AI/ragflow
1
0
Fork 0
mirror of https://git.mirrors.martin98.com/https://github.com/infiniflow/ragflow.git synced 2025-08-12 07:09:01 +08:00
Code Issues Packages Projects Releases Wiki Activity

fix: improve embedding model validation logic for dataset operations (#3235)

Browse Source 
What problem does this PR solve?
When creating or updating datasets with custom embedding models (e.g.,
Ollama), the validation logic was too restrictive and prevented valid
models from being used. The previous implementation would reject valid
custom models if they weren't in the predefined list, even when they
existed in TenantLLMService.

Changes:
- Simplify and improve the embedding model validation flow in
create/update endpoints
- Check TenantLLMService for custom models before rejecting
- Make validation logic more consistent between create and update
operations

### What problem does this PR solve?

This fix allows users to successfully create and update datasets with
custom embedding models while maintaining proper validation checks.

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)

---------

Co-authored-by: Jin Hai <haijin.chn@gmail.com>
Co-authored-by: Kevin Hu <kevinhu.sh@gmail.com>
Co-authored-by: liuhua <10215101452@stu.ecnu.edu.cn>
This commit is contained in:
Mohammed Tawileh 2024-11-07 05:36:28 +03:00 committed by GitHub
parent 1b3e39dd12
commit 5038552ed9
No known key found for this signature in database
GPG Key ID: B5690EEEBB952194
1 changed files with 11 additions and 22 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

33
api/apps/sdk/dataset.py
View File

@ -159,21 +159,15 @@ def create(tenant_id):
embd_model = LLMService.query(
llm_name=req["embedding_model"], model_type="embedding"
)
if embd_model:
if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(tenant_id=tenant_id,model_type="embedding",llm_name=req.get("embedding_model"),):
return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
if not embd_model:
embd_model=TenantLLMService.query(tenant_id=tenant_id,model_type="embedding", llm_name=req.get("embedding_model"))
if not embd_model:
return get_error_data_result(
f"`embedding_model` {req.get('embedding_model')} doesn't exist"
)
if embd_model:
if req[
"embedding_model"
] not in valid_embedding_models and not TenantLLMService.query(
tenant_id=tenant_id,
model_type="embedding",
llm_name=req.get("embedding_model"),
):
return get_error_data_result(
f"`embedding_model` {req.get('embedding_model')} doesn't exist"
)
key_mapping = {
"chunk_num": "chunk_count",
"doc_num": "document_count",
@ -403,21 +397,16 @@ def update(tenant_id, dataset_id):
embd_model = LLMService.query(
llm_name=req["embedding_model"], model_type="embedding"
)
if embd_model:
if req["embedding_model"] not in valid_embedding_models and not TenantLLMService.query(tenant_id=tenant_id,model_type="embedding",llm_name=req.get("embedding_model"),):
return get_error_data_result(f"`embedding_model` {req.get('embedding_model')} doesn't exist")
if not embd_model:
embd_model=TenantLLMService.query(tenant_id=tenant_id,model_type="embedding", llm_name=req.get("embedding_model"))
if not embd_model:
return get_error_data_result(
f"`embedding_model` {req.get('embedding_model')} doesn't exist"
)
if embd_model:
if req[
"embedding_model"
] not in valid_embedding_models and not TenantLLMService.query(
tenant_id=tenant_id,
model_type="embedding",
llm_name=req.get("embedding_model"),
):
return get_error_data_result(
f"`embedding_model` {req.get('embedding_model')} doesn't exist"
)
req["embd_id"] = req.pop("embedding_model")
if "name" in req:
req["name"] = req["name"].strip()