# Copyright (c) "Neo4j"
# Neo4j Sweden AB [https://neo4j.com]
# #
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# #
# https://www.apache.org/licenses/LICENSE-2.0
# #
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from neo4j_graphrag.embeddings.base import Embedder
from neo4j_graphrag.exceptions import EmbeddingsGenerationError
from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
try:
from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
except (ImportError, AttributeError):
TextEmbeddingModel = TextEmbeddingInput = None # type: ignore[misc, assignment]
if TYPE_CHECKING:
from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
[docs]
class VertexAIEmbeddings(Embedder):
"""
Vertex AI embeddings class.
This class uses the Vertex AI Python client to generate vector embeddings for text data.
Args:
model (str): The name of the Vertex AI text embedding model to use. Defaults to "text-embedding-004".
"""
def __init__(
self,
model: str = "text-embedding-004",
rate_limit_handler: Optional[RateLimitHandler] = None,
) -> None:
if TextEmbeddingModel is None:
raise ImportError(
"""Could not import Vertex AI Python client.
Please install it with `pip install "neo4j-graphrag[google]"`."""
)
super().__init__(rate_limit_handler)
self.model = TextEmbeddingModel.from_pretrained(model)
[docs]
@rate_limit_handler
def embed_query(
self, text: str, task_type: str = "RETRIEVAL_QUERY", **kwargs: Any
) -> list[float]:
"""
Generate embeddings for a given query using a Vertex AI text embedding model.
Args:
text (str): The text to generate an embedding for.
task_type (str): The type of the text embedding task. Defaults to "RETRIEVAL_QUERY". See https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/text-embeddings-api#tasktype for a full list.
**kwargs (Any): Additional keyword arguments to pass to the Vertex AI client's get_embeddings method.
"""
try:
# type annotation needed for mypy
inputs: list[str | TextEmbeddingInput] = [
TextEmbeddingInput(text, task_type)
]
embeddings = self.model.get_embeddings(inputs, **kwargs)
return list(embeddings[0].values)
except Exception as e:
raise EmbeddingsGenerationError(
f"Failed to generate embedding with VertexAI: {e}"
) from e