365 lines
17 KiB
Plaintext
365 lines
17 KiB
Plaintext
ns = params_kw.copy()
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kb_id = ns.get('kb_id', '')
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env = request._run_ns
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query = ns.get("query") or ns.get("value") or ns.get("keyword") or ns.get("text") or ""
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tag_ids_str = ns.get("tag_ids", "")
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# Defensive: legacy UI cached in browser double-encodes the keyword (%E5%88%86... arrives as-is).
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# Normal path: server already decoded once, no '%' present, this is a no-op.
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if "%" in query:
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try:
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from urllib.parse import unquote
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d = unquote(query)
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if d != query:
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info(f'[search_result] unquote fallback: {query!r} -> {d!r}')
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query = d
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except Exception:
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pass
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info(f'[search_result] params_kw={params_kw}')
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info(f'[search_result] parsed: kb_id={kb_id!r} query={query!r} tag_ids={tag_ids_str!r} method={request.method} url={request.url}')
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file_data = None
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file_name = None
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try:
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reader = await request.multipart()
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while True:
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part = await reader.next()
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if part is None: break
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if part.name == "file": file_data = await part.read(); file_name = part.filename; break
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except: pass
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if not file_data:
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try: file_data = await request.read();
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except: pass
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if file_data and len(file_data) < 10: file_data = None
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import base64, io
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top_k = int(ns.get('top_k', 5))
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# 0. Parse tag filter FIRST — find doc_ids that match ALL selected tags
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tag_doc_ids = None
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tag_info = ""
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if tag_ids_str:
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wanted_tags = [t.strip() for t in tag_ids_str.split(",") if t.strip()]
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if wanted_tags:
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try:
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async with get_sor_context(env, 'rag') as sor:
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placeholders = []
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nsq = {}
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kb_filter = ""
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if kb_id and kb_id != "all":
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kb_filter = "kb_id=${kb_id}$ AND "
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nsq["kb_id"] = kb_id
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for i, tid in enumerate(wanted_tags):
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placeholders.append("${tid_" + str(i) + "}$")
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nsq["tid_" + str(i)] = tid
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sql = ("SELECT media_id FROM rag_media_tags "
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"WHERE " + kb_filter + "media_type='document' AND tag_id IN (" + ",".join(placeholders) + ") "
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"GROUP BY media_id HAVING COUNT(DISTINCT tag_id)=" + str(len(wanted_tags)))
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recs = await sor.sqlExe(sql, nsq)
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tag_doc_ids = set(r.media_id for r in recs)
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tag_info = " (标签: " + ",".join(wanted_tags[:3]) + ("..." if len(wanted_tags)>3 else "") + ")"
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info(f'[search_result] tag filter: {len(tag_doc_ids)} docs match {len(wanted_tags)} tags')
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except Exception as e:
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info(f'[search_result] tag filter error: {e}')
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# Early return only when nothing is provided
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if not query and not tag_ids_str and not file_data:
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return json.dumps({
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"widgettype": "VBox", "options": {"padding": "20px", "spacing": "16px"},
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"subwidgets": [
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{"widgettype": "Text", "options": {"text": "🔍 知识检索", "cfontsize": 20, "fontWeight": "bold"}},
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{"widgettype": "Text", "options": {"text": "请输入检索内容或选择标签", "color": "#666", "marginTop": "20px"}}
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]
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}, ensure_ascii=False)
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hits = []
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raw_rows = []
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kw_rows = []
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# 读知识库向量引擎：bge-m3(在线文本) / qwen3-vl-embedding(在线多模态) / clip-vith14(GPU本地)
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emb_engine = 'clip-vith14'
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try:
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async with get_sor_context(env, 'rag') as sor:
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krecs = await sor.sqlExe("SELECT embedding_engine FROM rag_knowledge_bases WHERE id=${kb_id}$", {"kb_id": kb_id})
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if krecs:
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emb_engine = (getattr(krecs[0], 'embedding_engine', '') or 'clip-vith14').strip()
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except: pass
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is_vl = emb_engine == 'qwen3-vl-embedding'
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if emb_engine == 'bge-m3':
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emb_url = 'https://embedding.opencomputing.net:10443/txte/api/embed'
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emb_model = 'bge-m3'
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else:
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emb_url = 'https://embedding.opencomputing.net:10443/mme/api/embed'
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emb_model = 'CLIP-ViT-H-14'
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# VDB 服务地址：读 upapp.rag-vdb（生产已切内网），不硬编码
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try:
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async with get_sor_context(env, 'rag') as sor:
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_u = await sor.sqlExe("SELECT baseurl FROM upapp WHERE id='rag-vdb'", {})
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VDB_BASE = (_u[0].baseurl or '').rstrip('/') if _u else 'https://vectordb.opencomputing.net:10443'
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except:
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VDB_BASE = 'https://vectordb.opencomputing.net:10443'
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if query:
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# 1. Embed（在线：vl原生 / bge-m3兼容；GPU：CLIP /mme）
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vec = []
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if is_vl:
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try:
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from rag.vl_online import vl_embed_contents
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vec = await vl_embed_contents([{"text": query}]) or []
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except:
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vec = []
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elif emb_engine == 'bge-m3':
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try:
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from rag.init import _online_embed
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_vs = await _online_embed(env, [query])
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vec = _vs[0] if _vs else []
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except:
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vec = []
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else:
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try:
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client = StreamHttpClient()
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resp = await client.request('POST', emb_url,
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json={"texts": [query], "model": emb_model})
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emb = json.loads(resp)
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vec = emb.get("text_embeddings", emb.get("embeddings", [[]]))[0]
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except:
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pass
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if not vec:
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return json.dumps({"widgettype": "Text", "options": {"text": "向量化失败（引擎未配置或不可用，请到引擎配置页检查）", "cfontsize": 14, "color": "#e74c3c"}}, ensure_ascii=False)
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# 2. VDB search
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try:
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recall_n = top_k * 3
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client2 = StreamHttpClient()
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resp2 = await client2.request('POST', VDB_BASE + '/v1/query',
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json={"colname": kb_id, "vector": vec, "pagerows": recall_n, "output_fields": ["*"]})
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vdb = json.loads(resp2)
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raw_rows = vdb.get("data", {}).get("rows", [])
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if not isinstance(raw_rows, list):
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raw_rows = []
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except:
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pass
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# 3. Keyword recall (hybrid search: vector alone misses technical terms)
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kw_ids = set()
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try:
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tokens = [t for t in query.split() if t][:5] or [query]
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conds = []
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nsq = {"kb_id": kb_id}
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for i, t in enumerate(tokens):
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conds.append("content LIKE ${kw_" + str(i) + "}$")
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nsq["kw_" + str(i)] = "%" + t + "%"
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ksql = "SELECT id, doc_id, content FROM rag_document_chunks WHERE kb_id=${kb_id}$ AND (" + " OR ".join(conds) + ") LIMIT 20"
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async with get_sor_context(env, 'rag') as sor:
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krecs = await sor.sqlExe(ksql, nsq)
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for r in krecs:
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kw_ids.add(r.id)
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kw_rows.append({"id": r.id, "doc_id": r.doc_id or "", "score": 0.99, "text": r.content or '', "kw": True})
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except Exception as e:
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info('[search_result] keyword recall failed: %s' % e)
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seen = set()
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for row in raw_rows:
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rid = str(row.get("id", ""))
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score = row.get("score", 0)
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# Resolve doc_id from chunk id pattern "xxxx_c0"
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doc_id = rid.rsplit('_c', 1)[0] if '_c' in rid else rid
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# Look up chunk text from DB
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chunk_text = ''
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async with get_sor_context(env, 'rag') as sor:
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recs = await sor.sqlExe(
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"SELECT content, doc_id, metadata FROM rag_document_chunks WHERE id=${id}$",
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{"id": rid})
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chunk_meta = {}
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if recs:
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chunk_text = recs[0].content or ''
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doc_id = recs[0].doc_id or doc_id
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try:
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chunk_meta = json.loads(recs[0].metadata) if recs[0].metadata else {}
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except:
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pass
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# Tag filter
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if tag_doc_ids is not None and doc_id not in tag_doc_ids:
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continue
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if chunk_text:
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is_kw = rid in kw_ids
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hit = {"id": rid, "doc_id": doc_id, "score": 0.99 if is_kw else score, "text": chunk_text, "kw": is_kw}
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if chunk_meta:
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hit.update({k: chunk_meta[k] for k in ('bbox', 'start_time', 'end_time') if k in chunk_meta})
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hits.append(hit)
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seen.add(rid)
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for kr in kw_rows:
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if kr["id"] not in seen:
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if tag_doc_ids is not None and kr["doc_id"] not in tag_doc_ids:
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continue
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hits.append(kr)
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seen.add(kr["id"])
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# 在线重排（mm_rerank；未配置/失败则保持召回分排序）
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rr_applied = False
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try:
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from rag.vl_online import vl_rerank
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cand = hits[:top_k * 3]
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if cand:
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rr = await vl_rerank(query, [h.get("text", "") for h in cand])
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if rr and isinstance(rr.get("scores"), list) and len(rr["scores"]) == len(cand):
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for i, s in enumerate(rr["scores"]):
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cand[i]["score"] = s
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cand.sort(key=lambda x: x.get("score", 0), reverse=True)
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rr_applied = True
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except Exception as e:
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info('[search_result] rerank failed: %s' % e)
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if not rr_applied:
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hits.sort(key=lambda x: x.get("score", 0), reverse=True)
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hits = hits[:top_k]
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else:
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# Tag-only search: query documents table directly (handles text + non-text docs)
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if tag_doc_ids:
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try:
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async with get_sor_context(env, 'rag') as sor:
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placeholders2 = []
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nsq2 = {}
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kb_cond = ""
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if kb_id and kb_id != "all":
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kb_cond = "kb_id=${kb_id}$ AND "
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nsq2["kb_id"] = kb_id
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for i, did in enumerate(tag_doc_ids):
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placeholders2.append("${did_" + str(i) + "}$")
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nsq2["did_" + str(i)] = did
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# Get documents (exclude face/voice generated derivatives)
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sql2 = ("SELECT id, file_name, kb_id, file_path FROM rag_documents WHERE " + kb_cond +
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"id IN (" + ",".join(placeholders2) + ") AND (metadata IS NULL OR metadata NOT LIKE '%%\"face\"%%') ORDER BY created_at DESC LIMIT " + str(top_k * 2))
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docs = await sor.sqlExe(sql2, nsq2)
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doc_list = [(r.id, r.file_name or '', r.kb_id or '', r.file_path or '') for r in docs]
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if doc_list:
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doc_ids = [d[0] for d in doc_list]
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# Try to get chunks for these docs
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chunk_placeholders = []
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nsq_c = {}
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for i, did in enumerate(doc_ids):
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chunk_placeholders.append("${cdid_" + str(i) + "}$")
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nsq_c["cdid_" + str(i)] = did
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chunks_sql = ("SELECT id, doc_id, content FROM rag_document_chunks WHERE doc_id IN (" +
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",".join(chunk_placeholders) + ") ORDER BY created_at DESC LIMIT " + str(top_k))
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chunk_recs = await sor.sqlExe(chunks_sql, nsq_c)
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chunks_by_doc = {}
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for cr in chunk_recs:
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if cr.doc_id not in chunks_by_doc:
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chunks_by_doc[cr.doc_id] = []
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chunks_by_doc[cr.doc_id].append((cr.id, cr.content or ''))
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for did, fname, kbid, fpath in doc_list[:top_k]:
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chs = chunks_by_doc.get(did, [])
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if chs:
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for cid, ctext in chs[:2]: # up to 2 chunks per doc
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hits.append({"id": cid, "doc_id": did, "score": 1.0, "text": ctext, "file_name": fname, "file_path": fpath, "kw": False})
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else:
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# Non-text document: render as media widget
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ext = fname.rsplit('.', 1)[-1].lower() if '.' in fname else ''
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hits.append({"id": did, "doc_id": did, "score": 1.0, "text": "", "file_name": fname, "file_path": fpath, "file_ext": ext, "kw": False, "is_media": True})
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except Exception as e:
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info(f'[search_result] tag-only lookup error: {e}')
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header_text = f"🔍 检索: {query}" if query else "🔍 标签检索"
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# 补全 hits 的 file_name/file_path（向量/关键词召回路径只有 chunk 信息，缺文件名）
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try:
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need = sorted({h.get("doc_id", "") for h in hits if h.get("doc_id") and not h.get("file_name")})
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if need:
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ph = []
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nsq_d = {}
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for i, did in enumerate(need):
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ph.append("${ddid_" + str(i) + "}$")
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nsq_d["ddid_" + str(i)] = did
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async with get_sor_context(env, 'rag') as sor:
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drecs = await sor.sqlExe(
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"SELECT id, file_name, file_path FROM rag_documents WHERE id IN (" + ",".join(ph) + ")", nsq_d)
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dmap = {r.id: (r.file_name or '', r.file_path or '') for r in drecs}
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for h in hits:
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if not h.get("file_name") and h.get("doc_id") in dmap:
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fn, fp = dmap[h["doc_id"]]
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h["file_name"] = fn
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if not h.get("file_path"):
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h["file_path"] = fp
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except Exception as e:
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info('[search_result] enrich file_name failed: %s' % e)
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subwidgets = [
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{"widgettype": "Text", "options": {"text": header_text + tag_info, "cfontsize": 18, "fontWeight": "bold", "marginBottom": "8px"}},
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{"widgettype": "Text", "options": {"text": f"共 {len(hits)} 条结果" + (f" (召回 {len(raw_rows)} 条)" if raw_rows else ""), "cfontsize": 13, "color": "#888", "marginBottom": "16px"}}
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]
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if not hits:
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subwidgets.append({"widgettype": "Text", "options": {"text": "😔 未找到相关内容", "cfontsize": 14, "color": "#aaa", "halign": "center", "marginTop": "40px"}})
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else:
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def safe_url(p):
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if not p:
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return ''
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if p.startswith('/idfile'):
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return p
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return '/idfile' + p
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video_exts = {'mp4', 'avi', 'mov', 'mkv', 'webm'}
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audio_exts = {'mp3', 'wav', 'flac', 'ogg', 'm4a', 'aac'}
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image_exts = {'png', 'jpg', 'jpeg', 'gif', 'webp', 'bmp'}
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for i, h in enumerate(hits):
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is_media = h.get("is_media", False)
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score_pct = round(float(h["score"]) * 100, 1)
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color = "#3b82f6" if score_pct > 60 else ("#10b981" if score_pct > 30 else "#f59e0b")
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badges = [{"widgettype": "Text", "options": {"text": f"{score_pct}%", "cfontsize": 11, "bgcolor": color, "color": "#fff", "padding": "2px 8px", "borderRadius": "10px"}}]
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if h.get("kw"):
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badges.append({"widgettype": "Text", "options": {"text": "📌 关键词命中", "cfontsize": 11, "bgcolor": "#f59e0b", "color": "#fff", "padding": "2px 8px", "borderRadius": "10px", "marginLeft": "6px"}})
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card_subwidgets = [
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{"widgettype": "HBox", "options": {"alignItems": "center", "marginBottom": "6px"}, "subwidgets": [
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{"widgettype": "Text", "options": {"text": f"#{i+1}", "cfontsize": 12, "fontWeight": "bold", "color": color, "marginRight": "8px"}},
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*badges,
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{"widgettype": "Text", "options": {"text": f" {h.get('file_name') or (h.get('id') or '')[:16]}", "cfontsize": 11, "color": "#999", "marginLeft": "8px"}}
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]}
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]
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if is_media and h.get("file_ext"):
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media_url = entire_url(safe_url(h.get("file_path", "")))
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ext = h["file_ext"]
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if ext in video_exts:
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if media_url:
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card_subwidgets.append({"widgettype": "Html", "options": {"html": "<video controls autoplay muted playsinline style=\"width:100%;max-height:400px\" src=\"" + media_url + "\"></video>", "padding": "4px 0"}})
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elif ext in audio_exts:
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if media_url:
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card_subwidgets.append({"widgettype": "Html", "options": {"html": "<audio controls preload=\"metadata\" style=\"width:100%\" src=\"" + media_url + "\"></audio>", "padding": "4px 0"}})
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elif ext in image_exts:
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if media_url:
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card_subwidgets.append({"widgettype": "Image", "options": {"url": media_url, "width": "100%", "cheight": 14, "objectFit": "contain", "bgcolor": "#f0f0f0"}})
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else:
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card_subwidgets.append({"widgettype": "Text", "options": {"text": f"📎 {h.get('file_name') or ''}", "cfontsize": 13, "color": "#888"}})
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# Position info (bbox for images, timestamps for video/audio)
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meta_info = []
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bbox = h.get("bbox")
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if bbox and isinstance(bbox, dict):
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meta_info.append(f"📍 ({bbox.get('x1',0):.0f},{bbox.get('y1',0):.0f})-({bbox.get('x2',0):.0f},{bbox.get('y2',0):.0f})")
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start_t = h.get("start_time")
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end_t = h.get("end_time")
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if start_t is not None or end_t is not None:
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st = f"{start_t:.1f}s" if start_t is not None else "0s"
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et = f"{end_t:.1f}s" if end_t is not None else ""
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if et:
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meta_info.append(f"⏱ {st} → {et}")
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else:
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meta_info.append(f"⏱ {st}")
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if meta_info:
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card_subwidgets.append({"widgettype": "Text", "options": {"text": " · ".join(meta_info), "cfontsize": 10, "color": "#888", "marginTop": "2px"}})
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else:
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card_subwidgets.append({"widgettype": "Text", "options": {"text": h["text"][:300], "cfontsize": 13, "color": "#333", "lineHeight": "1.6"}})
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subwidgets.append({
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"widgettype": "VBox",
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"options": {"padding": "12px 16px", "marginBottom": "8px", "border": "1px solid #e0e0e0", "borderLeft": f"3px solid {color}", "bgcolor": "#fafafa", "borderRadius": "4px"},
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"subwidgets": card_subwidgets
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})
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return json.dumps({"widgettype": "VBox", "options": {"padding": "20px", "spacing": "4px"}, "subwidgets": subwidgets}, ensure_ascii=False, default=str)
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