# 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 os
from typing import Any
from neo4j_graphrag.embeddings.base import Embedder
from neo4j_graphrag.exceptions import EmbeddingsGenerationError
try:
from mistralai import Mistral
except ImportError:
Mistral = None # type: ignore
[docs]
class MistralAIEmbeddings(Embedder):
"""
Mistral AI embeddings class.
This class uses the Mistral AI Python client to generate vector embeddings for text data.
Args:
model (str): The name of the Mistral AI text embedding model to use. Defaults to "mistral-embed".
"""
def __init__(self, model: str = "mistral-embed", **kwargs: Any) -> None:
if Mistral is None:
raise ImportError(
"""Could not import mistralai.
Please install it with `pip install "neo4j-graphrag[mistralai]"`."""
)
api_key = kwargs.pop("api_key", None)
if api_key is None:
api_key = os.getenv("MISTRAL_API_KEY", "")
self.model = model
self.mistral_client = Mistral(api_key=api_key, **kwargs)
[docs]
def embed_query(self, text: str, **kwargs: Any) -> list[float]:
"""
Generate embeddings for a given query using a Mistral AI text embedding model.
Args:
text (str): The text to generate an embedding for.
**kwargs (Any): Additional keyword arguments to pass to the Mistral AI client.
"""
embeddings_batch_response = self.mistral_client.embeddings.create(
model=self.model, inputs=[text], **kwargs
)
if embeddings_batch_response is None or not embeddings_batch_response.data:
raise EmbeddingsGenerationError("Failed to retrieve embeddings.")
embedding = embeddings_batch_response.data[0].embedding
if not isinstance(embedding, list):
raise EmbeddingsGenerationError("Embedding is not a list of floats.")
return embedding