From 3425c091885fc14940b5313a2e2273f3d38102d0 Mon Sep 17 00:00:00 2001 From: ymq Date: Mon, 10 Aug 2026 11:20:00 +0800 Subject: [PATCH] =?UTF-8?q?feat:=20=E6=A0=87=E7=AD=BE=E6=94=AF=E6=8C=81?= =?UTF-8?q?=E5=A4=9A=E9=80=89=E5=90=8E=E7=9B=B4=E6=8E=A5=E6=A3=80=E7=B4=A2?= =?UTF-8?q?=EF=BC=8C=E6=97=A0=E5=85=B3=E9=94=AE=E8=AF=8D=E6=97=B6=E5=8F=AF?= =?UTF-8?q?=E7=BA=AF=E6=A0=87=E7=AD=BE=E6=A3=80=E7=B4=A2?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - search_result.dspy: 标签解析提前，query为空但标签有值时走直接DB查询 - search.ui: tag_selector添加changed事件触发检索，更新首页提示文案 --- wwwroot/knowledge_bases_list/search.ui | 18 +- .../knowledge_bases_list/search_result.dspy | 200 ++++++++++-------- 2 files changed, 130 insertions(+), 88 deletions(-) diff --git a/wwwroot/knowledge_bases_list/search.ui b/wwwroot/knowledge_bases_list/search.ui index 5d18e13..4071b59 100644 --- a/wwwroot/knowledge_bases_list/search.ui +++ b/wwwroot/knowledge_bases_list/search.ui @@ -3,7 +3,7 @@ "options": {"padding": "20px", "spacing": "12px", "width": "100%", "height": "100%", "css": "filler"}, "subwidgets": [ {"widgettype": "Text", "options": {"text": "🔍 知识检索", "cfontsize": 22, "fontWeight": "bold", "color": "#1a1a2e"}}, - {"widgettype": "Text", "options": {"text": "选择知识库，输入文字或上传文件检索", "cfontsize": 13, "color": "#888"}}, + {"widgettype": "Text", "options": {"text": "选择知识库，输入文字、选择标签或上传文件检索", "cfontsize": 13, "color": "#888"}}, { "widgettype": "HBox", "options": {"spacing": "12px", "alignItems": "center"}, @@ -64,6 +64,22 @@ } } }, + { + "wid": "tag_selector", + "event": "changed", + "actiontype": "urlwidget", + "target": "search_results", + "mode": "replace", + "options": { + "url": "/rag/knowledge_bases_list/search_result.dspy", + "params": { + "_webbricks_": 1, + "kb_id": "{{kb_selector}}", + "keyword": "{{search_bar}}", + "tag_ids": "{{tag_selector}}" + } + } + }, { "wid": "search_file", "event": "changed", diff --git a/wwwroot/knowledge_bases_list/search_result.dspy b/wwwroot/knowledge_bases_list/search_result.dspy index 5ce5dc4..6207698 100644 --- a/wwwroot/knowledge_bases_list/search_result.dspy +++ b/wwwroot/knowledge_bases_list/search_result.dspy @@ -34,16 +34,7 @@ if not file_data: import base64, io top_k = int(ns.get('top_k', 5)) -if not query: - return json.dumps({ - "widgettype": "VBox", "options": {"padding": "20px", "spacing": "16px"}, - "subwidgets": [ - {"widgettype": "Text", "options": {"text": "🔍 知识检索", "cfontsize": 20, "fontWeight": "bold"}}, - {"widgettype": "Text", "options": {"text": "请输入检索内容", "color": "#666", "marginTop": "20px"}} - ] - }, ensure_ascii=False) - -# 0. Parse tag filter — find doc_ids that match ALL selected tags +# 0. Parse tag filter FIRST — find doc_ids that match ALL selected tags tag_doc_ids = None tag_info = "" if tag_ids_str: @@ -65,93 +56,128 @@ if tag_ids_str: "GROUP BY media_id HAVING COUNT(DISTINCT tag_id)=" + str(len(wanted_tags))) recs = await sor.sqlExe(sql, nsq) tag_doc_ids = set(r.media_id for r in recs) - tag_info = " (标签过滤: " + ",".join(wanted_tags[:3]) + ("..." if len(wanted_tags)>3 else "") + ")" + tag_info = " (标签: " + ",".join(wanted_tags[:3]) + ("..." if len(wanted_tags)>3 else "") + ")" info(f'[search_result] tag filter: {len(tag_doc_ids)} docs match {len(wanted_tags)} tags') except Exception as e: info(f'[search_result] tag filter error: {e}') -# 1. Embed -vec = [] -try: - client = StreamHttpClient() - resp = await client.request('POST', 'https://embedding.opencomputing.net:10443/api/embed', - json={"texts": [query], "model": "CLIP-ViT-H-14"}) - emb = json.loads(resp) - vec = emb.get("text_embeddings", emb.get("embeddings", [[]]))[0] -except: - pass -if not vec: - return json.dumps({"widgettype": "Text", "options": {"text": "向量化失败", "cfontsize": 14, "color": "#e74c3c"}}, ensure_ascii=False) - -# 2. VDB search -raw_rows = [] -try: - recall_n = top_k * 3 - client2 = StreamHttpClient() - resp2 = await client2.request('POST', 'https://vectordb.opencomputing.net:10443/v1/query', - json={"colname": kb_id, "vector": vec, "pagerows": recall_n, "output_fields": ["*"]}) - vdb = json.loads(resp2) - raw_rows = vdb.get("data", {}).get("rows", []) - if not isinstance(raw_rows, list): - raw_rows = [] -except: - pass - -# 3. Keyword recall (hybrid search: vector alone misses technical terms) -kw_ids = set() -kw_rows = [] -try: - tokens = [t for t in query.split() if t][:5] or [query] - conds = [] - nsq = {"kb_id": kb_id} - for i, t in enumerate(tokens): - conds.append("content LIKE ${kw_" + str(i) + "}$") - nsq["kw_" + str(i)] = "%" + t + "%" - ksql = "SELECT id, doc_id, content FROM document_chunks WHERE kb_id=${kb_id}$ AND (" + " OR ".join(conds) + ") LIMIT 20" - async with get_sor_context(env, 'rag') as sor: - krecs = await sor.sqlExe(ksql, nsq) - for r in krecs: - kw_ids.add(r.id) - kw_rows.append({"id": r.id, "doc_id": r.doc_id or "", "score": 0.99, "text": r.content or '', "kw": True}) -except Exception as e: - info('[search_result] keyword recall failed: %s' % e) +# Early return only when nothing is provided +if not query and not tag_ids_str and not file_data: + return json.dumps({ + "widgettype": "VBox", "options": {"padding": "20px", "spacing": "16px"}, + "subwidgets": [ + {"widgettype": "Text", "options": {"text": "🔍 知识检索", "cfontsize": 20, "fontWeight": "bold"}}, + {"widgettype": "Text", "options": {"text": "请输入检索内容或选择标签", "color": "#666", "marginTop": "20px"}} + ] + }, ensure_ascii=False) hits = [] -seen = set() -for row in raw_rows: - rid = str(row.get("id", "")) - score = row.get("score", 0) - # Resolve doc_id from chunk id pattern "xxxx_c0" - doc_id = rid.rsplit('_c', 1)[0] if '_c' in rid else rid - # Look up chunk text from DB - chunk_text = '' - async with get_sor_context(env, 'rag') as sor: - recs = await sor.sqlExe( - "SELECT content, doc_id FROM document_chunks WHERE id=${id}$", - {"id": rid}) - if recs: - chunk_text = recs[0].content or '' - doc_id = recs[0].doc_id or doc_id - # Tag filter - if tag_doc_ids is not None and doc_id not in tag_doc_ids: - continue - if chunk_text: - is_kw = rid in kw_ids - hits.append({"id": rid, "doc_id": doc_id, "score": 0.99 if is_kw else score, "text": chunk_text, "kw": is_kw}) - seen.add(rid) -for kr in kw_rows: - if kr["id"] not in seen: - if tag_doc_ids is not None and kr["doc_id"] not in tag_doc_ids: +raw_rows = [] +kw_rows = [] + +if query: + # 1. Embed + vec = [] + try: + client = StreamHttpClient() + resp = await client.request('POST', 'https://embedding.opencomputing.net:10443/api/embed', + json={"texts": [query], "model": "CLIP-ViT-H-14"}) + emb = json.loads(resp) + vec = emb.get("text_embeddings", emb.get("embeddings", [[]]))[0] + except: + pass + if not vec: + return json.dumps({"widgettype": "Text", "options": {"text": "向量化失败", "cfontsize": 14, "color": "#e74c3c"}}, ensure_ascii=False) + + # 2. VDB search + try: + recall_n = top_k * 3 + client2 = StreamHttpClient() + resp2 = await client2.request('POST', 'https://vectordb.opencomputing.net:10443/v1/query', + json={"colname": kb_id, "vector": vec, "pagerows": recall_n, "output_fields": ["*"]}) + vdb = json.loads(resp2) + raw_rows = vdb.get("data", {}).get("rows", []) + if not isinstance(raw_rows, list): + raw_rows = [] + except: + pass + + # 3. Keyword recall (hybrid search: vector alone misses technical terms) + kw_ids = set() + try: + tokens = [t for t in query.split() if t][:5] or [query] + conds = [] + nsq = {"kb_id": kb_id} + for i, t in enumerate(tokens): + conds.append("content LIKE ${kw_" + str(i) + "}$") + nsq["kw_" + str(i)] = "%" + t + "%" + ksql = "SELECT id, doc_id, content FROM document_chunks WHERE kb_id=${kb_id}$ AND (" + " OR ".join(conds) + ") LIMIT 20" + async with get_sor_context(env, 'rag') as sor: + krecs = await sor.sqlExe(ksql, nsq) + for r in krecs: + kw_ids.add(r.id) + kw_rows.append({"id": r.id, "doc_id": r.doc_id or "", "score": 0.99, "text": r.content or '', "kw": True}) + except Exception as e: + info('[search_result] keyword recall failed: %s' % e) + + seen = set() + for row in raw_rows: + rid = str(row.get("id", "")) + score = row.get("score", 0) + # Resolve doc_id from chunk id pattern "xxxx_c0" + doc_id = rid.rsplit('_c', 1)[0] if '_c' in rid else rid + # Look up chunk text from DB + chunk_text = '' + async with get_sor_context(env, 'rag') as sor: + recs = await sor.sqlExe( + "SELECT content, doc_id FROM document_chunks WHERE id=${id}$", + {"id": rid}) + if recs: + chunk_text = recs[0].content or '' + doc_id = recs[0].doc_id or doc_id + # Tag filter + if tag_doc_ids is not None and doc_id not in tag_doc_ids: continue - hits.append(kr) - seen.add(kr["id"]) + if chunk_text: + is_kw = rid in kw_ids + hits.append({"id": rid, "doc_id": doc_id, "score": 0.99 if is_kw else score, "text": chunk_text, "kw": is_kw}) + seen.add(rid) + for kr in kw_rows: + if kr["id"] not in seen: + if tag_doc_ids is not None and kr["doc_id"] not in tag_doc_ids: + continue + hits.append(kr) + seen.add(kr["id"]) -hits.sort(key=lambda x: x.get("score", 0), reverse=True) -hits = hits[:top_k] + hits.sort(key=lambda x: x.get("score", 0), reverse=True) + hits = hits[:top_k] +else: + # Tag-only search: direct DB lookup for chunks of tagged documents + if tag_doc_ids: + try: + async with get_sor_context(env, 'rag') as sor: + kb_cond = "" + nsq2 = {} + if kb_id and kb_id != "all": + kb_cond = "kb_id=${kb_id}$ AND " + nsq2["kb_id"] = kb_id + placeholders2 = [] + for i, did in enumerate(tag_doc_ids): + placeholders2.append("${did_" + str(i) + "}$") + nsq2["did_" + str(i)] = did + sql2 = ("SELECT id, doc_id, content FROM document_chunks WHERE " + kb_cond + + "doc_id IN (" + ",".join(placeholders2) + ") ORDER BY updated_at DESC LIMIT " + str(top_k)) + crecs = await sor.sqlExe(sql2, nsq2) + for r in crecs: + hits.append({"id": r.id, "doc_id": r.doc_id or "", "score": 1.0, "text": r.content or '', "kw": False}) + except Exception as e: + info(f'[search_result] tag-only lookup error: {e}') + +header_text = f"🔍 检索: {query}" if query else "🔍 标签检索" subwidgets = [ - {"widgettype": "Text", "options": {"text": f"🔍 检索: {query}{tag_info}", "cfontsize": 18, "fontWeight": "bold", "marginBottom": "8px"}}, - {"widgettype": "Text", "options": {"text": f"共 {len(hits)} 条结果 (召回 {len(raw_rows)} 条)", "cfontsize": 13, "color": "#888", "marginBottom": "16px"}} + {"widgettype": "Text", "options": {"text": header_text + tag_info, "cfontsize": 18, "fontWeight": "bold", "marginBottom": "8px"}}, + {"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"}} ] if not hits: