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122 lines
5.6 KiB
Python
122 lines
5.6 KiB
Python
import base64
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import logging
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from typing import Optional, cast
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import numpy as np
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from sqlalchemy.exc import IntegrityError
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from core.model_manager import ModelInstance
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from core.model_runtime.entities.model_entities import ModelPropertyKey
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from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
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from core.rag.datasource.entity.embedding import Embeddings
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from extensions.ext_database import db
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from extensions.ext_redis import redis_client
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from libs import helper
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from models.dataset import Embedding
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logger = logging.getLogger(__name__)
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class CacheEmbedding(Embeddings):
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def __init__(self, model_instance: ModelInstance, user: Optional[str] = None) -> None:
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self._model_instance = model_instance
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self._user = user
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def embed_documents(self, texts: list[str]) -> list[list[float]]:
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"""Embed search docs in batches of 10."""
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# use doc embedding cache or store if not exists
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text_embeddings = [None for _ in range(len(texts))]
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embedding_queue_indices = []
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for i, text in enumerate(texts):
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hash = helper.generate_text_hash(text)
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embedding = db.session.query(Embedding).filter_by(model_name=self._model_instance.model,
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hash=hash,
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provider_name=self._model_instance.provider).first()
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if embedding:
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text_embeddings[i] = embedding.get_embedding()
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else:
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embedding_queue_indices.append(i)
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if embedding_queue_indices:
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embedding_queue_texts = [texts[i] for i in embedding_queue_indices]
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embedding_queue_embeddings = []
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try:
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model_type_instance = cast(TextEmbeddingModel, self._model_instance.model_type_instance)
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model_schema = model_type_instance.get_model_schema(self._model_instance.model,
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self._model_instance.credentials)
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max_chunks = model_schema.model_properties[ModelPropertyKey.MAX_CHUNKS] \
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if model_schema and ModelPropertyKey.MAX_CHUNKS in model_schema.model_properties else 1
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for i in range(0, len(embedding_queue_texts), max_chunks):
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batch_texts = embedding_queue_texts[i:i + max_chunks]
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embedding_result = self._model_instance.invoke_text_embedding(
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texts=batch_texts,
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user=self._user
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)
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for vector in embedding_result.embeddings:
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try:
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normalized_embedding = (vector / np.linalg.norm(vector)).tolist()
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embedding_queue_embeddings.append(normalized_embedding)
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except IntegrityError:
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db.session.rollback()
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except Exception as e:
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logging.exception('Failed transform embedding: ', e)
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cache_embeddings = []
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try:
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for i, embedding in zip(embedding_queue_indices, embedding_queue_embeddings):
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text_embeddings[i] = embedding
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hash = helper.generate_text_hash(texts[i])
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if hash not in cache_embeddings:
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embedding_cache = Embedding(model_name=self._model_instance.model,
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hash=hash,
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provider_name=self._model_instance.provider)
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embedding_cache.set_embedding(embedding)
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db.session.add(embedding_cache)
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cache_embeddings.append(hash)
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db.session.commit()
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except IntegrityError:
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db.session.rollback()
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except Exception as ex:
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db.session.rollback()
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logger.error('Failed to embed documents: ', ex)
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raise ex
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return text_embeddings
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def embed_query(self, text: str) -> list[float]:
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"""Embed query text."""
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# use doc embedding cache or store if not exists
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hash = helper.generate_text_hash(text)
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embedding_cache_key = f'{self._model_instance.provider}_{self._model_instance.model}_{hash}'
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embedding = redis_client.get(embedding_cache_key)
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if embedding:
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redis_client.expire(embedding_cache_key, 600)
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return list(np.frombuffer(base64.b64decode(embedding), dtype="float"))
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try:
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embedding_result = self._model_instance.invoke_text_embedding(
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texts=[text],
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user=self._user
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)
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embedding_results = embedding_result.embeddings[0]
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embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
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except Exception as ex:
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raise ex
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try:
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# encode embedding to base64
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embedding_vector = np.array(embedding_results)
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vector_bytes = embedding_vector.tobytes()
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# Transform to Base64
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encoded_vector = base64.b64encode(vector_bytes)
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# Transform to string
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encoded_str = encoded_vector.decode("utf-8")
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redis_client.setex(embedding_cache_key, 600, encoded_str)
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except IntegrityError:
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db.session.rollback()
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except:
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logging.exception('Failed to add embedding to redis')
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return embedding_results
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