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Code Issues Actions 4 Packages Projects Releases Wiki Activity

add embedding cache and clean embedding cache job (#3087)

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Co-authored-by: jyong <jyong@dify.ai>
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Jyong 2024-04-02 20:46:24 +08:00 committed by GitHub
parent 7f55ea0c53
commit 5e66a60f1c
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4 changed files with 91 additions and 30 deletions
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74
api/core/embedding/cached_embedding.py
View File

@ -12,6 +12,7 @@ from core.rag.datasource.entity.embedding import Embeddings
from extensions.ext_database import db
from extensions.ext_redis import redis_client
from libs import helper
from models.dataset import Embedding
logger = logging.getLogger(__name__)
@ -23,32 +24,55 @@ class CacheEmbedding(Embeddings):
def embed_documents(self, texts: list[str]) -> list[list[float]]:
"""Embed search docs in batches of 10."""
text_embeddings = []
try:
model_type_instance = cast(TextEmbeddingModel, self._model_instance.model_type_instance)
model_schema = model_type_instance.get_model_schema(self._model_instance.model, self._model_instance.credentials)
max_chunks = model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS] \
if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties else 1
for i in range(0, len(texts), max_chunks):
batch_texts = texts[i:i + max_chunks]
# use doc embedding cache or store if not exists
text_embeddings = [None for _ in range(len(texts))]
embedding_queue_indices = []
for i, text in enumerate(texts):
hash = helper.generate_text_hash(text)
embedding = db.session.query(Embedding).filter_by(model_name=self._model_instance.model,
hash=hash,
provider_name=self._model_instance.provider).first()
if embedding:
text_embeddings[i] = embedding.get_embedding()
else:
embedding_queue_indices.append(i)
if embedding_queue_indices:
embedding_queue_texts = [texts[i] for i in embedding_queue_indices]
embedding_queue_embeddings = []
try:
model_type_instance = cast(TextEmbeddingModel, self._model_instance.model_type_instance)
model_schema = model_type_instance.get_model_schema(self._model_instance.model, self._model_instance.credentials)
max_chunks = model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS] \
if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties else 1
for i in range(0, len(embedding_queue_texts), max_chunks):
batch_texts = embedding_queue_texts[i:i + max_chunks]
embedding_result = self._model_instance.invoke_text_embedding(
texts=batch_texts,
user=self._user
)
embedding_result = self._model_instance.invoke_text_embedding(
texts=batch_texts,
user=self._user
)
for vector in embedding_result.embeddings:
try:
normalized_embedding = (vector / np.linalg.norm(vector)).tolist()
text_embeddings.append(normalized_embedding)
except IntegrityError:
db.session.rollback()
except Exception as e:
logging.exception('Failed to add embedding to redis')
except Exception as ex:
logger.error('Failed to embed documents: ', ex)
raise ex
for vector in embedding_result.embeddings:
try:
normalized_embedding = (vector / np.linalg.norm(vector)).tolist()
embedding_queue_embeddings.append(normalized_embedding)
except IntegrityError:
db.session.rollback()
except Exception as e:
logging.exception('Failed transform embedding: ', e)
for i, embedding in zip(embedding_queue_indices, embedding_queue_embeddings):
text_embeddings[i] = embedding
hash = helper.generate_text_hash(texts[i])
embedding_cache = Embedding(model_name=self._model_instance.model,
hash=hash,
provider_name=self._model_instance.provider)
embedding_cache.set_embedding(embedding)
db.session.add(embedding_cache)
db.session.commit()
except Exception as ex:
db.session.rollback()
logger.error('Failed to embed documents: ', ex)
raise ex
return text_embeddings
@ -61,8 +85,6 @@ class CacheEmbedding(Embeddings):
if embedding:
redis_client.expire(embedding_cache_key, 600)
return list(np.frombuffer(base64.b64decode(embedding), dtype="float"))
try:
embedding_result = self._model_instance.invoke_text_embedding(
texts=[text],

4
api/extensions/ext_celery.py
View File

@ -46,11 +46,11 @@ def init_app(app: Flask) -> Celery:
beat_schedule = {
'clean_embedding_cache_task': {
'task': 'schedule.clean_embedding_cache_task.clean_embedding_cache_task',
'schedule': timedelta(days=7),
'schedule': timedelta(days=1),
},
'clean_unused_datasets_task': {
'task': 'schedule.clean_unused_datasets_task.clean_unused_datasets_task',
'schedule': timedelta(minutes=3),
'schedule': timedelta(days=1),
}
}
celery_app.conf.update(

34
api/migrations/versions/a8d7385a7b66_add_embeddings_provider_name.py Normal file
View File

@ -0,0 +1,34 @@
"""add-embeddings-provider-name
Revision ID: a8d7385a7b66
Revises: 17b5ab037c40
Create Date: 2024-04-02 12:17:22.641525
"""
import sqlalchemy as sa
from alembic import op
# revision identifiers, used by Alembic.
revision = 'a8d7385a7b66'
down_revision = '17b5ab037c40'
branch_labels = None
depends_on = None
def upgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('embeddings', schema=None) as batch_op:
batch_op.add_column(sa.Column('provider_name', sa.String(length=40), server_default=sa.text("''::character varying"), nullable=False))
batch_op.drop_constraint('embedding_hash_idx', type_='unique')
batch_op.create_unique_constraint('embedding_hash_idx', ['model_name', 'hash', 'provider_name'])
# ### end Alembic commands ###
def downgrade():
# ### commands auto generated by Alembic - please adjust! ###
with op.batch_alter_table('embeddings', schema=None) as batch_op:
batch_op.drop_constraint('embedding_hash_idx', type_='unique')
batch_op.create_unique_constraint('embedding_hash_idx', ['model_name', 'hash'])
batch_op.drop_column('provider_name')
# ### end Alembic commands ###

9
api/models/dataset.py
View File

@ -123,6 +123,7 @@ class Dataset(db.Model):
normalized_dataset_id = dataset_id.replace("-", "_")
return f'Vector_index_{normalized_dataset_id}_Node'
class DatasetProcessRule(db.Model):
__tablename__ = 'dataset_process_rules'
__table_args__ = (
@ -443,7 +444,8 @@ class DatasetKeywordTable(db.Model):
id = db.Column(UUID, primary_key=True, server_default=db.text('uuid_generate_v4()'))
dataset_id = db.Column(UUID, nullable=False, unique=True)
keyword_table = db.Column(db.Text, nullable=False)
data_source_type = db.Column(db.String(255), nullable=False, server_default=db.text("'database'::character varying"))
data_source_type = db.Column(db.String(255), nullable=False,
server_default=db.text("'database'::character varying"))
@property
def keyword_table_dict(self):
@ -457,6 +459,7 @@ class DatasetKeywordTable(db.Model):
if isinstance(node_idxs, list):
dct[keyword] = set(node_idxs)
return dct
# get dataset
dataset = Dataset.query.filter_by(
id=self.dataset_id
@ -481,7 +484,7 @@ class Embedding(db.Model):
__tablename__ = 'embeddings'
__table_args__ = (
db.PrimaryKeyConstraint('id', name='embedding_pkey'),
db.UniqueConstraint('model_name', 'hash', name='embedding_hash_idx')
db.UniqueConstraint('model_name', 'hash', 'provider_name', name='embedding_hash_idx')
)
id = db.Column(UUID, primary_key=True, server_default=db.text('uuid_generate_v4()'))
@ -490,6 +493,8 @@ class Embedding(db.Model):
hash = db.Column(db.String(64), nullable=False)
embedding = db.Column(db.LargeBinary, nullable=False)
created_at = db.Column(db.DateTime, nullable=False, server_default=db.text('CURRENT_TIMESTAMP(0)'))
provider_name = db.Column(db.String(40), nullable=False,
server_default=db.text("''::character varying"))
def set_embedding(self, embedding_data: list[float]):
self.embedding = pickle.dumps(embedding_data, protocol=pickle.HIGHEST_PROTOCOL)