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update qdrant migrate command (#2260)

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Co-authored-by: jyong <jyong@dify.ai>
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Jyong 2024-01-28 19:59:06 +08:00 committed by GitHub
parent 7076d41b29
commit 409e0c8e1c
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1 changed files with 16 additions and 49 deletions
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65
api/commands.py
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@ -339,26 +339,7 @@ def create_qdrant_indexes():
)
except Exception:
try:
embedding_model = model_manager.get_default_model_instance(
tenant_id=dataset.tenant_id,
model_type=ModelType.TEXT_EMBEDDING,
)
dataset.embedding_model = embedding_model.model
dataset.embedding_model_provider = embedding_model.provider
except Exception:
provider = Provider(
id='provider_id',
tenant_id=dataset.tenant_id,
provider_name='openai',
provider_type=ProviderType.SYSTEM.value,
encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
is_valid=True,
)
model_provider = OpenAIProvider(provider=provider)
embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
model_provider=model_provider)
continue
embeddings = CacheEmbedding(embedding_model)
from core.index.vector_index.qdrant_vector_index import QdrantConfig, QdrantVectorIndex
@ -405,7 +386,7 @@ def update_qdrant_indexes():
.order_by(Dataset.created_at.desc()).paginate(page=page, per_page=50)
except NotFound:
break
model_manager = ModelManager()
page += 1
for dataset in datasets:
if dataset.index_struct_dict:
@ -413,23 +394,15 @@ def update_qdrant_indexes():
try:
click.echo('Update dataset qdrant index: {}'.format(dataset.id))
try:
embedding_model = ModelFactory.get_embedding_model(
embedding_model = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
model_provider_name=dataset.embedding_model_provider,
model_name=dataset.embedding_model
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model
)
except Exception:
provider = Provider(
id='provider_id',
tenant_id=dataset.tenant_id,
provider_name='openai',
provider_type=ProviderType.CUSTOM.value,
encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
is_valid=True,
)
model_provider = OpenAIProvider(provider=provider)
embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
model_provider=model_provider)
continue
embeddings = CacheEmbedding(embedding_model)
from core.index.vector_index.qdrant_vector_index import QdrantConfig, QdrantVectorIndex
@ -524,23 +497,17 @@ def deal_dataset_vector(flask_app: Flask, dataset: Dataset, normalization_count:
try:
click.echo('restore dataset index: {}'.format(dataset.id))
try:
embedding_model = ModelFactory.get_embedding_model(
model_manager = ModelManager()
embedding_model = model_manager.get_model_instance(
tenant_id=dataset.tenant_id,
model_provider_name=dataset.embedding_model_provider,
model_name=dataset.embedding_model
provider=dataset.embedding_model_provider,
model_type=ModelType.TEXT_EMBEDDING,
model=dataset.embedding_model
)
except Exception:
provider = Provider(
id='provider_id',
tenant_id=dataset.tenant_id,
provider_name='openai',
provider_type=ProviderType.CUSTOM.value,
encrypted_config=json.dumps({'openai_api_key': 'TEST'}),
is_valid=True,
)
model_provider = OpenAIProvider(provider=provider)
embedding_model = OpenAIEmbedding(name="text-embedding-ada-002",
model_provider=model_provider)
pass
embeddings = CacheEmbedding(embedding_model)
dataset_collection_binding = db.session.query(DatasetCollectionBinding). \
filter(DatasetCollectionBinding.provider_name == embedding_model.model_provider.provider_name,