feat: LLM 모델명 외부 설정 + 포인트 추가 기능

- mcp-server: 하드코딩된 모델명을 llm-model.json 기반 config.py로 외부화 - C#: AppendPointsAsync로 기존 데이터 유지하면서 포인트 추가 - C#: LlmConfigController로 LLM 모델명 조회/저장 API - Frontend: LLM 설정 UI 카드 + 포인트 빌더에서 추가하기 버튼
2026-05-11 17:55:18 +09:00
parent de728f013a
commit 5cacc5dbb5
15 changed files with 544 additions and 35 deletions
--- a/.opencode/skills/experion-crawler/SKILL.md
+++ b/.opencode/skills/experion-crawler/SKILL.md
@@ -0,0 +1,278 @@
+---
+name: experion-crawler
+description: ExperionCrawler .NET 8 project — OPC UA data acquisition, PostgreSQL/TimescaleDB, P&ID DXF/PDF parsing, MCP server bridge, high-frequency data capture. Use when building, debugging, or modifying this project.
+license: MIT
+compatibility: opencode
+metadata:
+  domain: iiot
+  framework: dotnet
+---
+
+## Build / Run / Test
+
+| Action | Command | Working Dir |
+|--------|---------|-------------|
+| Build | `dotnet build src/Web/ExperionCrawler.csproj` | repo root |
+| Run (dev) | `dotnet run` | `src/Web/` |
+| Tests | `dotnet test` | repo root |
+| Publish | `dotnet publish -c Release -o /opt/ExperionCrawler` | `src/Web/` |
+
+Single project, linux-arm64 target. `src/Core/` and `src/Infrastructure/` are included via `<Compile Include>` globs.
+
+## Architecture
+
+```
+src/
+├── Core/            — Interfaces, Domain entities, DTOs, Application Services
+├── Infrastructure/  — OpcUa/, Database/, Certificates/, Csv/, Mcp/
+└── Web/             — Program.cs, Controllers/, wwwroot/ (SPA)
+```
+
+- Controllers: `src/Web/Controllers/ExperionControllers.cs` (single file)
+- Interfaces: `src/Core/Application/Interfaces/IExperionServices.cs` (single file)
+
+## Database — PostgreSQL (TimescaleDB)
+
+**Docker container** `iiot-timescaledb` (image: `timescale/timescaledb-ha:pg16`):
+- Port: `localhost:5432`
+- Volume: `iiot-pgdata` → `/var/lib/postgresql/data`
+- Init scripts: `/opt/iiot-platform/timescaledb/init/`
+- Container IP: `172.17.0.3`
+- Network: `bridge` (default)
+
+**Docker env credentials:**
+- DB: `iiot_platform`, User: `iiot_admin`, Pass: `ChangeMe2026`
+
+**appsettings.json connection strings** (`src/Web/appsettings.json`):
+```
+DefaultConnection:    Host=localhost;Port=5432;Database=iiot_platform;  Username=postgres;Password=postgres
+ExperionDbConnection: Host=localhost;Port=5432;Database=postgres;       Username=postgres;Password=postgres;Trust Server Certificate=true;Include Error Detail=true
+```
+
+**Key tables:** `raw_node_map`, `node_map_master`, `realtime_table`, `history_table`, `fast_session`, `fast_record`, `tag_metadata`, `pid_equipment`, `pid_graph_status`
+
+**Quick CLI access:** `psql -U postgres -d iiot_platform -h localhost`
+
+## Critical Convention — JSON camelCase
+
+`PropertyNamingPolicy = null` so C# PascalCase becomes JSON keys. Frontend expects camelCase:
+
+```csharp
+// ✅ Correct
+return Ok(new { id = x.Id, tagName = x.TagName, nodeId = x.NodeId, liveValue = x.LiveValue, timestamp = x.Timestamp });
+
+// ❌ Broken — shorthand = PascalCase keys
+return Ok(new { x.Id, x.TagName });
+// ❌ Broken — typed object = PascalCase keys
+return Ok(myDto);
+```
+
+**Checklist for every new endpoint:**
+- [ ] All anonymous object keys are camelCase (`id`, `tagName`, `nodeId` ...)
+- [ ] No `new { x.SomeProp }` shorthand anywhere
+- [ ] No typed record/class passed directly to `Ok()`
+- [ ] C# reserved words (`class`) prefixed with `@`
+
+## API Endpoints (all in `src/Web/Controllers/ExperionControllers.cs`)
+
+| Controller | Route | Method | Purpose |
+|---|---|---|---|
+| Certificate | `/api/certificate/status` | GET | Certificate status |
+| | `/api/certificate/create` | POST | Create certificate |
+| Connection | `/api/connection/test` | POST | Test OPC UA connection |
+| | `/api/connection/read` | POST | Read single tag |
+| | `/api/connection/browse` | POST | Browse nodes |
+| Crawl | `/api/crawl/nodemap` | POST | Full node map crawl → CSV |
+| | `/api/crawl/start` | POST | Sync crawl with tags |
+| Database | `/api/database/files` | GET | List CSV files |
+| | `/api/database/import` | POST | Import CSV → DB |
+| | `/api/database/records` | GET | Query records |
+| PointBuilder | `/api/pointbuilder/build` | POST | Build realtime_table from node_map_master |
+| | `/api/pointbuilder/preview` | POST | Preview matching points |
+| | `/api/pointbuilder/apply` | POST | Apply selected points |
+| | `/api/pointbuilder/points` | GET | Get realtime_table points |
+| | `/api/pointbuilder/add` | POST | Add point by node_id |
+| | `/api/pointbuilder/{id}` | DELETE | Delete point |
+| Tags | `/api/tags/metadata/reload` | POST | Reload metadata from OPC UA |
+| | `/api/tags/metadata` | GET | Get tag metadata |
+| Realtime | `/api/realtime/start` | POST | Start subscription |
+| | `/api/realtime/stop` | POST | Stop subscription |
+| | `/api/realtime/status` | GET | Subscription status |
+| History | `/api/history/tagnames` | GET | Tagnames from realtime_table |
+| | `/api/history/query` | GET | Query history (tags, time range, limit) |
+| OpcServer | `/api/opcserver/status` | GET | OPC UA server status |
+| | `/api/opcserver/start` | POST | Start OPC UA server |
+| | `/api/opcserver/stop` | POST | Stop OPC UA server |
+| | `/api/opcserver/rebuild` | POST | Rebuild address space |
+| NodeMap | `/api/nodemap/names` | GET | Distinct name values |
+| | `/api/nodemap/stats` | GET | node_map_master stats |
+| | `/api/nodemap/query` | GET | Query with filters + pagination |
+| Hypertable | `/api/experion/hypertable/status` | GET | TimescaleDB hypertable status |
+| | `/api/experion/hypertable/create` | POST | Create hypertable manually |
+| Fast | `/api/fast/start` | POST | Start fast session |
+| | `/api/fast/{id}/stop` | POST | Stop session |
+| | `/api/fast/sessions` | GET | List sessions |
+| | `/api/fast/{id}` | GET | Session details |
+| | `/api/fast/{id}/records` | GET | Records (long format) |
+| | `/api/fast/{id}/csv` | GET | Export CSV streaming |
+| | `/api/fast/{id}` | DELETE | Delete session |
+| | `/api/fast/{id}/pin` | POST | Pin/unpin session |
+| P&ID | `/api/pid/extract` | POST | Extract from DXF/PDF (100MB limit) |
+| | `/api/pid/equipment` | GET | Equipment list (paginated) |
+| | `/api/pid/statistics` | GET | P&ID statistics |
+| | `/api/pid/{id}/confidence` | PUT | Update confidence (0-1) |
+| | `/api/pid/{id}/activate` | POST | Activate equipment |
+| | `/api/pid/{id}/deactivate` | POST | Deactivate equipment |
+| | `/api/pid/mappings` | GET | Tag mappings (paginated) |
+| | `/api/pid/mappings` | POST | Create mapping |
+| | `/api/pid/mappings/{id}` | PUT | Update mapping |
+| | `/api/pid/mappings/{id}` | DELETE | Clear mapping |
+| | `/api/pid/mappings/available-tags` | GET | Available tags for mapping |
+| | `/api/pid/export/csv` | GET | Export CSV |
+| | `/api/pid/export/excel` | GET | Export Excel (.xlsx) |
+
+P&ID controllers are conditional — enabled by `PidControllers:Enabled` in config (default: true).
+
+## Service Interfaces & Implementations
+
+All interfaces in `src/Core/Application/Interfaces/IExperionServices.cs`.
+
+| Interface | Impl | Lifetime |
+|---|---|---|
+| `IExperionCertificateService` | `ExperionCertificateService` | Singleton |
+| `IExperionStatusCodeService` | `ExperionStatusCodeService` | Singleton |
+| `IOpcUaConfigProvider` | `OpcUaConfigProvider` | Singleton |
+| `IExperionOpcClient` | `ExperionOpcClient` | Scoped |
+| `IExperionCsvService` | `ExperionCsvService` | Scoped |
+| `IExperionDbService` | `ExperionDbService` | Scoped |
+| `IExperionRealtimeService` | `ExperionRealtimeService` | Singleton + HostedService |
+| `IExperionOpcServerService` | `ExperionOpcServerService` | Singleton + HostedService |
+| `IExperionFastService` | `ExperionFastService` | Singleton + HostedService |
+| `IMetadataLoaderService` | `MetadataLoaderService` | Singleton |
+| `ITextToSqlService` | — | Scoped |
+| `IMcpService` | `McpService` | Singleton |
+| `IPidExtractorService` | — | — |
+| `ITagMappingService` | — | — |
+
+Singleton + HostedService pattern: same instance shared via `sp.GetRequiredService<T>()`.
+
+## Background Services
+
+All Singleton + HostedService, registered in `Program.cs`:
+- `ExperionRealtimeService` — OPC UA subscription, 500ms batch flush
+- `ExperionHistoryService` — snapshot 60s realtime_table → history_table
+- `ExperionOpcServerService` — OPC UA server (port 4841)
+- `McpServerHostedService` — Python MCP server process lifecycle (`uv run server.py --http`)
+- `ExperionFastService` — high-frequency data capture
+- `ExperionFastCleanupService` — expired session cleanup
+
+Autostart flag files: `realtime_autostart.json`, `opcserver_autostart.json`
+
+## Frontend
+
+Vanilla JS SPA in `wwwroot/`. No build step. Tab navigation, no auto-fire on entry.
+
+**API helper:** `async function api(method, path, body)` → wraps fetch, JSON in/out, returns parsed JSON
+**All responses use camelCase:** `d.success`, `d.records`, `d.total`, `d.tagNames`, `d.nodeId`, `d.running`, `d.subscribedCount`, `d.sessionId`
+**`wwwroot/js/app.js`** (3075 lines) — main app logic
+**`wwwroot/js/pid-viewer.js`** (416 lines) — canvas-based P&ID graph viewer
+
+## appsettings.json — Key Config
+
+| Key | Value | Notes |
+|---|---|---|
+| `ConnectionStrings:DefaultConnection` | PostgreSQL `iiot_platform` | Main app DB |
+| `ConnectionStrings:ExperionDbConnection` | PostgreSQL `postgres` | Utility connection |
+| `OpcUaServer:Port` | 4841 | OPC UA server port |
+| `OpcUaServer:EnableSecurity` | false | No security for now |
+| `PidControllers:Enabled` | true | P&ID feature flag |
+| `Fast:MaxConcurrentSessions` | 3 | |
+| `Fast:MaxRowsPerSession` | 5000000 | |
+| `Fast:FlushIntervalMs` | 2000 | |
+| `McpServer:WorkingDirectory` | `../../mcp-server` | |
+| `Kestrel:Endpoints:Http:Url` | `http://0.0.0.0:5000` | App port |
+
+## OPC UA Gotchas
+
+- SDK v1.5.378.134 — use `DefaultSessionFactory.CreateAsync()` (not obsolete `Session.Create()`)
+- `Subscription.Create()`/`Delete()`/`ApplyChanges()` → async variants
+- Certificate validation AFTER `OpcUaConfigProvider.GetConfigAsync()`
+- Wrap `SelectEndpointAsync` with 10s CancellationTokenSource (OS default 127s)
+- Tag address format: `ns=3;s=ficq-6113.pv`
+
+## MCP Server (Python)
+
+**Location:** `mcp-server/` — Python FastMCP server on port `5001`
+
+**Startup:** `McpServerHostedService` launches `uv run server.py --http` in `McpServer:WorkingDirectory` (default `../../mcp-server`). Pings `localhost:5001` up to 30s to confirm ready.
+
+**C# → Python bridge:**
+- `IMcpService` → `McpService` → `McpClient` (Singleton)
+- `McpClient` uses JSON-RPC over HTTP to `http://localhost:5001/mcp`
+- All DTOs use `[JsonPropertyName]` for snake_case JSON keys
+- `McpQueryResult { Success, Error, Data }`
+
+**Python MCP server dependencies:** `mcp[cli]`, `fastapi`, `qdrant-client`, `sentence-transformers`, `openai`, `httpx`, `psycopg`, `ezdxf`, `paddleocr`, `pymupdf`
+
+**Python infra stack:**
+- **LLM:** vLLM serving Qwen3.6-27B-FP8 at `http://localhost:8000/v1`
+- **Embeddings:** Ollama `nomic-embed-text` at `http://localhost:11434`
+- **Vector DB:** Qdrant at `http://localhost:6333` (2 collections: codebase + OPC docs)
+- **Task workers:** `worker/rag_worker.py` (:5002), `worker/nl2sql_worker.py` (:5003)
+
+### MCP Tools (exposed to C# via `IMcpService`)
+
+| Tool | C# Method | Purpose |
+|---|---|---|
+| `run_sql` | `RunSqlAsync(sql)` | Execute SELECT SQL |
+| `query_pv_history` | `QueryPvHistoryAsync(...)` | History query by tag/time |
+| `get_tag_metadata` | `GetTagMetadataAsync(query, limit)` | Tag search |
+| `list_drawings` | `ListDrawingsAsync(unitNo)` | Drawing list |
+| `query_with_nl` | `QueryWithNlAsync(question)` | NL → LLM → SQL → pivot |
+
+## RAG (Retrieval-Augmented Generation)
+
+### Python-side RAG Tools (in `server.py`)
+
+| Tool | Purpose |
+|---|---|
+| `search_codebase(query, top_k)` | Qdrant search in ExperionCrawler C# code collection |
+| `search_r530_docs(query, top_k)` | Qdrant search in Honeywell Experion HS R530 docs collection (266 chunks from `.htm` files) |
+| `ask_iiot_llm(question, context)` | Direct Qwen3.6 Q&A with optional context |
+| `rag_query(question, search_code, search_docs)` | Search + LLM synthesis in one call |
+
+### C#-side Text-to-SQL (local, no MCP)
+
+**Service:** `ITextToSqlService` → `TextToSqlService` (Scoped), registered in `Program.cs`
+
+**Pipeline:**
+1. `ParseNaturalLanguageAsync(input)` — Korean NL parser (tag names, time ranges, aggregates), up to 8 tags
+2. `SqlValidator` — 7-stage validation pipeline:
+   - SELECT-only, dangerous keywords block, forbidden clauses, function whitelist, table allowlist, subquery depth (max 4), injection patterns
+3. `ExecuteQueryAsync(sql, limit)` — parameterized Npgsql, tag existence check
+4. `AnalyzeAsync(dto)` — per-tag statistics (AVG, MIN, MAX, STDDEV, FIRST, LAST)
+
+**Allowed tables (from `SqlValidatorOptions` in Program.cs):** `history_table`, `node_map_master`, `realtime_table`, `tag_metadata`, `v_tag_summary`
+
+### Text-to-SQL Controller (`/api/text-to-sql/*`)
+
+| Route | Method | Source | Description |
+|---|---|---|---|
+| `/parse` | POST | C# local | Korean NL → SQL |
+| `/query-nl` | POST | MCP | NL → LLM → SQL → pivot |
+| `/tools` | GET | MCP | List MCP tools |
+| `/execute-mcp` | POST | MCP | Execute SQL via MCP |
+| `/query-history` | POST | MCP | History query |
+| `/tags/search` | GET | MCP | Tag search |
+| `/drawings` | GET | MCP | Drawing list |
+| `/suggest` | GET | C# local | Autocomplete suggestions |
+| `/analyze` | POST | C# local | Time-series statistics |
+| `/query-history-interval` | POST | C# local | Custom-interval aggregation |
+
+### One-time Doc Indexing
+
+`mcp-server/index_opc_docs.py` — reads `.htm` files from `/home/windpacer/projects/Experion_opcua_documents`, chunks (600 chars, 100 overlap), embeds with Ollama, upserts to Qdrant `experion-opc-docs`.
+
+## Deploy
+
+`sudo bash deploy.sh` → publishes to `/opt/ExperionCrawler`, creates systemd service `experioncrawler`, runs as `www-data`.
--- a/mcp-server/config.py
+++ b/mcp-server/config.py
@@ -0,0 +1,21 @@
+import json
+import os
+
+_SERVER_DIR = os.path.dirname(os.path.abspath(__file__))
+_MODEL_FILE = os.path.join(_SERVER_DIR, "llm-model.json")
+
+_DEFAULT_MODEL = "Qwen3.6-27B-FP8"
+
+
+def get_vllm_model() -> str:
+    env = os.environ.get("VLLM_MODEL")
+    if env:
+        return env
+    if not os.path.isfile(_MODEL_FILE):
+        return _DEFAULT_MODEL
+    try:
+        with open(_MODEL_FILE, "r", encoding="utf-8") as f:
+            data = json.load(f)
+        return data.get("vllm_model", _DEFAULT_MODEL)
+    except Exception:
+        return _DEFAULT_MODEL
--- a/mcp-server/llm-model.json
+++ b/mcp-server/llm-model.json
@@ -0,0 +1,3 @@
+{
+  "vllm_model": "Qwen3.6-27B-FP8"
+}
--- a/mcp-server/pipeline/mapper.py
+++ b/mcp-server/pipeline/mapper.py
@@ -13,10 +13,11 @@ class MappingResult(BaseModel):
    confidence: float = Field(..., ge=0.0, le=1.0, description="Confidence score from 0 to 1")

 class IntelligentMapper:
-    def __init__(self, graph: nx.Graph, system_tags: List[str], api_client: Optional[AsyncOpenAI] = None):
-        self.graph = graph          # Phase 2에서 생성된 NetworkX 그래프
-        self.system_tags = system_tags # Experion 시스템의 전체 태그 리스트
+    def __init__(self, graph: nx.Graph, system_tags: List[str], api_client: Optional[AsyncOpenAI] = None, model_name: str = "Qwen3.6-27B-FP8"):
+        self.graph = graph
+        self.system_tags = system_tags
        self.client = api_client
+        self.model_name = model_name

    def get_node_context(self, node_id: str) -> str:
        """노드의 주변 위상 정보를 텍스트로 변환 (확장된 컨텍스트 제공)"""
@@ -84,7 +85,7 @@ class IntelligentMapper:
        
        try:
            response = await self.client.chat.completions.create(
-                model="Qwen3.6-27B-FP8", # MCP 서버 설정 모델 사용
+                model=model_name,
                messages=[{"role": "user", "content": prompt}],
                response_format={ "type": "json_object" }
            )
--- a/mcp-server/server.py
+++ b/mcp-server/server.py
@@ -1,7 +1,7 @@
 #!/usr/bin/env python3
 """
 ExperionCrawler Unified MCP Server
- RAG: Qdrant + Ollama nomic-embed-text + vLLM Qwen3.6-27B-FP8
+- RAG: Qdrant + Ollama nomic-embed-text + vLLM (llm-model.json)
 - NL2SQL: 자연어 → LLM SQL 생성 → PostgreSQL 실행
 - 사용처:
    stdio 모드 (기본): Claude Code MCP / Roo Code MCP
@@ -24,7 +24,8 @@ QDRANT_URL      = os.environ.get("QDRANT_URL",    "http://localhost:6333")
 OLLAMA_URL      = os.environ.get("OLLAMA_URL",    "http://localhost:11434")
 EMBED_MODEL     = os.environ.get("EMBED_MODEL",   "nomic-embed-text")
 VLLM_BASE_URL   = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
-VLLM_MODEL      = os.environ.get("VLLM_MODEL",    "Qwen3.6-27B-FP8")
+from config import get_vllm_model
+VLLM_MODEL      = get_vllm_model()

 # Qdrant 컬렉션
 COL_CODEBASE    = "ws-65f457145aee80b2"     # ExperionCrawler 소스코드
@@ -67,7 +68,7 @@ async def _embed(text: str) -> list[float]:
    
    return await asyncio.to_thread(_call_embed)

-# ── LLM (vLLM / Qwen3.6-27B-FP8) ─────────────────────────────────────
+    # ── LLM (vLLM) ──────────────────────────────────────────────────────

@lru_cache(maxsize=1)
 def _llm():
@@ -377,7 +378,7 @@ async def search_r530_docs(query: str, top_k: int = 5) -> str:

@mcp.tool()
 def ask_iiot_llm(question: str, context: str = "") -> str:
-    """Qwen3.6-27B-FP8에게 IIoT/OPC UA 질문 (컨텍스트 없이 LLM 직접 질문).
+    """LLM에게 IIoT/OPC UA 질문 (컨텍스트 없이 LLM 직접 질문).

    사용 시점: search_codebase 또는 search_r530_docs 결과를 context로 넘겨
    종합 분석·답변이 필요할 때. 또는 일반 IIoT/OPC UA 개념 질문.
@@ -393,7 +394,7 @@ def ask_iiot_llm(question: str, context: str = "") -> str:
    )
    user_msg = f"컨텍스트:\n{context}\n\n질문: {question}" if context else question
    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": system},
            {"role": "user",   "content": user_msg},
@@ -406,7 +407,7 @@ def ask_iiot_llm(question: str, context: str = "") -> str:

@mcp.tool()
 async def rag_query(question: str, search_code: bool = False, search_docs: bool = True) -> str:
-    """검색 → Qwen3.6-27B-FP8 답변 생성 (통합 RAG).
+    """검색 → LLM 답변 생성 (통합 RAG).

    기본값: Experion HS R530 공식 문서만 검색 (search_docs=True, search_code=False).
    ExperionCrawler 코드도 함께 보려면 search_code=True 추가.
@@ -612,7 +613,7 @@ async def query_with_nl(question: str) -> str:
    try:
        def _call_llm():
            return _llm().chat.completions.create(
-                model="Qwen3.6-27B-FP8",
+                model=VLLM_MODEL,
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user",   "content": question},
@@ -699,7 +700,7 @@ async def extract_pid_tags(text: str, source_type: str) -> str:

        def _call_llm():
            return _llm().chat.completions.create(
-                model="Qwen3.6-27B-FP8",
+                model=VLLM_MODEL,
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user", "content": f"Source: {source_type}\n\nText:\n{truncated_text}"},
@@ -805,7 +806,7 @@ async def match_pid_tags(pid_tags: list[str], experion_tags: list[str]) -> str:

        def _call_llm():
            return _llm().chat.completions.create(
-                model="Qwen3.6-27B-FP8",
+                model=VLLM_MODEL,
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user", "content": f"P&ID Tags:\n{pid_str}\n\nExperion Tags:\n{experion_str}"},
@@ -896,7 +897,7 @@ async def parse_pid_dxf(filepath: str) -> str:
        
        def _call_llm():
            return _llm().chat.completions.create(
-                model="Qwen3.6-27B-FP8",
+                model=VLLM_MODEL,
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user", "content": f"Source: dxf\n\nText:\n{truncated_text}"},
@@ -1009,7 +1010,7 @@ async def parse_pid_pdf(filepath: str, use_ocr: bool = True) -> str:
        
        def _call_llm():
            return _llm().chat.completions.create(
-                model="Qwen3.6-27B-FP8",
+                model=VLLM_MODEL,
                messages=[
                    {"role": "system", "content": system},
                    {"role": "user", "content": f"Source: pdf\n\nText:\n{truncated_text}"},
@@ -1111,7 +1112,7 @@ async def build_pid_graph_parallel(filepath: str) -> str:
        # Mapper 설정
        from openai import AsyncOpenAI
        api_client = AsyncOpenAI(base_url=VLLM_BASE_URL, api_key="dummy")
-        mapper = IntelligentMapper(builder.G, system_tags, api_client=api_client)
+        mapper = IntelligentMapper(builder.G, system_tags, api_client=api_client, model_name=VLLM_MODEL)
        
        # 분류별 노드 분리
        nodes = list(builder.G.nodes())
--- a/mcp-server/worker/nl2sql_worker.py
+++ b/mcp-server/worker/nl2sql_worker.py
@@ -34,7 +34,8 @@ DB_CONNECTION_STRING = os.environ.get("DB_CONNECTION_STRING", "postgresql://post
 DB_TIMEOUT = int(os.environ.get("DB_TIMEOUT", "10"))

 VLLM_BASE_URL = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
-VLLM_MODEL = os.environ.get("VLLM_MODEL", "Qwen3.6-27B-FP8")
+from config import get_vllm_model
+VLLM_MODEL = get_vllm_model()

 logging.basicConfig(
    level=logging.INFO,
@@ -150,7 +151,7 @@ async def _generate_sql(natural_language: str) -> str:
    )
    
    response = await client.chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": natural_language},
--- a/mcp-server/worker/pid_extract_template.py
+++ b/mcp-server/worker/pid_extract_template.py
@@ -10,7 +10,7 @@

 환경 변수:
    VLLM_BASE_URL: vLLM 엔드포인트 (기본: http://localhost:8000/v1)
-    VLLM_MODEL: 모델명 (기본: Qwen3.6-27B-FP8)
+    VLLM_MODEL: 모델명 (기본: llm-model.json 참조)
 """

 import argparse
@@ -22,6 +22,9 @@ import sys
 import time
 from typing import List

+sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+from config import get_vllm_model
+
 logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(name)s] %(levelname)s %(message)s",
@@ -84,7 +87,7 @@ def call_llm(system_prompt: str, user_text: str, max_tokens: int = 65536) -> Lis
    from openai import OpenAI
    
    base_url = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
-    model = os.environ.get("VLLM_MODEL", "Qwen3.6-27B-FP8")
+    model = os.environ.get("VLLM_MODEL") or get_vllm_model()
    
    client = OpenAI(base_url=base_url, api_key="dummy")
    
--- a/mcp-server/worker/pid_worker.py
+++ b/mcp-server/worker/pid_worker.py
@@ -30,7 +30,8 @@ import uvicorn
 # ── 설정 ─────────────────────────────────────────────────────────────────────

 VLLM_BASE_URL        = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
-VLLM_MODEL           = os.environ.get("VLLM_MODEL", "Qwen3.6-27B-FP8")
+from config import get_vllm_model
+VLLM_MODEL           = get_vllm_model()
 DB_CONNECTION_STRING = os.environ.get("DB_CONNECTION_STRING", "postgresql://postgres:postgres@localhost:5432/iiot_platform")
 DB_TIMEOUT           = int(os.environ.get("DB_TIMEOUT", "10"))

@@ -173,7 +174,7 @@ def _extract_pid_tags(text: str, source_type: str) -> str:
    )
    truncated = text[:100000]
    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": f"Source: {source_type}\n\nText:\n{truncated}"},
@@ -202,7 +203,7 @@ def _match_pid_tags(pid_tags: list, experion_tags: list) -> str:
        "- Output ONLY the JSON array.\n"
    )
    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": (
@@ -247,7 +248,7 @@ def _parse_pid_dxf(filepath: str) -> str:
                          ensure_ascii=False, indent=2)

    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": _TAG_EXTRACT_SYSTEM},
            {"role": "user", "content": f"Source: dxf\n\nText:\n{text[:8000]}"},
@@ -273,7 +274,7 @@ def _parse_pid_pdf(filepath: str, use_ocr: bool = True) -> str:
                          ensure_ascii=False, indent=2)

    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": _TAG_EXTRACT_SYSTEM},
            {"role": "user", "content": f"Source: pdf\n\nText:\n{text[:12000]}"},
--- a/mcp-server/worker/pid_worker_test.py
+++ b/mcp-server/worker/pid_worker_test.py
@@ -31,7 +31,8 @@ import uvicorn
 # ── 설정 ─────────────────────────────────────────────────────────────────────

 VLLM_BASE_URL        = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
-VLLM_MODEL           = os.environ.get("VLLM_MODEL", "Qwen3.6-27B-FP8")
+from config import get_vllm_model
+VLLM_MODEL           = get_vllm_model()
 DB_CONNECTION_STRING = os.environ.get("DB_CONNECTION_STRING", "postgresql://postgres:postgres@localhost:5432/iiot_platform")
 DB_TIMEOUT           = int(os.environ.get("DB_TIMEOUT", "10"))

@@ -174,7 +175,7 @@ def _extract_pid_tags(text: str, source_type: str) -> str:
    )
    truncated = text[:100000]
    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": f"Source: {source_type}\n\nText:\n{truncated}"},
@@ -203,7 +204,7 @@ def _match_pid_tags(pid_tags: list, experion_tags: list) -> str:
        "- Output ONLY the JSON array.\n"
    )
    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": system},
            {"role": "user", "content": (
@@ -248,7 +249,7 @@ def _parse_pid_dxf(filepath: str) -> str:
                          ensure_ascii=False, indent=2)

    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": _TAG_EXTRACT_SYSTEM},
            {"role": "user", "content": f"Source: dxf\n\nText:\n{text[:8000]}"},
@@ -274,7 +275,7 @@ def _parse_pid_pdf(filepath: str, use_ocr: bool = True) -> str:
                          ensure_ascii=False, indent=2)

    resp = _llm().chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": _TAG_EXTRACT_SYSTEM},
            {"role": "user", "content": f"Source: pdf\n\nText:\n{text[:12000]}"},
--- a/mcp-server/worker/rag_worker.py
+++ b/mcp-server/worker/rag_worker.py
@@ -32,7 +32,8 @@ import httpx
 OLLAMA_URL      = os.environ.get("OLLAMA_URL", "http://localhost:11434")
 QDRANT_URL      = os.environ.get("QDRANT_URL", "http://localhost:6333")
 VLLM_BASE_URL   = os.environ.get("VLLM_BASE_URL", "http://localhost:8000/v1")
-VLLM_MODEL      = os.environ.get("VLLM_MODEL", "Qwen3.6-27B-FP8")
+from config import get_vllm_model
+VLLM_MODEL      = get_vllm_model()
 EMBED_MODEL     = os.environ.get("EMBED_MODEL", "nomic-embed-text")

 COL_CODEBASE    = os.environ.get("COL_CODEBASE", "ws-65f457145aee80b2")
@@ -105,7 +106,7 @@ async def _ask_llm(question: str, context: str = "") -> str:
        prompt = question
    
    response = await client.chat.completions.create(
-        model="Qwen3.6-27B-FP8",
+        model=VLLM_MODEL,
        messages=[
            {"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": prompt},
--- a/src/Core/Application/Interfaces/IExperionServices.cs
+++ b/src/Core/Application/Interfaces/IExperionServices.cs
@@ -74,6 +74,7 @@ public interface IExperionDbService
    Task<int>  BuildRealtimeTableAsync(IEnumerable<PointBuilderGroupDto> groups);
    Task<PointBuilderPreviewResult> PreviewRealtimeBuildAsync(IEnumerable<(string GroupKey, PointBuilderGroupDto Group)> groups);
    Task<int> ApplySelectedPointsAsync(IEnumerable<string> selectedNodeIds);
+    Task<int> AppendPointsAsync(IEnumerable<string> nodeIds);
    Task<IEnumerable<RealtimePoint>> GetRealtimePointsAsync();
    Task<RealtimePoint> AddRealtimePointAsync(string nodeId);
    Task<bool> DeleteRealtimePointAsync(int id);
--- a/src/Infrastructure/Database/ExperionDbContext.cs
+++ b/src/Infrastructure/Database/ExperionDbContext.cs
@@ -704,6 +704,35 @@ public class ExperionDbService : IExperionDbService
        return points.Count;
    }

+    public async Task<int> AppendPointsAsync(IEnumerable<string> nodeIds)
+    {
+        var nodeIdsList = nodeIds.Where(n => !string.IsNullOrEmpty(n)).ToList();
+        if (nodeIdsList.Count == 0) return 0;
+
+        var existingNodeIds = await _ctx.RealtimePoints
+            .Where(p => nodeIdsList.Contains(p.NodeId))
+            .Select(p => p.NodeId)
+            .ToListAsync();
+
+        var newPoints = nodeIdsList
+            .Where(n => !existingNodeIds.Contains(n))
+            .Select(nodeId => new RealtimePoint
+            {
+                TagName   = ExtractTagName(nodeId),
+                NodeId    = nodeId,
+                LiveValue = null,
+                Timestamp = DateTime.UtcNow
+            }).ToList();
+
+        if (newPoints.Count == 0) return 0;
+
+        await _ctx.RealtimePoints.AddRangeAsync(newPoints);
+        await _ctx.SaveChangesAsync();
+
+        _logger.LogInformation("[ExperionDb] realtime_table 추가: {Count}건 (중복 제외)", newPoints.Count);
+        return newPoints.Count;
+    }
+
    public async Task<IEnumerable<RealtimePoint>> GetRealtimePointsAsync()
    {
        try
--- a/src/Web/Controllers/ExperionControllers.cs
+++ b/src/Web/Controllers/ExperionControllers.cs
@@ -380,6 +380,29 @@ public class ExperionPointBuilderController : ControllerBase
        return Ok(new { success = true, count, metaCount, message = $"{count}개 포인트 적용 완료 (메타데이터: {metaCount}개)" });
    }

+    /// <summary>선택된 포인트만 realtime_table에 추가 (기존 데이터 유지, 중복 제외)</summary>
+    [HttpPost("append")]
+    public async Task<IActionResult> Append([FromBody] PointBuilderApplyDto dto)
+    {
+        if (dto.SelectedNodeIds == null || dto.SelectedNodeIds.Count == 0)
+            return BadRequest(new { success = false, message = "선택된 포인트가 없습니다." });
+
+        var count = await _dbSvc.AppendPointsAsync(dto.SelectedNodeIds);
+
+        var metaCount = 0;
+        try
+        {
+            var cfg = GetServerConfig();
+            metaCount = await _metaSvc.ReloadMetadataAsync(cfg);
+        }
+        catch (Exception ex)
+        {
+            _logger.LogWarning(ex, "[PointBuilder] 메타데이터 자동 로드 실패 (포인트 추가는 완료됨)");
+        }
+
+        return Ok(new { success = true, count, metaCount, message = $"{count}개 포인트 추가 완료 (메타데이터: {metaCount}개)" });
+    }
+
    /// <summary>realtime_table 전체 조회</summary>
    [HttpGet("points")]
    public async Task<IActionResult> GetPoints()
@@ -1262,3 +1285,80 @@ public class EventHistoryController : ControllerBase
        }
    }
 }
+
+// ── LLM 설정 ──────────────────────────────────────────────────────────────────
+
+[ApiController]
+[Route("api/llm")]
+public class LlmConfigController : ControllerBase
+{
+    private readonly IConfiguration _config;
+    private readonly ILogger<LlmConfigController> _logger;
+
+    public LlmConfigController(
+        IConfiguration config,
+        ILogger<LlmConfigController> logger)
+    {
+        _config = config;
+        _logger = logger;
+    }
+
+    string LlmModelPath
+    {
+        get
+        {
+            var mcpDir = _config["McpServer:WorkingDirectory"] ?? "../../mcp-server";
+            if (!Path.IsPathRooted(mcpDir))
+                mcpDir = Path.GetFullPath(Path.Combine(Directory.GetCurrentDirectory(), mcpDir));
+            return Path.Combine(mcpDir, "llm-model.json");
+        }
+    }
+
+    [HttpGet("config")]
+    public IActionResult GetConfig()
+    {
+        try
+        {
+            var path = LlmModelPath;
+            if (!System.IO.File.Exists(path))
+            {
+                return Ok(new { success = false, error = "llm-model.json not found", vllmModel = "Qwen3.6-27B-FP8" });
+            }
+            var json = System.IO.File.ReadAllText(path);
+            var doc = System.Text.Json.JsonDocument.Parse(json);
+            var model = doc.RootElement.TryGetProperty("vllm_model", out var v)
+                ? v.GetString() ?? "Qwen3.6-27B-FP8"
+                : "Qwen3.6-27B-FP8";
+            return Ok(new { success = true, vllmModel = model });
+        }
+        catch (Exception ex)
+        {
+            _logger.LogError(ex, "[LlmConfigController] 설정 조회 실패");
+            return Ok(new { success = false, error = ex.Message, vllmModel = (string?)null });
+        }
+    }
+
+    [HttpPost("config")]
+    public IActionResult SetConfig([FromBody] System.Text.Json.JsonElement body)
+    {
+        try
+        {
+            var path = LlmModelPath;
+            var model = body.TryGetProperty("vllm_model", out var v) ? v.GetString() : null;
+            if (string.IsNullOrEmpty(model))
+            {
+                return Ok(new { success = false, error = "vllm_model is required" });
+            }
+
+            var json = $"{{\"vllm_model\": \"{model}\"}}";
+            System.IO.File.WriteAllText(path, json);
+            _logger.LogInformation("[LlmConfigController] 모델 변경: {Model}", model);
+            return Ok(new { success = true, vllmModel = model });
+        }
+        catch (Exception ex)
+        {
+            _logger.LogError(ex, "[LlmConfigController] 설정 저장 실패");
+            return Ok(new { success = false, error = ex.Message });
+        }
+    }
+}
--- a/src/Web/wwwroot/index.html
+++ b/src/Web/wwwroot/index.html
@@ -176,6 +176,16 @@
        <div id="tag-box" class="tag-box hidden"></div>
      </div>

+      <div class="card">
+        <div class="card-cap">LLM 설정</div>
+        <div class="row-inp">
+          <input id="llm-model" class="inp flex1" placeholder="모델명" />
+          <button class="btn-b" onclick="llmLoadConfig()">🔄 불러오기</button>
+          <button class="btn-a" onclick="llmSaveConfig()">💾 저장</button>
+        </div>
+        <div id="llm-status" class="kv-box" style="margin-top:8px"></div>
+      </div>
+
      <div id="conn-log"    class="logbox  hidden"></div>
      <div id="browse-wrap" class="bwrap   hidden"></div>
    </section>
@@ -624,6 +634,7 @@
            <div class="btn-row" style="margin-top:10px;margin-bottom:0">
              <button class="btn-b" onclick="pbCancelPreview()">취소</button>
              <button class="btn-a" id="pb-apply-btn" onclick="pbApplySelected()">✓ 선택된 포인트 적용하기</button>
+              <button class="btn-a" onclick="pbAppendSelected()">＋ 기존 데이터에 추가하기</button>
            </div>
          </div>
        </div>
--- a/src/Web/wwwroot/js/app.js
+++ b/src/Web/wwwroot/js/app.js
@@ -156,18 +156,55 @@ async function connRead() {
  }
 }

+async function llmLoadConfig() {
+  const statusEl = document.getElementById('llm-status');
+  const input = document.getElementById('llm-model');
+  statusEl.innerHTML = '<span class="placeholder">불러오는 중...</span>';
+  try {
+    const d = await api('GET', '/api/llm/config');
+    if (d.success) {
+      input.value = d.vllmModel || '';
+      statusEl.innerHTML = `<span style="color:var(--color-success)">✓ 현재 모델: ${esc(d.vllmModel)}</span>`;
+    } else {
+      statusEl.innerHTML = `<span style="color:var(--color-danger)">✗ ${esc(d.error || '조회 실패')}</span>`;
+    }
+  } catch (e) {
+    statusEl.innerHTML = `<span style="color:var(--color-danger)">✗ ${esc(e.message)}</span>`;
+  }
+}
+
+async function llmSaveConfig() {
+  const statusEl = document.getElementById('llm-status');
+  const model = document.getElementById('llm-model').value.trim();
+  if (!model) {
+    statusEl.innerHTML = '<span style="color:var(--color-danger)">모델명을 입력하세요</span>';
+    return;
+  }
+  statusEl.innerHTML = '<span class="placeholder">저장 중...</span>';
+  try {
+    const d = await api('POST', '/api/llm/config', { vllm_model: model });
+    if (d.success) {
+      statusEl.innerHTML = `<span style="color:var(--color-success)">✓ 모델 변경 완료: ${esc(d.vllmModel)} (다음 LLM 요청 시 반영)</span>`;
+    } else {
+      statusEl.innerHTML = `<span style="color:var(--color-danger)">✗ ${esc(d.error || '저장 실패')}</span>`;
+    }
+  } catch (e) {
+    statusEl.innerHTML = `<span style="color:var(--color-danger)">✗ ${esc(e.message)}</span>`;
+  }
+}
+
 async function connBrowse() {
  const wrap = document.getElementById('browse-wrap');
  const logEl = document.getElementById('conn-log');
  setGlobal('busy', '노드 탐색');
-  
+
  // 로그 초기화
  logEl.classList.remove('hidden');
  logEl.innerHTML = '<div class="ll inf">[진행] 노드 탐색 시작...</div>';
-  
+
  try {
    logEl.innerHTML += '<div class="ll inf">[진행] 서버: ' + getServerCfg('x').serverHostName + ':' + getServerCfg('x').port + '</div>';
-    
+
    const d = await api('POST', '/api/connection/browse', {
      serverConfig: getServerCfg('x'), startNodeId: null
    });
@@ -780,6 +817,26 @@ async function pbApplySelected() {
  }
 }

+async function pbAppendSelected() {
+  const selected = pbPreviewData.filter(p => p.selected).map(p => p.nodeId);
+  if (selected.length === 0) {
+    setGlobal('err', '추가할 포인트를 선택하세요.');
+    return;
+  }
+
+  setGlobal('busy', `${selected.length}개 포인트 추가 중`);
+  try {
+    const d = await api('POST', '/api/pointbuilder/append', { selectedNodeIds: selected });
+    setGlobal(d.success ? 'ok' : 'err', d.success ? `${d.count}개 포인트 추가 완료` : '추가 실패');
+    if (d.success) {
+      pbCancelPreview();
+      await pbRefresh();
+    }
+  } catch (e) {
+    setGlobal('err', '추가 오류');
+  }
+}
+
 async function pbRefresh() {
  try {
    const d = await api('GET', '/api/pointbuilder/points');