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

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