mirror of
https://github.com/hwchase17/langchain.git
synced 2025-08-11 13:55:03 +00:00
openai: Update API Reference docs for AzureOpenAI Embeddings (#25312)
Update AzureOpenAI Embeddings docs
This commit is contained in:
parent
056c7c2983
commit
217a915b29
@ -13,19 +13,92 @@ from langchain_openai.embeddings.base import OpenAIEmbeddings
|
|||||||
|
|
||||||
|
|
||||||
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
|
class AzureOpenAIEmbeddings(OpenAIEmbeddings):
|
||||||
"""`Azure OpenAI` Embeddings API.
|
"""AzureOpenAI embedding model integration.
|
||||||
|
|
||||||
To use, you should have the
|
Setup:
|
||||||
environment variable ``AZURE_OPENAI_API_KEY`` set with your API key or pass it
|
To access AzureOpenAI embedding models you'll need to create an Azure account,
|
||||||
as a named parameter to the constructor.
|
get an API key, and install the `langchain-openai` integration package.
|
||||||
|
|
||||||
Example:
|
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
|
.. code-block:: python
|
||||||
|
|
||||||
from langchain_openai import AzureOpenAIEmbeddings
|
from langchain_openai import AzureOpenAIEmbeddings
|
||||||
|
|
||||||
openai = AzureOpenAIEmbeddings(model="text-embedding-3-large")
|
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: Union[str, None] = None
|
azure_endpoint: Union[str, None] = None
|
||||||
"""Your Azure endpoint, including the resource.
|
"""Your Azure endpoint, including the resource.
|
||||||
|
Loading…
Reference in New Issue
Block a user