提取操作使用千问plus大模型

.env

@@ -1,10 +1,13 @@
-# Required — fill in before running
-ZHIPUAI_API_KEY=your-zhipuai-api-key-here
-STORAGE_ACCESS_KEY=your-storage-access-key
-STORAGE_SECRET_KEY=your-storage-secret-key
-STORAGE_ENDPOINT=http://rustfs:9000
+# Required - fill in before running
+ZHIPUAI_API_KEY=b11404531c574043b1b0750186cd9d79.LfoyJjtj1fnGbTLl
+DASHSCOPE_API_KEY=sk-d0ebc07bad2d4666bcd284f80d3fe138
+STORAGE_ACCESS_KEY=admin
+STORAGE_SECRET_KEY=your_strong_password
+STORAGE_ENDPOINT=http://39.107.112.174:9000
 
 # Optional overrides
-BACKEND_CALLBACK_URL=http://label-backend:8080/api/ai/callback
+DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+DASHSCOPE_FINE_TUNE_BASE_URL=https://dashscope.aliyuncs.com/api/v1
+BACKEND_CALLBACK_URL=http://localhost:18082/api/ai/callback
 LOG_LEVEL=INFO
-# MAX_VIDEO_SIZE_MB=200
+MAX_VIDEO_SIZE_MB=500MB

Dockerfile

@@ -1,18 +1,22 @@
-FROM python:3.12-slim
+FROM registry.bjzgzp.com:4433/library/python3.12:base
 
 WORKDIR /app
 
-RUN apt-get update && apt-get install -y --no-install-recommends \
+ARG APT_MIRROR=mirrors.tuna.tsinghua.edu.cn
+ARG PIP_INDEX_URL=https://pypi.tuna.tsinghua.edu.cn/simple
+
+RUN sed -i "s|deb.debian.org|${APT_MIRROR}|g; s|security.debian.org|${APT_MIRROR}|g" /etc/apt/sources.list.d/debian.sources \
+    && apt-get update && apt-get install -y --no-install-recommends \
     libgl1 \
     libglib2.0-0 \
     curl \
     && rm -rf /var/lib/apt/lists/*
 
 COPY requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt
+RUN pip install --no-cache-dir -i "${PIP_INDEX_URL}" -r requirements.txt
 
 COPY . .
 
-EXPOSE 8000
+EXPOSE 18000
 
-CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]
+CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "18000"]

README.md

@@ -1,5 +1,7 @@
 # label_ai_service
 
+> 2026-04-16 update: 默认 LLM 适配器已切换为阿里云百炼/千问（DashScope OpenAI-compatible API）。文本默认模型为 `qwen3.6-plus`，视觉默认模型为 `qwen-vl-plus`；旧的 `ZhipuAIClient` 代码保留在仓库中，但默认依赖注入不再使用。
+
 `label_ai_service` 是知识图谱智能标注平台的 AI 计算服务，基于 FastAPI 提供独立部署的推理与预处理能力。它不直接访问数据库，而是通过 ZhipuAI GLM 系列模型完成结构化抽取，通过 RustFS 读写原始文件和处理结果，并通过 HTTP 回调把异步视频任务结果通知上游后端。
 
 当前服务覆盖 6 类核心能力：
@@ -128,9 +130,14 @@ label_ai_service/
 
 ```yaml
 server:
-  port: 8000
+  port: 18000
   log_level: INFO
 
+dashscope:
+  api_key: ""
+  base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
+  fine_tune_base_url: "https://dashscope.aliyuncs.com/api/v1"
+
 storage:
   buckets:
     source_data: "source-data"
@@ -140,12 +147,12 @@ backend: {}
 
 video:
   frame_sample_count: 8
-  max_file_size_mb: 200
+  max_file_size_mb: 500
   keyframe_diff_threshold: 30.0
 
 models:
-  default_text: "glm-4-flash"
-  default_vision: "glm-4v-flash"
+  default_text: "qwen3.6-plus"
+  default_vision: "qwen-vl-plus"
 ```
 
 ### .env
@@ -154,7 +161,9 @@ models:
 
 | 变量名 | 必填 | 说明 |
 |---|---|---|
-| `ZHIPUAI_API_KEY` | 是 | ZhipuAI API Key |
+| `DASHSCOPE_API_KEY` | 是 | DashScope API Key |
+| `DASHSCOPE_BASE_URL` | 否 | DashScope OpenAI-compatible base URL |
+| `DASHSCOPE_FINE_TUNE_BASE_URL` | 否 | DashScope fine-tune API base URL |
 | `STORAGE_ACCESS_KEY` | 是 | RustFS/S3 Access Key |
 | `STORAGE_SECRET_KEY` | 是 | RustFS/S3 Secret Key |
 | `STORAGE_ENDPOINT` | 是 | RustFS/S3 Endpoint，例如 `http://rustfs:9000` |
@@ -165,7 +174,9 @@ models:
 `.env` 示例：
 
 ```ini
-ZHIPUAI_API_KEY=your-zhipuai-api-key-here
+DASHSCOPE_API_KEY=your-dashscope-api-key-here
+DASHSCOPE_BASE_URL=https://dashscope.aliyuncs.com/compatible-mode/v1
+DASHSCOPE_FINE_TUNE_BASE_URL=https://dashscope.aliyuncs.com/api/v1
 STORAGE_ACCESS_KEY=your-storage-access-key
 STORAGE_SECRET_KEY=your-storage-secret-key
 STORAGE_ENDPOINT=http://rustfs:9000
@@ -318,7 +329,7 @@ curl -X POST http://localhost:8000/api/v1/finetune/start \
   -H "Content-Type: application/json" \
   -d '{
     "jsonl_url": "https://example.com/train.jsonl",
-    "base_model": "glm-4-flash",
+    "base_model": "qwen3-14b",
     "hyperparams": {
       "epochs": 3,
       "learning_rate": 0.0001

app/clients/llm/qwen_client.py

@@ -0,0 +1,132 @@
+from typing import Any
+
+import httpx
+
+from app.clients.llm.base import LLMClient
+from app.core.exceptions import LLMCallError
+from app.core.logging import get_logger
+
+logger = get_logger(__name__)
+
+
+class QwenClient(LLMClient):
+    def __init__(
+        self,
+        api_key: str,
+        base_url: str = "https://dashscope.aliyuncs.com/compatible-mode/v1",
+        fine_tune_base_url: str | None = None,
+        transport: httpx.BaseTransport | None = None,
+    ) -> None:
+        self._api_key = api_key
+        self._base_url = base_url.rstrip("/")
+        self._fine_tune_base_url = (
+            fine_tune_base_url.rstrip("/")
+            if fine_tune_base_url
+            else self._base_url.replace("/compatible-mode/v1", "/api/v1")
+        )
+        self._transport = transport
+
+    async def chat(self, model: str, messages: list[dict]) -> str:
+        return await self._chat(model, messages)
+
+    async def chat_vision(self, model: str, messages: list[dict]) -> str:
+        return await self._chat(model, messages)
+
+    async def submit_finetune(self, jsonl_url: str, base_model: str, hyperparams: dict) -> str:
+        try:
+            file_bytes = await self._download_training_file(jsonl_url)
+            file_id = await self._upload_training_file(file_bytes)
+            payload = {
+                "model": base_model,
+                "training_file_ids": [file_id],
+            }
+            if hyperparams:
+                payload["hyper_parameters"] = hyperparams
+            data = await self._post_json(self._fine_tune_base_url, "/fine-tunes", payload)
+            output = data.get("output", {})
+            job_id = output.get("job_id") or data.get("job_id")
+            if not job_id:
+                raise LLMCallError("千问微调任务提交失败: 缺少 job_id")
+            return job_id
+        except LLMCallError:
+            raise
+        except Exception as exc:
+            raise LLMCallError(f"千问微调任务提交失败: {exc}") from exc
+
+    async def get_finetune_status(self, job_id: str) -> dict:
+        try:
+            data = await self._get_json(self._fine_tune_base_url, f"/fine-tunes/{job_id}")
+            output = data.get("output", {})
+            return {
+                "job_id": output.get("job_id") or job_id,
+                "status": output.get("status", "").lower(),
+                "progress": output.get("progress"),
+                "error_message": output.get("message"),
+            }
+        except LLMCallError:
+            raise
+        except Exception as exc:
+            raise LLMCallError(f"查询千问微调任务失败: {exc}") from exc
+
+    async def _chat(self, model: str, messages: list[dict]) -> str:
+        try:
+            data = await self._post_json(
+                self._base_url,
+                "/chat/completions",
+                {"model": model, "messages": messages},
+            )
+            content = data["choices"][0]["message"]["content"]
+            if isinstance(content, list):
+                return "".join(
+                    part.get("text", "") if isinstance(part, dict) else str(part)
+                    for part in content
+                )
+            logger.info("llm_call", extra={"model": model, "response_len": len(content)})
+            return content
+        except LLMCallError:
+            raise
+        except Exception as exc:
+            logger.error("llm_call_error", extra={"model": model, "error": str(exc)})
+            raise LLMCallError(f"千问大模型调用失败: {exc}") from exc
+
+    async def _download_training_file(self, jsonl_url: str) -> bytes:
+        async with self._build_client() as client:
+            response = await client.get(jsonl_url)
+            response.raise_for_status()
+            return response.content
+
+    async def _upload_training_file(self, file_bytes: bytes) -> str:
+        async with self._build_client(base_url=self._base_url) as client:
+            response = await client.post(
+                "/files",
+                data={"purpose": "fine-tune"},
+                files={"file": ("training.jsonl", file_bytes, "application/jsonl")},
+            )
+            response.raise_for_status()
+            data = response.json()
+            file_id = data.get("id")
+            if not file_id:
+                raise LLMCallError("千问训练文件上传失败: 缺少 file id")
+            return file_id
+
+    async def _post_json(self, base_url: str, path: str, payload: dict[str, Any]) -> dict[str, Any]:
+        async with self._build_client(base_url=base_url) as client:
+            response = await client.post(path, json=payload)
+            response.raise_for_status()
+            return response.json()
+
+    async def _get_json(self, base_url: str, path: str) -> dict[str, Any]:
+        async with self._build_client(base_url=base_url) as client:
+            response = await client.get(path)
+            response.raise_for_status()
+            return response.json()
+
+    def _build_client(self, base_url: str | None = None) -> httpx.AsyncClient:
+        return httpx.AsyncClient(
+            base_url=base_url or self._base_url,
+            headers={
+                "Authorization": f"Bearer {self._api_key}",
+            },
+            transport=self._transport,
+            timeout=60,
+        )

app/clients/storage/rustfs_client.py

@@ -28,6 +28,7 @@ class RustFSClient(StorageClient):
             )
             return resp["Body"].read()
         except ClientError as exc:
+            print(exc)
             raise StorageError(f"存储下载失败 [{bucket}/{path}]: {exc}") from exc
 
     async def upload_bytes(

app/core/config.py

@@ -10,6 +10,9 @@ load_dotenv()
 
 # Maps environment variable names to nested YAML key paths
 _ENV_OVERRIDES: dict[str, list[str]] = {
+    "DASHSCOPE_API_KEY": ["dashscope", "api_key"],
+    "DASHSCOPE_BASE_URL": ["dashscope", "base_url"],
+    "DASHSCOPE_FINE_TUNE_BASE_URL": ["dashscope", "fine_tune_base_url"],
     "ZHIPUAI_API_KEY": ["zhipuai", "api_key"],
     "STORAGE_ACCESS_KEY": ["storage", "access_key"],
     "STORAGE_SECRET_KEY": ["storage", "secret_key"],

app/core/dependencies.py

@@ -1,7 +1,7 @@
 from functools import lru_cache
 
 from app.clients.llm.base import LLMClient
-from app.clients.llm.zhipuai_client import ZhipuAIClient
+from app.clients.llm.qwen_client import QwenClient
 from app.clients.storage.base import StorageClient
 from app.clients.storage.rustfs_client import RustFSClient
 from app.core.config import get_config
@@ -10,7 +10,12 @@ from app.core.config import get_config
 @lru_cache(maxsize=1)
 def get_llm_client() -> LLMClient:
     cfg = get_config()
-    return ZhipuAIClient(api_key=cfg["zhipuai"]["api_key"])
+    dashscope_cfg = cfg["dashscope"]
+    return QwenClient(
+        api_key=dashscope_cfg["api_key"],
+        base_url=dashscope_cfg.get("base_url", "https://dashscope.aliyuncs.com/compatible-mode/v1"),
+        fine_tune_base_url=dashscope_cfg.get("fine_tune_base_url"),
+    )
 
 
 @lru_cache(maxsize=1)

app/main.py

@@ -47,4 +47,5 @@ app.include_router(finetune.router, prefix="/api/v1")
 
 if __name__ == "__main__":
     import uvicorn
+
     uvicorn.run(app, host="0.0.0.0", port=8000)

app/services/text_service.py

@@ -6,7 +6,7 @@ import docx
 from app.clients.llm.base import LLMClient
 from app.clients.storage.base import StorageClient
 from app.core.config import get_config
-from app.core.exceptions import UnsupportedFileTypeError
+from app.core.exceptions import StorageError, UnsupportedFileTypeError
 from app.core.json_utils import extract_json
 from app.core.logging import get_logger
 from app.models.text_models import (
@@ -61,7 +61,21 @@ async def extract_triples(
     bucket = cfg["storage"]["buckets"]["source_data"]
     model = req.model or cfg["models"]["default_text"]
 
-    data = await storage.download_bytes(bucket, req.file_path)
+    try:
+        data = await storage.download_bytes(bucket, req.file_path)
+        logger.info("文件下载成功", extra={
+            "file_name": req.file_name,
+            "size_bytes": len(data)
+        })
+    except Exception as e:
+        logger.error("文件下载失败", extra={
+            "file_name": req.file_name,
+            "file_path": req.file_path,
+            "bucket": bucket,
+            "error_type": type(e).__name__,
+            "error_message": str(e)
+        }, exc_info=True)
+        raise StorageError(f"下载文件失败: {str(e)}") from e
 
     if ext == ".txt":
         text = _parse_txt(data)

config.yaml

@@ -1,19 +1,24 @@
 server:
-  port: 8000
+  port: 18000
   log_level: INFO
 
+dashscope:
+  api_key: ""  # override with DASHSCOPE_API_KEY in .env or environment
+  base_url: "https://dashscope.aliyuncs.com/compatible-mode/v1"
+  fine_tune_base_url: "https://dashscope.aliyuncs.com/api/v1"
+
 storage:
   buckets:
-    source_data: "source-data"
+    source_data: "label-source-data"
     finetune_export: "finetune-export"
 
 backend: {}   # callback_url injected via BACKEND_CALLBACK_URL env var
 
 video:
   frame_sample_count: 8    # uniform frames sampled for video-to-text
-  max_file_size_mb: 200    # video size limit (override with MAX_VIDEO_SIZE_MB)
+  max_file_size_mb: 500    # video size limit (override with MAX_VIDEO_SIZE_MB)
   keyframe_diff_threshold: 30.0  # grayscale mean-diff threshold for keyframe detection
 
 models:
-  default_text: "glm-4-flash"
-  default_vision: "glm-4v-flash"
+  default_text: "qwen-plus"
+  default_vision: "qwen-vl-plus"

docker-compose.python.yml

@@ -0,0 +1,12 @@
+version: "3.9"
+
+services:
+  python-service:
+    image: label-ai-service:latest
+    build:
+      context: .
+      dockerfile: Dockerfile
+    container_name: label-ai-service
+    ports:
+      - "18000:18000"
+    restart: unless-stopped

requirements.txt

@@ -1,16 +1,61 @@
-fastapi>=0.111.0
-uvicorn[standard]>=0.29.0
-pydantic>=2.7.0
-zhipuai>=2.1.0
-boto3>=1.34.0
-pdfplumber>=0.11.0
-python-docx>=1.1.0
-opencv-python-headless>=4.9.0
-numpy>=1.26.0
-httpx>=0.27.0
-python-dotenv>=1.0.0
-pyyaml>=6.0.0
-
-# Testing
-pytest>=8.0.0
-pytest-asyncio>=0.23.0
+# ============================================
+# Core Framework
+# ============================================
+fastapi==0.135.3
+uvicorn[standard]==0.44.0
+pydantic==2.12.5
+
+# ============================================
+# AI & LLM
+# ============================================
+zhipuai==2.1.5.20250825
+
+# ============================================
+# Storage (S3 Compatible)
+# ============================================
+boto3==1.42.87
+
+# ============================================
+# Configuration
+# ============================================
+python-dotenv==1.2.2
+PyYAML==6.0.2
+
+# ============================================
+# Document Processing
+# ============================================
+pdfplumber==0.11.9
+python-docx==1.2.0
+lxml==6.0.3
+
+# ============================================
+# Image & Video Processing
+# ============================================
+opencv-python-headless==4.13.0.92
+pillow==12.2.0
+numpy==2.4.4
+
+# ============================================
+# Authentication & Security
+# ============================================
+PyJWT==2.8.0
+cryptography==46.0.7
+
+# ============================================
+# HTTP Client 用于测试客户端
+# ============================================
+httpx==0.28.1
+
+# ============================================
+# Testing (Development Only)
+# ============================================
+pytest==9.0.3
+pytest-asyncio==1.3.0
+
+# ============================================
+# Type Checking & Async Support
+# ============================================
+typing_extensions==4.14.1
+sniffio==1.3.1
+
+

start.sh

@@ -0,0 +1,27 @@
+#!/usr/bin/env bash
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+cd "$SCRIPT_DIR"
+
+COMPOSE_CMD="docker compose"
+if ! docker compose version >/dev/null 2>&1; then
+  if command -v docker-compose >/dev/null 2>&1; then
+    COMPOSE_CMD="docker-compose"
+  else
+    echo "Error: docker compose and docker-compose are both unavailable." >&2
+    exit 1
+  fi
+fi
+
+echo "==> Pulling latest code..."
+git pull
+
+echo "==> Building image..."
+docker build -t label-ai-service:latest -f Dockerfile .
+
+echo "==> Starting service..."
+$COMPOSE_CMD -f docker-compose.python.yml up -d
+
+echo "==> Service started. Check logs with:"
+echo "    $COMPOSE_CMD -f docker-compose.python.yml logs -f python-service"