AzureOpenAIEmbeddings

Reference docs

This page contains reference documentation for AzureOpenAIEmbeddings. See the docs for conceptual guides, tutorials, and examples on using AzureOpenAIEmbeddings.

langchain_openai.embeddings.AzureOpenAIEmbeddings

Bases: 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.

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.

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

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

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

[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Embed multiple texts

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])

2
[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]

Async

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

# multiple:
# await embed.aembed_documents(input_texts)

[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]

METHOD	DESCRIPTION
embed_documents	Call out to OpenAI's embedding endpoint for embedding search docs.
embed_query	Call out to OpenAI's embedding endpoint for embedding query text.
aembed_documents	Call out to OpenAI's embedding endpoint async for embedding search docs.
aembed_query	Call out to OpenAI's embedding endpoint async for embedding query text.
build_extra	Build extra kwargs from additional params that were passed in.
validate_environment	Validate that api key and python package exists in environment.

dimensions class-attribute instance-attribute

dimensions: int | None = None

The number of dimensions the resulting output embeddings should have.

Only supported in text-embedding-3 and later models.

openai_api_base class-attribute instance-attribute

openai_api_base: str | None = Field(
    alias="base_url", default_factory=from_env("OPENAI_API_BASE", default=None)
)

Base URL path for API requests, leave blank if not using a proxy or service emulator.

embedding_ctx_length class-attribute instance-attribute

embedding_ctx_length: int = 8191

The maximum number of tokens to embed at once.

openai_organization class-attribute instance-attribute

openai_organization: str | None = Field(
    alias="organization",
    default_factory=from_env(["OPENAI_ORG_ID", "OPENAI_ORGANIZATION"], default=None),
)

Automatically inferred from env var OPENAI_ORG_ID if not provided.

max_retries class-attribute instance-attribute

max_retries: int = 2

Maximum number of retries to make when generating.

request_timeout class-attribute instance-attribute

request_timeout: float | tuple[float, float] | Any | None = Field(
    default=None, alias="timeout"
)

Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

tiktoken_enabled class-attribute instance-attribute

tiktoken_enabled: bool = True

Set this to False for non-OpenAI implementations of the embeddings API, e.g. the --extensions openai extension for text-generation-webui

tiktoken_model_name class-attribute instance-attribute

tiktoken_model_name: str | None = None

The model name to pass to tiktoken when using this class. Tiktoken is used to count the number of tokens in documents to constrain them to be under a certain limit. By default, when set to None, this will be the same as the embedding model name. However, there are some cases where you may want to use this Embedding class with a model name not supported by tiktoken. This can include when using Azure embeddings or when using one of the many model providers that expose an OpenAI-like API but with different models. In those cases, in order to avoid erroring when tiktoken is called, you can specify a model name to use here.

show_progress_bar class-attribute instance-attribute

show_progress_bar: bool = False

Whether to show a progress bar when embedding.

model_kwargs class-attribute instance-attribute

model_kwargs: dict[str, Any] = Field(default_factory=dict)

Holds any model parameters valid for create call not explicitly specified.

skip_empty class-attribute instance-attribute

skip_empty: bool = False

Whether to skip empty strings when embedding or raise an error.

retry_min_seconds class-attribute instance-attribute

retry_min_seconds: int = 4

Min number of seconds to wait between retries

retry_max_seconds class-attribute instance-attribute

retry_max_seconds: int = 20

Max number of seconds to wait between retries

http_client class-attribute instance-attribute

http_client: Any | None = None

Optional httpx.Client. Only used for sync invocations. Must specify http_async_client as well if you'd like a custom client for async invocations.

http_async_client class-attribute instance-attribute

http_async_client: Any | None = None

Optional httpx.AsyncClient. Only used for async invocations. Must specify http_client as well if you'd like a custom client for sync invocations.

check_embedding_ctx_length class-attribute instance-attribute

check_embedding_ctx_length: bool = True

Whether to check the token length of inputs and automatically split inputs longer than embedding_ctx_length.

azure_endpoint class-attribute instance-attribute

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 class-attribute instance-attribute

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.

openai_api_key class-attribute instance-attribute

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 class-attribute instance-attribute

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 class-attribute instance-attribute

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.

azure_ad_token_provider class-attribute instance-attribute

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 class-attribute instance-attribute

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.

chunk_size class-attribute instance-attribute

chunk_size: int = 2048

Maximum number of texts to embed in each batch

embed_documents

embed_documents(
    texts: list[str], chunk_size: int | None = None, **kwargs: Any
) -> list[list[float]]

Call out to OpenAI's embedding endpoint for embedding search docs.

PARAMETER	DESCRIPTION
texts	The list of texts to embed. TYPE: list[str]
chunk_size	The chunk size of embeddings. If None, will use the chunk size specified by the class. TYPE: int | None DEFAULT: None
kwargs	Additional keyword arguments to pass to the embedding API. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings, one for each text.

embed_query

embed_query(text: str, **kwargs: Any) -> list[float]

Call out to OpenAI's embedding endpoint for embedding query text.

PARAMETER	DESCRIPTION
text	The text to embed. TYPE: str
kwargs	Additional keyword arguments to pass to the embedding API. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
list[float]	Embedding for the text.

aembed_documents async

aembed_documents(
    texts: list[str], chunk_size: int | None = None, **kwargs: Any
) -> list[list[float]]

Call out to OpenAI's embedding endpoint async for embedding search docs.

PARAMETER	DESCRIPTION
texts	The list of texts to embed. TYPE: list[str]
chunk_size	The chunk size of embeddings. If None, will use the chunk size specified by the class. TYPE: int | None DEFAULT: None
kwargs	Additional keyword arguments to pass to the embedding API. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
list[list[float]]	List of embeddings, one for each text.

aembed_query async

aembed_query(text: str, **kwargs: Any) -> list[float]

Call out to OpenAI's embedding endpoint async for embedding query text.

PARAMETER	DESCRIPTION
text	The text to embed. TYPE: str
kwargs	Additional keyword arguments to pass to the embedding API. TYPE: Any DEFAULT: {}

RETURNS	DESCRIPTION
list[float]	Embedding for the text.

build_extra classmethod

build_extra(values: dict[str, Any]) -> Any

Build extra kwargs from additional params that were passed in.

validate_environment

validate_environment() -> Self

Validate that api key and python package exists in environment.