Source code for langchain_openai.embeddings.azure

"""Azure OpenAI embeddings wrapper."""

from __future__ import annotations

from typing import Callable, Dict, Optional, Union

import openai
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
from langchain_core.utils import from_env, secret_from_env

from langchain_openai.embeddings.base import OpenAIEmbeddings


[docs]class AzureOpenAIEmbeddings(OpenAIEmbeddings):
    """AzureOpenAI embedding model integration.

    Setup:
        To access AzureOpenAI embedding models you'll need to create an Azure account,
        get an API key, and install the `langchain-openai` integration package.

        You’ll need to have an Azure OpenAI instance deployed.
        You can deploy a version on Azure Portal following this
        [guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).

        Once you have your instance running, make sure you have the name of your
        instance and key. You can find the key in the Azure Portal,
        under the “Keys and Endpoint” section of your instance.

        .. code-block:: bash

            pip install -U langchain_openai

            # Set up your environment variables (or pass them directly to the model)
            export AZURE_OPENAI_API_KEY="your-api-key"
            export AZURE_OPENAI_ENDPOINT="https://<your-endpoint>.openai.azure.com/"
            export AZURE_OPENAI_API_VERSION="2024-02-01"

    Key init args — completion params:
        model: str
            Name of AzureOpenAI model to use.
        dimensions: Optional[int]
            Number of dimensions for the embeddings. Can be specified only
            if the underlying model supports it.

    Key init args — client params:
      api_key: Optional[SecretStr]

    See full list of supported init args and their descriptions in the params section.

    Instantiate:
        .. code-block:: python

            from langchain_openai import AzureOpenAIEmbeddings

            embeddings = AzureOpenAIEmbeddings(
                model="text-embedding-3-large"
                # dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models
                # azure_endpoint="https://<your-endpoint>.openai.azure.com/", If not provided, will read env variable AZURE_OPENAI_ENDPOINT
                # api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY
                # openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION
            )

    Embed single text:
        .. code-block:: python

            input_text = "The meaning of life is 42"
            vector = embed.embed_query(input_text)
            print(vector[:3])

        .. code-block:: python

            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

    Embed multiple texts:
        .. code-block:: python

             input_texts = ["Document 1...", "Document 2..."]
            vectors = embed.embed_documents(input_texts)
            print(len(vectors))
            # The first 3 coordinates for the first vector
            print(vectors[0][:3])

        .. code-block:: python

            2
            [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

    Async:
        .. code-block:: python

            vector = await embed.aembed_query(input_text)
           print(vector[:3])

            # multiple:
            # await embed.aembed_documents(input_texts)

        .. code-block:: python

            [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
    """  # noqa: E501

    azure_endpoint: Optional[str] = Field(
        default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
    )
    """Your Azure endpoint, including the resource.

        Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.

        Example: `https://example-resource.azure.openai.com/`
    """
    deployment: Optional[str] = Field(default=None, alias="azure_deployment")
    """A model deployment.

        If given sets the base client URL to include `/deployments/{azure_deployment}`.
        Note: this means you won't be able to use non-deployment endpoints.
    """
    # Check OPENAI_KEY for backwards compatibility.
    # TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
    # other forms of azure credentials.
    openai_api_key: Optional[SecretStr] = Field(
        alias="api_key",
        default_factory=secret_from_env(
            ["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
        ),
    )
    """Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
    openai_api_version: Optional[str] = Field(
        default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15"),
        alias="api_version",
    )
    """Automatically inferred from env var `OPENAI_API_VERSION` if not provided.
    
    Set to "2023-05-15" by default if env variable `OPENAI_API_VERSION` is not set.
    """
    azure_ad_token: Optional[SecretStr] = Field(
        default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
    )
    """Your Azure Active Directory token.

        Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.

        For more:
        https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
    """
    azure_ad_token_provider: Union[Callable[[], str], None] = None
    """A function that returns an Azure Active Directory token.

        Will be invoked on every request.
    """
    openai_api_type: Optional[str] = Field(
        default_factory=from_env("OPENAI_API_TYPE", default="azure")
    )
    validate_base_url: bool = True
    chunk_size: int = 2048
    """Maximum number of texts to embed in each batch"""

    @root_validator(pre=False, skip_on_failure=True)
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        # For backwards compatibility. Before openai v1, no distinction was made
        # between azure_endpoint and base_url (openai_api_base).
        openai_api_base = values["openai_api_base"]
        if openai_api_base and values["validate_base_url"]:
            if "/openai" not in openai_api_base:
                values["openai_api_base"] += "/openai"
                raise ValueError(
                    "As of openai>=1.0.0, Azure endpoints should be specified via "
                    "the `azure_endpoint` param not `openai_api_base` "
                    "(or alias `base_url`). "
                )
            if values["deployment"]:
                raise ValueError(
                    "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 `deployment` (or alias `azure_deployment`) "
                    "and `azure_endpoint`."
                )
        client_params = {
            "api_version": values["openai_api_version"],
            "azure_endpoint": values["azure_endpoint"],
            "azure_deployment": values["deployment"],
            "api_key": (
                values["openai_api_key"].get_secret_value()
                if values["openai_api_key"]
                else None
            ),
            "azure_ad_token": (
                values["azure_ad_token"].get_secret_value()
                if values["azure_ad_token"]
                else None
            ),
            "azure_ad_token_provider": values["azure_ad_token_provider"],
            "organization": values["openai_organization"],
            "base_url": values["openai_api_base"],
            "timeout": values["request_timeout"],
            "max_retries": values["max_retries"],
            "default_headers": values["default_headers"],
            "default_query": values["default_query"],
        }
        if not values.get("client"):
            sync_specific = {"http_client": values["http_client"]}
            values["client"] = openai.AzureOpenAI(
                **client_params, **sync_specific
            ).embeddings
        if not values.get("async_client"):
            async_specific = {"http_client": values["http_async_client"]}
            values["async_client"] = openai.AsyncAzureOpenAI(
                **client_params, **async_specific
            ).embeddings
        return values

    @property
    def _llm_type(self) -> str:
        return "azure-openai-chat"