chore: initial baseline with P0-safety .gitignore
This commit is contained in:
Simon
@@ -0,0 +1,35 @@
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# =============================================================================
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# RAGFlow 集成模块
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# =============================================================================
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from .client import RagflowClient
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from .config import get_ragflow_client
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from .exceptions import (
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RagflowApiError,
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RagflowAuthError,
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RagflowConfigError,
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RagflowConnectionError,
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RagflowError,
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)
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from .models import (
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DatasetInfo,
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DocAggregate,
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DocumentInfo,
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RetrievalChunk,
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RetrievalResult,
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)
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__all__ = [
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"RagflowClient",
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"get_ragflow_client",
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"RagflowError",
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"RagflowConfigError",
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"RagflowAuthError",
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"RagflowApiError",
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"RagflowConnectionError",
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"RetrievalChunk",
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"DocAggregate",
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"RetrievalResult",
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"DatasetInfo",
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"DocumentInfo",
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]
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@@ -0,0 +1,449 @@
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# =============================================================================
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# RAGFlow API 客户端
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# =============================================================================
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# 说明：封装 RAGFlow 知识检索引擎的 API 调用
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# 核心功能：
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# 1. 知识检索 — POST /api/v1/retrieval（核心接口）
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# 2. 数据集管理 — 列出/创建/删除知识库
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# 3. 文档管理 — 上传/列出/删除文档
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# 4. 测试连接 — 验证 API Key 是否有效
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# 认证方式：Authorization: Bearer <API_KEY>
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# 参考文档：https://ragflow.io/docs/http_api_reference
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# =============================================================================
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import logging
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from typing import Any, Dict, List, Optional
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import httpx
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from .exceptions import (
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RagflowApiError,
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RagflowAuthError,
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RagflowConfigError,
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RagflowConnectionError,
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RagflowError,
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)
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from .models import (
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DatasetInfo,
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DocAggregate,
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DocumentInfo,
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RetrievalChunk,
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RetrievalResult,
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)
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logger = logging.getLogger(__name__)
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# 默认请求超时（秒）
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DEFAULT_TIMEOUT = 30.0
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# 默认分页大小
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DEFAULT_PAGE_SIZE = 20
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class RagflowClient:
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"""RAGFlow API 客户端。
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封装 RAGFlow 知识检索引擎的 API 调用，支持：
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- 知识检索（核心功能）
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- 数据集（知识库）管理
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- 文档管理
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- 连接测试
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使用方式：
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client = RagflowClient(
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api_key="sk-xxx",
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base_url="http://10.80.0.85:9380"
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)
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result = await client.retrieval("VPN怎么连？", dataset_ids=["xxx"])
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"""
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def __init__(
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self,
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api_key: str,
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base_url: str = "http://10.80.0.85:9380",
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timeout: float = DEFAULT_TIMEOUT,
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):
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"""初始化 RAGFlow 客户端。
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Args:
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api_key: RAGFlow API Key（Bearer Token）
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base_url: RAGFlow API 基础地址（不含尾部斜杠）
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timeout: 默认请求超时（秒）
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Raises:
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RagflowConfigError: API Key 为空
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"""
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if not api_key:
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raise RagflowConfigError("RAGFlow API Key 不能为空")
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self.api_key = api_key
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self.base_url = base_url.rstrip("/")
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self.timeout = timeout
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def _headers(self) -> Dict[str, str]:
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"""构建请求头。
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Returns:
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Dict: 包含 Authorization 和 Content-Type 的请求头
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"""
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return {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json",
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}
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async def _request(
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self,
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method: str,
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path: str,
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json_data: Optional[Dict] = None,
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params: Optional[Dict] = None,
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timeout: Optional[float] = None,
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) -> Dict[str, Any]:
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"""统一请求封装。
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Args:
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method: HTTP 方法（GET/POST/PUT/DELETE）
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path: API 路径（如 /api/v1/retrieval）
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json_data: JSON 请求体
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params: 查询参数
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timeout: 覆盖默认超时
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Returns:
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Dict: API 响应的 JSON 数据
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Raises:
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RagflowAuthError: 认证失败（401）
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RagflowApiError: API 返回错误
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RagflowConnectionError: 网络连接失败
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"""
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url = f"{self.base_url}{path}"
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req_timeout = timeout or self.timeout
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try:
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async with httpx.AsyncClient() as client:
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response = await client.request(
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method=method,
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url=url,
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headers=self._headers(),
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json=json_data,
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params=params,
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timeout=req_timeout,
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)
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# 处理 HTTP 错误
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if response.status_code == 401:
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raise RagflowAuthError("RAGFlow API Key 无效或已过期")
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if response.status_code >= 400:
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try:
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err_body = response.json()
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err_msg = err_body.get("message", response.text)
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except Exception:
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err_msg = response.text
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raise RagflowApiError(
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code=response.status_code,
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message=f"RAGFlow API 错误 ({response.status_code}): {err_msg}",
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)
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# 解析响应
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result = response.json()
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# RAGFlow 统一响应格式：{code: 0, data: ..., message: ...}
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if result.get("code") != 0:
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raise RagflowApiError(
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code=result.get("code", -1),
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message=result.get("message", "未知错误"),
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)
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return result
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except httpx.TimeoutException:
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raise RagflowConnectionError(f"RAGFlow 请求超时 ({req_timeout}s): {path}")
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except httpx.ConnectError:
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raise RagflowConnectionError(f"RAGFlow 连接失败: {self.base_url}")
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except (RagflowAuthError, RagflowApiError, RagflowConnectionError):
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raise
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except Exception as e:
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raise RagflowError(f"RAGFlow 请求异常: {str(e)}")
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# ==========================================================================
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# 测试连接
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# ==========================================================================
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async def test_connection(self) -> Dict[str, Any]:
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"""测试 RAGFlow API 连接。
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通过列出数据集（limit=1）验证 API Key 是否有效。
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Returns:
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Dict: {success: bool, message: str}
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"""
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try:
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result = await self.list_datasets(page=1, page_size=1)
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return {
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"success": True,
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"message": f"连接成功，共 {result.get('total', 0)} 个知识库",
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}
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except RagflowAuthError:
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return {"success": False, "message": "API Key 无效或已过期"}
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except RagflowConnectionError as e:
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return {"success": False, "message": f"连接失败: {e.message}"}
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except RagflowError as e:
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return {"success": False, "message": e.message}
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# ==========================================================================
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# 知识检索（核心接口）
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# ==========================================================================
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async def retrieval(
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self,
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question: str,
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dataset_ids: Optional[List[str]] = None,
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document_ids: Optional[List[str]] = None,
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similarity_threshold: float = 0.2,
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vector_similarity_weight: float = 0.3,
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top_k: int = 1024,
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keyword: bool = False,
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highlight: bool = False,
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) -> RetrievalResult:
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"""知识检索 — 从知识库中搜索相关文档片段。
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这是 RAGFlow 的核心接口，用于根据用户问题检索最相关的文本块。
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Args:
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question: 用户查询问题
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dataset_ids: 要搜索的数据集ID列表（与 document_ids 二选一）
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document_ids: 要搜索的文档ID列表
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similarity_threshold: 最小相似度阈值（0-1），默认 0.2
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vector_similarity_weight: 向量相似度权重（0-1），默认 0.3
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top_k: 参与计算的块数量，默认 1024
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keyword: 是否启用关键词匹配，默认 False
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highlight: 是否高亮匹配术语，默认 False
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Returns:
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RetrievalResult: 检索结果（含文本块、文档聚合、总数）
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Raises:
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RagflowError: 检索失败
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"""
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body: Dict[str, Any] = {
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"question": question,
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"similarity_threshold": similarity_threshold,
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"vector_similarity_weight": vector_similarity_weight,
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"top_k": top_k,
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"keyword": keyword,
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"highlight": highlight,
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}
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if dataset_ids:
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body["dataset_ids"] = dataset_ids
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if document_ids:
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body["document_ids"] = document_ids
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result = await self._request("POST", "/api/v1/retrieval", json_data=body)
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data = result.get("data", {})
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# 解析文本块
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chunks = [
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RetrievalChunk.model_validate(chunk)
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for chunk in data.get("chunks", [])
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]
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# 解析文档聚合
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doc_aggs = [
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DocAggregate.model_validate(agg)
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for agg in data.get("doc_aggs", [])
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]
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return RetrievalResult(
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chunks=chunks,
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doc_aggs=doc_aggs,
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total=data.get("total", 0),
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)
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# ==========================================================================
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# 数据集（知识库）管理
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# ==========================================================================
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async def list_datasets(
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self,
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page: int = 1,
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page_size: int = DEFAULT_PAGE_SIZE,
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) -> Dict[str, Any]:
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"""列出所有数据集（知识库）。
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Args:
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page: 页码
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page_size: 每页条数
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Returns:
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Dict: {items: List[DatasetInfo], total: int}
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"""
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result = await self._request(
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"GET",
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"/api/v1/datasets",
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params={"page": page, "page_size": page_size},
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)
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data = result.get("data", {})
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items = [
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DatasetInfo.model_validate(ds)
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for ds in data.get("datasets", [])
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]
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return {"items": items, "total": data.get("total", 0)}
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async def create_dataset(
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self,
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name: str,
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embedding_model: str = "BAAI/bge-m3@BAAI",
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chunk_method: str = "naive",
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permission: str = "me",
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) -> DatasetInfo:
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"""创建数据集（知识库）。
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Args:
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name: 数据集名称
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embedding_model: 向量模型
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chunk_method: 分块方法（naive/qa/book/laws 等）
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permission: 权限（me/team）
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Returns:
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DatasetInfo: 创建的数据集信息
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"""
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body = {
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"name": name,
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"embedding_model": embedding_model,
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"chunk_method": chunk_method,
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"permission": permission,
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}
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result = await self._request("POST", "/api/v1/datasets", json_data=body)
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return DatasetInfo.model_validate(result.get("data", {}))
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async def delete_dataset(self, dataset_ids: List[str]) -> bool:
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"""删除数据集。
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Args:
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dataset_ids: 要删除的数据集ID列表
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Returns:
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bool: 是否成功
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"""
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await self._request(
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"DELETE",
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"/api/v1/datasets",
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json_data={"ids": dataset_ids},
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)
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return True
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# ==========================================================================
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# 文档管理
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# ==========================================================================
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async def list_documents(
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self,
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dataset_id: str,
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page: int = 1,
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page_size: int = DEFAULT_PAGE_SIZE,
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) -> Dict[str, Any]:
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"""列出数据集中的文档。
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Args:
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dataset_id: 数据集ID
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page: 页码
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page_size: 每页条数
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Returns:
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Dict: {items: List[DocumentInfo], total: int}
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"""
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result = await self._request(
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"GET",
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f"/api/v1/datasets/{dataset_id}/documents",
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params={"page": page, "page_size": page_size},
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)
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data = result.get("data", {})
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items = [
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DocumentInfo.model_validate(doc)
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for doc in data.get("documents", [])
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]
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return {"items": items, "total": data.get("total", 0)}
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async def upload_document(
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self,
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dataset_id: str,
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file_path: str,
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file_name: Optional[str] = None,
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) -> DocumentInfo:
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"""上传文档到数据集。
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Args:
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dataset_id: 数据集ID
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file_path: 本地文件路径
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file_name: 文件名（可选，默认取 file_path 的文件名）
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Returns:
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DocumentInfo: 上传的文档信息
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"""
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import os
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if not os.path.exists(file_path):
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raise RagflowError(f"文件不存在: {file_path}")
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||||
fname = file_name or os.path.basename(file_path)
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url = f"{self.base_url}/api/v1/datasets/{dataset_id}/documents"
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try:
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async with httpx.AsyncClient() as client:
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with open(file_path, "rb") as f:
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response = await client.post(
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url=url,
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headers={"Authorization": f"Bearer {self.api_key}"},
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files={"file": (fname, f)},
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timeout=60.0,
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)
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if response.status_code == 401:
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raise RagflowAuthError()
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result = response.json()
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if result.get("code") != 0:
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raise RagflowApiError(
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code=result.get("code", -1),
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||||
message=result.get("message", "上传失败"),
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)
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||||
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docs = result.get("data", {}).get("documents", [])
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if docs:
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return DocumentInfo.model_validate(docs[0])
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return DocumentInfo(name=fname)
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|
||||
except (RagflowAuthError, RagflowApiError):
|
||||
raise
|
||||
except Exception as e:
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||||
raise RagflowError(f"文档上传失败: {str(e)}")
|
||||
|
||||
async def delete_documents(
|
||||
self,
|
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dataset_id: str,
|
||||
document_ids: List[str],
|
||||
) -> bool:
|
||||
"""删除文档。
|
||||
|
||||
Args:
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||||
dataset_id: 数据集ID
|
||||
document_ids: 要删除的文档ID列表
|
||||
|
||||
Returns:
|
||||
bool: 是否成功
|
||||
"""
|
||||
await self._request(
|
||||
"DELETE",
|
||||
f"/api/v1/datasets/{dataset_id}/documents",
|
||||
json_data={"ids": document_ids},
|
||||
)
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||||
return True
|
||||
@@ -0,0 +1,61 @@
|
||||
# =============================================================================
|
||||
# RAGFlow 配置加载器
|
||||
# =============================================================================
|
||||
# 说明：从数据库 system_configs 表加载 RAGFlow 配置，创建客户端实例
|
||||
# 配置项：integration_ragflow_api_url + integration_ragflow_api_key
|
||||
|
||||
import logging
|
||||
from typing import Optional
|
||||
|
||||
from sqlalchemy import select
|
||||
from sqlalchemy.ext.asyncio import AsyncSession
|
||||
|
||||
from app.models.system_config import SystemConfig
|
||||
|
||||
from .client import RagflowClient
|
||||
from .exceptions import RagflowConfigError
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# 默认 RAGFlow API 地址（生产环境）
|
||||
DEFAULT_RAGFLOW_BASE_URL = "http://10.80.0.85:9380"
|
||||
|
||||
|
||||
async def _get_config(db: AsyncSession, key: str) -> str:
|
||||
"""从数据库读取单个配置值。"""
|
||||
result = await db.execute(
|
||||
select(SystemConfig.config_value).where(SystemConfig.config_key == key)
|
||||
)
|
||||
row = result.scalar()
|
||||
return row if row else ""
|
||||
|
||||
|
||||
async def get_ragflow_client(db: AsyncSession) -> RagflowClient:
|
||||
"""从数据库配置创建 RAGFlow 客户端实例。
|
||||
|
||||
读取 system_configs 表中的：
|
||||
- integration_ragflow_api_url: RAGFlow API 地址
|
||||
- integration_ragflow_api_key: RAGFlow API Key
|
||||
|
||||
Args:
|
||||
db: 数据库会话
|
||||
|
||||
Returns:
|
||||
RagflowClient: 客户端实例
|
||||
|
||||
Raises:
|
||||
RagflowConfigError: 配置缺失
|
||||
"""
|
||||
api_url = await _get_config(db, "integration_ragflow_api_url")
|
||||
api_key = await _get_config(db, "integration_ragflow_api_key")
|
||||
|
||||
# 如果数据库没有配置，使用默认地址
|
||||
if not api_url:
|
||||
api_url = DEFAULT_RAGFLOW_BASE_URL
|
||||
|
||||
if not api_key:
|
||||
raise RagflowConfigError(
|
||||
"RAGFlow API Key 未配置，请在管理后台 → 集成管理 → RAGFlow 中设置"
|
||||
)
|
||||
|
||||
return RagflowClient(api_key=api_key, base_url=api_url)
|
||||
@@ -0,0 +1,35 @@
|
||||
# =============================================================================
|
||||
# RAGFlow API 异常定义
|
||||
# =============================================================================
|
||||
|
||||
|
||||
class RagflowError(Exception):
|
||||
"""RAGFlow 基础异常。"""
|
||||
def __init__(self, message: str = "RAGFlow 错误"):
|
||||
self.message = message
|
||||
super().__init__(self.message)
|
||||
|
||||
|
||||
class RagflowConfigError(RagflowError):
|
||||
"""配置错误（缺少 API Key 或 Base URL）。"""
|
||||
def __init__(self, message: str = "RAGFlow 配置缺失"):
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
class RagflowAuthError(RagflowError):
|
||||
"""认证失败（API Key 无效）。"""
|
||||
def __init__(self, message: str = "RAGFlow 认证失败"):
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
class RagflowApiError(RagflowError):
|
||||
"""API 调用失败（非 200 响应）。"""
|
||||
def __init__(self, code: int = 0, message: str = "RAGFlow API 错误"):
|
||||
self.code = code
|
||||
super().__init__(message)
|
||||
|
||||
|
||||
class RagflowConnectionError(RagflowError):
|
||||
"""网络连接失败。"""
|
||||
def __init__(self, message: str = "RAGFlow 连接失败"):
|
||||
super().__init__(message)
|
||||
@@ -0,0 +1,110 @@
|
||||
# =============================================================================
|
||||
# RAGFlow API 数据模型
|
||||
# =============================================================================
|
||||
# 说明：定义 RAGFlow API 请求/响应的 Pydantic 数据模型
|
||||
# 参考：https://ragflow.io/docs/http_api_reference
|
||||
|
||||
from typing import Any, Dict, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class RetrievalChunk(BaseModel):
|
||||
"""检索返回的单个文本块。
|
||||
|
||||
Attributes:
|
||||
id: 块唯一ID
|
||||
content: 块内容文本
|
||||
document_id: 所属文档ID
|
||||
document_keyword: 所属文档名称
|
||||
similarity: 综合相似度分数
|
||||
term_similarity: 关键词相似度
|
||||
vector_similarity: 向量相似度
|
||||
highlight: 高亮标记的内容（可选）
|
||||
"""
|
||||
id: str = Field(default="", description="块唯一ID")
|
||||
content: str = Field(default="", description="块内容文本")
|
||||
document_id: str = Field(default="", description="所属文档ID")
|
||||
document_keyword: str = Field(default="", description="所属文档名称")
|
||||
similarity: float = Field(default=0.0, description="综合相似度分数")
|
||||
term_similarity: float = Field(default=0.0, description="关键词相似度")
|
||||
vector_similarity: float = Field(default=0.0, description="向量相似度")
|
||||
highlight: Optional[str] = Field(default=None, description="高亮标记的内容")
|
||||
|
||||
model_config = {"from_attributes": True}
|
||||
|
||||
|
||||
class DocAggregate(BaseModel):
|
||||
"""文档聚合统计。
|
||||
|
||||
Attributes:
|
||||
doc_id: 文档ID
|
||||
doc_name: 文档名称
|
||||
count: 命中的块数量
|
||||
"""
|
||||
doc_id: str = Field(default="", description="文档ID")
|
||||
doc_name: str = Field(default="", description="文档名称")
|
||||
count: int = Field(default=0, description="命中块数量")
|
||||
|
||||
model_config = {"from_attributes": True}
|
||||
|
||||
|
||||
class RetrievalResult(BaseModel):
|
||||
"""检索结果。
|
||||
|
||||
Attributes:
|
||||
chunks: 命中的文本块列表
|
||||
doc_aggs: 按文档聚合统计
|
||||
total: 命中总数
|
||||
"""
|
||||
chunks: List[RetrievalChunk] = Field(default_factory=list, description="命中文本块列表")
|
||||
doc_aggs: List[DocAggregate] = Field(default_factory=list, description="文档聚合统计")
|
||||
total: int = Field(default=0, description="命中总数")
|
||||
|
||||
model_config = {"from_attributes": True}
|
||||
|
||||
|
||||
class DatasetInfo(BaseModel):
|
||||
"""数据集（知识库）信息。
|
||||
|
||||
Attributes:
|
||||
id: 数据集ID
|
||||
name: 数据集名称
|
||||
chunk_method: 分块方法
|
||||
permission: 权限
|
||||
document_count: 文档数量
|
||||
embedding_model: 向量模型
|
||||
create_time: 创建时间
|
||||
update_time: 更新时间
|
||||
"""
|
||||
id: str = Field(default="", description="数据集ID")
|
||||
name: str = Field(default="", description="数据集名称")
|
||||
chunk_method: str = Field(default="naive", description="分块方法")
|
||||
permission: str = Field(default="me", description="权限")
|
||||
document_count: int = Field(default=0, description="文档数量")
|
||||
embedding_model: str = Field(default="", description="向量模型")
|
||||
create_time: Optional[str] = Field(default=None, description="创建时间")
|
||||
update_time: Optional[str] = Field(default=None, description="更新时间")
|
||||
|
||||
model_config = {"from_attributes": True}
|
||||
|
||||
|
||||
class DocumentInfo(BaseModel):
|
||||
"""文档信息。
|
||||
|
||||
Attributes:
|
||||
id: 文档ID
|
||||
name: 文档名称
|
||||
chunk_method: 分块方法
|
||||
chunk_count: 块数量
|
||||
create_time: 创建时间
|
||||
update_time: 更新时间
|
||||
"""
|
||||
id: str = Field(default="", description="文档ID")
|
||||
name: str = Field(default="", description="文档名称")
|
||||
chunk_method: str = Field(default="naive", description="分块方法")
|
||||
chunk_count: int = Field(default=0, description="块数量")
|
||||
create_time: Optional[str] = Field(default=None, description="创建时间")
|
||||
update_time: Optional[str] = Field(default=None, description="更新时间")
|
||||
|
||||
model_config = {"from_attributes": True}
|
||||
Reference in New Issue
Block a user