tx_flow_analysis/venv/lib/python3.10/site-packages/langchain_openai/embeddings/azure.py

"""Azure OpenAI embeddings wrapper."""

from __future__ import annotations

from collections.abc import Awaitable, Callable
from typing import cast

import openai
from langchain_core.utils import from_env, secret_from_env
from pydantic import Field, SecretStr, model_validator
from typing_extensions import Self

from langchain_openai.embeddings.base import OpenAIEmbeddings


class AzureOpenAIEmbeddings(OpenAIEmbeddings):  # type: ignore[override]
    """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.

        ```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:
            Name of `AzureOpenAI` model to use.
        dimensions:
            Number of dimensions for the embeddings. Can be specified only if the
            underlying model supports it.

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

    Instantiate:
        ```python
        from langchain_openai import AzureOpenAIEmbeddings

        embeddings = AzureOpenAIEmbeddings(
            model="text-embedding-3-large"
            # dimensions: int | None = 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:
        ```python
        input_text = "The meaning of life is 42"
        vector = embed.embed_query(input_text)
        print(vector[:3])
        ```
        ```python
        [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
        ```

    Embed multiple texts:
        ```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])
        ```
        ```python
        2
        [-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
        ```

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

        # multiple:
        # await embed.aembed_documents(input_texts)
        ```
        ```python
        [-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
        ```
    """  # noqa: E501

    azure_endpoint: str | None = 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: str | None = 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: SecretStr | None = 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: str | None = 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: SecretStr | None = 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, see this page.](https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id)
    """
    azure_ad_token_provider: Callable[[], str] | None = None
    """A function that returns an Azure Active Directory token.

        Will be invoked on every sync request. For async requests,
        will be invoked if `azure_ad_async_token_provider` is not provided.
    """
    azure_ad_async_token_provider: Callable[[], Awaitable[str]] | None = None
    """A function that returns an Azure Active Directory token.

        Will be invoked on every async request.
    """
    openai_api_type: str | None = 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"""

    @model_validator(mode="after")
    def validate_environment(self) -> Self:
        """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 = self.openai_api_base
        if openai_api_base and self.validate_base_url:
            # Only validate openai_api_base if azure_endpoint is not provided
            if not self.azure_endpoint and "/openai" not in openai_api_base:
                self.openai_api_base = cast(str, self.openai_api_base) + "/openai"
                msg = (
                    "As of openai>=1.0.0, Azure endpoints should be specified via "
                    "the `azure_endpoint` param not `openai_api_base` "
                    "(or alias `base_url`). "
                )
                raise ValueError(msg)
            if self.deployment:
                msg = (
                    "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`."
                )
                raise ValueError(msg)
        client_params: dict = {
            "api_version": self.openai_api_version,
            "azure_endpoint": self.azure_endpoint,
            "azure_deployment": self.deployment,
            "api_key": (
                self.openai_api_key.get_secret_value() if self.openai_api_key else None
            ),
            "azure_ad_token": (
                self.azure_ad_token.get_secret_value() if self.azure_ad_token else None
            ),
            "azure_ad_token_provider": self.azure_ad_token_provider,
            "organization": self.openai_organization,
            "base_url": self.openai_api_base,
            "timeout": self.request_timeout,
            "max_retries": self.max_retries,
            "default_headers": {
                **(self.default_headers or {}),
                "User-Agent": "langchain-partner-python-azure-openai",
            },
            "default_query": self.default_query,
        }
        if not self.client:
            sync_specific: dict = {"http_client": self.http_client}
            self.client = openai.AzureOpenAI(
                **client_params,  # type: ignore[arg-type]
                **sync_specific,
            ).embeddings
        if not self.async_client:
            async_specific: dict = {"http_client": self.http_async_client}

            if self.azure_ad_async_token_provider:
                client_params["azure_ad_token_provider"] = (
                    self.azure_ad_async_token_provider
                )

            self.async_client = openai.AsyncAzureOpenAI(
                **client_params,  # type: ignore[arg-type]
                **async_specific,
            ).embeddings
        return self

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