mirror of
https://github.com/hwchase17/langchain.git
synced 2025-04-28 11:55:21 +00:00
8c6734325b
Using `pyupgrade` to get all `partners` code up to 3.9 standards (mostly, fixing old `typing` imports).
231 lines
9.0 KiB
Python
231 lines
9.0 KiB
Python
"""Azure OpenAI embeddings wrapper."""
|
||
|
||
from __future__ import annotations
|
||
|
||
from collections.abc import Awaitable
|
||
from typing import Callable, Optional, Union, 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.
|
||
|
||
.. 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
|
||
|
||
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:
|
||
.. 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: Optional[str] = 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: Optional[str] = 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: Optional[SecretStr] = 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: Optional[str] = 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: Optional[SecretStr] = 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:
|
||
https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
|
||
"""
|
||
azure_ad_token_provider: Union[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: Union[Callable[[], Awaitable[str]], None] = None
|
||
"""A function that returns an Azure Active Directory token.
|
||
|
||
Will be invoked on every async request.
|
||
"""
|
||
openai_api_type: Optional[str] = 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:
|
||
if "/openai" not in openai_api_base:
|
||
self.openai_api_base = cast(str, self.openai_api_base) + "/openai"
|
||
raise ValueError(
|
||
"As of openai>=1.0.0, Azure endpoints should be specified via "
|
||
"the `azure_endpoint` param not `openai_api_base` "
|
||
"(or alias `base_url`). "
|
||
)
|
||
if self.deployment:
|
||
raise ValueError(
|
||
"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`."
|
||
)
|
||
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"
|