feat: Add Azure OpenAI embedding provider

nemoguardrails/embeddings/providers/__init__.py

@@ -18,7 +18,7 @@
 
 from typing import Optional, Type
 
-from . import fastembed, nim, openai, sentence_transformers
+from . import azureopenai, fastembed, nim, openai, sentence_transformers
 from .base import EmbeddingModel
 from .registry import EmbeddingProviderRegistry
 
@@ -65,6 +65,7 @@ def register_embedding_provider(
 
 register_embedding_provider(fastembed.FastEmbedEmbeddingModel)
 register_embedding_provider(openai.OpenAIEmbeddingModel)
+register_embedding_provider(azureopenai.AzureEmbeddingModel)
 register_embedding_provider(sentence_transformers.SentenceTransformerEmbeddingModel)
 register_embedding_provider(nim.NIMEmbeddingModel)
 register_embedding_provider(nim.NVIDIAAIEndpointsEmbeddingModel)

nemoguardrails/embeddings/providers/azureopenai.py

@@ -0,0 +1,105 @@
+# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# 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
+#
+# http://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.
+
+import asyncio
+import os
+from typing import List
+
+from .base import EmbeddingModel
+
+
+def get_executor():
+    from . import embeddings_executor
+
+    return embeddings_executor
+
+class AzureEmbeddingModel(EmbeddingModel):
+    """Embedding model using Azure OpenAI.
+
+    This class represents an embedding model that utilizes the Azure OpenAI API
+    for generating text embeddings.
+
+    Args:
+        embedding_model (str): The name of the Azure OpenAI deployment model (e.g., "text-embedding-ada-002").
+    """
+
+    engine_name = "AzureOpenAI"
+
+    # Lookup table for model embedding dimensions
+    MODEL_DIMENSIONS = {
+        "text-embedding-ada-002": 1536,
+        # Add more models and their dimensions here if needed
+    }
+
+    def __init__(self, embedding_model: str):
+        try:
+            from openai import AzureOpenAI
+        except ImportError:
+            raise ImportError(
+                "Could not import openai, please install it with "
+                "`pip install openai`."
+            )
+        # Set Azure OpenAI API credentials
+        self.client = AzureOpenAI(
+            api_key=os.getenv("AZURE_OPENAI_API_KEY"),
+            api_version=os.getenv("AZURE_OPENAI_API_VERSION"),
+            azure_endpoint=os.getenv("AZURE_OPENAI_ENDPOINT"),
+        )
+
+        self.embedding_model = embedding_model
+        self.embedding_size = self._get_embedding_dimension()
+
+    def _get_embedding_dimension(self):
+        """Retrieve the embedding dimension for the specified model."""
+        if self.embedding_model in self.MODEL_DIMENSIONS:
+            return self.MODEL_DIMENSIONS[self.embedding_model]
+        else:
+            raise ValueError(
+                f"Unknown model: {self.embedding_model}. Please add its dimensions to MODEL_DIMENSIONS."
+            )
+
+    async def encode_async(self, documents: List[str]) -> List[List[float]]:
+        """Asynchronously encode a list of documents into their corresponding embeddings.
+
+        Args:
+            documents (List[str]): The list of documents to be encoded.
+
+        Returns:
+            List[List[float]]: The list of embeddings, where each embedding is a list of floats.
+        """
+        loop = asyncio.get_running_loop()
+        result = await loop.run_in_executor(get_executor(), self.encode, documents)
+        return result
+
+    def encode(self, documents: List[str]) -> List[List[float]]:
+        """Encode a list of documents into their corresponding embeddings.
+
+        Args:
+            documents (List[str]): The list of documents to be encoded.
+
+        Returns:
+            List[List[float]]: The list of embeddings, where each embedding is a list of floats.
+
+        Raises:
+            RuntimeError: If the API call fails.
+        """
+        try:
+            response = self.client.embeddings.create(
+                model=self.embedding_model, input=documents
+            )
+            embeddings = [record.embedding for record in response.data]
+            return embeddings
+        except Exception as e:
+            raise RuntimeError(f"Failed to retrieve embeddings: {e}")