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83 lines
3.2 KiB
Python
83 lines
3.2 KiB
Python
import logging
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from typing import List
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import numpy as np
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from langchain.embeddings.base import Embeddings
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from sqlalchemy.exc import IntegrityError
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from core.model_providers.models.embedding.base import BaseEmbedding
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from extensions.ext_database import db
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from libs import helper
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from models.dataset import Embedding
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class CacheEmbedding(Embeddings):
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def __init__(self, embeddings: BaseEmbedding):
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self._embeddings = embeddings
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Embed search docs."""
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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._embeddings.name, hash=hash).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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try:
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embedding_results = self._embeddings.client.embed_documents([texts[i] for i in embedding_queue_indices])
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except Exception as ex:
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raise self._embeddings.handle_exceptions(ex)
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for i, indice in enumerate(embedding_queue_indices):
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hash = helper.generate_text_hash(texts[indice])
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try:
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embedding = Embedding(model_name=self._embeddings.name, hash=hash)
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vector = embedding_results[i]
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normalized_embedding = (vector / np.linalg.norm(vector)).tolist()
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text_embeddings[indice] = normalized_embedding
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embedding.set_embedding(normalized_embedding)
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db.session.add(embedding)
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db.session.commit()
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except IntegrityError:
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db.session.rollback()
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continue
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except:
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logging.exception('Failed to add embedding to db')
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continue
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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 = db.session.query(Embedding).filter_by(model_name=self._embeddings.name, hash=hash).first()
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if embedding:
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return embedding.get_embedding()
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try:
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embedding_results = self._embeddings.client.embed_query(text)
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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 self._embeddings.handle_exceptions(ex)
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try:
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embedding = Embedding(model_name=self._embeddings.name, hash=hash)
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embedding.set_embedding(embedding_results)
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db.session.add(embedding)
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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:
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logging.exception('Failed to add embedding to db')
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return embedding_results
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