youngkingdom/ragflow
1
0
Fork 0
mirror of https://github.com/infiniflow/ragflow.git synced 2026-05-05 17:57:47 +08:00
Code Issues Packages Projects Releases Wiki Activity

Feat/tenant model (#13072)

Browse Source 
### What problem does this PR solve?

Add id for table tenant_llm and apply in LLMBundle.

### Type of change

- [x] Refactoring

---------

Co-authored-by: Yingfeng <yingfeng.zhang@gmail.com>
Co-authored-by: Liu An <asiro@qq.com>
This commit is contained in:
Lynn
2026-03-05 17:27:17 +08:00
committed by GitHub
parent 47540a4147
commit 62cb292635
54 changed files with 1754 additions and 361 deletions
Download Patch File Download Diff File Expand all files Collapse all files

42
api/apps/sdk/doc.py
View File

@ -36,6 +36,7 @@ from api.db.services.knowledgebase_service import KnowledgebaseService
from api.db.services.llm_service import LLMBundle
from api.db.services.tenant_llm_service import TenantLLMService
from api.db.services.task_service import TaskService, queue_tasks, cancel_all_task_of
from api.db.joint_services.tenant_model_service import get_model_config_by_id, get_tenant_default_model_by_type, get_model_config_by_type_and_name
from common.metadata_utils import meta_filter, convert_conditions
from api.utils.api_utils import check_duplicate_ids, construct_json_result, get_error_data_result, get_parser_config, get_result, server_error_response, token_required, \
get_request_json
@ -1248,8 +1249,13 @@ async def add_chunk(tenant_id, dataset_id, document_id):
d["tag_kwd"] = req["tag_kwd"]
if "tag_feas" in req:
d["tag_feas"] = req["tag_feas"]
embd_id = DocumentService.get_embd_id(document_id)
embd_mdl = TenantLLMService.model_instance(tenant_id, LLMType.EMBEDDING.value, embd_id)
tenant_embd_id = DocumentService.get_tenant_embd_id(document_id)
if tenant_embd_id:
model_config = get_model_config_by_id(tenant_embd_id)
else:
embd_id = DocumentService.get_embd_id(document_id)
model_config = get_model_config_by_type_and_name(tenant_id, LLMType.EMBEDDING.value, embd_id)
embd_mdl = TenantLLMService.model_instance(model_config)
v, c = embd_mdl.encode([doc.name, req["content"] if not d["question_kwd"] else "\n".join(d["question_kwd"])])
v = 0.1 * v[0] + 0.9 * v[1]
d["q_%d_vec" % len(v)] = v.tolist()
@ -1446,8 +1452,13 @@ async def update_chunk(tenant_id, dataset_id, document_id, chunk_id):
d["tag_kwd"] = req["tag_kwd"]
if "tag_feas" in req:
d["tag_feas"] = req["tag_feas"]
embd_id = DocumentService.get_embd_id(document_id)
embd_mdl = TenantLLMService.model_instance(tenant_id, LLMType.EMBEDDING.value, embd_id)
tenant_embd_id = DocumentService.get_tenant_embd_id(document_id)
if tenant_embd_id:
model_config = get_model_config_by_id(tenant_embd_id)
else:
embd_id = DocumentService.get_embd_id(document_id)
model_config = get_model_config_by_type_and_name(tenant_id, LLMType.EMBEDDING.value, embd_id)
embd_mdl = TenantLLMService.model_instance(model_config)
if doc.parser_id == ParserType.QA:
arr = [t for t in re.split(r"[\n\t]", d["content_with_weight"]) if len(t) > 1]
if len(arr) != 2:
@ -1616,17 +1627,26 @@ async def retrieval_test(tenant_id):
e, kb = KnowledgebaseService.get_by_id(kb_ids[0])
if not e:
return get_error_data_result(message="Dataset not found!")
embd_mdl = LLMBundle(kb.tenant_id, LLMType.EMBEDDING, llm_name=kb.embd_id)
if kb.tenant_embd_id:
embd_model_config = get_model_config_by_id(kb.tenant_embd_id)
else:
embd_model_config = get_model_config_by_type_and_name(kb.tenant_id, LLMType.EMBEDDING, kb.embd_id)
embd_mdl = LLMBundle(kb.tenant_id, embd_model_config)
rerank_mdl = None
if req.get("rerank_id"):
rerank_mdl = LLMBundle(kb.tenant_id, LLMType.RERANK, llm_name=req["rerank_id"])
if req.get("tenant_rerank_id"):
rerank_model_config = get_model_config_by_id(req["tenant_rerank_id"])
rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
elif req.get("rerank_id"):
rerank_model_config = get_model_config_by_type_and_name(kb.tenant_id, LLMType.RERANK, req["rerank_id"])
rerank_mdl = LLMBundle(kb.tenant_id, rerank_model_config)
if langs:
question = await cross_languages(kb.tenant_id, None, question, langs)
if req.get("keyword", False):
chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
chat_model_config = get_tenant_default_model_by_type(kb.tenant_id, LLMType.CHAT)
chat_mdl = LLMBundle(kb.tenant_id, chat_model_config)
question += await keyword_extraction(chat_mdl, question)
ranks = await settings.retriever.retrieval(
@ -1645,13 +1665,15 @@ async def retrieval_test(tenant_id):
rank_feature=label_question(question, kbs),
)
if toc_enhance:
chat_mdl = LLMBundle(kb.tenant_id, LLMType.CHAT)
chat_model_config = get_tenant_default_model_by_type(kb.tenant_id, LLMType.CHAT)
chat_mdl = LLMBundle(kb.tenant_id, chat_model_config)
cks = await settings.retriever.retrieval_by_toc(question, ranks["chunks"], tenant_ids, chat_mdl, size)
if cks:
ranks["chunks"] = cks
ranks["chunks"] = settings.retriever.retrieval_by_children(ranks["chunks"], tenant_ids)
if use_kg:
ck = await settings.kg_retriever.retrieval(question, [k.tenant_id for k in kbs], kb_ids, embd_mdl, LLMBundle(kb.tenant_id, LLMType.CHAT))
chat_model_config = get_tenant_default_model_by_type(kb.tenant_id, LLMType.CHAT)
ck = await settings.kg_retriever.retrieval(question, [k.tenant_id for k in kbs], kb_ids, embd_mdl, LLMBundle(kb.tenant_id, chat_model_config))
if ck["content_with_weight"]:
ranks["chunks"].insert(0, ck)