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Refa: refine search app (#9536)

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### What problem does this PR solve?

Refine search app.

### Type of change

- [x] Refactoring
This commit is contained in:
Yongteng Lei
2025-08-19 09:33:33 +08:00
committed by GitHub
parent dad97869b6
commit 188c0f614b
4 changed files with 192 additions and 15 deletions
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44
api/db/services/dialog_service.py
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@ -687,7 +687,30 @@ def tts(tts_mdl, text):
return binascii.hexlify(bin).decode("utf-8")
def ask(question, kb_ids, tenant_id, chat_llm_name=None):
def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
similarity_threshold = 0.1,
vector_similarity_weight = 0.3,
top = 1024,
doc_ids = []
rerank_id = ""
rerank_mdl = None
if search_config:
if search_config.get("kb_ids", []):
kb_ids = search_config.get("kb_ids", [])
if search_config.get("chat_id", ""):
chat_llm_name = search_config.get("chat_id", "")
if search_config.get("similarity_threshold", 0.1):
similarity_threshold = search_config.get("similarity_threshold", 0.1)
if search_config.get("vector_similarity_weight", 0.3):
vector_similarity_weight = search_config.get("vector_similarity_weight", 0.3)
if search_config.get("top_k", 1024):
top = search_config.get("top_k", 1024)
if search_config.get("doc_ids", []):
doc_ids = search_config.get("doc_ids", [])
if search_config.get("rerank_id", ""):
rerank_id = search_config.get("rerank_id", "")
kbs = KnowledgebaseService.get_by_ids(kb_ids)
embedding_list = list(set([kb.embd_id for kb in kbs]))
@ -696,9 +719,26 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None):
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name)
if rerank_id:
rerank_mdl = LLMBundle(tenant_id, LLMType.RERANK, rerank_id)
max_tokens = chat_mdl.max_length
tenant_ids = list(set([kb.tenant_id for kb in kbs]))
kbinfos = retriever.retrieval(question, embd_mdl, tenant_ids, kb_ids, 1, 12, 0.1, 0.3, aggs=False, rank_feature=label_question(question, kbs))
kbinfos = retriever.retrieval(
question = question,
embd_mdl=embd_mdl,
tenant_ids=tenant_ids,
kb_ids=kb_ids,
page=1,
page_size=12,
similarity_threshold=similarity_threshold,
vector_similarity_weight=vector_similarity_weight,
top=top,
doc_ids=doc_ids,
aggs=False,
rerank_mdl=rerank_mdl,
rank_feature=label_question(question, kbs)
)
knowledges = kb_prompt(kbinfos, max_tokens)
prompt = """
Role: You're a smart assistant. Your name is Miss R.