mirror of
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216 lines
8.4 KiB
Python
216 lines
8.4 KiB
Python
"""Azure OpenAI embeddings wrapper."""
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from __future__ import annotations
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from typing import Callable, Dict, Optional, Union
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import openai
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import from_env, secret_from_env
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from langchain_openai.embeddings.base import OpenAIEmbeddings
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class AzureOpenAIEmbeddings(OpenAIEmbeddings):
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"""AzureOpenAI embedding model integration.
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Setup:
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To access AzureOpenAI embedding models you'll need to create an Azure account,
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get an API key, and install the `langchain-openai` integration package.
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You’ll need to have an Azure OpenAI instance deployed.
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You can deploy a version on Azure Portal following this
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[guide](https://learn.microsoft.com/en-us/azure/ai-services/openai/how-to/create-resource?pivots=web-portal).
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Once you have your instance running, make sure you have the name of your
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instance and key. You can find the key in the Azure Portal,
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under the “Keys and Endpoint” section of your instance.
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.. code-block:: bash
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pip install -U langchain_openai
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# Set up your environment variables (or pass them directly to the model)
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export AZURE_OPENAI_API_KEY="your-api-key"
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export AZURE_OPENAI_ENDPOINT="https://<your-endpoint>.openai.azure.com/"
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export AZURE_OPENAI_API_VERSION="2024-02-01"
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Key init args — completion params:
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model: str
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Name of AzureOpenAI model to use.
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dimensions: Optional[int]
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Number of dimensions for the embeddings. Can be specified only
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if the underlying model supports it.
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Key init args — client params:
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api_key: Optional[SecretStr]
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See full list of supported init args and their descriptions in the params section.
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Instantiate:
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.. code-block:: python
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from langchain_openai import AzureOpenAIEmbeddings
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embeddings = AzureOpenAIEmbeddings(
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model="text-embedding-3-large"
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# dimensions: Optional[int] = None, # Can specify dimensions with new text-embedding-3 models
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# azure_endpoint="https://<your-endpoint>.openai.azure.com/", If not provided, will read env variable AZURE_OPENAI_ENDPOINT
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# api_key=... # Can provide an API key directly. If missing read env variable AZURE_OPENAI_API_KEY
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# openai_api_version=..., # If not provided, will read env variable AZURE_OPENAI_API_VERSION
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)
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Embed single text:
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.. code-block:: python
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input_text = "The meaning of life is 42"
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vector = embed.embed_query(input_text)
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print(vector[:3])
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.. code-block:: python
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[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
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Embed multiple texts:
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.. code-block:: python
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input_texts = ["Document 1...", "Document 2..."]
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vectors = embed.embed_documents(input_texts)
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print(len(vectors))
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# The first 3 coordinates for the first vector
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print(vectors[0][:3])
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.. code-block:: python
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2
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[-0.024603435769677162, -0.007543657906353474, 0.0039630369283258915]
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Async:
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.. code-block:: python
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vector = await embed.aembed_query(input_text)
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print(vector[:3])
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# multiple:
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# await embed.aembed_documents(input_texts)
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.. code-block:: python
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[-0.009100092574954033, 0.005071679595857859, -0.0029193938244134188]
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""" # noqa: E501
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azure_endpoint: Optional[str] = Field(
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default_factory=from_env("AZURE_OPENAI_ENDPOINT", default=None)
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)
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"""Your Azure endpoint, including the resource.
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Automatically inferred from env var `AZURE_OPENAI_ENDPOINT` if not provided.
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Example: `https://example-resource.azure.openai.com/`
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"""
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deployment: Optional[str] = Field(default=None, alias="azure_deployment")
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"""A model deployment.
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If given sets the base client URL to include `/deployments/{azure_deployment}`.
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Note: this means you won't be able to use non-deployment endpoints.
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"""
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# Check OPENAI_KEY for backwards compatibility.
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# TODO: Remove OPENAI_API_KEY support to avoid possible conflict when using
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# other forms of azure credentials.
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openai_api_key: Optional[SecretStr] = Field(
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alias="api_key",
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default_factory=secret_from_env(
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["AZURE_OPENAI_API_KEY", "OPENAI_API_KEY"], default=None
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),
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)
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"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
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openai_api_version: Optional[str] = Field(
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default_factory=from_env("OPENAI_API_VERSION", default="2023-05-15"),
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alias="api_version",
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)
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"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided.
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Set to "2023-05-15" by default if env variable `OPENAI_API_VERSION` is not set.
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"""
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azure_ad_token: Optional[SecretStr] = Field(
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default_factory=secret_from_env("AZURE_OPENAI_AD_TOKEN", default=None)
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)
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"""Your Azure Active Directory token.
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Automatically inferred from env var `AZURE_OPENAI_AD_TOKEN` if not provided.
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For more:
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https://www.microsoft.com/en-us/security/business/identity-access/microsoft-entra-id.
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"""
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azure_ad_token_provider: Union[Callable[[], str], None] = None
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"""A function that returns an Azure Active Directory token.
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Will be invoked on every request.
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"""
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openai_api_type: Optional[str] = Field(
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default_factory=from_env("OPENAI_API_TYPE", default="azure")
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)
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validate_base_url: bool = True
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chunk_size: int = 2048
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"""Maximum number of texts to embed in each batch"""
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@root_validator(pre=False, skip_on_failure=True)
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key and python package exists in environment."""
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# For backwards compatibility. Before openai v1, no distinction was made
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# between azure_endpoint and base_url (openai_api_base).
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openai_api_base = values["openai_api_base"]
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if openai_api_base and values["validate_base_url"]:
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if "/openai" not in openai_api_base:
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values["openai_api_base"] += "/openai"
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raise ValueError(
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"As of openai>=1.0.0, Azure endpoints should be specified via "
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"the `azure_endpoint` param not `openai_api_base` "
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"(or alias `base_url`). "
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)
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if values["deployment"]:
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raise ValueError(
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"As of openai>=1.0.0, if `deployment` (or alias "
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"`azure_deployment`) is specified then "
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"`openai_api_base` (or alias `base_url`) should not be. "
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"Instead use `deployment` (or alias `azure_deployment`) "
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"and `azure_endpoint`."
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)
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client_params = {
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"api_version": values["openai_api_version"],
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"azure_endpoint": values["azure_endpoint"],
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"azure_deployment": values["deployment"],
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"api_key": (
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values["openai_api_key"].get_secret_value()
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if values["openai_api_key"]
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else None
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),
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"azure_ad_token": (
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values["azure_ad_token"].get_secret_value()
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if values["azure_ad_token"]
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else None
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),
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"azure_ad_token_provider": values["azure_ad_token_provider"],
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"organization": values["openai_organization"],
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"base_url": values["openai_api_base"],
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"timeout": values["request_timeout"],
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"max_retries": values["max_retries"],
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"default_headers": values["default_headers"],
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"default_query": values["default_query"],
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}
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if not values.get("client"):
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sync_specific = {"http_client": values["http_client"]}
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values["client"] = openai.AzureOpenAI(
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**client_params, **sync_specific
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).embeddings
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if not values.get("async_client"):
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async_specific = {"http_client": values["http_async_client"]}
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values["async_client"] = openai.AsyncAzureOpenAI(
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**client_params, **async_specific
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).embeddings
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return values
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@property
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def _llm_type(self) -> str:
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return "azure-openai-chat"
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