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recreate qdrant vector (#1049)

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Co-authored-by: jyong <jyong@dify.ai>
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Jyong 2023-08-29 15:00:36 +08:00 committed by GitHub
parent a43e80dd9c
commit b5953039de
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1 changed files with 47 additions and 45 deletions
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92
api/commands.py
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@ -318,53 +318,55 @@ def create_qdrant_indexes():
page += 1
for dataset in datasets:
try:
click.echo('Create dataset qdrant index: {}'.format(dataset.id))
try:
embedding_model = ModelFactory.get_embedding_model(
tenant_id=dataset.tenant_id,
model_provider_name=dataset.embedding_model_provider,
model_name=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)
embeddings = CacheEmbedding(embedding_model)
if dataset.index_struct_dict:
if dataset.index_struct_dict['type'] != 'qdrant':
try:
click.echo('Create dataset qdrant index: {}'.format(dataset.id))
try:
embedding_model = ModelFactory.get_embedding_model(
tenant_id=dataset.tenant_id,
model_provider_name=dataset.embedding_model_provider,
model_name=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)
embeddings = CacheEmbedding(embedding_model)
from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig
from core.index.vector_index.qdrant_vector_index import QdrantVectorIndex, QdrantConfig
index = QdrantVectorIndex(
dataset=dataset,
config=QdrantConfig(
endpoint=current_app.config.get('QDRANT_URL'),
api_key=current_app.config.get('QDRANT_API_KEY'),
root_path=current_app.root_path
),
embeddings=embeddings
)
if index:
index_struct = {
"type": 'qdrant',
"vector_store": {"class_prefix": dataset.index_struct_dict['vector_store']['class_prefix']}
}
dataset.index_struct = json.dumps(index_struct)
db.session.commit()
index.create_qdrant_dataset(dataset)
create_count += 1
else:
click.echo('passed.')
except Exception as e:
click.echo(
click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)), fg='red'))
continue
index = QdrantVectorIndex(
dataset=dataset,
config=QdrantConfig(
endpoint=current_app.config.get('QDRANT_URL'),
api_key=current_app.config.get('QDRANT_API_KEY'),
root_path=current_app.root_path
),
embeddings=embeddings
)
if index:
index.create_qdrant_dataset(dataset)
index_struct = {
"type": 'qdrant',
"vector_store": {"class_prefix": dataset.index_struct_dict['vector_store']['class_prefix']}
}
dataset.index_struct = json.dumps(index_struct)
db.session.commit()
create_count += 1
else:
click.echo('passed.')
except Exception as e:
click.echo(
click.style('Create dataset index error: {} {}'.format(e.__class__.__name__, str(e)), fg='red'))
continue
click.echo(click.style('Congratulations! Create {} dataset indexes.'.format(create_count), fg='green'))