/Dockerfile
+++ b/app/services/papers_service/Dockerfile
@@ -1,27 +1,25 @@
 FROM python:3.9-slim
 
+# 1. 필수 패키지 + gdown 설치
+RUN apt-get update && \
+    apt-get install -y --no-install-recommends python3-pip git && \
+    pip3 install --no-cache-dir gdown && \
+    rm -rf /var/lib/apt/lists/*
+# 2. 프로젝트 코드 복사
+#    (컨텍스트 최상단에서 COPY . . 라면 /app 구조도 함께 포함됩니다)
 WORKDIR /app
-
-# 1. 의존성 설치
-COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
-
-RUN apt-get update && apt-get install -y tar
-
-# 2. gdown 설치
-RUN pip install gdown beautifulsoup4
-
-# 3. Google Drive에서 tar.gz 파일 다운로드 → data/ 안에 저장
-RUN mkdir -p data && \
-    gdown --id 1tFYFjScIyu9RvAHWWWf-SGHwTs7F5866 -O data/inductive_test_checkpoint_collected.tar.gz
-
-# 2. data/ 폴더 안에서 압축 해제 + 압축 파일 삭제
-RUN tar -xzf data/inductive_test_checkpoint_collected.tar.gz -C data/ && \
-    rm data/inductive_test_checkpoint_collected.tar.gz
-
-# 4. 코드 복사 (덮어쓰지 않도록 이후에)
-COPY data/kw2pids.json data/
-COPY papers_service.py ./
+COPY . .
+
+# 3. Google Drive에서 tar.gz 다운로드 → data/ 안에 저장·압축 해제
+RUN mkdir -p app/data && \
+    gdown --id 13KL8qntiJlyxc3ObqjB7t8NEXoRyYyXX \
+          -O app/data/paper_db.tar.gz && \
+    tar -xzf app/data/paper_db.tar.gz \
+        -C app/data && \
+    rm  app/data/paper_db.tar.gz
+
+# 4. 파이썬 의존성 설치
+RUN pip3 install --no-cache-dir -r requirements.txt   # 경로는 필요에 맞게
 
 # 5. 앱 실행
 CMD ["uvicorn", "papers_service:app", "--host", "0.0.0.0", "--port", "8000"]
\ No newline at end of file
diff --git a/app/services/papers_service/data/dummy_data.py b/app/services/papers_service/data/dummy_data.py
deleted file mode 100644
index 56c8b38..0000000
--- a/app/services/papers_service/data/dummy_data.py
+++ /dev/null
@@ -1,128 +0,0 @@
-# papers_service/dummy_data.py
-
-def get_dummy_papers():
-    """
-    더미 논문 리스트 반환
-    """
-    return [
-        {
-            "paper_id":       "p1",
-            "title":          "Dummy Paper A",
-            "abstract":       "This is the abstract of dummy paper A.",
-            "authors":        ["Alice"],
-            "year":           2023,
-            "citation_count": 5,
-            "sim_score":      0.90,
-            "summary":        "이 논문은 A에 대해 간략히 설명합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p2",
-            "title":          "Dummy Paper B",
-            "abstract":       "This is the abstract of dummy paper B.",
-            "authors":        ["Bob", "Carol"],
-            "year":           2020,
-            "citation_count": 3,
-            "sim_score":      0.85,
-            "summary":        "이 논문은 B의 주요 기여를 요약합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p3",
-            "title":          "Dummy Paper C",
-            "abstract":       "This is the abstract of dummy paper C.",
-            "authors":        ["Dave"],
-            "year":           2022,
-            "citation_count": 7,
-            "sim_score":      0.80,
-            "summary":        "이 논문에서는 C를 제안하고 실험 결과를 제공합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain": "Mathematic"
-        },
-        {
-            "paper_id":       "p4",
-            "title":          "Dummy Paper D",
-            "abstract":       "This is the abstract of dummy paper D.",
-            "authors":        ["Eve", "Frank"],
-            "year":           2021,
-            "citation_count": 10,
-            "sim_score":      0.75,
-            "summary":        "이 논문은 D 기법의 유효성을 평가합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p5",
-            "title":          "Dummy Paper E",
-            "abstract":       "This is the abstract of dummy paper E.",
-            "authors":        ["Grace"],
-            "year":           2019,
-            "citation_count": 12,
-            "sim_score":      0.70,
-            "summary":        "이 논문에서는 E 알고리즘을 제안합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p6",
-            "title":          "Dummy Paper F",
-            "abstract":       "This is the abstract of dummy paper F.",
-            "authors":        ["Heidi"],
-            "year":           2020,
-            "citation_count": 8,
-            "sim_score":      0.65,
-            "summary":        "이 논문은 F 시스템의 성능을 분석합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p7",
-            "title":          "Dummy Paper G",
-            "abstract":       "This is the abstract of dummy paper G.",
-            "authors":        ["Ivan", "Judy"],
-            "year":           2022,
-            "citation_count": 6,
-            "sim_score":      0.60,
-            "summary":        "이 논문에서는 G 모델을 제안하고 평가합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p8",
-            "title":          "Dummy Paper H",
-            "abstract":       "This is the abstract of dummy paper H.",
-            "authors":        ["Kevin"],
-            "year":           2021,
-            "citation_count": 9,
-            "sim_score":      0.55,
-            "summary":        "이 논문은 H 프로토콜의 보안성을 검증합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic",
-        },
-        {
-            "paper_id":       "p9",
-            "title":          "Dummy Paper I",
-            "abstract":       "This is the abstract of dummy paper I.",
-            "authors":        ["Laura"],
-            "year":           2023,
-            "citation_count": 4,
-            "sim_score":      0.50,
-            "summary":        "이 논문에서는 I 프레임워크를 소개합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        },
-        {
-            "paper_id":       "p10",
-            "title":          "Dummy Paper J",
-            "abstract":       "This is the abstract of dummy paper J.",
-            "authors":        ["Mallory", "Niaj"],
-            "year":           2018,
-            "citation_count": 15,
-            "sim_score":      0.45,
-            "summary":        "이 논문은 J 방법론의 활용 사례를 제시합니다.",
-            "url":            "https://www.semanticscholar.org",
-            "domain":         "Mathematic"
-        }
-    ]
\ No newline at end of file
diff --git a/app/services/papers_service/data/kw2pids.json b/app/services/papers_service/data/kw2pids.json
deleted file mode 100644
index 368bd49..0000000
--- a/app/services/papers_service/data/kw2pids.json
+++ /dev/null
@@ -1,53 +0,0 @@
-{
-  "machine learning": [
-    "40108038",
-    "59572248",
-    "5799960",
-    "14188576",
-    "119242784"
-  ],
-  "neural network": [
-    "41418788",
-    "119472164",
-    "11501607",
-    "119111722",
-    "14909482"
-  ],
-  "graph representation": [
-    "55836730",
-    "118751294",
-    "51183683",
-    "118849608",
-    "118816857",
-    "5734610"
-  ],
-  "text summarization": [
-    "59572248",
-    "5799960",
-    "14188576",
-    "51183683",
-    "85459157",
-    "56099032"
-  ],
-  "natural language processing": [
-    "11501607",
-    "59572248",
-    "14909482"
-  ],
-  "deep learning": [
-    "11534505",
-    "59572248",
-    "13074624",
-    "53641451"
-  ],
-  "transformer": [
-    "41418788",
-    "5799960",
-    "11501607"
-  ],
-  "embedding": [
-    "59408549",
-    "14516333",
-    "119144587"
-  ]
-}
\ No newline at end of file
diff --git a/app/services/papers_service/papers_service.py b/app/services/papers_service/papers_service.py
index 1f02272..16b7d91 100644
--- a/app/services/papers_service/papers_service.py
+++ b/app/services/papers_service/papers_service.py
@@ -1,146 +1,142 @@
-# papers_service/main.py
-import random
-
-from fastapi import FastAPI, HTTPException, Query
-from pydantic import BaseModel
-from typing import List, Optional
-import json
-import os
-
-app = FastAPI(title="Papers Service")
-
-
-class Author(BaseModel):
-    name: str
-
-
-class Citation(BaseModel):
-    paperId: str
-    title: Optional[str]
-    year: Optional[int]
-
-
-class Reference(BaseModel):
-    paperId: str
-    title: Optional[str]
-    year: Optional[int]
-
-
-# --- 논문 객체 정의 ---
-class Paper(BaseModel):
-    paper_id: str
-    abstract: Optional[str]
-    title: Optional[str]
-
-    url: Optional[str]
-    venue: Optional[str]
-    year: Optional[int]
-
-    reference_count: Optional[int]
-    citation_count: Optional[int]
-    influentialCitationCount: Optional[int]
-
-    fieldsOfStudy: Optional[List[str]]
-    tldr: Optional[str]
-    authors: List[Author]
-
-    sim_score: float
-
-
-# --- 응답 모델 ---
-class PapersResponse(BaseModel):
-    total_results: int
-    max_display: int
-    page: int
-    page_size: int
-    papers: List[Paper]
-
-
-
-def make_cache_key(root, top1, top2):
-    key_str = f"{root}|{top1}|{top2}"
-    return "keyword_tree:graph:" + hashlib.sha256(key_str.encode()).hexdigest()
-
-
-def fetch_keyword_tree_from_graph_service(query: str) -> dict:
-    response = requests.get("http://graph-service:8002/keyword_tree", params={"query": query})
-    return response.json()
-
-@app.get("/papers", response_model=PapersResponse)
-def get_papers_by_keyword(
-        kw: str = Query(..., description="클릭한 키워드"),
-        page: int = Query(1, ge=1),
-        page_size: int = Query(20, ge=1, le=100)
-):
-
-    response = requests.post("http://graph-service:8002/graph", json={
-        "root": kw,
-        "top1": 10,
-        "top2": 3
-    })
-
-
-    keyword_tree = response.json().get("keyword_tree")
-
-    response = requests.get("http://graph-service:8002/keyword_tree", params={"query": "AI"})
-    kw2pids = response.json()
-
-
-
-    keyword_tree_json = redis.get(f"keyword_tree:{cache_key}")
-    keyword_tree = json.loads(keyword_tree_json)
-
-    key = make_cache_key("pid123", "AI", 5, 3)
-    cached = redis.get(key)
-    if cached:
-        kw2pids = json.loads(cached)
-
-
-    if kw not in kw2pids:
-        raise HTTPException(status_code=404, detail=f"Keyword '{kw}' not found.")
-    else:
-        all_pids = kw2pids[kw]
-
-    # 페이징
-    total = len(all_pids)
-    start = (page - 1) * page_size
-    end = min(start + page_size, total)
-    sliced = all_pids[start:end]
-
-    papers = []
-    for pid in sliced:
-        if pid not in paper_db:
-            continue
-        entry = paper_db[pid]
-        papers.append(Paper(
-            paper_id=pid,
-            title=entry.get("title"),
-            abstract=entry.get("abstract"),
-            url=entry.get("url"),
-            venue=entry.get("venue"),
-            year=entry.get("year"),
-            reference_count=entry.get("referenceCount"),
-            citation_count=entry.get("citationCount"),
-            influentialCitationCount=entry.get("influentialCitationCount"),
-            fieldsOfStudy=entry.get("fieldsOfStudy"),
-            tldr=entry.get("tldr", {}).get("text") if entry.get("tldr") else None,
-            authors=[Author(name=a["name"]) for a in entry.get("authors", [])],
-            sim_score=random.uniform(0, 1)  # Stub score
-        ))
-
-    return PapersResponse(
-        total_results=total,
-        max_display=len(sliced),
-        page=page,
-        page_size=page_size,
-        papers=papers
-    )
-
-@app.get("/keyword_tree")
-async def get_keyword_tree(query: str = Query(...)):
-    cache_key = f"kw2pids:{query}"
-    if redis:
-        cached = await redis.get(cache_key)
-        if cached:
-            return json.loads(cached)
-    return {"message": "No cached keyword->pids mapping found."}
+# papers_service/main.py 
+
+import os, json, random, asyncio
+from typing import List, Dict, Optional
+
+from fastapi import FastAPI, Query, HTTPException
+from pydantic import BaseModel
+import httpx
+
+app = FastAPI(title="Papers Service")
+
+# ───────── Pydantic Models ─────────
+class Author(BaseModel):
+    name: str
+
+class Paper(BaseModel):
+    paper_id: str
+    title:      Optional[str]
+    abstract:   Optional[str]
+    url:        Optional[str]
+    venue:      Optional[str]
+    year:       Optional[int]
+    reference_count:          Optional[int]
+    citation_count:           Optional[int]
+    influentialCitationCount: Optional[int]
+    fieldsOfStudy:            Optional[List[str]]
+    tldr:       Optional[str]
+    authors:    List[Author]
+    sim_score:  float
+
+class PapersResponse(BaseModel):
+    total_results: int
+    max_display:   int
+    page:          int
+    page_size:     int
+    papers:        List[Paper]
+
+# ───────── Data Load ─────────
+BASE_DIR = os.path.join(os.path.dirname(__file__), "data")
+
+def safe_load(fname: str) -> dict:
+    path = os.path.join(BASE_DIR, fname)
+    try:
+        with open(path, encoding="utf-8") as f:
+            return json.load(f)
+    except FileNotFoundError:
+        print(f"⚠️  {fname} not found → 빈 dict 사용")
+        return {}
+
+paper_db: Dict[str, dict]        = safe_load("paper_db.json")
+kw2pids:   Dict[str, List[str]]  = safe_load("kw2pids.json")
+save_lock  = asyncio.Lock()                      # 파일 캐시 동시 접근 보호
+
+# ────────────── Helper ──────────────
+def build_paper(pid: str) -> Paper:
+    e = paper_db[pid]
+    return Paper(
+        paper_id              = pid,
+        title                 = e.get("title"),
+        abstract              = e.get("abstract"),
+        url                   = e.get("url"),
+        venue                 = e.get("venue"),
+        year                  = e.get("year"),
+        reference_count       = e.get("referenceCount"),
+        citation_count        = e.get("citationCount"),
+        influentialCitationCount = e.get("influentialCitationCount"),
+        fieldsOfStudy         = e.get("fieldsOfStudy"),
+        tldr                  = (e.get("tldr") or {}).get("text"),  # ← 수정
+        authors               = [Author(name=a["name"])
+                                 for a in e.get("authors", [])],
+        sim_score             = random.uniform(0, 1),   # stub
+    )
+
+def paginate(ids: List[str], page: int, page_size: int):
+    total = len(ids)
+    start = (page - 1) * page_size
+    return ids[start:start + page_size], total
+
+async def build_papers(ids: List[str]) -> List[Paper]:
+    papers: List[Paper] = []
+    for pid in ids:
+        if await ensure_paper(pid):
+            papers.append(build_paper(pid))
+    return papers
+
+# ───────── API ─────────
+@app.get("/papers", response_model=PapersResponse)
+async def get_papers(
+    root: str = Query(..., description="검색 루트(처음 입력)"),
+    kw:   str = Query(..., description="사용자가 선택한 키워드"),
+    page: int = Query(1, ge=1),
+    page_size: int = Query(20, ge=1, le=100),
+):
+    ids = await ensure_kw2pids(root, kw)
+    if not ids:
+        raise HTTPException(404, f"Keyword '{kw}' not found")
+
+    sliced, total = paginate(ids, page, page_size)
+
+    # paper_db 에 존재하는 PID 만 반환
+    papers = [build_paper(pid) for pid in sliced if pid in paper_db]
+
+    return PapersResponse(
+        total_results = total,
+        max_display   = len(papers),
+        page          = page,
+        page_size     = page_size,
+        papers        = papers,
+    )
+
+GRAPH_BASE = os.getenv("GRAPH_URL", "http://graph-service:8002")
+
+async def ensure_kw2pids(root: str, keyword: str,
+                         top1: int = 5, top2: int = 3) -> List[str]:
+    """keyword 가 캐시에 없으면 graph-service 를 호출해 kw2pids 갱신"""
+    if keyword in kw2pids:
+        return kw2pids[keyword]
+
+    async with httpx.AsyncClient() as client:
+        resp = await client.post(
+            f"{GRAPH_BASE}/graph",
+            json={"root": keyword, "top1": top1, "top2": top2},
+            timeout=15
+        )
+    if resp.status_code != 200:
+        raise HTTPException(502, "graph_service error")
+
+    data = resp.json()
+    kw2pids.update(data["kw2pids"])
+
+    # 🔽 파일 캐시 저장 시도 → 읽기 전용이면 무시
+    try:
+        async with save_lock:
+            path = os.path.join(BASE_DIR, "kw2pids.json")
+            with open(path, "w") as f:
+                json.dump(kw2pids, f, ensure_ascii=False)
+    except OSError:
+        # read-only · 컨테이너 환경에선 무시하고 넘어감
+        pass
+
+    return kw2pids.get(keyword, [])
+    
\ No newline at end of file
diff --git a/app/services/papers_service/requirements.txt b/app/services/papers_service/requirements.txt
index 4174eb1..bf2e342 100644
--- a/app/services/papers_service/requirements.txt
+++ b/app/services/papers_service/requirements.txt
@@ -1,3 +1,4 @@
 fastapi
 uvicorn[standard]
-pydantic
\ No newline at end of file
+pydantic
+httpx>=0.26
\ No newline at end of file
diff --git a/app/services/requirements.txt b/app/services/requirements.txt
index c9b6004..81a4930 100644
--- a/app/services/requirements.txt
+++ b/app/services/requirements.txt
@@ -1,3 +1,3 @@
 fastapi
 uvicorn
-pydantic
+pydantic
\ No newline at end of file
diff --git a/docker-compose.yml b/docker-compose.yml
index 549ed4b..e14d2c1 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -8,14 +8,24 @@ services:
       - "6379:6379"
     restart: unless-stopped
   
-  papers-service:
-    build:
-      context: ./app/services/papers_service
-      dockerfile: Dockerfile
-    ports:
-      - "8000:8000"
-    depends_on:
-      - searchforest-ai
+  # runtime_infer:
+  #   build:
+  #     context: ./app
+  #     dockerfile: runtime/Dockerfile
+  #   container_name: searchforest-infer
+  #   environment:
+  #     - NVIDIA_VISIBLE_DEVICES=all
+  #   volumes:
+  #     - ./app/indices:/app/indices:ro  # 인덱스 실시간 사용
+  #   ports:
+  #     - "8004:8004"
+  #   deploy:
+  #     resources:
+  #       reservations:
+  #         devices:
+  #           - driver: nvidia
+  #             count: 1
+  #             capabilities: [gpu]
 
   graph-service:
     build:
@@ -25,31 +35,26 @@ services:
     ports:
       - "8002:8002"
     environment:
-      # Redis URL을 환경변수로 주입
-      REDIS_URL: "redis://redis:6379"
-      # (필요하다면) CONFIG_PATH, INDEX_PATH 등도
+      - REDIS_URL=redis://redis:6379
+      - INFER_URL=http://host.docker.internal:8004
     depends_on:
       - redis
-      - searchforest-ai
     restart: unless-stopped
-  
-  runtime_infer:
+
+  papers-service:
     build:
-      context: ./app/runtime
-      dockerfile: app/runtime/Dockerfile
-    container_name: searchforest-infer
-    runtime: nvidia            # GPU 전달
+      context: ./app/services/papers_service
+      dockerfile: Dockerfile
+    container_name: papers_service
+    ports:
+      - "8000:8000"
     environment:
-      - NVIDIA_VISIBLE_DEVICES=all
+      - REDIS_URL=redis://redis:6379
+      - GRAPH_URL=http://graph-service:8002
+    depends_on:
+      - redis
+      - graph-service
+    restart: unless-stopped
     volumes:
-      - ./indices:/workspace/indices  # 인덱스 실시간 사용
-    ports:
-      - "8004:8004"
-    deploy:
-      resources:
-        reservations:
-          devices:
-            - driver: nvidia
-              count: 1
-              capabilities: [gpu]
+      - ./app/data:/app/data:ro