참고소스 수정본

참고/neo4j-graphrag-python-main/src/neo4j_graphrag/embeddings/mistral.py

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+#  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, 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 mistralai import Mistral
+except ImportError:
+    Mistral = None  # type: ignore
+
+
+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",
+        rate_limit_handler: Optional[RateLimitHandler] = None,
+        **kwargs: Any,
+    ) -> None:
+        if Mistral is None:
+            raise ImportError(
+                """Could not import mistralai.
+                Please install it with `pip install "neo4j-graphrag[mistralai]"`."""
+            )
+        super().__init__(rate_limit_handler)
+        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)
+
+    @rate_limit_handler
+    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.
+        """
+        try:
+            embeddings_batch_response = self.mistral_client.embeddings.create(
+                model=self.model, inputs=[text], **kwargs
+            )
+        except Exception as e:
+            raise EmbeddingsGenerationError(
+                f"Failed to generate embedding with MistralAI: {e}"
+            ) from e
+
+        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