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test: add base_fetch and transformation extended tests
- base_fetch.py: WebFetchResponse model + BaseFetchConfig ABC (6 tests) - transformation_extended.py: transform_request, transform_response, build messages, format (22 tests)
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Thomas Menard
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2 changed files with 381 additions and 0 deletions
74
tests/webfetch_tests/test_base_fetch.py
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74
tests/webfetch_tests/test_base_fetch.py
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"""Tests for base_llm/fetch/transformation.py.
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Covers BaseFetchConfig validation and WebFetchResponse model.
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"""
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import pytest
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from pydantic import ValidationError
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from litellm.llms.base_llm.fetch.transformation import (
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BaseFetchConfig,
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WebFetchResponse,
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)
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class TestWebFetchResponse:
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"""Test WebFetchResponse Pydantic model."""
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def test_basic_creation(self):
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"""Create with required fields."""
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response = WebFetchResponse(
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url="https://example.com",
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content="Hello World",
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)
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assert response.url == "https://example.com"
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assert response.content == "Hello World"
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assert response.title is None
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assert response.metadata is None
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def test_with_optional_fields(self):
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"""Create with all fields."""
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response = WebFetchResponse(
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url="https://example.com",
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title="Test Page",
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content="Hello",
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metadata={"author": "test"},
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)
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assert response.title == "Test Page"
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assert response.metadata["author"] == "test"
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def test_extra_fields_allowed(self):
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"""Extra fields are allowed via ConfigDict."""
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response = WebFetchResponse(
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url="https://example.com",
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content="Hello",
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custom_field="value",
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)
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assert response.custom_field == "value"
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def test_missing_url_raises(self):
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"""Missing required URL raises ValidationError."""
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with pytest.raises(ValidationError):
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WebFetchResponse(content="Hello")
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def test_missing_content_raises(self):
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"""Missing required content raises ValidationError."""
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with pytest.raises(ValidationError):
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WebFetchResponse(url="https://example.com")
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class TestBaseFetchConfig:
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"""Test BaseFetchConfig ABC."""
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def test_cannot_instantiate(self):
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"""BaseFetchConfig is abstract."""
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with pytest.raises(TypeError):
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BaseFetchConfig()
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def test_required_methods(self):
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"""Subclasses must implement abstract methods."""
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# Verify the abstract methods exist
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assert hasattr(BaseFetchConfig, "validate_environment")
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assert hasattr(BaseFetchConfig, "ui_friendly_name")
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assert hasattr(BaseFetchConfig, "scrape_url")
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assert hasattr(BaseFetchConfig, "afetch_url")
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307
tests/webfetch_tests/test_transformation_extended.py
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307
tests/webfetch_tests/test_transformation_extended.py
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"""Extended tests for transformation.py to cover all uncovered methods.
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Cover transform_response, _build_anthropic_messages, _build_openai_messages,
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convert_native_to_litellm, and format_fetch_response.
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"""
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import json
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import pytest
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from unittest.mock import MagicMock
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from litellm.integrations.webfetch_interception.transformation import (
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WebFetchTransformation,
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)
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from litellm.llms.base_llm.fetch.transformation import WebFetchResponse
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class TestTransformRequest:
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"""Test transform_request."""
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def test_streaming_return_false(self):
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"""Streaming responses return no tool calls."""
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result = WebFetchTransformation.transform_request({}, stream=True)
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assert result == (False, [])
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def test_openai_format(self):
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"""OpenAI format detection."""
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response = {
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"choices": [{
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"message": {
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"tool_calls": [{
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"id": "call-1",
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"function": {
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"name": "litellm-web-fetch",
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"arguments": json.dumps({"url": "https://example.com"}),
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}
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}]
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}
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}]
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}
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result = WebFetchTransformation.transform_request(
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response, stream=False, response_format="openai"
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)
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assert result[0] is True
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assert len(result[1]) == 1
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def test_anthropic_format(self):
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"""Anthropic format detection."""
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response = {
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"content": [{
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"type": "tool_use",
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"name": "litellm-web-fetch",
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"id": "call-1",
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"input": {"url": "https://example.com"},
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}]
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}
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result = WebFetchTransformation.transform_request(
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response, stream=False, response_format="anthropic"
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)
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assert result[0] is True
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assert len(result[1]) == 1
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def test_no_content(self):
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"""Empty content returns no tool calls."""
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result = WebFetchTransformation.transform_request(
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{"content": []}, stream=False
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)
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assert result == (False, [])
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def test_object_response(self):
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"""Response as object with .content attribute."""
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mock_response = MagicMock()
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mock_response.content = [{
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"type": "tool_use",
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"name": "litellm-web-fetch",
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"id": "call-1",
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"input": {"url": "https://example.com"},
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}]
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result = WebFetchTransformation.transform_request(
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mock_response, stream=False
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)
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assert result[0] is True
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def test_no_content_attribute(self):
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"""Response without content attribute returns no tool calls."""
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mock_response = MagicMock()
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result = WebFetchTransformation.transform_request(
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mock_response, stream=False
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)
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assert result == (False, [])
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class TestDetectFromOpenAIResponse:
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"""Test _detect_from_openai_response."""
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def test_no_choices(self):
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"""No choices returns no tool calls."""
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result = WebFetchTransformation._detect_from_openai_response({})
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assert result == (False, [])
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def test_no_tool_calls(self):
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"""No tool_calls returns no tool calls."""
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response = {"choices": [{"message": {}}]}
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result = WebFetchTransformation._detect_from_openai_response(response)
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assert result == (False, [])
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def test_model_response_object(self):
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"""ModelResponse object with choices."""
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mock_choice = MagicMock()
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mock_choice.message = MagicMock()
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mock_choice.message.tool_calls = None
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mock_response = MagicMock()
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mock_response.choices = [mock_choice]
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result = WebFetchTransformation._detect_from_openai_response(mock_response)
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assert result == (False, [])
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def test_json_string_arguments(self):
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"""JSON string arguments are parsed."""
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response = {
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"choices": [{
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"message": {
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"tool_calls": [{
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"id": "call-1",
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"function": {
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"name": "litellm-web-fetch",
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"arguments": '{"url": "https://example.com"}',
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}
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}]
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}
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}]
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}
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result = WebFetchTransformation._detect_from_openai_response(response)
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assert result[0] is True
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assert isinstance(result[1][0]["function"]["arguments"], dict)
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def test_invalid_json_arguments(self):
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"""Invalid JSON string defaults to empty dict."""
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response = {
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"choices": [{
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"message": {
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"tool_calls": [{
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"id": "call-1",
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"function": {
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"name": "litellm-web-fetch",
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"arguments": "not json",
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}
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}]
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}
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}]
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}
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result = WebFetchTransformation._detect_from_openai_response(response)
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assert result[0] is True
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assert result[1][0]["function"]["arguments"] == {}
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class TestTransformResponse:
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"""Test transform_response."""
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def test_anthropic_format(self):
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"""Anthropic format transformation."""
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tool_calls = [{"id": "call-1", "name": "litellm-web-fetch", "input": {"url": "https://example.com"}}]
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fetch_results = ["# Hello\nWorld"]
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result = WebFetchTransformation.transform_response(
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tool_calls,
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fetch_results,
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response_format="anthropic",
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)
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assert "assistant_message" in result
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assert "tool_result_message" in result
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assert result["assistant_message"]["role"] == "assistant"
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def test_openai_format(self):
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"""OpenAI format transformation."""
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tool_calls = [{"id": "call-1", "function": {"name": "litellm-web-fetch", "arguments": {"url": "https://example.com"}}}]
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fetch_results = ["Test content"]
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result = WebFetchTransformation.transform_response(
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tool_calls,
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fetch_results,
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response_format="openai",
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)
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assert "assistant_message" in result
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assert "tool_result_message" in result
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assert result["assistant_message"]["role"] == "assistant"
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def test_with_thinking_blocks(self):
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"""Preserve thinking blocks in response."""
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tool_calls = [{"id": "call-1", "name": "litellm-web-fetch", "input": {"url": "https://example.com"}}]
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fetch_results = ["Content"]
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thinking_blocks = [{"type": "thinking", "thinking": "reasoning"}]
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result = WebFetchTransformation.transform_response(
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tool_calls,
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fetch_results,
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response_format="anthropic",
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thinking_blocks=thinking_blocks,
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)
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assert "content" in result["assistant_message"]
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def test_empty_tool_calls(self):
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"""Empty tool calls handled gracefully."""
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result = WebFetchTransformation.transform_response(
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[],
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[],
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response_format="anthropic",
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)
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assert result[0] == {}
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class TestBuildAnthropicMessages:
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"""Test _build_anthropic_messages."""
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def test_basic(self):
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"""Build messages from tool calls and results."""
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tool_calls = [{"id": "call-1", "name": "litellm-web-fetch", "input": {"url": "https://example.com"}}]
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fetch_results = ["# Title\nContent"]
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result = WebFetchTransformation._build_anthropic_messages(
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tool_calls, fetch_results
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)
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assert result[0]["role"] == "assistant"
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assert result[1]["role"] == "user"
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assert "# Title" in result[1]["content"][0]["text"]
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class TestBuildOpenAIMessages:
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"""Test _build_openai_messages."""
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def test_basic(self):
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"""Build OpenAI messages."""
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tool_calls = [{"id": "call-1", "function": {"name": "litellm-web-fetch", "arguments": {"url": "https://example.com"}}}]
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fetch_results = ["OpenAI result"]
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result = WebFetchTransformation._build_openai_messages(
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tool_calls, fetch_results
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)
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assert result[0]["role"] == "assistant"
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assert result[1]["role"] == "tool"
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assert "OpenAI result" in result[1]["content"]
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class TestFormatFetchResponse:
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"""Test format_fetch_response."""
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def test_with_all_fields(self):
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"""Format with title, URL, and content."""
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response = WebFetchResponse(
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url="https://example.com",
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title="Test Page",
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content="Hello World",
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)
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result = WebFetchTransformation.format_fetch_response(response)
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assert "Test Page" in result
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assert "https://example.com" in result
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assert "Hello World" in result
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def test_no_title(self):
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"""Format without title."""
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response = WebFetchResponse(
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url="https://example.com",
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content="Hello",
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)
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result = WebFetchTransformation.format_fetch_response(response)
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assert "https://example.com" in result
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assert "Hello" in result
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def test_empty_content(self):
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"""Format with empty content."""
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response = WebFetchResponse(
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url="https://example.com",
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title="Empty",
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content="",
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)
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result = WebFetchTransformation.format_fetch_response(response)
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assert "Empty" in result
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class TestConvertNativeToLitellm:
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"""Test convert_native_to_litellm."""
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def test_anthropic_native(self):
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"""Convert Anthropic native tool to LiteLLM format."""
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native_tool = {
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"type": "web_fetch_20250305",
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"name": "web_fetch",
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"input_schema": {
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"properties": {"url": {"type": "string"}},
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"required": ["url"],
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},
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}
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result = WebFetchTransformation.convert_native_to_litellm(native_tool)
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assert result["name"] == "litellm-web-fetch"
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assert "url" in result["input_schema"]["properties"]
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def test_missing_input_schema(self):
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"""Convert with missing input_schema."""
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native_tool = {"type": "web_fetch_20250305"}
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result = WebFetchTransformation.convert_native_to_litellm(native_tool)
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assert result["name"] == "litellm-web-fetch"
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assert result["input_schema"]["properties"]["url"]["type"] == "string"
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