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Adding a python http_request wrapper to create external tools #28

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3 changes: 3 additions & 0 deletions src/databricks_ai_bridge/__init__.py
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from databricks_ai_bridge.external_tool_request import http_request

__all__ = ["http_request"]
57 changes: 57 additions & 0 deletions src/databricks_ai_bridge/external_tool_request.py
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import json as js
from typing import Any, Dict, Optional

import requests
from databricks.sdk import WorkspaceClient

from databricks_ai_bridge.utils.annotations import experimental


@experimental
def http_request(
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We should think about the module and API name a little more, maybe something like databricks_ai_bridge.external_tools.http_request? Or even databricks_ai_bridge.external_tools.request? Just thinking about how this looks in code where you might mix this with generic requests.request calls to some public APIs

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Another option is databricks_ai_bridge.uc_connections.http_request - I know we wanna present our product as external tools but what we're actually using is the UC connection to make an HTTP request in the process of defining a tool, ratehr than using a tool itself

conn: str,
method: str,
path: str,
*,
json: Optional[Any] = None,
headers: Optional[Dict[str, str]] = None,
params: Optional[Dict[str, Any]] = None,
) -> requests.Response:
"""
Makes an HTTP request to an external function through the Databricks Workspace.
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Suggested change
Makes an HTTP request to an external function through the Databricks Workspace.
Makes an HTTP request to a remote API using authentication from a Unity Catalog HTTP connection.


Args:
conn (str): The connection name to use. This is required to identify the external connection.
method (str): The HTTP method to use (e.g., "GET", "POST"). This is required.
path (str): The relative path for the API endpoint. This is required.
json (Optional[Any]): JSON payload for the request.
headers (Optional[Dict[str, str]]): Additional headers for the request.
If not provided, only auth headers from connections would be passed.
params (Optional[Dict[str, Any]]): Query parameters for the request.

Returns:
requests.Response: The HTTP response from the external function.

Example Usage:
response = http_request(
conn="my_connection",
method="POST",
path="/api/v1/resource",
json={"key": "value"},
headers={"extra_header_key": "extra_header_value"},
params={"query": "example"}
)
"""
workspaceConfig = WorkspaceClient().config
url = f"{workspaceConfig.host}/external-functions"
request_headers = workspaceConfig._header_factory()
payload = {
"connection_name": conn,
"method": method,
"path": path,
"json": js.dumps(json),
"header": headers,
"params": params,
}

return requests.post(url, headers=request_headers, json=payload)
65 changes: 65 additions & 0 deletions src/databricks_ai_bridge/utils/annotations.py
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import inspect
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Are there tests for these annotations that we should also include + include a backlink to the OSS MLflow code (I'm guessing) that we copied from?

import re
import types
from typing import Any, Callable, TypeVar, Union

C = TypeVar("C", bound=Callable[..., Any])


def _get_min_indent_of_docstring(docstring_str: str) -> str:
"""
Get the minimum indentation string of a docstring, based on the assumption
that the closing triple quote for multiline comments must be on a new line.
Note that based on ruff rule D209, the closing triple quote for multiline
comments must be on a new line.

Args:
docstring_str: string with docstring

Returns:
Whitespace corresponding to the indent of a docstring.
"""

if not docstring_str or "\n" not in docstring_str:
return ""

return re.match(r"^\s*", docstring_str.rsplit("\n", 1)[-1]).group()


def experimental(api_or_type: Union[C, str]) -> C:
"""Decorator / decorator creator for marking APIs experimental in the docstring.

Args:
api_or_type: An API to mark, or an API typestring for which to generate a decorator.

Returns:
Decorated API (if a ``api_or_type`` is an API) or a function that decorates
the specified API type (if ``api_or_type`` is a typestring).
"""
if isinstance(api_or_type, str):

def f(api: C) -> C:
return _experimental(api=api, api_type=api_or_type)

return f
elif inspect.isclass(api_or_type):
return _experimental(api=api_or_type, api_type="class")
elif inspect.isfunction(api_or_type):
return _experimental(api=api_or_type, api_type="function")
elif isinstance(api_or_type, (property, types.MethodType)):
return _experimental(api=api_or_type, api_type="property")
else:
return _experimental(api=api_or_type, api_type=str(type(api_or_type)))


def _experimental(api: C, api_type: str) -> C:
indent = _get_min_indent_of_docstring(api.__doc__)
notice = (
indent + f".. Note:: Experimental: This {api_type} may change or "
"be removed in a future release without warning.\n\n"
)
if api_type == "property":
api.__doc__ = api.__doc__ + "\n\n" + notice if api.__doc__ else notice
else:
api.__doc__ = notice + api.__doc__ if api.__doc__ else notice
return api
89 changes: 89 additions & 0 deletions tests/databricks_ai_bridge/test_external_tool_request.py
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from unittest.mock import MagicMock, patch

from databricks_ai_bridge import http_request


@patch("databricks_ai_bridge.external_tool_request.WorkspaceClient")
@patch("databricks_ai_bridge.external_tool_request.requests.post")
def test_http_request_success(mock_post, mock_workspace_client):
# Mock the WorkspaceClient config
mock_workspace_config = MagicMock()
mock_workspace_config.host = "https://mock-host"
mock_workspace_config._header_factory.return_value = {"Authorization": "Bearer mock-token"}
mock_workspace_client.return_value.config = mock_workspace_config

# Mock the POST request
mock_response = MagicMock()
mock_response.status_code = 200
mock_response.json.return_value = {"success": True}
mock_post.return_value = mock_response

# Call the function
response = http_request(
conn="mock_connection",
method="POST",
path="/mock-path",
json={"key": "value"},
headers={"Custom-Header": "HeaderValue"},
params={"query": "test"},
)

# Assertions
assert response.status_code == 200
assert response.json() == {"success": True}
mock_post.assert_called_once_with(
"https://mock-host/external-functions",
headers={
"Authorization": "Bearer mock-token",
},
json={
"connection_name": "mock_connection",
"method": "POST",
"path": "/mock-path",
"json": '{"key": "value"}',
"header": {
"Custom-Header": "HeaderValue",
},
"params": {"query": "test"},
},
)


@patch("databricks_ai_bridge.external_tool_request.WorkspaceClient")
@patch("databricks_ai_bridge.external_tool_request.requests.post")
def test_http_request_error_response(mock_post, mock_workspace_client):
# Mock the WorkspaceClient config
mock_workspace_config = MagicMock()
mock_workspace_config.host = "https://mock-host"
mock_workspace_config._header_factory.return_value = {"Authorization": "Bearer mock-token"}
mock_workspace_client.return_value.config = mock_workspace_config

# Mock the POST request to return an error
mock_response = MagicMock()
mock_response.status_code = 400
mock_response.json.return_value = {"error": "Bad Request"}
mock_post.return_value = mock_response

# Call the function
response = http_request(
conn="mock_connection",
method="POST",
path="/mock-path",
json={"key": "value"},
)

# Assertions
assert response.status_code == 400
assert response.json() == {"error": "Bad Request"}
mock_post.assert_called_once_with(
"https://mock-host/external-functions",
headers={"Authorization": "Bearer mock-token"},
json={
"connection_name": "mock_connection",
"method": "POST",
"path": "/mock-path",
"json": '{"key": "value"}',
"header": None,
"params": None,
},
)
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