85 lines
3.0 KiB
Python
85 lines
3.0 KiB
Python
# Copyright (c) "Neo4j"
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# Neo4j Sweden AB [https://neo4j.com]
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# #
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# #
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# https://www.apache.org/licenses/LICENSE-2.0
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# #
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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import os
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from typing import Any, Optional
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from neo4j_graphrag.embeddings.base import Embedder
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from neo4j_graphrag.exceptions import EmbeddingsGenerationError
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from neo4j_graphrag.utils.rate_limit import RateLimitHandler, rate_limit_handler
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try:
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from mistralai import Mistral
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except ImportError:
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Mistral = None # type: ignore
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class MistralAIEmbeddings(Embedder):
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"""
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Mistral AI embeddings class.
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This class uses the Mistral AI Python client to generate vector embeddings for text data.
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Args:
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model (str): The name of the Mistral AI text embedding model to use. Defaults to "mistral-embed".
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"""
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def __init__(
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self,
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model: str = "mistral-embed",
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rate_limit_handler: Optional[RateLimitHandler] = None,
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**kwargs: Any,
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) -> None:
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if Mistral is None:
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raise ImportError(
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"""Could not import mistralai.
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Please install it with `pip install "neo4j-graphrag[mistralai]"`."""
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)
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super().__init__(rate_limit_handler)
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api_key = kwargs.pop("api_key", None)
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if api_key is None:
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api_key = os.getenv("MISTRAL_API_KEY", "")
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self.model = model
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self.mistral_client = Mistral(api_key=api_key, **kwargs)
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@rate_limit_handler
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def embed_query(self, text: str, **kwargs: Any) -> list[float]:
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"""
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Generate embeddings for a given query using a Mistral AI text embedding model.
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Args:
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text (str): The text to generate an embedding for.
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**kwargs (Any): Additional keyword arguments to pass to the Mistral AI client.
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"""
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try:
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embeddings_batch_response = self.mistral_client.embeddings.create(
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model=self.model, inputs=[text], **kwargs
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)
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except Exception as e:
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raise EmbeddingsGenerationError(
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f"Failed to generate embedding with MistralAI: {e}"
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) from e
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if embeddings_batch_response is None or not embeddings_batch_response.data:
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raise EmbeddingsGenerationError("Failed to retrieve embeddings.")
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embedding = embeddings_batch_response.data[0].embedding
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if not isinstance(embedding, list):
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raise EmbeddingsGenerationError("Embedding is not a list of floats.")
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return embedding
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