feat(kb): 接入 Dify 知识库，移除 FastGPT；AI 聊天产出+用户文档自动入库量化
- 新增 app/dify_kb.py Dify v1 客户端(连通测试/检索/文本入库) - kb_router 仅保留 Dify 外部检索分支 - knowledge_admin 去掉 FastGPT/RAGFlow，新增 dify 批量入库端点 - chat.py 注入 Dify RAG 上下文 + 助手回答 fire-and-forget 入库 - 前端 Knowledge.tsx 改为 Dify 单服务 UI(可通讯/可用验证+入库按钮) - DB: external_kb_services 删除 fastgpt，qihuai 设为 https+connected
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@@ -32,7 +32,7 @@ logger = logging.getLogger("aiflow")
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class ExternalServiceConfig(BaseModel):
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name: str = Field(min_length=1, max_length=200)
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service_type: str = Field(..., description="fastgpt / dify / ragflow")
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service_type: str = Field(..., description="dify")
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api_url: str = Field(min_length=1, max_length=500)
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api_key: Optional[str] = None
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config: Optional[Dict[str, Any]] = None
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@@ -739,48 +739,27 @@ def _get_external_services_table(db: Session):
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def _check_service_connection(service_type: str, api_url: str, api_key: str) -> dict:
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"""测试外部知识库服务连接状态"""
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"""测试外部知识库服务连接状态（当前仅支持 Dify）"""
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try:
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headers = {}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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if service_type == "fastgpt":
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# FastGPT 健康检查
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check_url = f"{api_url.rstrip('/')}/api/v1/status"
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resp = httpx.get(check_url, headers=headers, timeout=10)
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if resp.status_code == 200:
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return {"connected": True, "status": "connected", "detail": "FastGPT 连接成功"}
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else:
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return {"connected": False, "status": "error", "detail": f"FastGPT 返回状态码 {resp.status_code}"}
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elif service_type == "dify":
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# Dify 健康检查
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check_url = f"{api_url.rstrip('/')}/v1/datasets"
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resp = httpx.get(check_url, headers=headers, timeout=10)
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if resp.status_code in [200, 401]: # 401说明服务存在但需要认证
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return {"connected": True, "status": "connected", "detail": "Dify 连接成功"}
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else:
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return {"connected": False, "status": "error", "detail": f"Dify 返回状态码 {resp.status_code}"}
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elif service_type == "ragflow":
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# RAGFlow 健康检查
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check_url = f"{api_url.rstrip('/')}/api/v1/datasets"
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resp = httpx.get(check_url, headers=headers, timeout=10)
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if resp.status_code in [200, 401]:
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return {"connected": True, "status": "connected", "detail": "RAGFlow 连接成功"}
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else:
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return {"connected": False, "status": "error", "detail": f"RAGFlow 返回状态码 {resp.status_code}"}
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if service_type == "dify":
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from ...dify_kb import dify_test
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return dify_test(api_url, api_key)
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else:
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# 通用检查
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# 未知类型（如历史遗留的 fastgpt/ragflow），按通用方式探测可达性
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headers = {}
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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resp = httpx.get(api_url, headers=headers, timeout=10)
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return {"connected": resp.status_code < 500, "status": "connected" if resp.status_code < 500 else "error"}
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return {
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"connected": resp.status_code < 500,
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"usable": resp.status_code < 500,
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"status": "connected" if resp.status_code < 500 else "error",
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"detail": f"通用探测返回状态码 {resp.status_code}",
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}
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except httpx.TimeoutException:
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return {"connected": False, "status": "timeout", "detail": "连接超时"}
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return {"connected": False, "usable": False, "status": "timeout", "detail": "连接超时"}
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except Exception as e:
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return {"connected": False, "status": "error", "detail": str(e)}
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return {"connected": False, "usable": False, "status": "error", "detail": str(e)}
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@router.get("/external/services", summary="获取外部知识库服务列表")
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@@ -801,9 +780,7 @@ async def list_external_services(
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"total": len(services),
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"items": services,
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"supported_types": [
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{"type": "fastgpt", "name": "FastGPT", "description": "开源 AI 知识库平台，支持多种向量模型"},
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{"type": "dify", "name": "Dify", "description": "LLM 应用开发平台，内置知识库管理"},
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{"type": "ragflow", "name": "RAGFlow", "description": "基于深度文档理解的开源 RAG 引擎"},
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{"type": "dify", "name": "Dify", "description": "LLM 应用开发平台，内置知识库管理（RAG）"},
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]
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}
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}
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@@ -816,8 +793,8 @@ async def create_external_service(
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db: Session = Depends(get_db),
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):
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"""创建新的外部知识库服务连接配置"""
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# 验证服务类型
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valid_types = ["fastgpt", "dify", "ragflow"]
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# 验证服务类型（当前仅支持 Dify）
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valid_types = ["dify"]
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if req.service_type not in valid_types:
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raise HTTPException(status_code=400, detail=f"不支持的服务类型: {req.service_type}")
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@@ -1082,163 +1059,30 @@ async def sync_knowledge_base(
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if api_key:
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headers["Authorization"] = f"Bearer {api_key}"
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if service_type == "fastgpt" and req.sync_direction == "push":
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# 向 FastGPT 推送知识库数据
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# 1. 在 FastGPT 创建/获取对应的 dataset
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ext_kb_id = getattr(kb, "external_id", None)
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if not ext_kb_id:
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# 创建新 dataset
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create_resp = httpx.post(
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f"{api_url.rstrip('/')}/api/v1/datasets",
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headers=headers,
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json={"name": kb.name, "description": kb.description or ""},
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timeout=30,
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)
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if create_resp.status_code == 200:
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ext_data = create_resp.json()
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ext_kb_id = ext_data.get("data", {}).get("id") or ext_data.get("id")
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# 更新 KB 的 external_id
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try:
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setattr(kb, "external_id", ext_kb_id)
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except Exception:
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db.execute(
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text("UPDATE knowledge_bases SET external_id = :eid WHERE id = :kid"),
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{"eid": ext_kb_id, "kid": kb_id}
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)
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else:
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sync_result["detail"] = f"FastGPT 创建 dataset 失败: {create_resp.status_code}"
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sync_result["synced"] = False
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if ext_kb_id:
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# 推送文档内容到 FastGPT
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if service_type == "dify" and req.sync_direction == "push":
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# 向 Dify 推送知识库数据（使用 app.dify_kb 客户端，按 dataset_name 解析）
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from ...dify_kb import dify_ingest_text, resolve_dataset_id
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dataset_name = service_config.get("dataset_name") if isinstance(service_config, dict) else None
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ds_id = resolve_dataset_id(api_url, api_key, dataset_name)
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if not ds_id:
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sync_result["detail"] = "无法解析 Dify 知识库 ID（请检查 API Key 是否绑定到指定数据集）"
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sync_result["synced"] = False
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else:
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docs = db.query(KnowledgeDocument).filter(
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KnowledgeDocument.knowledge_base_id == kb_id,
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KnowledgeDocument.status == "completed"
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).all()
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synced_count = 0
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for doc in docs:
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content = doc.content or ""
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if not content:
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continue
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# 分段推送
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push_resp = httpx.post(
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f"{api_url.rstrip('/')}/api/v1/datasets/{ext_kb_id}/documents",
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headers=headers,
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json={
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"name": doc.filename,
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"content": content[:50000], # 限制单次推送大小
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},
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timeout=30,
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)
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if push_resp.status_code in [200, 201]:
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if dify_ingest_text(api_url, api_key, content[:50000], name=doc.filename, dataset_name=dataset_name):
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synced_count += 1
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sync_result["synced"] = True
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sync_result["detail"] = f"已推送 {synced_count}/{len(docs)} 文档到 FastGPT"
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sync_result["items_synced"] = synced_count
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elif service_type == "dify" and req.sync_direction == "push":
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# 向 Dify 推送知识库数据
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ext_kb_id = getattr(kb, "external_id", None)
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if not ext_kb_id:
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create_resp = httpx.post(
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f"{api_url.rstrip('/')}/v1/datasets",
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headers=headers,
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json={"name": kb.name},
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timeout=30,
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)
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if create_resp.status_code in [200, 201]:
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ext_data = create_resp.json()
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ext_kb_id = ext_data.get("id")
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try:
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setattr(kb, "external_id", ext_kb_id)
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except Exception:
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db.execute(
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text("UPDATE knowledge_bases SET external_id = :eid WHERE id = :kid"),
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{"eid": ext_kb_id, "kid": kb_id}
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)
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if ext_kb_id:
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docs = db.query(KnowledgeDocument).filter(
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KnowledgeDocument.knowledge_base_id == kb_id,
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KnowledgeDocument.status == "completed"
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).all()
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synced_count = 0
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for doc in docs:
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content = doc.content or ""
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if not content:
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continue
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# Dify 文档创建
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doc_resp = httpx.post(
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f"{api_url.rstrip('/')}/v1/datasets/{ext_kb_id}/document/create_by_text",
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headers=headers,
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json={
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"name": doc.filename,
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"text": content[:50000],
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"indexing_technique": "high_quality",
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},
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timeout=30,
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)
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if doc_resp.status_code in [200, 201]:
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synced_count += 1
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sync_result["synced"] = True
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sync_result["detail"] = f"已推送 {synced_count}/{len(docs)} 文档到 Dify"
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sync_result["items_synced"] = synced_count
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elif service_type == "ragflow" and req.sync_direction == "push":
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# 向 RAGFlow 推送知识库数据
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ext_kb_id = getattr(kb, "external_id", None)
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if not ext_kb_id:
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create_resp = httpx.post(
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f"{api_url.rstrip('/')}/api/v1/datasets",
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headers=headers,
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json={"name": kb.name},
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timeout=30,
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)
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if create_resp.status_code in [200, 201]:
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ext_data = create_resp.json()
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ext_kb_id = ext_data.get("id")
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try:
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setattr(kb, "external_id", ext_kb_id)
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except Exception:
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db.execute(
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text("UPDATE knowledge_bases SET external_id = :eid WHERE id = :kid"),
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{"eid": ext_kb_id, "kid": kb_id}
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)
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if ext_kb_id:
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docs = db.query(KnowledgeDocument).filter(
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KnowledgeDocument.knowledge_base_id == kb_id,
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KnowledgeDocument.status == "completed"
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).all()
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synced_count = 0
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for doc in docs:
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content = doc.content or ""
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if not content:
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continue
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doc_resp = httpx.post(
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f"{api_url.rstrip('/')}/api/v1/datasets/{ext_kb_id}/documents",
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headers=headers,
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json={"name": doc.filename, "content": content[:50000]},
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timeout=30,
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)
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if doc_resp.status_code in [200, 201]:
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synced_count += 1
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sync_result["synced"] = True
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sync_result["detail"] = f"已推送 {synced_count}/{len(docs)} 文档到 RAGFlow"
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sync_result["items_synced"] = synced_count
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elif req.sync_direction == "pull":
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# 从外部服务拉取知识库数据
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sync_result["detail"] = f"从 {service_type} 拉取数据（暂未实现完整拉取逻辑）"
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@@ -1288,6 +1132,106 @@ async def sync_knowledge_base(
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return {"code": 0, "data": sync_result}
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# ============================================================
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# 平台内容批量入库到 Dify（AI聊天产出 + 用户上传文档）
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# ============================================================
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@router.post("/external/dify/ingest", summary="将平台内容批量入库到 Dify 知识库")
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async def ingest_platform_to_dify(
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admin_user=Depends(get_current_admin),
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db: Session = Depends(get_db),
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):
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"""把平台中「可量化的内容」推送到 Dify 知识库（qihuai）:
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- AI 聊天的助手回答（文本、非工具/非媒体、长度 > 120）
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- 用户上传的文档资产（assets, category='file' 且含文本内容）
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返回各来源的入库数量。
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"""
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from ...dify_kb import dify_ingest_text, resolve_dataset_id
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# 读取已启用的 Dify 服务
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try:
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row = db.execute(
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text("SELECT api_url, api_key, config FROM external_kb_services "
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"WHERE service_type='dify' AND is_enabled=1 LIMIT 1")
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).fetchone()
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except Exception:
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row = None
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if not row:
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raise HTTPException(status_code=400, detail="未配置已启用的 Dify 知识库服务，请先添加 Dify 服务并测试连接")
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api_url, api_key, config_raw = row
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dataset_name = None
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if config_raw:
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try:
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cfg = config_raw if isinstance(config_raw, dict) else json.loads(config_raw)
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dataset_name = cfg.get("dataset_name")
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except Exception:
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pass
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ds_id = resolve_dataset_id(api_url, api_key, dataset_name)
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if not ds_id:
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raise HTTPException(status_code=400, detail="无法解析 Dify 知识库 ID（请检查 API Key 与 dataset_name）")
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stats = {"chat_answers": 0, "user_docs": 0, "skipped": 0, "failed": 0}
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# 1. AI 聊天助手回答
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try:
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from ...models import Message
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answers = db.query(Message).filter(
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Message.role == "assistant"
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).order_by(Message.created_at.asc()).limit(2000).all()
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for m in answers:
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content = getattr(m, "content", None) or ""
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content = content.strip()
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# 跳过媒体/工具类消息与过短内容
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if len(content) < 120:
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stats["skipped"] += 1
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continue
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if content.startswith("[") and ("MEDIA" in content or "IMAGE" in content or "VIDEO" in content):
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stats["skipped"] += 1
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continue
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conv_id = getattr(m, "conversation_id", None) or "unknown"
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if dify_ingest_text(api_url, api_key, content, name=f"chat_{conv_id}_{m.id}", dataset_name=dataset_name):
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stats["chat_answers"] += 1
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else:
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stats["failed"] += 1
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except Exception as e:
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logger.error(f"ingest chat answers failed: {e}")
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# 2. 用户上传的文档资产
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try:
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from ...models import Asset
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assets = db.query(Asset).filter(
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getattr(Asset, "category", "file") == "file"
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).order_by(getattr(Asset, "created_at", text("id")).asc()).limit(2000).all()
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for a in assets:
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content = getattr(a, "content", None) or ""
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content = content.strip()
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if len(content) < 50:
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stats["skipped"] += 1
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continue
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fname = getattr(a, "filename", None) or getattr(a, "name", None) or "asset"
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if dify_ingest_text(api_url, api_key, content[:50000], name=f"asset_{a.id}_{fname}", dataset_name=dataset_name):
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stats["user_docs"] += 1
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else:
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stats["failed"] += 1
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except Exception as e:
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logger.error(f"ingest assets failed: {e}")
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LogService.log_audit(
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admin_id=admin_user.id,
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admin_name=admin_user.username,
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action="ingest_platform_to_dify",
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target_type="external_kb_service",
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target_id=ds_id,
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detail=f"平台内容入库 Dify: 聊天回答 {stats['chat_answers']} 条, 用户文档 {stats['user_docs']} 条, 跳过 {stats['skipped']}, 失败 {stats['failed']}",
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)
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return {
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"code": 0,
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"message": f"入库完成：聊天回答 {stats['chat_answers']} 条，用户文档 {stats['user_docs']} 条",
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"data": stats,
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}
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# ============================================================
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# 批量操作
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# ============================================================
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+116
-64
@@ -490,84 +490,122 @@ def _retrieve_kb_context(db: Session, kb_ids: list, query: str, max_chars: int =
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2. 构建文档列表(content+title+metadata)
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3. BM25检索最相关的段落
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4. 拼接为上下文注入system prompt
|
||||
同时叠加 Dify 外部知识库(RAG)检索结果。
|
||||
"""
|
||||
if not kb_ids:
|
||||
return ""
|
||||
local_context = ""
|
||||
|
||||
kbs = db.query(KnowledgeBase).filter(KnowledgeBase.id.in_(kb_ids)).all()
|
||||
if not kbs:
|
||||
return ""
|
||||
if kb_ids:
|
||||
kbs = db.query(KnowledgeBase).filter(KnowledgeBase.id.in_(kb_ids)).all()
|
||||
if kbs:
|
||||
# 收集所有文档段落
|
||||
# Stage C.1: 优先使用 KnowledgeDocumentChunk 表的预分段数据
|
||||
all_segments = []
|
||||
for kb in kbs:
|
||||
# 先查chunk表
|
||||
chunks = db.query(KnowledgeDocumentChunk).filter(
|
||||
KnowledgeDocumentChunk.knowledge_base_id == kb.id
|
||||
).order_by(KnowledgeDocumentChunk.chunk_index).limit(30).all()
|
||||
|
||||
# 收集所有文档段落
|
||||
# Stage C.1: 优先使用 KnowledgeDocumentChunk 表的预分段数据
|
||||
all_segments = []
|
||||
for kb in kbs:
|
||||
# 先查chunk表
|
||||
chunks = db.query(KnowledgeDocumentChunk).filter(
|
||||
KnowledgeDocumentChunk.knowledge_base_id == kb.id
|
||||
).order_by(KnowledgeDocumentChunk.chunk_index).limit(30).all()
|
||||
|
||||
if chunks:
|
||||
# 使用预分段数据
|
||||
for chunk in chunks:
|
||||
if chunk.content and len(chunk.content.strip()) >= 20:
|
||||
all_segments.append({
|
||||
"content": "[{}/{}/段{}]: {}".format(
|
||||
kb.name, chunk.document_id[:8], chunk.chunk_index, chunk.content[:500]
|
||||
),
|
||||
"raw_content": chunk.content,
|
||||
"kb_name": kb.name,
|
||||
})
|
||||
else:
|
||||
# 退化: 使用原始文档分段
|
||||
docs = db.query(KnowledgeDocument).filter(
|
||||
KnowledgeDocument.knowledge_base_id == kb.id,
|
||||
KnowledgeDocument.status == "completed"
|
||||
).order_by(KnowledgeDocument.updated_at.desc()).limit(20).all()
|
||||
|
||||
for doc in docs:
|
||||
content = doc.content or ""
|
||||
if not content:
|
||||
continue
|
||||
|
||||
# 如果文档较长, 分段处理 (每段~800字)
|
||||
if len(content) > 800:
|
||||
paragraphs = re.split(r'\n\n+', content)
|
||||
for pi, para in enumerate(paragraphs):
|
||||
if len(para.strip()) >= 20:
|
||||
if chunks:
|
||||
# 使用预分段数据
|
||||
for chunk in chunks:
|
||||
if chunk.content and len(chunk.content.strip()) >= 20:
|
||||
all_segments.append({
|
||||
"content": "[{}/{}/段{}]: {}".format(
|
||||
kb.name, doc.name, pi+1, para.strip()[:500]
|
||||
kb.name, chunk.document_id[:8], chunk.chunk_index, chunk.content[:500]
|
||||
),
|
||||
"raw_content": para.strip(),
|
||||
"raw_content": chunk.content,
|
||||
"kb_name": kb.name,
|
||||
})
|
||||
else:
|
||||
# 退化: 使用原始文档分段
|
||||
docs = db.query(KnowledgeDocument).filter(
|
||||
KnowledgeDocument.knowledge_base_id == kb.id,
|
||||
KnowledgeDocument.status == "completed"
|
||||
).order_by(KnowledgeDocument.updated_at.desc()).limit(20).all()
|
||||
|
||||
for doc in docs:
|
||||
content = doc.content or ""
|
||||
if not content:
|
||||
continue
|
||||
|
||||
# 如果文档较长, 分段处理 (每段~800字)
|
||||
if len(content) > 800:
|
||||
paragraphs = re.split(r'\n\n+', content)
|
||||
for pi, para in enumerate(paragraphs):
|
||||
if len(para.strip()) >= 20:
|
||||
all_segments.append({
|
||||
"content": "[{}/{}/段{}]: {}".format(
|
||||
kb.name, doc.name, pi+1, para.strip()[:500]
|
||||
),
|
||||
"raw_content": para.strip(),
|
||||
"kb_name": kb.name,
|
||||
"doc_name": doc.name,
|
||||
})
|
||||
else:
|
||||
all_segments.append({
|
||||
"content": "[{}/{}]: {}".format(kb.name, doc.name, content[:500]),
|
||||
"raw_content": content,
|
||||
"kb_name": kb.name,
|
||||
"doc_name": doc.name,
|
||||
})
|
||||
else:
|
||||
all_segments.append({
|
||||
"content": "[{}/{}]: {}".format(kb.name, doc.name, content[:500]),
|
||||
"raw_content": content,
|
||||
"kb_name": kb.name,
|
||||
"doc_name": doc.name,
|
||||
})
|
||||
|
||||
if not all_segments:
|
||||
# 退化: 使用原始朴素拼接
|
||||
kb_names = ", ".join([k.name for k in kbs])
|
||||
return f"参考知识库: {kb_names}"
|
||||
if not all_segments:
|
||||
# 退化: 使用原始朴素拼接
|
||||
kb_names = ", ".join([k.name for k in kbs])
|
||||
local_context = f"参考知识库: {kb_names}"
|
||||
else:
|
||||
# BM25检索最相关的段落
|
||||
top_segments = _bm25_search(query, all_segments, top_k=8)
|
||||
context_parts = [seg["content"] for seg in top_segments]
|
||||
local_context = "\n".join(context_parts)
|
||||
if len(local_context) > max_chars:
|
||||
local_context = local_context[:max_chars] + "...(内容截断)"
|
||||
|
||||
# BM25检索最相关的段落
|
||||
top_segments = _bm25_search(query, all_segments, top_k=8)
|
||||
# 叠加 Dify 外部知识库(RAG)检索
|
||||
dify_context = ""
|
||||
try:
|
||||
from app.dify_kb import get_dify_kb_context
|
||||
dify_context = get_dify_kb_context(db, query, top_k=5)
|
||||
except Exception as e:
|
||||
logger.warning(f"Dify KB context retrieval failed: {e}")
|
||||
|
||||
# 拼接上下文
|
||||
context_parts = [seg["content"] for seg in top_segments]
|
||||
context = "\n".join(context_parts)
|
||||
parts = []
|
||||
if local_context:
|
||||
parts.append(local_context)
|
||||
if dify_context:
|
||||
parts.append("【Dify 知识库检索结果】\n" + dify_context)
|
||||
return "\n\n".join(parts)
|
||||
|
||||
# 限制总长度
|
||||
if len(context) > max_chars:
|
||||
context = context[:max_chars] + "...(内容截断)"
|
||||
|
||||
return context
|
||||
async def _ingest_answer_to_dify(conv_id: str, msg_id: str, content: str):
|
||||
"""后台任务：将助手回答入库到 Dify 知识库（量化后可供 RAG 检索复用）。"""
|
||||
try:
|
||||
from app.dify_kb import aingest_text
|
||||
from app.database import SessionLocal
|
||||
from sqlalchemy import text as _t
|
||||
db = SessionLocal()
|
||||
try:
|
||||
row = db.execute(_t(
|
||||
"SELECT api_url, api_key, config FROM external_kb_services "
|
||||
"WHERE service_type='dify' AND is_enabled=1 LIMIT 1"
|
||||
)).fetchone()
|
||||
finally:
|
||||
db.close()
|
||||
if not row:
|
||||
return
|
||||
api_url, api_key, config_raw = row
|
||||
dataset_name = None
|
||||
if config_raw:
|
||||
try:
|
||||
import json
|
||||
cfg = config_raw if isinstance(config_raw, dict) else json.loads(config_raw)
|
||||
dataset_name = cfg.get("dataset_name")
|
||||
except Exception:
|
||||
pass
|
||||
await aingest_text(api_url, api_key, content, name=f"chat_{conv_id}_{msg_id}", dataset_name=dataset_name)
|
||||
except Exception as e:
|
||||
logger.warning(f"后台入库 Dify 失败: {e}")
|
||||
|
||||
|
||||
# ============================================================
|
||||
@@ -1203,6 +1241,20 @@ async def send_message(
|
||||
)
|
||||
db.add(ai_msg)
|
||||
|
||||
# 后台异步：将实质性文本回答入库到 Dify 知识库（量化后可供 RAG 复用）
|
||||
_ingest_text = (ai_content or "").strip()
|
||||
if len(_ingest_text) > 120 and not (
|
||||
_ingest_text.startswith("[MEDIA")
|
||||
or _ingest_text.startswith("data:")
|
||||
or _ingest_text.startswith("http")
|
||||
or "<img" in _ingest_text
|
||||
or "![]( " in _ingest_text
|
||||
):
|
||||
try:
|
||||
asyncio.create_task(_ingest_answer_to_dify(conv_id, ai_msg.id, _ingest_text))
|
||||
except Exception:
|
||||
pass
|
||||
|
||||
# 更新used_skills
|
||||
used_skills = []
|
||||
if skills:
|
||||
|
||||
@@ -0,0 +1,248 @@
|
||||
"""
|
||||
Dify 知识库客户端
|
||||
================
|
||||
封装与 Dify 知识库(v1 API)的交互：连通性测试、检索(RAG)、文本入库。
|
||||
设计用于替换原 FastGPT 外部知识库对接。
|
||||
|
||||
已验证的接入点（2026-07-14 实测）:
|
||||
- Base: https://dify.qihuyun.com/v1
|
||||
（直接 HTTP 访问会被 nginx 301 跳转到 HTTPS，必须 HTTPS 直连，
|
||||
且内网自签证书需 verify=False）
|
||||
- Auth: Bearer {api_key}
|
||||
- GET /v1/datasets 列出知识库(可据此解析 dataset id)
|
||||
- POST /v1/datasets/{id}/document/create-by-text 文本入库
|
||||
- POST /v1/datasets/{id}/document/create-by-file 文件入库(multipart)
|
||||
- POST /v1/datasets/{id}/retrieve 检索(RAG)
|
||||
retrieve 必需完整的 retrieval_model 字典，否则返回 400。
|
||||
"""
|
||||
import logging
|
||||
import threading
|
||||
import time
|
||||
import httpx
|
||||
|
||||
logger = logging.getLogger("aiflow.dify_kb")
|
||||
|
||||
# dataset id 缓存: key -> (dataset_id, ts)
|
||||
_dataset_cache: dict = {}
|
||||
_dataset_cache_lock = threading.Lock()
|
||||
_DATASET_CACHE_TTL = 3600
|
||||
|
||||
|
||||
def _normalize_base(api_url: str) -> str:
|
||||
"""统一 base url -> https 形式，且不带 /v1 后缀。"""
|
||||
u = (api_url or "").strip()
|
||||
if not u:
|
||||
u = "https://dify.qihuyun.com/v1"
|
||||
u = u.rstrip("/")
|
||||
if u.endswith("/v1"):
|
||||
u = u[: -len("/v1")]
|
||||
if u.startswith("http://"):
|
||||
u = "https://" + u[len("http://"):]
|
||||
return u
|
||||
|
||||
|
||||
def dify_test(api_url: str, api_key: str) -> dict:
|
||||
"""验证 Dify 知识库「可通讯 + 可用」。
|
||||
|
||||
返回:
|
||||
{
|
||||
"connected": bool,
|
||||
"usable": bool,
|
||||
"status": "connected" | "auth_error" | "timeout" | "error",
|
||||
"detail": str,
|
||||
"datasets": [{"id":..., "name":...}, ...]
|
||||
}
|
||||
"""
|
||||
base = _normalize_base(api_url)
|
||||
headers = {"Authorization": f"Bearer {api_key}"}
|
||||
try:
|
||||
with httpx.Client(base_url=base, timeout=15, verify=False) as client:
|
||||
r = client.get("/v1/datasets", headers=headers)
|
||||
if r.status_code == 200:
|
||||
data = r.json()
|
||||
datasets = data.get("data", []) or []
|
||||
names = [d.get("name") for d in datasets]
|
||||
# 能列出知识库即代表鉴权通过、可读取 —— 视为可用。
|
||||
return {
|
||||
"connected": True,
|
||||
"usable": True,
|
||||
"status": "connected",
|
||||
"detail": f"Dify 连接成功，发现 {len(datasets)} 个知识库: {', '.join([n for n in names if n]) or '无'}",
|
||||
"datasets": [{"id": d.get("id"), "name": d.get("name")} for d in datasets],
|
||||
}
|
||||
elif r.status_code == 401:
|
||||
return {
|
||||
"connected": True,
|
||||
"usable": False,
|
||||
"status": "auth_error",
|
||||
"detail": "已连通 Dify，但 API Key 鉴权失败(401)，请检查 API Key。",
|
||||
"datasets": [],
|
||||
}
|
||||
else:
|
||||
return {
|
||||
"connected": False,
|
||||
"usable": False,
|
||||
"status": "error",
|
||||
"detail": f"Dify 返回状态码 {r.status_code}",
|
||||
"datasets": [],
|
||||
}
|
||||
except httpx.TimeoutException:
|
||||
return {"connected": False, "usable": False, "status": "timeout", "detail": "连接超时", "datasets": []}
|
||||
except Exception as e:
|
||||
return {"connected": False, "usable": False, "status": "error", "detail": str(e), "datasets": []}
|
||||
|
||||
|
||||
def resolve_dataset_id(api_url: str, api_key: str, dataset_name: str = None) -> str:
|
||||
"""解析 dataset id（优先按名称匹配，否则取列表第一个），带缓存。"""
|
||||
base = _normalize_base(api_url)
|
||||
headers = {"Authorization": f"Bearer {api_key}"}
|
||||
cache_key = f"{base}|{api_key}|{dataset_name}"
|
||||
now = time.time()
|
||||
with _dataset_cache_lock:
|
||||
c = _dataset_cache.get(cache_key)
|
||||
if c and now - c[1] < _DATASET_CACHE_TTL:
|
||||
return c[0]
|
||||
ds_id = None
|
||||
try:
|
||||
with httpx.Client(base_url=base, timeout=15, verify=False) as client:
|
||||
r = client.get("/v1/datasets", headers=headers)
|
||||
if r.status_code == 200:
|
||||
datasets = r.json().get("data", []) or []
|
||||
if dataset_name:
|
||||
match = [d for d in datasets if d.get("name") == dataset_name]
|
||||
if match:
|
||||
ds_id = match[0].get("id")
|
||||
if not ds_id and datasets:
|
||||
ds_id = datasets[0].get("id")
|
||||
except Exception as e:
|
||||
logger.error(f"resolve_dataset_id failed: {e}")
|
||||
if ds_id:
|
||||
with _dataset_cache_lock:
|
||||
_dataset_cache[cache_key] = (ds_id, now)
|
||||
return ds_id
|
||||
|
||||
|
||||
def dify_retrieve(api_url, api_key, query, top_k=5, dataset_name=None) -> list:
|
||||
"""检索，返回 [{content, score, source, metadata}]。"""
|
||||
ds_id = resolve_dataset_id(api_url, api_key, dataset_name)
|
||||
if not ds_id:
|
||||
return []
|
||||
base = _normalize_base(api_url)
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
payload = {
|
||||
"query": query,
|
||||
"retrieval_model": {
|
||||
"search_method": "semantic_search",
|
||||
"reranking_enable": False,
|
||||
"score_threshold_enabled": False,
|
||||
"top_k": top_k,
|
||||
},
|
||||
}
|
||||
try:
|
||||
with httpx.Client(base_url=base, timeout=30, verify=False) as client:
|
||||
r = client.post(f"/v1/datasets/{ds_id}/retrieve", headers=headers, json=payload)
|
||||
if r.status_code == 200:
|
||||
data = r.json()
|
||||
out = []
|
||||
for item in data.get("records", []):
|
||||
seg = item.get("segment", {})
|
||||
out.append({
|
||||
"content": seg.get("content", ""),
|
||||
"score": float(item.get("score", 0.5)),
|
||||
"source": f"Dify: {seg.get('document_id', '')}",
|
||||
"metadata": item,
|
||||
})
|
||||
return out
|
||||
else:
|
||||
logger.warning(f"Dify retrieve returned {r.status_code}: {r.text[:200]}")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"Dify retrieve error: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def dify_ingest_text(api_url, api_key, text, name=None, dataset_name=None) -> bool:
|
||||
"""文本入库（create-by-text）。返回是否成功。"""
|
||||
if not text or not text.strip():
|
||||
return False
|
||||
ds_id = resolve_dataset_id(api_url, api_key, dataset_name)
|
||||
if not ds_id:
|
||||
return False
|
||||
base = _normalize_base(api_url)
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
payload = {
|
||||
"name": name or f"doc-{int(time.time())}",
|
||||
"text": text,
|
||||
"indexing_technique": "high_quality",
|
||||
}
|
||||
try:
|
||||
with httpx.Client(base_url=base, timeout=60, verify=False) as client:
|
||||
r = client.post(f"/v1/datasets/{ds_id}/document/create-by-text", headers=headers, json=payload)
|
||||
if r.status_code in (200, 201):
|
||||
return True
|
||||
else:
|
||||
logger.warning(f"Dify ingest text returned {r.status_code}: {r.text[:200]}")
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.error(f"Dify ingest text error: {e}")
|
||||
return False
|
||||
|
||||
|
||||
async def aingest_text(api_url, api_key, text, name=None, dataset_name=None) -> bool:
|
||||
"""异步文本入库（供 chat 实时落库使用）。"""
|
||||
if not text or not text.strip():
|
||||
return False
|
||||
ds_id = resolve_dataset_id(api_url, api_key, dataset_name)
|
||||
if not ds_id:
|
||||
return False
|
||||
base = _normalize_base(api_url)
|
||||
headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
|
||||
payload = {
|
||||
"name": name or f"doc-{int(time.time())}",
|
||||
"text": text,
|
||||
"indexing_technique": "high_quality",
|
||||
}
|
||||
try:
|
||||
async with httpx.AsyncClient(base_url=base, timeout=60, verify=False) as client:
|
||||
r = await client.post(f"/v1/datasets/{ds_id}/document/create-by-text", headers=headers, json=payload)
|
||||
if r.status_code in (200, 201):
|
||||
return True
|
||||
else:
|
||||
logger.warning(f"Dify aingest returned {r.status_code}: {r.text[:200]}")
|
||||
return False
|
||||
except Exception as e:
|
||||
logger.error(f"Dify aingest error: {e}")
|
||||
return False
|
||||
|
||||
|
||||
def get_dify_kb_context(db, query: str, top_k: int = 5) -> str:
|
||||
"""读取已启用的 Dify 外部服务，检索并返回拼接后的上下文文本（供 chat 注入）。"""
|
||||
from sqlalchemy import text as _text
|
||||
try:
|
||||
result = db.execute(_text(
|
||||
"SELECT api_url, api_key, config FROM external_kb_services "
|
||||
"WHERE service_type='dify' AND is_enabled=1 AND status='connected' LIMIT 1"
|
||||
))
|
||||
row = result.fetchone()
|
||||
if not row:
|
||||
return ""
|
||||
api_url, api_key, config_raw = row
|
||||
dataset_name = None
|
||||
if config_raw:
|
||||
try:
|
||||
cfg = config_raw if isinstance(config_raw, dict) else __import__("json").loads(config_raw)
|
||||
dataset_name = cfg.get("dataset_name")
|
||||
except Exception:
|
||||
pass
|
||||
hits = dify_retrieve(api_url, api_key, query, top_k=top_k, dataset_name=dataset_name)
|
||||
if not hits:
|
||||
return ""
|
||||
parts = []
|
||||
for i, h in enumerate(hits, 1):
|
||||
content = (h.get("content") or "").strip()
|
||||
if content:
|
||||
parts.append(f"[{i}] {content}")
|
||||
return "\n\n".join(parts)
|
||||
except Exception as e:
|
||||
logger.error(f"get_dify_kb_context error: {e}")
|
||||
return ""
|
||||
+25
-117
@@ -1,7 +1,7 @@
|
||||
"""
|
||||
知识库路由层
|
||||
============
|
||||
将检索请求智能路由到本地BM25、向量检索、或外部KB服务(FastGPT/Dify/RAGFlow)。
|
||||
将检索请求智能路由到本地BM25、向量检索、或外部KB服务(Dify)。
|
||||
根据知识库配置自动选择最优检索策略。
|
||||
|
||||
使用方式:
|
||||
@@ -9,17 +9,14 @@
|
||||
results = await kb_router_search(query, kb_id, top_k=5)
|
||||
"""
|
||||
|
||||
import os
|
||||
import json
|
||||
import logging
|
||||
import httpx
|
||||
from typing import List, Dict, Optional
|
||||
|
||||
logger = logging.getLogger("aiflow.kb_router")
|
||||
|
||||
|
||||
async def _get_external_services(db_session, enabled_only: bool = True) -> List[Dict]:
|
||||
"""获取已配置的外部KB服务"""
|
||||
"""获取已配置的外部KB服务（仅 dify）"""
|
||||
from sqlalchemy import text
|
||||
try:
|
||||
query = "SELECT * FROM external_kb_services"
|
||||
@@ -34,123 +31,34 @@ async def _get_external_services(db_session, enabled_only: bool = True) -> List[
|
||||
return []
|
||||
|
||||
|
||||
async def _search_fastgpt(api_url: str, api_key: str, query: str, top_k: int = 5) -> List[Dict]:
|
||||
"""搜索 FastGPT 知识库"""
|
||||
async def _search_dify(service: Dict, query: str, top_k: int = 5) -> List[Dict]:
|
||||
"""检索 Dify 知识库（走 app.dify_kb 客户端）"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30) as client:
|
||||
resp = await client.post(
|
||||
f"{api_url.rstrip('/')}/api/v1/dataset/search",
|
||||
headers={
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={
|
||||
"query": query,
|
||||
"similarity": 0.5,
|
||||
"limit": top_k,
|
||||
},
|
||||
)
|
||||
if resp.status_code == 200:
|
||||
data = resp.json()
|
||||
results = []
|
||||
for item in data.get("data", {}).get("list", []):
|
||||
results.append({
|
||||
"content": item.get("q", item.get("content", "")),
|
||||
"score": float(item.get("score", 0.5)),
|
||||
"source": f"FastGPT: {item.get('collectionId', '')}",
|
||||
"metadata": item,
|
||||
})
|
||||
return results
|
||||
else:
|
||||
logger.warning(f"FastGPT search returned {resp.status_code}")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"FastGPT search error: {e}")
|
||||
return []
|
||||
|
||||
|
||||
async def _search_dify(api_url: str, api_key: str, query: str, top_k: int = 5) -> List[Dict]:
|
||||
"""搜索 Dify 知识库"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30) as client:
|
||||
resp = await client.post(
|
||||
f"{api_url.rstrip('/')}/v1/datasets/retrieval",
|
||||
headers={
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={
|
||||
"query": query,
|
||||
"retrieval_mode": "semantic",
|
||||
"top_k": top_k,
|
||||
},
|
||||
)
|
||||
if resp.status_code == 200:
|
||||
data = resp.json()
|
||||
results = []
|
||||
for item in data.get("records", []):
|
||||
segment = item.get("segment", {})
|
||||
results.append({
|
||||
"content": segment.get("content", ""),
|
||||
"score": float(item.get("score", 0.5)),
|
||||
"source": f"Dify: {segment.get('id', '')}",
|
||||
"metadata": item,
|
||||
})
|
||||
return results
|
||||
else:
|
||||
logger.warning(f"Dify search returned {resp.status_code}")
|
||||
return []
|
||||
from app.dify_kb import dify_retrieve
|
||||
api_url = service.get("api_url", "")
|
||||
api_key = service.get("api_key", "")
|
||||
dataset_name = None
|
||||
cfg = service.get("config")
|
||||
if isinstance(cfg, str):
|
||||
try:
|
||||
import json
|
||||
cfg = json.loads(cfg)
|
||||
except Exception:
|
||||
cfg = None
|
||||
if isinstance(cfg, dict):
|
||||
dataset_name = cfg.get("dataset_name")
|
||||
hits = dify_retrieve(api_url, api_key, query, top_k=top_k, dataset_name=dataset_name)
|
||||
return hits
|
||||
except Exception as e:
|
||||
logger.error(f"Dify search error: {e}")
|
||||
return []
|
||||
|
||||
|
||||
async def _search_ragflow(api_url: str, api_key: str, query: str, top_k: int = 5) -> List[Dict]:
|
||||
"""搜索 RAGFlow 知识库"""
|
||||
try:
|
||||
async with httpx.AsyncClient(timeout=30) as client:
|
||||
resp = await client.post(
|
||||
f"{api_url.rstrip('/')}/api/v1/retrieval",
|
||||
headers={
|
||||
"Authorization": f"Bearer {api_key}",
|
||||
"Content-Type": "application/json",
|
||||
},
|
||||
json={
|
||||
"question": query,
|
||||
"top_k": top_k,
|
||||
},
|
||||
)
|
||||
if resp.status_code == 200:
|
||||
data = resp.json()
|
||||
results = []
|
||||
for item in data.get("data", {}).get("chunks", []):
|
||||
results.append({
|
||||
"content": item.get("content", ""),
|
||||
"score": float(item.get("similarity", 0.5)),
|
||||
"source": f"RAGFlow: {item.get('document_id', '')}",
|
||||
"metadata": item,
|
||||
})
|
||||
return results
|
||||
else:
|
||||
logger.warning(f"RAGFlow search returned {resp.status_code}")
|
||||
return []
|
||||
except Exception as e:
|
||||
logger.error(f"RAGFlow search error: {e}")
|
||||
return []
|
||||
|
||||
|
||||
async def _search_external(service: Dict, query: str, top_k: int = 5) -> List[Dict]:
|
||||
"""路由到对应的外部KB服务"""
|
||||
"""路由到对应的外部KB服务（当前仅支持 dify）"""
|
||||
service_type = service.get("service_type", "")
|
||||
api_url = service.get("api_url", "")
|
||||
api_key = service.get("api_key", "")
|
||||
|
||||
if service_type == "fastgpt":
|
||||
return await _search_fastgpt(api_url, api_key, query, top_k)
|
||||
elif service_type == "dify":
|
||||
return await _search_dify(api_url, api_key, query, top_k)
|
||||
elif service_type == "ragflow":
|
||||
return await _search_ragflow(api_url, api_key, query, top_k)
|
||||
if service_type == "dify":
|
||||
return await _search_dify(service, query, top_k)
|
||||
else:
|
||||
logger.warning(f"Unknown external KB type: {service_type}")
|
||||
return []
|
||||
@@ -171,7 +79,7 @@ async def kb_router_search(
|
||||
检索策略:
|
||||
1. 本地 BM25 检索 (MySQL, 已有)
|
||||
2. 向量语义检索 (PostgreSQL+pgvector, 新增)
|
||||
3. 外部KB检索 (FastGPT/Dify/RAGFlow, 按配置)
|
||||
3. 外部KB检索 (Dify, 按配置)
|
||||
|
||||
融合方式: Reciprocal Rank Fusion (RRF)
|
||||
|
||||
@@ -218,7 +126,7 @@ async def kb_router_search(
|
||||
except Exception as e:
|
||||
logger.warning(f"Vector search failed: {e}")
|
||||
|
||||
# 3. 外部KB检索
|
||||
# 3. 外部KB检索 (Dify)
|
||||
if use_external and db_session:
|
||||
services = await _get_external_services(db_session)
|
||||
for service in services:
|
||||
@@ -228,7 +136,7 @@ async def kb_router_search(
|
||||
all_results.append({
|
||||
"content": item.get("content", ""),
|
||||
"score": item.get("score", 0.5),
|
||||
"source": item.get("source", f"external_{service['service_type']}"),
|
||||
"source": item.get("source", "external_dify"),
|
||||
"rank": rank + 1,
|
||||
"metadata": item.get("metadata", {}),
|
||||
})
|
||||
|
||||
@@ -66,7 +66,10 @@ const Knowledge: React.FC = () => {
|
||||
// External Services
|
||||
const [extServices, setExtServices] = useState<ExternalService[]>([]);
|
||||
const [showExtModal, setShowExtModal] = useState(false);
|
||||
const [extForm, setExtForm] = useState({ name: '', service_type: 'fastgpt', api_url: '', api_key: '' });
|
||||
const [extForm, setExtForm] = useState({ name: 'Dify 知识库', service_type: 'dify', api_url: 'https://dify.qihuyun.com/v1', api_key: '', dataset_name: 'qihuai' });
|
||||
const [extMsg, setExtMsg] = useState('');
|
||||
const [ingesting, setIngesting] = useState(false);
|
||||
const [ingestResult, setIngestResult] = useState<any>(null);
|
||||
|
||||
// KB Modal
|
||||
const [showModal, setShowModal] = useState(false);
|
||||
@@ -210,7 +213,19 @@ const Knowledge: React.FC = () => {
|
||||
};
|
||||
|
||||
const handleCreateExtService = async () => {
|
||||
try { const res = await adminAPI.createAdminKnowledgeExtService(extForm); if (res.code === 0) { setShowExtModal(false); setExtForm({ name: '', service_type: 'fastgpt', api_url: '', api_key: '' }); fetchExtServices(); } } catch (e) {}
|
||||
try {
|
||||
const payload = {
|
||||
...extForm,
|
||||
config: { dataset_name: extForm.dataset_name || 'qihuai' },
|
||||
};
|
||||
const res = await adminAPI.createAdminKnowledgeExtService(payload);
|
||||
if (res.code === 0) {
|
||||
setShowExtModal(false);
|
||||
setExtForm({ name: 'Dify 知识库', service_type: 'dify', api_url: 'https://dify.qihuyun.com/v1', api_key: '', dataset_name: 'qihuai' });
|
||||
setExtMsg('');
|
||||
fetchExtServices();
|
||||
}
|
||||
} catch (e: any) { setExtMsg(`添加失败: ${e?.message || '未知错误'}`); }
|
||||
};
|
||||
|
||||
const handleDeleteExtService = async (id: string) => {
|
||||
@@ -219,7 +234,38 @@ const Knowledge: React.FC = () => {
|
||||
};
|
||||
|
||||
const handleCheckExtStatus = async (id: string) => {
|
||||
try { const res = await adminAPI.checkAdminKnowledgeExtStatus(id); if (res.code === 0) { alert(`连接状态: ${res.data.connected ? '已连接' : '未连接'}\n详情: ${res.data.detail || '无'}`); fetchExtServices(); } } catch (e) {}
|
||||
try {
|
||||
const res = await adminAPI.checkAdminKnowledgeExtStatus(id);
|
||||
if (res.code === 0) {
|
||||
const d = res.data || {};
|
||||
const statusText = d.connected ? (d.usable ? '可通讯且可用 ✅' : '可通讯但不可用 ⚠️') : '不可通讯 ❌';
|
||||
alert(`连接状态: ${statusText}\n\n详情: ${d.detail || '无'}`);
|
||||
fetchExtServices();
|
||||
}
|
||||
} catch (e) {}
|
||||
};
|
||||
|
||||
const handleIngestPlatform = async () => {
|
||||
if (!confirm('将把平台「AI聊天回答 + 用户上传文档」中有价值的内容批量入库到 Dify 知识库（量化后可被 AI 聊天检索复用）。确认执行？')) return;
|
||||
setIngesting(true); setIngestResult(null);
|
||||
try {
|
||||
const token = localStorage.getItem('aiflow_token');
|
||||
const res = await fetch(`/app/api/admin/knowledge/external/dify/ingest`, {
|
||||
method: 'POST',
|
||||
headers: { 'Authorization': `Bearer ${token}` },
|
||||
});
|
||||
const data = await res.json();
|
||||
if (data.code === 0) {
|
||||
setIngestResult(data.data);
|
||||
setExtMsg(`✅ ${data.message}`);
|
||||
} else {
|
||||
setExtMsg(`❌ ${data.message || '入库失败'}`);
|
||||
}
|
||||
} catch (e: any) {
|
||||
setExtMsg(`❌ 入库失败: ${e?.message || '网络错误'}`);
|
||||
} finally {
|
||||
setIngesting(false);
|
||||
}
|
||||
};
|
||||
|
||||
const statusBadge = (status: string) => {
|
||||
@@ -487,7 +533,7 @@ const Knowledge: React.FC = () => {
|
||||
{extServices.map(svc => (
|
||||
<tr key={svc.id}>
|
||||
<td>{svc.name}</td>
|
||||
<td><span style={{ fontSize: 12, padding: '2px 6px', background: '#F3F4FF', color: '#8B5CF6', borderRadius: 3 }}>{svc.service_type === 'fastgpt' ? 'FastGPT' : svc.service_type === 'dify' ? 'Dify' : 'RAGFlow'}</span></td>
|
||||
<td><span style={{ fontSize: 12, padding: '2px 6px', background: '#F3F4FF', color: '#8B5CF6', borderRadius: 3 }}>Dify</span></td>
|
||||
<td style={{ fontSize: 11, maxWidth: 200, overflow: 'hidden', textOverflow: 'ellipsis' }}>{svc.api_url}</td>
|
||||
<td>{statusBadge(svc.status)}</td>
|
||||
<td>
|
||||
@@ -501,17 +547,36 @@ const Knowledge: React.FC = () => {
|
||||
</div>
|
||||
)}
|
||||
<div style={{ borderTop: '1px solid #E5E7EB', paddingTop: 16 }}>
|
||||
<h4 style={{ margin: '0 0 12px 0', fontSize: 14 }}>添加新服务</h4>
|
||||
<h4 style={{ margin: '0 0 12px 0', fontSize: 14 }}>配置 Dify 知识库</h4>
|
||||
<div style={{ display: 'grid', gridTemplateColumns: '1fr 1fr', gap: 12 }}>
|
||||
<div><label style={labelStyle}>名称</label><input value={extForm.name} onChange={e => setExtForm({ ...extForm, name: e.target.value })} placeholder="如: 生产环境 FastGPT" style={fullInput} /></div>
|
||||
<div><label style={labelStyle}>类型</label><select value={extForm.service_type} onChange={e => setExtForm({ ...extForm, service_type: e.target.value })} style={fullInput}><option value="fastgpt">FastGPT</option><option value="dify">Dify</option><option value="ragflow">RAGFlow</option></select></div>
|
||||
<div><label style={labelStyle}>名称</label><input value={extForm.name} onChange={e => setExtForm({ ...extForm, name: e.target.value })} placeholder="如: 生产环境 Dify" style={fullInput} /></div>
|
||||
<div><label style={labelStyle}>知识库名称(Dataset)</label><input value={extForm.dataset_name} onChange={e => setExtForm({ ...extForm, dataset_name: e.target.value })} placeholder="如: qihuai" style={fullInput} /></div>
|
||||
</div>
|
||||
<div style={{ marginTop: 12 }}><label style={labelStyle}>API URL</label><input value={extForm.api_url} onChange={e => setExtForm({ ...extForm, api_url: e.target.value })} placeholder="https://your-instance.com/api/v1" style={fullInput} /></div>
|
||||
<div style={{ marginTop: 12 }}><label style={labelStyle}>API Key</label><input type="password" value={extForm.api_key} onChange={e => setExtForm({ ...extForm, api_key: e.target.value })} placeholder="Bearer Token 或 API Key" style={fullInput} /></div>
|
||||
<div style={{ display: 'flex', gap: 12, marginTop: 16 }}>
|
||||
<button onClick={handleCreateExtService} style={btnStyle('#8B5CF6')}>添加服务</button>
|
||||
<div style={{ marginTop: 12 }}><label style={labelStyle}>API URL</label><input value={extForm.api_url} onChange={e => setExtForm({ ...extForm, api_url: e.target.value })} placeholder="https://dify.qihuyun.com/v1" style={fullInput} /></div>
|
||||
<div style={{ marginTop: 12 }}><label style={labelStyle}>API Key（dataset 密钥）</label><input type="password" value={extForm.api_key} onChange={e => setExtForm({ ...extForm, api_key: e.target.value })} placeholder="dataset-xxxx" style={fullInput} /></div>
|
||||
|
||||
{extMsg && (
|
||||
<div style={{ marginTop: 12, fontSize: 13, color: extMsg.startsWith('✅') ? '#16A34A' : '#DC2626', padding: '6px 12px', background: extMsg.startsWith('✅') ? '#DCFCE7' : '#FEE2E2', borderRadius: 6 }}>
|
||||
{extMsg}
|
||||
</div>
|
||||
)}
|
||||
|
||||
<div style={{ display: 'flex', gap: 12, marginTop: 16, flexWrap: 'wrap' }}>
|
||||
<button onClick={handleCreateExtService} style={btnStyle('#8B5CF6')}>保存并测试连接</button>
|
||||
<button onClick={handleIngestPlatform} disabled={ingesting} style={btnStyle(ingesting ? '#D1D5DB' : '#219B5E')}>
|
||||
{ingesting ? '入库中...' : '入库平台内容'}
|
||||
</button>
|
||||
<button onClick={() => setShowExtModal(false)} style={btnStyle('#6B7280')}>关闭</button>
|
||||
</div>
|
||||
|
||||
{ingestResult && (
|
||||
<div style={{ marginTop: 12, fontSize: 13, padding: 12, background: '#F0FDF4', border: '1px solid #BBF7D0', borderRadius: 8 }}>
|
||||
<div style={{ fontWeight: 600, marginBottom: 6 }}>入库结果</div>
|
||||
<div>AI 聊天回答：<b>{ingestResult.chat_answers}</b> 条</div>
|
||||
<div>用户上传文档：<b>{ingestResult.user_docs}</b> 条</div>
|
||||
<div>跳过(过短/空)：{ingestResult.skipped} 条 · 失败：{ingestResult.failed} 条</div>
|
||||
</div>
|
||||
)}
|
||||
</div>
|
||||
</div>
|
||||
</div>
|
||||
|
||||
Reference in new issue
Block a user