AzureOpenAIModel

Environment variables: AZURE_OPENAI_API_KEY, AZURE_OPENAI_ENDPOINT, OPENAI_API_VERSION, AZURE_OPENAI_DEPLOYMENT_NAME

import os
from elsai_model.azure_openai import AzureOpenAIModel

model = AzureOpenAIModel(
    model_id=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    client_args={
        "azure_endpoint": os.environ["AZURE_OPENAI_ENDPOINT"],
        "api_key": os.environ["AZURE_OPENAI_API_KEY"],
        "api_version": os.environ["OPENAI_API_VERSION"],
    },
    params={"temperature": 0.2},
)
messages = [{"role": "user", "content": "Say hello in one short sentence."}]

response = model.invoke(messages)
print(response.choices[0].message.content)

for chunk in model.stream_text(messages):
    print(chunk, end="", flush=True)

Or via the factory:

from elsai_model import LLM, Provider

model = LLM(
    provider=Provider.AZURE_OPENAI,
    model=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    api_key=os.environ["AZURE_OPENAI_API_KEY"],
    client_args={
        "azure_endpoint": os.environ["AZURE_OPENAI_ENDPOINT"],
        "api_version": os.environ["OPENAI_API_VERSION"],
    },
    params={"temperature": 0.2},
)

LangChain backend

pip install --extra-index-url https://core-packages.elsai.ai/root/ "elsai-model[langchain]==2.1.0"

model = AzureOpenAIModel(
    model_id=os.environ["AZURE_OPENAI_DEPLOYMENT_NAME"],
    client_args={
        "azure_endpoint": os.environ["AZURE_OPENAI_ENDPOINT"],
        "api_key": os.environ["AZURE_OPENAI_API_KEY"],
        "api_version": os.environ["OPENAI_API_VERSION"],
    },
    params={"temperature": 0.2},
    implementation="langchain",
)

---

See also

• LLM Models overview — install, extras, factory
• LLM factory