Kye
11 months ago
parent
ce0dd1f5a6
commit
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from swarms.models.azure_openai_llm import AzureOpenAI
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# Initialize Azure OpenAI
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model = AzureOpenAI()
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# Run the model
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model(
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"Create a youtube script for a video on how to use the swarms"
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" framework"
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)
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from swarms import Agent, OpenAIChat, SequentialWorkflow
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# Example usage
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llm = OpenAIChat(
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temperature=0.5,
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max_tokens=3000,
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)
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# Initialize the Agent with the language agent
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agent1 = Agent(
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agent_name="John the writer",
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llm=llm,
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max_loops=1,
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dashboard=False,
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)
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# Create another Agent for a different task
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agent2 = Agent("Summarizer", llm=llm, max_loops=1, dashboard=False)
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# Create the workflow
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workflow = SequentialWorkflow(
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name="Blog Generation Workflow",
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description=(
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"Generate a youtube transcript on how to deploy agents into production"
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),
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max_loops=1,
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autosave=True,
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dashboard=False,
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agents=[agent1, agent2],
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)
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# Run the workflow
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workflow.run()
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# # # Output the results
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# for task in workflow.tasks:
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# print(f"Task: {task.description}, Result: {task.result}")
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from __future__ import annotations
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import logging
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import os
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from typing import Any, Callable, Dict, List, Mapping, Optional, Union
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import openai
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from langchain_core.pydantic_v1 import (
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Field,
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SecretStr,
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root_validator,
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)
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from langchain_core.utils import (
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convert_to_secret_str,
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get_from_dict_or_env,
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)
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from langchain_openai.llms.base import BaseOpenAI
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logger = logging.getLogger(__name__)
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class AzureOpenAI(BaseOpenAI):
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"""Azure-specific OpenAI large language models.
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To use, you should have the ``openai`` python package installed, and the
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environment variable ``OPENAI_API_KEY`` set with your API key.
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Any parameters that are valid to be passed to the openai.create call can be passed
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in, even if not explicitly saved on this class.
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Example:
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.. code-block:: python
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from swarms import AzureOpenAI
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openai = AzureOpenAI(model_name="gpt-3.5-turbo-instruct")
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"""
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azure_endpoint: Union[str, None] = None
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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_name: Union[str, None] = Field(
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default=None, alias="azure_deployment"
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)
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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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openai_api_version: str = Field(default="", alias="api_version")
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"""Automatically inferred from env var `OPENAI_API_VERSION` if not provided."""
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openai_api_key: Optional[SecretStr] = Field(
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default=None, alias="api_key"
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)
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"""Automatically inferred from env var `AZURE_OPENAI_API_KEY` if not provided."""
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azure_ad_token: Optional[SecretStr] = None
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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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""" # noqa: E501
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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: str = ""
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"""Legacy, for openai<1.0.0 support."""
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validate_base_url: bool = True
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"""For backwards compatibility. If legacy val openai_api_base is passed in, try to
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infer if it is a base_url or azure_endpoint and update accordingly.
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"""
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "llms", "openai"]
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@root_validator()
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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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if values["n"] < 1:
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raise ValueError("n must be at least 1.")
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if values["streaming"] and values["n"] > 1:
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raise ValueError("Cannot stream results when n > 1.")
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if values["streaming"] and values["best_of"] > 1:
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raise ValueError(
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"Cannot stream results when best_of > 1."
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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 = (
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values["openai_api_key"]
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or os.getenv("AZURE_OPENAI_API_KEY")
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or os.getenv("OPENAI_API_KEY")
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)
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values["openai_api_key"] = (
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convert_to_secret_str(openai_api_key)
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if openai_api_key
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else None
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)
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values["azure_endpoint"] = values[
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"azure_endpoint"
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] or os.getenv("AZURE_OPENAI_ENDPOINT")
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azure_ad_token = values["azure_ad_token"] or os.getenv(
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"AZURE_OPENAI_AD_TOKEN"
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)
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values["azure_ad_token"] = (
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convert_to_secret_str(azure_ad_token)
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if azure_ad_token
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else None
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)
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values["openai_api_base"] = values[
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"openai_api_base"
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] or os.getenv("OPENAI_API_BASE")
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values["openai_proxy"] = get_from_dict_or_env(
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values,
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"openai_proxy",
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"OPENAI_PROXY",
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default="",
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)
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values["openai_organization"] = (
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values["openai_organization"]
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or os.getenv("OPENAI_ORG_ID")
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or os.getenv("OPENAI_ORGANIZATION")
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)
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values["openai_api_version"] = values[
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"openai_api_version"
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] or os.getenv("OPENAI_API_VERSION")
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values["openai_api_type"] = get_from_dict_or_env(
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values,
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"openai_api_type",
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"OPENAI_API_TYPE",
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default="azure",
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)
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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"] = (
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values["openai_api_base"].rstrip("/") + "/openai"
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)
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raise ValueError(
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"As of openai>=1.0.0, Azure endpoints should be"
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" specified via the `azure_endpoint` param not"
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" `openai_api_base` (or alias `base_url`)."
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)
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if values["deployment_name"]:
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raise ValueError(
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"As of openai>=1.0.0, if `deployment_name` (or"
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" alias `azure_deployment`) is specified then"
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" `openai_api_base` (or alias `base_url`) should"
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" not be. Instead use `deployment_name` (or alias"
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" `azure_deployment`) and `azure_endpoint`."
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)
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values["deployment_name"] = None
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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_name"],
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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[
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"azure_ad_token_provider"
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],
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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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"http_client": values["http_client"],
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}
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values["client"] = openai.AzureOpenAI(
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**client_params
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).completions
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values["async_client"] = openai.AsyncAzureOpenAI(
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**client_params
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).completions
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return values
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@property
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def _identifying_params(self) -> Mapping[str, Any]:
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return {
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**{"deployment_name": self.deployment_name},
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**super()._identifying_params,
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}
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@property
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def _invocation_params(self) -> Dict[str, Any]:
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openai_params = {"model": self.deployment_name}
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return {**openai_params, **super()._invocation_params}
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "azure"
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@property
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def lc_attributes(self) -> Dict[str, Any]:
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return {
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"openai_api_type": self.openai_api_type,
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"openai_api_version": self.openai_api_version,
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}
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