231 lines
8.9 KiB
Python
231 lines
8.9 KiB
Python
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"""Azure OpenAI embeddings wrapper."""
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from __future__ import annotations
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from collections.abc import Awaitable, Callable
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from typing import cast
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import openai
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from langchain_core.utils import from_env, secret_from_env
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from pydantic import Field, SecretStr, model_validator
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from typing_extensions import Self
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from langchain_openai.embeddings.base import OpenAIEmbeddings
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class AzureOpenAIEmbeddings(OpenAIEmbeddings): # type: ignore[override]
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"""AzureOpenAI embedding model integration.
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Setup:
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To access AzureOpenAI embedding models you'll need to create an Azure account,
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get an API key, and install the `langchain-openai` integration package.
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You'll need to have an Azure OpenAI instance deployed.
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You can deploy a version on Azure Portal following this
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[guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).
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Once you have your instance running, make sure you have the name of your
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instance and key. You can find the key in the Azure Portal,
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under the “Keys and Endpoint” section of your instance.
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```bash
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pip install -U langchain_openai
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# Set up your environment variables (or pass them directly to the model)
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export AZURE_OPENAI_API_KEY="your-api-key"
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export AZURE_OPENAI_ENDPOINT="https://<your-endpoint>.openai.azure.com/"
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export AZURE_OPENAI_API_VERSION="2024-02-01"
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```
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Key init args — completion params:
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model:
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Name of `AzureOpenAI` model to use.
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dimensions:
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Number of dimensions for the embeddings. Can be specified only if the
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underlying model supports it.
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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```python
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from langchain_openai import AzureOpenAIEmbeddings
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embeddings = AzureOpenAIEmbeddings(
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model="text-embedding-3-large"
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# dimensions: int | None = None, # Can specify dimensions with new text-embedding-3 models
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# azure_endpoint="https://<your-endpoint>.openai.azure.com/", If not provided, will read env variable AZURE_OPENAI_ENDPOINT
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# api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY
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# openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION
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)
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```
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Embed single text:
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```python
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input_text = "The meaning of life is 42"
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vector = embed.embed_query(input_text)
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print(vector[:3])
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```
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```python
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[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
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```
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Embed multiple texts:
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```python
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input_texts = ["Document 1...", "Document 2..."]
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vectors = embed.embed_documents(input_texts)
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print(len(vectors))
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# The first 3 coordinates for the first vector
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print(vectors[0][:3])
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```
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```python
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2
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[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
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```
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Async:
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```python
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vector = await embed.aembed_query(input_text)
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print(vector[:3])
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# multiple:
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# await embed.aembed_documents(input_texts)
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```
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```python
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[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
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```
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""" # noqa: E501
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azure_endpoint: str | None = Field(
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default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
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)
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"""Your Azure endpoint, including the resource.
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Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
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Example: `https://example-resource.azure.openai.com/`
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"""
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deployment: str | None = Field(default=None, alias="azure_deployment")
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"""A model deployment.
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If given sets the base client URL to include `/deployments/{azure_deployment}`.
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!!! note
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This means you won't be able to use non-deployment endpoints.
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"""
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# Check OPENAI_KEY for backwards compatibility.
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# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
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# other forms of azure credentials.
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openai_api_key: SecretStr | None = Field(
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alias="api_key",
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default_factory=secret_from_env(
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["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
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),
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)
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"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
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openai_api_version: str | None = Field(
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default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15"),
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alias="api_version",
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)
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"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided.
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Set to `'2023-05-15'` by default if env variable `OPENAI_API_VERSION` is not
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set.
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"""
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azure_ad_token: SecretStr | None = Field(
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default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
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)
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"""Your Azure Active Directory token.
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Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
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[For more, see this page.](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id)
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"""
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azure_ad_token_provider: Callable[[], str] | None = None
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"""A function that returns an Azure Active Directory token.
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Will be invoked on every sync request. For async requests,
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will be invoked if `azure_ad_async_token_provider` is not provided.
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"""
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azure_ad_async_token_provider: Callable[[], Awaitable[str]] | None = None
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"""A function that returns an Azure Active Directory token.
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Will be invoked on every async request.
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"""
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openai_api_type: str | None = Field(
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default_factory=from_env("OPENAI_API_TYPE", default="azure")
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)
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validate_base_url: bool = True
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chunk_size: int = 2048
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"""Maximum number of texts to embed in each batch"""
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@model_validator(mode="after")
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def validate_environment(self) -> Self:
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"""Validate that api key and python package exists in environment."""
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# For backwards compatibility. Before openai v1, no distinction was made
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# between azure_endpoint and base_url (openai_api_base).
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openai_api_base = self.openai_api_base
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if openai_api_base and self.validate_base_url:
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# Only validate openai_api_base if azure_endpoint is not provided
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if not self.azure_endpoint and "/openai" not in openai_api_base:
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self.openai_api_base = cast(str, self.openai_api_base) + "/openai"
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msg = (
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"As of openai>=1.0.0, Azure endpoints should be specified via "
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"the `azure_endpoint` param not `openai_api_base` "
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"(or alias `base_url`). "
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)
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raise ValueError(msg)
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if self.deployment:
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msg = (
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"As of openai>=1.0.0, if `deployment` (or alias "
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"`azure_deployment`) is specified then "
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"`openai_api_base` (or alias `base_url`) should not be. "
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"Instead use `deployment` (or alias `azure_deployment`) "
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"and `azure_endpoint`."
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)
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raise ValueError(msg)
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client_params: dict = {
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"api_version": self.openai_api_version,
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"azure_endpoint": self.azure_endpoint,
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"azure_deployment": self.deployment,
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"api_key": (
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self.openai_api_key.get_secret_value() if self.openai_api_key else None
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),
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"azure_ad_token": (
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self.azure_ad_token.get_secret_value() if self.azure_ad_token else None
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),
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"azure_ad_token_provider": self.azure_ad_token_provider,
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"organization": self.openai_organization,
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"base_url": self.openai_api_base,
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"timeout": self.request_timeout,
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"max_retries": self.max_retries,
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"default_headers": {
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**(self.default_headers or {}),
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"User-Agent": "langchain-partner-python-azure-openai",
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},
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"default_query": self.default_query,
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}
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if not self.client:
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sync_specific: dict = {"http_client": self.http_client}
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self.client = openai.AzureOpenAI(
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**client_params, # type: ignore[arg-type]
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**sync_specific,
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).embeddings
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if not self.async_client:
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async_specific: dict = {"http_client": self.http_async_client}
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if self.azure_ad_async_token_provider:
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client_params["azure_ad_token_provider"] = (
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self.azure_ad_async_token_provider
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)
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self.async_client = openai.AsyncAzureOpenAI(
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**client_params, # type: ignore[arg-type]
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**async_specific,
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).embeddings
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return self
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@property
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def _llm_type(self) -> str:
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return "azure-openai-chat"
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