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feat: server multi models support (#799)

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takatost 2023-08-12 00:57:00 +08:00 • committed by GitHub
commit 5fa2161b05
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213 changed files with 10556 additions and 2579 deletions
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48
api/controllers/console/datasets/datasets_document.py
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@ -18,7 +18,9 @@ from controllers.console.datasets.error import DocumentAlreadyFinishedError, Inv
from controllers.console.setup import setup_required
from controllers.console.wraps import account_initialization_required
from core.indexing_runner import IndexingRunner
from core.llm.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError
from core.model_providers.error import ProviderTokenNotInitError, QuotaExceededError, ModelCurrentlyNotSupportError, \
LLMBadRequestError
from core.model_providers.model_factory import ModelFactory
from extensions.ext_redis import redis_client
from libs.helper import TimestampField
from extensions.ext_database import db
@ -280,6 +282,15 @@ class DatasetDocumentListApi(Resource):
# validate args
DocumentService.document_create_args_validate(args)
try:
ModelFactory.get_embedding_model(
tenant_id=current_user.current_tenant_id
)
except LLMBadRequestError:
raise ProviderNotInitializeError(
f"No Embedding Model available. Please configure a valid provider "
f"in the Settings -> Model Provider.")
try:
documents, batch = DocumentService.save_document_with_dataset_id(dataset, args, current_user)
except ProviderTokenNotInitError as ex:
@ -319,6 +330,15 @@ class DatasetInitApi(Resource):
parser.add_argument('doc_form', type=str, default='text_model', required=False, nullable=False, location='json')
args = parser.parse_args()
try:
ModelFactory.get_embedding_model(
tenant_id=current_user.current_tenant_id
)
except LLMBadRequestError:
raise ProviderNotInitializeError(
f"No Embedding Model available. Please configure a valid provider "
f"in the Settings -> Model Provider.")
# validate args
DocumentService.document_create_args_validate(args)
@ -384,7 +404,13 @@ class DocumentIndexingEstimateApi(DocumentResource):
indexing_runner = IndexingRunner()
response = indexing_runner.file_indexing_estimate([file], data_process_rule_dict)
try:
response = indexing_runner.file_indexing_estimate(current_user.current_tenant_id, [file],
data_process_rule_dict)
except LLMBadRequestError:
raise ProviderNotInitializeError(
f"No Embedding Model available. Please configure a valid provider "
f"in the Settings -> Model Provider.")
return response
@ -445,12 +471,24 @@ class DocumentBatchIndexingEstimateApi(DocumentResource):
raise NotFound("File not found.")
indexing_runner = IndexingRunner()
response = indexing_runner.file_indexing_estimate(file_details, data_process_rule_dict)
try:
response = indexing_runner.file_indexing_estimate(current_user.current_tenant_id, file_details,
data_process_rule_dict)
except LLMBadRequestError:
raise ProviderNotInitializeError(
f"No Embedding Model available. Please configure a valid provider "
f"in the Settings -> Model Provider.")
elif dataset.data_source_type:
indexing_runner = IndexingRunner()
response = indexing_runner.notion_indexing_estimate(info_list,
data_process_rule_dict)
try:
response = indexing_runner.notion_indexing_estimate(current_user.current_tenant_id,
info_list,
data_process_rule_dict)
except LLMBadRequestError:
raise ProviderNotInitializeError(
f"No Embedding Model available. Please configure a valid provider "
f"in the Settings -> Model Provider.")
else:
raise ValueError('Data source type not support')
return response