深度专题四十二 FDE 现场工具箱(可照抄的命令/脚本/模板速查)
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本专题是给 FDE/FDSE 在客户现场"开干"用的速查手册。所有命令、脚本、配置均来自真实交付经验,可直接复制粘贴到任意一台装好基础环境的 Linux/GPU 机器上跑。原则:先跑通,再优化;先把客户问题压住,再谈架构美化。
一、环境与部署:从一台裸机到 vLLM 可用
1.1 GPU 检测与显存体检
bash# 基础一次性查看
nvidia-smi
# 持续监控(每 2 秒刷新,排查显存泄漏/训练-推理混跑占用)
watch -n 2 nvidia-smi
# 只看显存占用百分比与进程 PID(写脚本时常用)
nvidia-smi --query-gpu=index,name,memory.total,memory.used,utilization.gpu --format=csv
# 看是哪个进程在吃显存(拿到 PID 后)
nvidia-smi --query-compute-apps=pid,process_name,used_memory --format=csv
# 驱动/CUDA 版本核对(客户现场最常见的"为什么跑不起来"根因)
nvidia-smi | grep -E "Driver Version|CUDA Version"
nvcc --version # CUDA toolkit 版本,需与 PyTorch 编译版本匹配
经验:客户现场的"模型跑不起来",80% 是 CUDA 驱动版本 < 535(跑不了新架构 H100/H200)、x86 容器没装 nvidia-container-toolkit、或 PyTorch 的 CUDA build 与系统 CUDA 不匹配。先核这三项。
1.2 Python 环境隔离:conda 与 venv
bash# conda(推荐,能锁 CUDA 版本)
conda create -n fde python=3.11 -y
conda activate fde
# 锁定 cudatoolkit 版本,避免和服务端 CUDA 冲突
conda install -c nvidia cuda-runtime=12.1 -y
# venv(轻量,客户机器不想装 conda 时)
python3.11 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
# 导出/复刻环境(交付物必备)
pip freeze > requirements.txt
pip install -r requirements.txt
# 更稳的锁法
pip install pip-tools
pip-compile requirements.in # 生成带 hash 的 requirements.txt
pip-sync requirements.txt
1.3 Docker + nvidia-container-toolkit(生产部署标配)
bash# Ubuntu 22.04 安装 nvidia-container-toolkit
distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \
sed s#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g | \
sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit
sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker
# 验证容器内能看见 GPU
docker run --rm --gpus all nvidia/cuda:12.1.0-base-ubuntu22.04 nvidia-smi
1.4 docker-compose 起 vLLM + Milvus + Redis(一套现场 RAG 底座)
yaml# docker-compose.yml —— 客户内网一套起
version: "3.9"
services:
vllm:
image: vllm/vllm-openai:v0.6.3
runtime: nvidia
environment:
- HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}
volumes:
- ./models:/models
- ./hf-cache:/root/.cache/huggingface
command:
- --model=/models/Qwen2.5-14B-Instruct
- --served-model-name=qwen2.5-14b
- --tensor-parallel-size=2
- --gpu-memory-utilization=0.90
- --max-model-len=8192
- --quantization=gptq
- --trust-remote-code
ports: ["8000:8000"]
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: 2
capabilities: [gpu]
etcd:
image: quay.io/coreos/etcd:v3.5.5
environment:
- ETCD_AUTO_COMPACTION_MODE=revision
- ETCD_AUTO_COMPACTION_RETENTION=1000
command: etcd -advertise-client-urls=http://etcd:2379 -listen-client-urls http://0.0.0.0:2379
minio:
image: minio/minio:RELEASE.2024-09-13T20-26-02Z
environment:
MINIO_ROOT_USER: minioadmin
MINIO_ROOT_PASSWORD: minioadmin
command: minio server /minio_data --console-address ":9001"
milvus:
image: milvusdb/milvus:v2.4.13
command: ["milvus", "run", "standalone"]
environment:
ETCD_ENDPOINTS: etcd:2379
MINIO_ADDRESS: minio:9000
depends_on: [etcd, minio]
ports: ["19530:19530"]
redis:
image: redis:7-alpine
command: redis-server --maxmemory 2gb --maxmemory-policy allkeys-lru
ports: ["6379:6379"]
bashdocker compose up -d
docker compose logs -f vllm # 看模型加载日志,卡在 90%+ 是正常(权重到显存)
curl http://localhost:8000/v1/models # 验证 OpenAI 兼容接口
1.5 离线/内网依赖打包(客户现场无外网时的命根子)
bash# 在有网的机器上下载全部 wheel + 模型
mkdir -p offline/{wheels,models,hf}
pip download -r requirements.txt -d offline/wheels
# 用 huggingface-cli 把模型整包拉下来(避免 lazy load 时回源)
pip install -U "huggingface_hub[cli]"
hf download Qwen/Qwen2.5-14B-Instruct --local-dir offline/models/Qwen2.5-14B-Instruct
# 或走镜像(国内/内网 HF 镜像)
HF_ENDPOINT=https://hf-mirror.com hf download Qwen/Qwen2.5-14B-Instruct --local-dir offline/models/Qwen2.5-14B-Instruct
# 打成 tar.zst 拷贝进客户内网
tar --use-compress-program= zstd -19 -T0 -cf offline.tar.zst offline/
# 内网安装
tar -xf offline.tar.zst
pip install --no-index --find-links=offline/wheels -r requirements.txt
# 模型:设置 HF_HUB_OFFLINE=1 走本地缓存
export HF_HUB_OFFLINE=1
export TRANSFORMERS_OFFLINE=1
二、推理服务:vLLM 启动、量化、压测
2.1 vLLM 启动参数速查(按场景)
bash# 场景 A:FP16 单卡,Qwen2.5-7B,中等并发
vllm serve Qwen/Qwen2.5-7B-Instruct \
--port 8000 \
--max-model-len 8192 \
--gpu-memory-utilization 0.90 \
--enforce-eager # 关闭 CUDA Graph,冷启动更快,适合 PoC
# 场景 B:双卡张量并行 + AWQ 4bit 量化(省显存提并发)
vllm serve Qwen/Qwen2.5-14B-Instruct-AWQ \
--tensor-parallel-size 2 \
--quantization awq \
--max-model-len 16384 \
--gpu-memory-utilization 0.92 \
--swap-space 8 # CPU 交换空间 GB,缓解 KV cache 压力
# 场景 C:对接外部 Embedding 走 RAG,需要长上下文
vllm serve Qwen/Qwen2.5-32B-Instruct-AWQ \
--tensor-parallel-size 4 \
--quantization awq \
--max-model-len 32768 \
--max-num-seqs 64 # 长上下文下,批次并发别拉太高
# 场景 D:OpenAI 兼容 + 多模型同卡(SkyPilot/LiteLLM 风格)
vllm serve meta-llama/Meta-Llama-3-8B-Instruct \
--served-model-name llama3-8b \
--api-key sk-fde-local \
--disable-log-requests # 生产环境关请求日志,磁盘别被刷爆
关键参数取舍:--gpu-memory-utilization 实测 0.88–0.92 是甜区,过低浪费、过高触发 OOM;--max-model-len 直接决定 KV cache 上限,长上下文是显存大头;--max-num-seqs 在长输出场景应下调。
2.2 离线量化(GPTQ/AWQ/llama.cpp)
bash# AutoGPTQ 对 HF 模型做 GPTQ 4bit
pip install auto-gptq optimum
python - << PY
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
model_id = "Qwen/Qwen2.5-7B-Instruct"
quant_conf = BaseQuantizeConfig(bits=4, group_size=128, desc_act=True)
tokenizer = AutoTokenizer.from_pretrained(model_id)
# 准备 128-256 条校准样本(业务真实问答对,别用 wikitext)
calib = [tokenizer("用户问:...\n助手:...") for _ in calibration_texts]
model = AutoGPTQForCausalLM.from_pretrained(model_id, quant_conf)
model.quantize(calib)
model.save_quantized("./Qwen2.5-7B-gptq", use_safetensors=True)
PY
# llama.cpp 转 GGUF(给 CPU/边缘端用)
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp
pip install -r requirements.txt
python convert_hf_to_gguf.py /models/Qwen2.5-7B --outfile qwen25-7b.gguf
./llama-quantize qwen25-7b.gguf qwen25-7b-Q4_K_M.gguf Q4_K_M
2.3 压测:看清你的真实 QPS/TTFT/TPOT
bash# vLLM 自带 benchmark_serving(最有用,跑真实业务 prompt 分布)
python -m vllm.entrypoints.openai.api.benchmark_serving \
--backend vllm --base-url http://localhost:8000 \
--model qwen2.5-14b \
--dataset-name random --random-input-len 1024 --random-output-len 512 \
--num-prompts 500 --request-rate 10 \
--save-result --result-dir ./bench
# 关键指标解读:
# TTFT (Time To First Token) < 800ms 才像样
# TPOT (Time Per Output Token) < 50ms/tok
# 成功率 > 99% (低于此值说明 PagedAllocator 在打架)
# Locust 做业务流量回放
pip install locust
cat > locustfile.py << PY
from locust import HttpUser, task, between
import json, random
PROMPTS = ["帮我总结这份合同","这段代码有 bug 吗","..."]
class ChatUser(HttpUser):
wait_time = between(1, 3)
@task
def chat(self):
self.client.post("/v1/chat/completions", json={
"model": "qwen2.5-14b",
"messages": [{"role":"user","content": random.choice(PROMPTS)}],
"max_tokens": 512, "temperature": 0.3,
}, timeout=120)
PY
locust --headless -u 50 -r 5 -H http://localhost:8000 --run-time 5m
三、RAG 全链路:解析 → 切分 → 嵌入 → 入库 → 重排
3.1 文档解析:Unstructured 与 Marker
bashpip install "unstructured[pdf,docx,pptx]" marker-pdf
python# Unstructured:适合混合格式办公文档
from unstructured.partition.auto import partition
elements = partition("contracts/甲方合同.pdf", strategy="hi_res",
infer_table_structure=True)
chunks_text = [e.text for e in elements if e.text.strip()]
# Marker:对扫描件/复杂版式 PDF 召回更好,直接出 Markdown
from marker.converters.pdf import PdfConverter
from marker.models import create_model_dict
converter = PdfConverter(artifact_dict=create_model_dict())
md = converter("contracts/扫描件.pdf").markdown
3.2 切分:递归 + 表格保护
pythonfrom langchain_text_splitters import RecursiveCharacterTextSplitter
splitter = RecursiveCharacterTextSplitter(
chunk_size=512, chunk_overlap=64,
separators=["\n\n", "\n", "。", ";", " ", ""],
# 表格单独成块,不要被句子切分打散
def smart_split(text, tables):
base = splitter.split_text(text)
return base + [t.to_markdown() for t in tables]
3.3 嵌入与入库 Milvus
pythonfrom FlagEmbedding import FlagModel
from pymilvus import MilvusClient
embedder = FlagModel("BAAI/bge-large-zh-v1.5",
query_instruction_for_retrieval="为这个句子生成表示用于检索相关文章:")
client = MilvusClient("http://localhost:19530")
client.create_collection("rag", dimension=1024, metric_type="COSINE")
# 批量入库(别一条一条 insert)
vectors = embedder.encode(chunks)
data = [{"id": i, "vector": v, "text": chunks[i]} for i, v in enumerate(vectors)]
client.insert("rag", data)
3.4 检索 + bge-reranker 重排
python# 一阶段:向量粗召回 top-30
q_vec = embedder.encode_queries([query])[0]
hits = client.search("rag", [q_vec], limit=30, output_fields=["text"])
# 二阶段:Cross-encoder 精排 top-5
from FlagEmbedding import FlagReranker
reranker = FlagReranker("BAAI/bge-reranker-v2-m3", use_fp16=True)
pairs = [[query, h["entity"]["text"]] for h in hits[0]]
scores = reranker.compute_score(pairs, normalize=True)
top5 = [h for _, h in sorted(zip(scores, hits[0]), reverse=True)[:5]]
经验:粗召回收 30 条,重排留 5 条是性价比最高的配置。只走向量召回在多义词/同义改写下召回掉得厉害,加一层 reranker 几乎没有副作用,延迟 +100ms 换准确率 +15%。
四、Agent 框架:LangGraph / CrewAI / MCP / HITL
4.1 LangGraph 最小可跑图(带条件路由)
pythonfrom langgraph.graph import StateGraph, END
from typing import TypedDict, Annotated
import operator
class S(TypedDict):
query: str
retrieved: Annotated[list, operator.add]
answer: str
def retrieve(s): return {"retrieved": top5(s["query"])}
def grade(s):
# 命中关键词则直接答,否则转工具
return "answer" if any(k in s["retrieved"][0] for k in ["合同","条款"]) else "tool"
def answer(s): return {"answer": llm(s["retrieved"], s["query"])}
def call_tool(s): return {"answer": llm_with_tools(s["query"])}
g = StateGraph(S)
g.add_node("retrieve", retrieve); g.add_node("answer", answer); g.add_node("tool", call_tool)
g.set_entry_point("retrieve")
g.add_conditional_edges("retrieve", grade, {"answer":"answer","tool":"tool"})
g.add_edge("answer", END); g.add_edge("tool", END)
app = g.compile()
print(app.invoke({"query":"合同里违约金是多少","retrieved":[]}))
4.2 CrewAI 多 Agent 协作
pythonfrom crewai import Agent, Task, Crew, Process
researcher = Agent(role="行业研究员",
goal="收集客户所在行业数字化现状", backstory="...", llm="qwen2.5-14b", tools=[search])
analyst = Agent(role="方案分析师",
goal="把研究产出转成可落地建议", backstory="...", llm="qwen2.5-14b")
c = Crew(agents=[researcher, analyst], process=Process.sequential,
tasks=[
Task(description="调研 {company} 所在行业", agent=researcher, expected_output="结构化报告"),
Task(description="基于研究产出给 3 条落地建议", agent=analyst, expected_output="建议清单"),
])
c.kickoff(inputs={"company":"某城投"})
4.3 MCP Server 最小实现(把客户内部 API 暴露成工具)
python# pip install "mcp[cli]"
from mcp.server.fastmcp import FastMCP
mcp = FastMCP("fde-tools")
@mcp.tool()
def query_oa_system(ticket_id: str) -> str:
"""根据工单号查询 OA 系统进度"""
return call_internal_oa(ticket_id) # 客户内部接口
@mcp.resource("config://{key}")
def get_config(key: str) -> str:
return load_client_config(key)
if __name__ == "__main__":
mcp.run(transport="stdio")
4.4 HITL:人在回路打断审批
pythonfrom langgraph.checkpoint.memory import MemorySaver
from langgraph.types import interrupt, Command
def human_approve(s):
decision = interrupt({"draft": s["answer"], "ask":"确认发送邮件吗?(yes/no)"})
if decision == "yes":
send_email(s["answer"])
return {"answer":"已发送"}
return {"answer":"已取消"}
graph = builder.compile(checkpointer=MemorySaver())
config = {"configurable":{"thread_id":"t1"}}
# 第一次调用会在 human_approve 处 interrupt
result = graph.invoke({"query":"帮我回客户邮件"}, config)
# 拿到用户输入后,用 Command 续跑
result = graph.invoke(Command(resume="yes"), config)
五、数据质量与同步
5.1 Great Expectations 数据质量门禁
pythonimport great_expectations as gx
ctx = gx.get_context()
ds = ctx.data_sources.add_pandas("ds")
asset = ds.add_dataframe_asset("orders")
batch = asset.add_batch_definition_whole_dataframe().get_batch(batch_parameters={"df": df})
# 期望套件(客户现场必备:非空、唯一、范围、外键)
batch.expect_column_values_to_not_be_null("order_id")
batch.expect_column_values_to_be_unique("order_id")
batch.expect_column_values_to_be_between("amount", 0, 1_000_000)
batch.expect_column_values_to_be_in_set("status", ["paid","shipped","refunded"])
result = batch.validate()
assert result.success, f"数据质量门禁失败: {result}"
5.2 CDC 增量同步(MySQL → Kafka → 特征/索引)
yaml# Debezium 连接器配置(精简)
database.hostname: mysql-prod
database.server.id: 184054
database.allowPublicKeyRetrieval: true
database.user: debezium
database.password: ***
table.include.list: orders,customers
topic.prefix: cdc_fde
特征增量计算(Flink/Spark Structured Streaming 任选):
python# 简化版:从 Kafka 流式聚合,落特征存储
from pyspark.sql import functions as F
df = (spark.readStream.format("kafka")
.option("kafka.bootstrap.servers","kafka:9092")
.option("subscribe","cdc_fde.orders").load())
agg = (df.selectExpr("CAST(value AS STRING) as json")
.selectExpr("get_json_object(json, $.after.customer_id ) as cid",
"get_json_object(json, $.after.amount ) as amt")
.groupBy("cid").agg(F.sum("amt").alias("total_30d")))
agg.writeStream.format("redis").option("checkpointLocation","/ckpt").start()
六、评估:RAGAS 与 LLM-as-judge
6.1 RAGAS 跑一次端到端评估
pythonfrom ragas import evaluate
from ragas.metrics import faithfulness, answer_relevancy, context_precision, context_recall
from datasets import Dataset
ds = Dataset.from_dict({
"question": ["合同违约金多少？","数据出境需要哪些审批？"],
"answer": [gen1, gen2],
"contexts": [[retrieved1], [retrieved2]],
"ground_truth":["按日万分之五","需经网信办+省级网信办评估"],
})
result = evaluate(ds, metrics=[faithfulness, answer_relevancy,
context_precision, context_recall],
llm=evaluator_llm, embeddings=eval_emb)
print(result) # { faithfulness :0.82, context_recall :0.76, ...}
6.2 LLM-as-judge 批量打分脚本
pythonJUDGE_PROMPT = """你是严格评审。根据参考答案打分。
问题:{q}
参考答案:{ref}
模型答案:{pred}
评分维度:正确性(0-5)、完整性(0-5)、简洁性(0-5)。只输出 JSON:{{"correct":x,"complete":y,"concise":z}}"""
def judge(q, ref, pred):
resp = openai.chat.completions.create(
model="qwen2.5-14b",
messages=[{"role":"user","content":JUDGE_PROMPT.format(q=q,ref=ref,pred=pred)}],
temperature=0)
return json.loads(resp.choices[0].message.content)
# 批量跑,结果落 CSV 形成回归基线
import pandas as pd
df = pd.DataFrame([judge(**r) for r in eval_set])
df.to_csv("judge_baseline.csv", index=False)
七、可观测:Langfuse + Prometheus
python# Langfuse OpenAI 包装(三行接入)
from langfuse.openai import openai
resp = openai.chat.completions.create(model="qwen2.5-14b",
messages=[{"role":"user","content":query}], metadata={"customer":"acme","user":uid})
# 自动记录:prompt、completion、token、延迟、cost,按 trace_id 串起 RAG 各步
yaml# Prometheus 抓取 vLLM 指标
scrape_configs:
- job_name: vllm
metrics_path: /metrics
static_configs: [{targets: ["vllm:8000"]}]
# 关键指标(配 Grafana 告警):
# vllm:num_requests_running 运行中请求数
# vllm:num_requests_waiting 排队数 >0 说明吞吐到顶
# vllm:gpu_cache_usage_perc KV cache 占用率,接近 1.0 要扩容
# vllm:e2e_request_latency_seconds P95 延迟
八、安全:输入护栏与注入检测
python# 轻量关键词 + 正则护栏(不上 LLM 也能挡住 80% 注入)
import re
BLOCK_PATTERNS = [
r"忽略(以上|前面|之前).{0,10}(指令|提示|规则)",
r"(reveal|show|print).{0,10}(system|secret|api[_-]?key)",
r"<\|im_start\|>|</?script>",
PII_PATTERNS = {
"phone": r"1[3-9]\d{9}",
"idcard": r"\d{17}[\dXx]",
"bankcard": r"\d{16,19}",
def guard(input_text: str) -> tuple[bool, str]:
for p in BLOCK_PATTERNS:
if re.search(p, input_text, re.I):
return False, f"命中注入模式:{p}"
masked = input_text
for k, p in PII_PATTERNS.items():
masked = re.sub(p, f"[{k}]", masked)
return True, masked
# 输出侧同样跑一遍护栏 + 敏感词,防止模型泄露训练数据中的密钥
python# 进阶:用小模型做指令注入分类(BERT/护栏 LLM)
from transformers import pipeline
clf = pipeline("text-classification", model="protectai/deberta-v3-base-prompt-injection-v2")
if clf(query)[0]["label"] == "INJECTION":
reject(query)
九、运维:日志、显存溢出、回滚
bash# 排查 vLLM 日志(过滤掉健康检查噪音)
docker compose logs vllm | grep -vE "/health|GET /v1/models" | tail -200
# OOM 经典三连查
dmesg -T | grep -iE "killed process|out of memory" # 系统级 OOM
docker stats --no-stream # 容器内存
nvidia-smi --query-gpu=memory.used,memory.total --format=csv # 显存
# 模型回滚(镜像 tag 即版本,生产禁用 latest)
docker compose down
# 改 image tag 到上一个稳定版本,例如 v0.6.3 -> v0.6.2
docker compose up -d
# 如需回滚模型权重(保存多版本目录)
ln -sfn /models/Qwen2.5-14B-r3 /models/current # 软链切版本,秒级回滚
显存溢出处理顺序:降 max-model-len → 降 max-num-seqs → 降 gpu-memory-utilization → 换量化版本(AWQ/GPTQ) → 加卡张量并行。优先调参数,最后才加硬件。
十、客户现场常用排查命令合集
bash# 网络:模型/API 通不通
curl -w "\n%{http_code} %{time_total}s\n" -o /dev/null -s http://localhost:8000/v1/models
nc -zv milvus 19530
nslookup hf-mirror.com
# 磁盘:HF 缓存爆盘是常事
du -sh ~/.cache/huggingface /models
df -h
# 端口占用(8000 被占是高频事故)
ss -lntp | grep :8000
# GPU 进程残留(训推混跑导致 OOM)
fuser -v /dev/nvidia*
# 看模型到底用了几个 GPU、batch 多大
curl -s http://localhost:8000/metrics | grep -E "num_requests_running|gpu_cache_usage"
# 一次性把客户机器全貌输出成报告(交付文档附录必备)
echo "## 主机"; hostname; uname -a; cat /etc/os-release | head -2
echo "## CPU/内存"; nproc; free -h
echo "## 磁盘"; df -h | grep -v tmpfs
echo "## GPU"; nvidia-smi --query-gpu=index,name,driver_version,memory.total --format=csv
echo "## Docker"; docker --version; docker compose version
echo "## Python"; python3 --version; pip --version
} > env_snapshot.txt
本专题小结
FDE 在客户现场真正缺的不是"理论",而是一份能照着敲就能跑通的命令清单。本专题按 环境 → 推理 → RAG → Agent → 数据 → 评估 → 可观测 → 安全 → 运维 → 排查 十个现场最高频场景,给出了可直接复制的命令、脚本与配置。三条贯穿性原则:其一,先跑通再优化,所有命令默认配的是稳妥参数,而不是极限参数;其二,离线优先,内网交付场景下 wheel 包、模型权重、HF 镜像三件套必须提前备好;其三,可观测优先,Langfuse + Prometheus + 环境快照三件套,既是运维抓手,也是交付文档的真实数据来源。把这份手册打印贴在客户机房墙上,比任何架构图都管用。
本专题来源
vLLM 官方文档与 benchmark_serving 实践参数(v0.6.x)
Hugging Face transformers、auto-gptq、FlagEmbedding(bge/reranker)官方示例
Milvus 2.4.x Standalone 部署文档与 pymilvus API
LangGraph(条件路由、interrupt/HITL)、CrewAI、Model Context Protocol Python SDK 官方示例
Great Expectations 1.x、Debezium MySQL CDC、Spark Structured Streaming 文档
RAGAS、Langfuse OpenAI 集成、Prometheus vLLM metrics 指标定义
作者在政企/金融/制造客户内网交付现场整理的可照抄版本,已脱敏