update azure embedding docs (#13091)

docs/docs/integrations/text_embedding/azureopenai.ipynb

@@ -7,7 +7,95 @@
    "source": [
     "# AzureOpenAI\n",
     "\n",
-    "Let's load the OpenAI Embedding class with environment variables set to indicate to use Azure endpoints."
+    "Let's load the Azure OpenAI Embedding class with environment variables set to indicate to use Azure endpoints."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8a6ed30d-806f-4800-b5fd-d04126be9060",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "\n",
+    "os.environ[\"AZURE_OPENAI_API_KEY\"] = \"...\"\n",
+    "os.environ[\"AZURE_OPENAI_ENDPOINT\"] = \"https://<yout-endpoint>.openai.azure.com/\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "20179bc7-3f71-4909-be12-d38bce009b18",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from langchain.embeddings import AzureOpenAIEmbeddings\n",
+    "\n",
+    "embeddings = AzureOpenAIEmbeddings(azure_deployment=\"<your-embeddings-deployment-name>\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "f8cb9dca-738b-450f-9986-5c3efd3c6eb3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "text = \"this is a test document\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "0fae0295-b117-4a5a-8b98-500c79306551",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "query_result = embeddings.embed_query(text)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "65a01ddd-0bbf-444f-a87f-93af25ef902c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "doc_result = embeddings.embed_documents([text])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "45771052-68ca-4e03-9c4f-a0c7796d9442",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[-0.012222584727053133,\n",
+       " 0.0072103982392216145,\n",
+       " -0.014818063280923775,\n",
+       " -0.026444746872933557,\n",
+       " -0.0034330499700826883]"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "doc_result[0][:5]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e66ec1f2-6768-4ee5-84bf-a2d76adc20c8",
+   "metadata": {},
+   "source": [
+    "## [Legacy] When using `openai<1`"
    ]
   },
   {
@@ -79,9 +167,9 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
+   "display_name": "poetry-venv",
    "language": "python",
-   "name": "python3"
+   "name": "poetry-venv"
   },
   "language_info": {
    "codemirror_mode": {

libs/langchain/langchain/embeddings/azure_openai.py

@@ -21,7 +21,7 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
         
         Example: `https://example-resource.azure.openai.com/`
     """
-    azure_deployment: Optional[str] = None
+    deployment: Optional[str] = Field(default=None, alias="azure_deployment")
     """A model deployment. 
 
         If given sets the base client URL to include `/deployments/{azure_deployment}`.
@@ -104,15 +104,15 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
                         f"(or alias `base_url`). Updating `openai_api_base` from "
                         f"{openai_api_base} to {values['openai_api_base']}."
                     )
-                if values["azure_deployment"]:
+                if values["deployment"]:
                     warnings.warn(
-                        "As of openai>=1.0.0, if `azure_deployment` (or alias "
+                        "As of openai>=1.0.0, if `deployment` (or alias "
                         "`azure_deployment`) is specified then "
                         "`openai_api_base` (or alias `base_url`) should not be. "
-                        "Instead use `azure_deployment` (or alias `azure_deployment`) "
+                        "Instead use `deployment` (or alias `azure_deployment`) "
                         "and `azure_endpoint`."
                     )
-                    if values["azure_deployment"] not in values["openai_api_base"]:
+                    if values["deployment"] not in values["openai_api_base"]:
                         warnings.warn(
                             "As of openai>=1.0.0, if `openai_api_base` "
                             "(or alias `base_url`) is specified it is expected to be "
@@ -122,13 +122,13 @@ class AzureOpenAIEmbeddings(OpenAIEmbeddings):
                             f"{values['openai_api_base']}."
                         )
                         values["openai_api_base"] += (
-                            "/deployments/" + values["azure_deployment"]
+                            "/deployments/" + values["deployment"]
                         )
-                    values["azure_deployment"] = None
+                    values["deployment"] = None
             client_params = {
                 "api_version": values["openai_api_version"],
                 "azure_endpoint": values["azure_endpoint"],
-                "azure_deployment": values["azure_deployment"],
+                "azure_deployment": values["deployment"],
                 "api_key": values["openai_api_key"],
                 "azure_ad_token": values["azure_ad_token"],
                 "azure_ad_token_provider": values["azure_ad_token_provider"],

libs/langchain/langchain/embeddings/openai.py

@@ -17,6 +17,7 @@ from typing import (
     Set,
     Tuple,
     Union,
+    cast,
 )
 
 import numpy as np
@@ -182,7 +183,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
     async_client: Any = None  #: :meta private:
     model: str = "text-embedding-ada-002"
     # to support Azure OpenAI Service custom deployment names
-    deployment: str = model
+    deployment: Optional[str] = model
     # TODO: Move to AzureOpenAIEmbeddings.
     openai_api_version: Optional[str] = Field(default=None, alias="api_version")
     """Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
@@ -546,7 +547,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         """
         # NOTE: to keep things simple, we assume the list may contain texts longer
         #       than the maximum context and use length-safe embedding function.
-        return self._get_len_safe_embeddings(texts, engine=self.deployment)
+        engine = cast(str, self.deployment)
+        return self._get_len_safe_embeddings(texts, engine=engine)
 
     async def aembed_documents(
         self, texts: List[str], chunk_size: Optional[int] = 0
@@ -563,7 +565,8 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
         """
         # NOTE: to keep things simple, we assume the list may contain texts longer
         #       than the maximum context and use length-safe embedding function.
-        return await self._aget_len_safe_embeddings(texts, engine=self.deployment)
+        engine = cast(str, self.deployment)
+        return await self._aget_len_safe_embeddings(texts, engine=engine)
 
     def embed_query(self, text: str) -> List[float]:
         """Call out to OpenAI's embedding endpoint for embedding query text.