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Revert "Refa: make RAGFlow more asynchronous 2" (#11669)

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Reverts infiniflow/ragflow#11664
This commit is contained in:
Kevin Hu
2025-12-02 19:42:05 +08:00
committed by GitHub
parent 1388c4420d
commit a6681d6366
14 changed files with 292 additions and 622 deletions
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56
api/apps/sdk/session.py
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@ -13,7 +13,6 @@
# See the License for the specific language governing permissions and
# limitations under the License.
#
import asyncio
import json
import re
import time
@ -788,7 +787,7 @@ Reason:
- At the same time, related terms can also help search engines better understand user needs and return more accurate search results.
"""
ans = await chat_mdl.async_chat(
ans = chat_mdl.chat(
prompt,
[
{
@ -964,30 +963,28 @@ async def retrieval_test_embedded():
use_kg = req.get("use_kg", False)
top = int(req.get("top_k", 1024))
langs = req.get("cross_languages", [])
tenant_ids = []
tenant_id = objs[0].tenant_id
if not tenant_id:
return get_error_data_result(message="permission denined.")
def _retrieval_sync():
local_doc_ids = list(doc_ids) if doc_ids else []
tenant_ids = []
_question = question
if req.get("search_id", ""):
search_config = SearchService.get_detail(req.get("search_id", "")).get("search_config", {})
meta_data_filter = search_config.get("meta_data_filter", {})
metas = DocumentService.get_meta_by_kbs(kb_ids)
if meta_data_filter.get("method") == "auto":
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", ""))
filters: dict = gen_meta_filter(chat_mdl, metas, _question)
local_doc_ids.extend(meta_filter(metas, filters["conditions"], filters.get("logic", "and")))
if not local_doc_ids:
local_doc_ids = None
elif meta_data_filter.get("method") == "manual":
local_doc_ids.extend(meta_filter(metas, meta_data_filter["manual"], meta_data_filter.get("logic", "and")))
if meta_data_filter["manual"] and not local_doc_ids:
local_doc_ids = ["-999"]
if req.get("search_id", ""):
search_config = SearchService.get_detail(req.get("search_id", "")).get("search_config", {})
meta_data_filter = search_config.get("meta_data_filter", {})
metas = DocumentService.get_meta_by_kbs(kb_ids)
if meta_data_filter.get("method") == "auto":
chat_mdl = LLMBundle(tenant_id, LLMType.CHAT, llm_name=search_config.get("chat_id", ""))
filters: dict = gen_meta_filter(chat_mdl, metas, question)
doc_ids.extend(meta_filter(metas, filters["conditions"], filters.get("logic", "and")))
if not doc_ids:
doc_ids = None
elif meta_data_filter.get("method") == "manual":
doc_ids.extend(meta_filter(metas, meta_data_filter["manual"], meta_data_filter.get("logic", "and")))
if meta_data_filter["manual"] and not doc_ids:
doc_ids = ["-999"]
try:
tenants = UserTenantService.query(user_id=tenant_id)
for kb_id in kb_ids:
for tenant in tenants:
@ -1003,7 +1000,7 @@ async def retrieval_test_embedded():
return get_error_data_result(message="Knowledgebase not found!")
if langs:
_question = cross_languages(kb.tenant_id, None, _question, langs)
question = cross_languages(kb.tenant_id, None, question, langs)
embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING.value, llm_name=kb.embd_id)
@ -1013,15 +1010,15 @@ async def retrieval_test_embedded():
if req.get("keyword", False):
chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
_question += keyword_extraction(chat_mdl, _question)
question += keyword_extraction(chat_mdl, question)
labels = label_question(_question, [kb])
labels = label_question(question, [kb])
ranks = settings.retriever.retrieval(
_question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top,
local_doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels
question, embd_mdl, tenant_ids, kb_ids, page, size, similarity_threshold, vector_similarity_weight, top,
doc_ids, rerank_mdl=rerank_mdl, highlight=req.get("highlight"), rank_feature=labels
)
if use_kg:
ck = settings.kg_retriever.retrieval(_question, tenant_ids, kb_ids, embd_mdl,
ck = settings.kg_retriever.retrieval(question, tenant_ids, kb_ids, embd_mdl,
LLMBundle(kb.tenant_id, LLMType.CHAT))
if ck["content_with_weight"]:
ranks["chunks"].insert(0, ck)
@ -1031,9 +1028,6 @@ async def retrieval_test_embedded():
ranks["labels"] = labels
return get_json_result(data=ranks)
try:
return await asyncio.to_thread(_retrieval_sync)
except Exception as e:
if str(e).find("not_found") > 0:
return get_json_result(data=False, message="No chunk found! Check the chunk status please!",
@ -1070,7 +1064,7 @@ async def related_questions_embedded():
gen_conf = search_config.get("llm_setting", {"temperature": 0.9})
prompt = load_prompt("related_question")
ans = await chat_mdl.async_chat(
ans = chat_mdl.chat(
prompt,
[
{