# 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
import abc
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
if TYPE_CHECKING:
import openai
class BaseOpenAIEmbeddings(Embedder, abc.ABC):
"""
Abstract base class for OpenAI embeddings.
"""
client: openai.OpenAI
def __init__(
self,
model: str = "text-embedding-ada-002",
rate_limit_handler: Optional[RateLimitHandler] = None,
**kwargs: Any,
) -> None:
try:
import openai
except ImportError:
raise ImportError(
"""Could not import openai python client.
Please install it with `pip install "neo4j-graphrag[openai]"`."""
)
super().__init__(rate_limit_handler)
self.openai = openai
self.model = model
self.client = self._initialize_client(**kwargs)
@abc.abstractmethod
def _initialize_client(self, **kwargs: Any) -> Any:
"""
Initialize the OpenAI client.
Must be implemented by subclasses.
"""
pass
@rate_limit_handler
def embed_query(self, text: str, **kwargs: Any) -> list[float]:
"""
Generate embeddings for a given query using an OpenAI text embedding model.
Args:
text (str): The text to generate an embedding for.
**kwargs (Any): Additional arguments to pass to the OpenAI embedding generation function.
"""
try:
response = self.client.embeddings.create(
input=text, model=self.model, **kwargs
)
embedding: list[float] = response.data[0].embedding
return embedding
except Exception as e:
raise EmbeddingsGenerationError(
f"Failed to generate embedding with OpenAI: {e}"
) from e
[docs]
class OpenAIEmbeddings(BaseOpenAIEmbeddings):
"""
OpenAI embeddings class.
This class uses the OpenAI python client to generate embeddings for text data.
Args:
model (str): The name of the OpenAI embedding model to use. Defaults to "text-embedding-ada-002".
kwargs: All other parameters will be passed to the openai.OpenAI init.
"""
def _initialize_client(self, **kwargs: Any) -> Any:
return self.openai.OpenAI(**kwargs)
[docs]
class AzureOpenAIEmbeddings(BaseOpenAIEmbeddings):
"""
Azure OpenAI embeddings class.
This class uses the Azure OpenAI python client to generate embeddings for text data.
Args:
model (str): The name of the Azure OpenAI embedding model to use. Defaults to "text-embedding-ada-002".
kwargs: All other parameters will be passed to the openai.AzureOpenAI init.
"""
def _initialize_client(self, **kwargs: Any) -> Any:
return self.openai.AzureOpenAI(**kwargs)