kplam
db568740c5
- 新增 Ingest.vue 独立入库页（文本/文件/批量 + 任务进度列表），移除入库 Drawer - 导航栏去掉「入库进度」，新增「文档入库」入口；知识库页「入库」跳转独立页 - API 说明页接口清单与后端对齐（补齐用户管理/上传批量/任务重试删除/reingest 等，修正鉴权标注） - 更新 qmdsearch-agent skill 与 api-examples（鉴权说明、会话 token、新端点示例）
6.9 KiB
6.9 KiB
QMDSearch API 示例与 Python 客户端封装
基址占位符
{BASE}替换为实际服务地址，例如http://localhost:8000或http://<nas-ip>:8000。
一、curl 示例
登录
curl -s -X POST {BASE}/api/v1/auth/login \
-H "Content-Type: application/json" \
-d '{"username":"admin","password":"<password>"}'
文本入库（202 + task_id）
curl -s -X POST {BASE}/api/v1/documents \
-H "Authorization: Bearer <token>" \
-H "Content-Type: application/json" \
-d '{"text":"QMDSearch 是面向 AI Agent 的分层信息检索服务……","title":"QMDSearch 介绍","source":"notes"}'
文件上传（202 + task_id）
curl -s -X POST {BASE}/api/v1/documents/upload \
-H "Authorization: Bearer <token>" \
-F "file=@document.pdf" \
-F "source=manual"
轮询任务状态
curl -s {BASE}/api/v1/documents/tasks/<task_id>
批量文件上传（202，逐文件建任务）
curl -s -X POST {BASE}/api/v1/documents/upload-batch \
-H "Authorization: Bearer <token>" \
-F "files=@a.pdf" -F "files=@b.md"
列出入库任务 / 重试 / 删除任务
curl -s -H "Authorization: Bearer <token>" "{BASE}/api/v1/documents/tasks?limit=20"
curl -s -X POST -H "Authorization: Bearer <token>" {BASE}/api/v1/documents/tasks/<task_id>/retry
curl -s -X DELETE -H "Authorization: Bearer <token>" {BASE}/api/v1/documents/tasks/<task_id>
重新摘要入库（读原文件 → 删旧 → 重跑）
curl -s -X POST -H "Authorization: Bearer <token>" {BASE}/api/v1/documents/<doc_id>/reingest
检索（免登录）
curl -s -X POST {BASE}/api/v1/search \
-H "Content-Type: application/json" \
-d '{"query":"如何配置 Redis 缓存","top_k":5,"summarize":false}'
二、Python 客户端封装
可直接在 Agent 工具代码里复用：
import time
import requests
class QMDSearchClient:
"""QMDSearch 最小客户端：登录、入库（文本/文件）、轮询、检索。"""
def __init__(self, base_url: str, username: str, password: str,
poll_interval: float = 1.5, poll_timeout: float = 120.0):
self.base = base_url.rstrip("/")
self.username = username
self.password = password
self.poll_interval = poll_interval
self.poll_timeout = poll_timeout
self.token: str | None = None
# ---- 鉴权 ----
def login(self) -> str:
r = requests.post(
f"{self.base}/api/v1/auth/login",
json={"username": self.username, "password": self.password},
timeout=30,
)
r.raise_for_status()
body = r.json()
if body.get("code") != 0:
raise RuntimeError(f"登录失败: {body}")
self.token = body["data"]["token"]
return self.token
def _headers(self) -> dict:
if not self.token:
self.login()
return {"Authorization": f"Bearer {self.token}"}
def _ok(self, resp: requests.Response):
resp.raise_for_status()
body = resp.json()
if body.get("code") != 0:
raise RuntimeError(f"API 错误 code={body.get('code')} msg={body.get('message')}")
return body["data"]
# ---- 入库 ----
def ingest_text(self, text: str, title: str = "", source: str = "",
metadata: dict | None = None) -> str:
data = {"text": text}
if title:
data["title"] = title
if source:
data["source"] = source
if metadata:
data["metadata"] = {str(k): str(v) for k, v in metadata.items()}
resp = requests.post(f"{self.base}/api/v1/documents",
headers=self._headers(), json=data, timeout=30)
return self._ok(resp)["task_id"]
def upload_file(self, path: str, title: str = "", source: str = "",
metadata: dict | None = None) -> str:
files = {"file": open(path, "rb")}
data = {}
if title:
data["title"] = title
if source:
data["source"] = source
if metadata:
data["metadata"] = str({str(k): str(v) for k, v in metadata.items()})
resp = requests.post(f"{self.base}/api/v1/documents/upload",
headers=self._headers(), files=files, data=data, timeout=60)
return self._ok(resp)["task_id"]
def upload_batch(self, paths: list[str]) -> dict:
"""批量上传多个文件：返回 {tasks:[{filename,task_id}], failed:[{filename,error}]}"""
files = [("files", open(p, "rb")) for p in paths]
resp = requests.post(f"{self.base}/api/v1/documents/upload-batch",
headers=self._headers(), files=files, timeout=60)
return self._ok(resp)
# ---- 轮询 ----
def wait_task(self, task_id: str) -> dict:
deadline = time.time() + self.poll_timeout
while time.time() < deadline:
resp = requests.get(f"{self.base}/api/v1/documents/tasks/{task_id}", timeout=30)
data = self._ok(resp)
if data["status"] == "done":
return data["result"]
if data["status"] == "failed":
raise RuntimeError(f"入库失败: {data.get('error')}")
time.sleep(self.poll_interval)
raise TimeoutError(f"任务 {task_id} 轮询超时")
def ingest_text_wait(self, *args, **kwargs) -> dict:
return self.wait_task(self.ingest_text(*args, **kwargs))
def upload_file_wait(self, *args, **kwargs) -> dict:
return self.wait_task(self.upload_file(*args, **kwargs))
# ---- 检索 ----
def search(self, query: str, top_k: int | None = None,
summarize: bool = False) -> dict:
payload = {"query": query, "summarize": summarize}
if top_k is not None:
payload["top_k"] = top_k
resp = requests.post(f"{self.base}/api/v1/search",
headers=self._headers(), json=payload, timeout=60)
return self._ok(resp)
# 用法
if __name__ == "__main__":
client = QMDSearchClient("http://localhost:8000", "admin", "<password>")
client.login()
# 文本入库并等待完成
result = client.ingest_text_wait("这是一篇关于分层检索的笔记……", title="笔记")
print("document_id =", result["document_id"])
# 检索
hits = client.search("分层检索是什么", top_k=5)["hits"]
for h in hits:
print(f"[{h['score']:.3f}] {h['title']}: {h['text'][:80]}")
三、响应结构速记
- 文本 / 文件 / 批量入库
202→data = {task_id, status:"pending"[, saved_path]}；批量返回{tasks, failed} - 任务
done→data.result = {document_id, summary:{l1_summary,l2_outline,l3_content_outline,level}, category, chunks_count, tags, category_confidence, deduplicated} - 检索 →
data = {query, hits:[{text,doc_id,title,section_path,score,doc_summary}], routed_categories, fallback, extracted_info, summary?}