AzureOpenAIEmbeddings Class — langchain Architecture

Architecture documentation for the AzureOpenAIEmbeddings class in azure.py from the langchain codebase.

Class python

Entity Profile

Dependency Diagram

graph TD
  b39b2417_4449_2d2f_168b_475b9217cf18["AzureOpenAIEmbeddings"]
  02ce964e_7ae0_baca_8a6a_784328c5c8a2["OpenAIEmbeddings"]
  b39b2417_4449_2d2f_168b_475b9217cf18 -->|extends| 02ce964e_7ae0_baca_8a6a_784328c5c8a2
  fe99e450_f39e_b29c_2ae7_ae092f3a28f1["azure.py"]
  b39b2417_4449_2d2f_168b_475b9217cf18 -->|defined in| fe99e450_f39e_b29c_2ae7_ae092f3a28f1
  cfd54c5b_d807_5122_ac12_7fb10c9f92ad["validate_environment()"]
  b39b2417_4449_2d2f_168b_475b9217cf18 -->|method| cfd54c5b_d807_5122_ac12_7fb10c9f92ad
  8cf6af83_ed48_711c_2988_f3f77d12397a["_llm_type()"]
  b39b2417_4449_2d2f_168b_475b9217cf18 -->|method| 8cf6af83_ed48_711c_2988_f3f77d12397a

Relationship Graph

Source Code

libs/partners/openai/langchain_openai/embeddings/azure.py lines 16–230

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