dify/api/core/embedding/cached_embedding.py

import base64
import logging
from typing import Optional, cast

import numpy as np
from langchain.embeddings.base import Embeddings
from sqlalchemy.exc import IntegrityError

from core.model_manager import ModelInstance
from core.model_runtime.entities.model_entities import ModelPropertyKey
from core.model_runtime.model_providers.__base.text_embedding_model import TextEmbeddingModel
from extensions.ext_database import db
from extensions.ext_redis import redis_client
from libs import helper

logger = logging.getLogger(__name__)


class CacheEmbedding(Embeddings):
    def __init__(self, model_instance: ModelInstance, user: Optional[str] = None) -> None:
        self._model_instance = model_instance
        self._user = user

    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]

                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

        return text_embeddings

    def embed_query(self, text: str) -> list[float]:
        """Embed query text."""
        # use doc embedding cache or store if not exists
        hash = helper.generate_text_hash(text)
        embedding_cache_key = f'{self._model_instance.provider}_{self._model_instance.model}_{hash}'
        embedding = redis_client.get(embedding_cache_key)
        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],
                user=self._user
            )

            embedding_results = embedding_result.embeddings[0]
            embedding_results = (embedding_results / np.linalg.norm(embedding_results)).tolist()
        except Exception as ex:
            raise ex

        try:
            # encode embedding to base64
            embedding_vector = np.array(embedding_results)
            vector_bytes = embedding_vector.tobytes()
            # Transform to Base64
            encoded_vector = base64.b64encode(vector_bytes)
            # Transform to string
            encoded_str = encoded_vector.decode("utf-8")
            redis_client.setex(embedding_cache_key, 600, encoded_str)

        except IntegrityError:
            db.session.rollback()
        except:
            logging.exception('Failed to add embedding to redis')

        return embedding_results