AzureOpenAIEmbeddings#

class langchain_openai.embeddings.azure.AzureOpenAIEmbeddings[source]#

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](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.

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:

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:

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]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

param allowed_special: Literal['all'] | Set[str] | None = None#

param azure_ad_token: SecretStr | None [Optional]#

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.

param azure_ad_token_provider: Callable[[], str] | None = None#

A function that returns an Azure Active Directory token.

Will be invoked on every request.

param azure_endpoint: str | None [Optional]#

Your Azure endpoint, including the resource.

Automatically inferred from env var AZURE_OPENAI_ENDPOINT if not provided.

Example: https://example-resource.azure.openai.com/

param check_embedding_ctx_length: bool = True#: Whether to check the token length of inputs and automatically split inputs longer than embedding_ctx_length.

param chunk_size: int = 2048#: Maximum number of texts to embed in each batch

param default_headers: Mapping[str, str] | None = None#

param default_query: Mapping[str, object] | None = None#

param deployment: str | None = 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.

param dimensions: int | None = None#

The number of dimensions the resulting output embeddings should have.

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

param disallowed_special: Literal['all'] | Set[str] | Sequence[str] | None = None#

param embedding_ctx_length: int = 8191#: The maximum number of tokens to embed at once.

param headers: Any = None#

param 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.

param 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.

param max_retries: int = 2#: Maximum number of retries to make when generating.

param model: str = 'text-embedding-ada-002'#

param model_kwargs: Dict[str, Any] [Optional]#: Holds any model parameters valid for create call not explicitly specified.

param openai_api_base: str | None [Optional] (alias 'base_url')#: Base URL path for API requests, leave blank if not using a proxy or service emulator.

param openai_api_key: SecretStr | None [Optional] (alias 'api_key')#: Automatically inferred from env var AZURE_OPENAI_API_KEY if not provided.

param openai_api_type: str | None [Optional]#

param openai_api_version: str | None [Optional] (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.

param openai_organization: str | None [Optional] (alias 'organization')#: Automatically inferred from env var OPENAI_ORG_ID if not provided.

param openai_proxy: str | None [Optional]#

param request_timeout: float | Tuple[float, float] | Any | None = None (alias 'timeout')#: Timeout for requests to OpenAI completion API. Can be float, httpx.Timeout or None.

param retry_max_seconds: int = 20#: Max number of seconds to wait between retries

param retry_min_seconds: int = 4#: Min number of seconds to wait between retries

param show_progress_bar: bool = False#: Whether to show a progress bar when embedding.

param skip_empty: bool = False#: Whether to skip empty strings when embedding or raise an error. Defaults to not skipping.

param 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

param 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.

param validate_base_url: bool = True#

async aembed_documents(texts: List[str], chunk_size: int | None = 0) → List[List[float]]#

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

Parameters:

texts (List[str]) – The list of texts to embed.
chunk_size (int | None) – The chunk size of embeddings. If None, will use the chunk size specified by the class.

Returns:

List of embeddings, one for each text.

Return type:

List[List[float]]

async aembed_query(text: str) → List[float]#

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

Parameters:: text (str) – The text to embed.
Returns:: Embedding for the text.
Return type:: List[float]

embed_documents(texts: List[str], chunk_size: int | None = 0) → List[List[float]]#

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

Parameters:

texts (List[str]) – The list of texts to embed.
chunk_size (int | None) – The chunk size of embeddings. If None, will use the chunk size specified by the class.

Returns:

List of embeddings, one for each text.

Return type:

List[List[float]]

embed_query(text: str) → List[float]#

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

Parameters:: text (str) – The text to embed.
Returns:: Embedding for the text.
Return type:: List[float]

Examples using AzureOpenAIEmbeddings