35 lines
1.8 KiB
Plaintext
35 lines
1.8 KiB
Plaintext
ns = params_kw.copy()
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db = DBPools()
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dbname = get_module_dbname('rag')
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async with db.sqlorContext(dbname) as sor:
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env = request._run_ns
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userorgid = await env.get_userorgid()
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kb_id = uuid()
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name = ns.get("name", "")
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desc = ns.get("description", "")
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# 向量引擎：bge-m3=文本(在线) / qwen3-vl-embedding=多媒体(在线) / clip-vith14=多媒体(GPU本地CLIP)
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emb_type = ns.get("embedding_type", "") or ns.get("embedding_engine", "")
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if emb_type not in ("bge-m3", "clip-vith14", "qwen3-vl-embedding"):
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emb_type = "bge-m3"
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# 知识库级角色权限：维护角色/检索角色（多选 checkbox 提交 list，兼容逗号串）
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def _roles_csv(v):
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if isinstance(v, (list, tuple)):
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return ','.join([str(x) for x in v if str(x).strip()])
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return str(v or '')
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maintain_roles = _roles_csv(ns.get("maintain_roles", ""))
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search_roles = _roles_csv(ns.get("search_roles", ""))
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await sor.sqlExe(
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"INSERT INTO rag_knowledge_bases (id, name, description, org_id, embedding_engine, vdb_collection, doc_count, total_size, chunk_count, status, maintain_roles, search_roles, created_at) "
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"VALUES (${id}$, ${name}$, ${desc}$, ${org_id}$, ${emb}$, 'rag_collection', 0, 0, 0, 'active', ${mr}$, ${sr}$, NOW())",
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{"id": kb_id, "name": name, "desc": desc, "org_id": userorgid, "emb": emb_type,
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"mr": maintain_roles, "sr": search_roles})
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# 机构 rags 目录下建知识库子目录：{workspace_base}/{org}/rags/{kb}/
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from rag.init import get_rags_base, ensure_kb_dir
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rags_base = await get_rags_base(sor)
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ensure_kb_dir(rags_base, userorgid, kb_id)
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return {
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"widgettype": "urlwidget",
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"options": {"url": entire_url('/rag/knowledge_bases_list/index.ui')}
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}
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return {"error": "failed"}
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