AI_POC/TERES_fastapi_backend
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v0.21.1-fastapi

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dangzerong
2025-11-04 16:06:36 +08:00
parent 3e58c3d0e9
commit d57b5d76ae
218 changed files with 19617 additions and 72339 deletions
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14
api/db/services/dialog_service.py
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@@ -370,7 +370,7 @@ def chat(dialog, messages, stream=True, **kwargs):
chat_mdl.bind_tools(toolcall_session, tools)
bind_models_ts = timer()
retriever = settings.retrievaler
retriever = settings.retriever
questions = [m["content"] for m in messages if m["role"] == "user"][-3:]
attachments = kwargs["doc_ids"].split(",") if "doc_ids" in kwargs else []
if "doc_ids" in messages[-1]:
@@ -466,13 +466,17 @@ def chat(dialog, messages, stream=True, **kwargs):
rerank_mdl=rerank_mdl,
rank_feature=label_question(" ".join(questions), kbs),
)
if prompt_config.get("toc_enhance"):
cks = retriever.retrieval_by_toc(" ".join(questions), kbinfos["chunks"], tenant_ids, chat_mdl, dialog.top_n)
if cks:
kbinfos["chunks"] = cks
if prompt_config.get("tavily_api_key"):
tav = Tavily(prompt_config["tavily_api_key"])
tav_res = tav.retrieve_chunks(" ".join(questions))
kbinfos["chunks"].extend(tav_res["chunks"])
kbinfos["doc_aggs"].extend(tav_res["doc_aggs"])
if prompt_config.get("use_kg"):
ck = settings.kg_retrievaler.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl,
ck = settings.kg_retriever.retrieval(" ".join(questions), tenant_ids, dialog.kb_ids, embd_mdl,
LLMBundle(dialog.tenant_id, LLMType.CHAT))
if ck["content_with_weight"]:
kbinfos["chunks"].insert(0, ck)
@@ -658,7 +662,7 @@ Please write the SQL, only SQL, without any other explanations or text.
logging.debug(f"{question} get SQL(refined): {sql}")
tried_times += 1
return settings.retrievaler.sql_retrieval(sql, format="json"), sql
return settings.retriever.sql_retrieval(sql, format="json"), sql
tbl, sql = get_table()
if tbl is None:
@@ -752,7 +756,7 @@ def ask(question, kb_ids, tenant_id, chat_llm_name=None, search_config={}):
embedding_list = list(set([kb.embd_id for kb in kbs]))
is_knowledge_graph = all([kb.parser_id == ParserType.KG for kb in kbs])
retriever = settings.retrievaler if not is_knowledge_graph else settings.kg_retrievaler
retriever = settings.retriever if not is_knowledge_graph else settings.kg_retriever
embd_mdl = LLMBundle(tenant_id, LLMType.EMBEDDING, embedding_list[0])
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, chat_llm_name)
@@ -848,7 +852,7 @@ def gen_mindmap(question, kb_ids, tenant_id, search_config={}):
if not doc_ids:
doc_ids = None
ranks = settings.retrievaler.retrieval(
ranks = settings.retriever.retrieval(
question=question,
embd_mdl=embd_mdl,
tenant_ids=tenant_ids,