23 lines
1.1 KiB
Plaintext
23 lines
1.1 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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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, created_at) "
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"VALUES (${id}$, ${name}$, ${desc}$, ${org_id}$, ${emb}$, 'rag_collection', 0, 0, 0, 'active', NOW())",
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{"id": kb_id, "name": name, "desc": desc, "org_id": userorgid, "emb": emb_type})
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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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