Add support for AzureOpenAI (#131)

**Objective**

Added a new TextVectorizer for the AzureOpenAI API, which is based on
the AzureOpenAI and AsyncAzureOpenAI classes from openai>=1.0.0.

**Reason**

Compatibility with Azure OpenAI is an important feature for developers
that are building enterprise AI applications with Azure Cloud.
Particularly for use cases where privacy is a concern, and data must
stay within their cloud tenant.

My team particularly wants to integrate Semantic Cache (with Azure Cache
for Redis Enterprise). Since we rely on the Azure OpenAI API, I created
this new vectorizer to add that functionality.

---------

Co-authored-by: Anibal <a8065384@banorte.com>
Co-authored-by: Tyler Hutcherson <tyler.hutcherson@redis.com>

Author: 3 people

conftest.py

@@ -21,6 +21,14 @@ def client(redis_url):
 def openai_key():
     return os.getenv("OPENAI_API_KEY")
 
+@pytest.fixture
+def openai_version():
+    return os.getenv("OPENAI_API_VERSION")
+
+@pytest.fixture
+def azure_endpoint():
+    return os.getenv("AZURE_OPENAI_ENDPOINT")
+
 @pytest.fixture
 def cohere_key():
     return os.getenv("COHERE_API_KEY")

docs/user_guide/vectorizers_04.ipynb

@@ -64,7 +64,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -176,6 +176,115 @@
     "print(\"Number of Embeddings:\", len(embeddings))\n"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Azure OpenAI\n",
+    "\n",
+    "The ``AzureOpenAITextVectorizer`` is a variation of the OpenAI vectorizer that calls OpenAI models within Azure. If you've already installed ``openai``, then you're ready to use Azure OpenAI.\n",
+    "\n",
+    "The only practical difference between OpenAI and Azure OpenAI is the variables required to call the API."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# additionally to the API Key, setup the API endpoint and version\n",
+    "api_version = os.environ.get(\"OPENAI_API_VERSION\") or getpass.getpass(\"Enter your AzureOpenAI API version: \")\n",
+    "azure_endpoint = os.environ.get(\"AZURE_OPENAI_ENDPOINT\") or getpass.getpass(\"Enter your AzureOpenAI API endpoint: \")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Vector dimensions:  1536\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "[-0.0010088568087667227,\n",
+       " -0.003142790636047721,\n",
+       " 0.0024922797456383705,\n",
+       " -0.004522906616330147,\n",
+       " -0.010369433090090752,\n",
+       " 0.012739036232233047,\n",
+       " -0.005365503951907158,\n",
+       " -0.0029668458737432957,\n",
+       " -0.007141091860830784,\n",
+       " -0.03383301943540573]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from redisvl.utils.vectorize import AzureOpenAITextVectorizer\n",
+    "\n",
+    "# create a vectorizer\n",
+    "az_oai = AzureOpenAITextVectorizer(\n",
+    "    model=\"text-embedding-ada-002\", # Must be your custom deployment name\n",
+    "    api_config={\n",
+    "        \"api_key\": api_key,\n",
+    "        \"api_version\": api_version,\n",
+    "        \"azure_endpoint\": azure_endpoint\n",
+    "    },\n",
+    ")\n",
+    "\n",
+    "test = az_oai.embed(\"This is a test sentence.\")\n",
+    "print(\"Vector dimensions: \", len(test))\n",
+    "test[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.017460526898503304,\n",
+       " -6.895032856846228e-05,\n",
+       " 0.0013909287517890334,\n",
+       " -0.025688467547297478,\n",
+       " -0.019813183695077896,\n",
+       " 0.016087085008621216,\n",
+       " -0.003729278687387705,\n",
+       " 0.0009211922879330814,\n",
+       " 0.006606514099985361,\n",
+       " -0.025128915905952454]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Just like OpenAI, AzureOpenAI supports batching embeddings and asynchronous requests.\n",
+    "sentences = [\n",
+    "    \"That is a happy dog\",\n",
+    "    \"That is a happy person\",\n",
+    "    \"Today is a sunny day\"\n",
+    "]\n",
+    "\n",
+    "embeddings = await az_oai.aembed_many(sentences)\n",
+    "embeddings[0][:10]"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -547,7 +656,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.12"
+   "version": "3.11.5"
   },
   "orig_nbformat": 4,
   "vscode": {

redisvl/utils/vectorize/__init__.py

@@ -1,4 +1,5 @@
 from redisvl.utils.vectorize.base import BaseVectorizer
+from redisvl.utils.vectorize.text.azureopenai import AzureOpenAITextVectorizer
 from redisvl.utils.vectorize.text.cohere import CohereTextVectorizer
 from redisvl.utils.vectorize.text.huggingface import HFTextVectorizer
 from redisvl.utils.vectorize.text.openai import OpenAITextVectorizer
@@ -10,4 +11,5 @@
     "HFTextVectorizer",
     "OpenAITextVectorizer",
     "VertexAITextVectorizer",
+    "AzureOpenAITextVectorizer",
 ]