Add AzureML endpoint LLM wrapper (#6580)

### Description

We have added a new LLM integration `azureml_endpoint` that allows users
to leverage models from the AzureML platform. Microsoft recently
announced the release of [Azure Foundation

Models](https://learn.microsoft.com/en-us/azure/machine-learning/concept-foundation-models?view=azureml-api-2)
which users can find in the AzureML Model Catalog. The Model Catalog
contains a variety of open source and Hugging Face models that users can
deploy on AzureML. The `azureml_endpoint` allows LangChain users to use
the deployed Azure Foundation Models.

### Dependencies

No added dependencies were required for the change.

### Tests

Integration tests were added in
`tests/integration_tests/llms/test_azureml_endpoint.py`.

### Notebook

A Jupyter notebook demonstrating how to use `azureml_endpoint` was added
to `docs/modules/llms/integrations/azureml_endpoint_example.ipynb`.

### Twitters

[Prakhar Gupta](https://twitter.com/prakhar_in)
[Matthew DeGuzman](https://twitter.com/matthew_d13)

---------

Co-authored-by: Matthew DeGuzman <91019033+matthewdeguzman@users.noreply.github.com>
Co-authored-by: prakharg-msft <75808410+prakharg-msft@users.noreply.github.com>

tests/integration_tests/llms/test_azureml_endpoint.py

@@ -0,0 +1,151 @@
+"""Test AzureML Endpoint wrapper."""
+
+import json
+import os
+from pathlib import Path
+from typing import Dict
+from urllib.request import HTTPError
+
+import pytest
+
+from langchain.llms.azureml_endpoint import (
+    AzureMLOnlineEndpoint,
+    ContentFormatterBase,
+    DollyContentFormatter,
+    HFContentFormatter,
+    OSSContentFormatter,
+)
+from langchain.llms.loading import load_llm
+
+
+def test_oss_call() -> None:
+    """Test valid call to Open Source Foundation Model."""
+    llm = AzureMLOnlineEndpoint(
+        endpoint_api_key=os.getenv("OSS_ENDPOINT_API_KEY"),
+        endpoint_url=os.getenv("OSS_ENDPOINT_URL"),
+        deployment_name=os.getenv("OSS_DEPLOYMENT_NAME"),
+        content_formatter=OSSContentFormatter(),
+    )
+    output = llm("Foo")
+    assert isinstance(output, str)
+
+
+def test_hf_call() -> None:
+    """Test valid call to HuggingFace Foundation Model."""
+    llm = AzureMLOnlineEndpoint(
+        endpoint_api_key=os.getenv("HF_ENDPOINT_API_KEY"),
+        endpoint_url=os.getenv("HF_ENDPOINT_URL"),
+        deployment_name=os.getenv("HF_DEPLOYMENT_NAME"),
+        content_formatter=HFContentFormatter(),
+    )
+    output = llm("Foo")
+    assert isinstance(output, str)
+
+
+def test_dolly_call() -> None:
+    """Test valid call to dolly-v2-12b."""
+    llm = AzureMLOnlineEndpoint(
+        endpoint_api_key=os.getenv("DOLLY_ENDPOINT_API_KEY"),
+        endpoint_url=os.getenv("DOLLY_ENDPOINT_URL"),
+        deployment_name=os.getenv("DOLLY_DEPLOYMENT_NAME"),
+        content_formatter=DollyContentFormatter(),
+    )
+    output = llm("Foo")
+    assert isinstance(output, str)
+
+
+def test_custom_formatter() -> None:
+    """Test ability to create a custom content formatter."""
+
+    class CustomFormatter(ContentFormatterBase):
+        content_type = "application/json"
+        accepts = "application/json"
+
+        def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
+            input_str = json.dumps(
+                {
+                    "inputs": [prompt],
+                    "parameters": model_kwargs,
+                    "options": {"use_cache": False, "wait_for_model": True},
+                }
+            )
+            return input_str.encode("utf-8")
+
+        def format_response_payload(self, output: bytes) -> str:
+            response_json = json.loads(output)
+            return response_json[0]["summary_text"]
+
+    llm = AzureMLOnlineEndpoint(
+        endpoint_api_key=os.getenv("BART_ENDPOINT_API_KEY"),
+        endpoint_url=os.getenv("BART_ENDPOINT_URL"),
+        deployment_name=os.getenv("BART_DEPLOYMENT_NAME"),
+        content_formatter=CustomFormatter(),
+    )
+    output = llm("Foo")
+    assert isinstance(output, str)
+
+
+def test_missing_content_formatter() -> None:
+    """Test AzureML LLM without a content_formatter attribute"""
+    with pytest.raises(AttributeError):
+        llm = AzureMLOnlineEndpoint(
+            endpoint_api_key=os.getenv("OSS_ENDPOINT_API_KEY"),
+            endpoint_url=os.getenv("OSS_ENDPOINT_URL"),
+            deployment_name=os.getenv("OSS_DEPLOYMENT_NAME"),
+        )
+        llm("Foo")
+
+
+def test_invalid_request_format() -> None:
+    """Test invalid request format."""
+
+    class CustomContentFormatter(ContentFormatterBase):
+        content_type = "application/json"
+        accepts = "application/json"
+
+        def format_request_payload(self, prompt: str, model_kwargs: Dict) -> bytes:
+            input_str = json.dumps(
+                {
+                    "incorrect_input": {"input_string": [prompt]},
+                    "parameters": model_kwargs,
+                }
+            )
+            return str.encode(input_str)
+
+        def format_response_payload(self, output: bytes) -> str:
+            response_json = json.loads(output)
+            return response_json[0]["0"]
+
+    with pytest.raises(HTTPError):
+        llm = AzureMLOnlineEndpoint(
+            endpoint_api_key=os.getenv("OSS_ENDPOINT_API_KEY"),
+            endpoint_url=os.getenv("OSS_ENDPOINT_URL"),
+            deployment_name=os.getenv("OSS_DEPLOYMENT_NAME"),
+            content_formatter=CustomContentFormatter(),
+        )
+        llm("Foo")
+
+
+def test_incorrect_key() -> None:
+    """Testing AzureML Endpoint for incorrect key"""
+    with pytest.raises(HTTPError):
+        llm = AzureMLOnlineEndpoint(
+            endpoint_api_key="incorrect-key",
+            endpoint_url=os.getenv("OSS_ENDPOINT_URL"),
+            deployment_name=os.getenv("OSS_DEPLOYMENT_NAME"),
+            content_formatter=OSSContentFormatter(),
+        )
+        llm("Foo")
+
+
+def test_saving_loading_llm(tmp_path: Path) -> None:
+    """Test saving/loading an AzureML Foundation Model LLM."""
+
+    save_llm = AzureMLOnlineEndpoint(
+        deployment_name="databricks-dolly-v2-12b-4",
+        model_kwargs={"temperature": 0.03, "top_p": 0.4, "max_tokens": 200},
+    )
+    save_llm.save(file_path=tmp_path / "azureml.yaml")
+    loaded_llm = load_llm(tmp_path / "azureml.yaml")
+
+    assert loaded_llm == save_llm