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feat: hybrid search - add keyword LIKE recall merged with vector results, mark kw hits

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yumoqing 2026-08-03 19:19:32 +08:00
parent bd03743c6f
commit dfad26d3c7
1 changed files with 31 additions and 2 deletions
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33
wwwroot/knowledge_bases_list/search_result.dspy
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@ -58,7 +58,27 @@ try:
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)
hits = []
seen = set()
for row in raw_rows:
rid = str(row.get("id", ""))
score = row.get("score", 0)
@ -75,7 +95,13 @@ for row in raw_rows:
chunk_text = recs[0].content or ''
doc_id = recs[0].doc_id or doc_id
if chunk_text:
hits.append({"id": rid, "doc_id": doc_id, "score": score, "text": 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:
hits.append(kr)
seen.add(kr["id"])
hits.sort(key=lambda x: x.get("score", 0), reverse=True)
hits = hits[:top_k]
@ -91,13 +117,16 @@ else:
for i, h in enumerate(hits):
score_pct = round(float(h["score"]) * 100, 1)
color = "#3b82f6" if score_pct > 60 else ("#10b981" if score_pct > 30 else "#f59e0b")
badges = [{"widgettype": "Text", "options": {"text": f"{score_pct}%", "cfontsize": 11, "bgcolor": color, "color": "#fff", "padding": "2px 8px", "borderRadius": "10px"}}]
if h.get("kw"):
badges.append({"widgettype": "Text", "options": {"text": "📌 关键词命中", "cfontsize": 11, "bgcolor": "#f59e0b", "color": "#fff", "padding": "2px 8px", "borderRadius": "10px", "marginLeft": "6px"}})
subwidgets.append({
"widgettype": "VBox",
"options": {"padding": "12px 16px", "marginBottom": "8px", "border": "1px solid #e0e0e0", "borderLeft": f"3px solid {color}", "bgcolor": "#fafafa", "borderRadius": "4px"},
"subwidgets": [
{"widgettype": "HBox", "options": {"alignItems": "center", "marginBottom": "6px"}, "subwidgets": [
{"widgettype": "Text", "options": {"text": f"#{i+1}", "cfontsize": 12, "fontWeight": "bold", "color": color, "marginRight": "8px"}},
{"widgettype": "Text", "options": {"text": f"{score_pct}%", "cfontsize": 11, "bgcolor": color, "color": "#fff", "padding": "2px 8px", "borderRadius": "10px"}},
*badges,
{"widgettype": "Text", "options": {"text": f" {h['id'][:16]}", "cfontsize": 11, "color": "#999", "marginLeft": "8px"}}
]},
{"widgettype": "Text", "options": {"text": h["text"][:300], "cfontsize": 13, "color": "#333", "lineHeight": "1.6"}}