forked from phoenix/litellm-mirror
239 lines
7.1 KiB
Python
239 lines
7.1 KiB
Python
"""
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This tests the pattern matching router
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Pattern matching router is used to match patterns like openai/*, vertex_ai/*, anthropic/* etc. (wildcard matching)
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"""
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import sys, os, time
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import traceback, asyncio
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import pytest
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sys.path.insert(
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0, os.path.abspath("../..")
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) # Adds the parent directory to the system path
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import litellm
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from litellm import Router
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from litellm.router import Deployment, LiteLLM_Params, ModelInfo
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from concurrent.futures import ThreadPoolExecutor
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from collections import defaultdict
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from dotenv import load_dotenv
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from unittest.mock import patch, MagicMock, AsyncMock
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load_dotenv()
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from litellm.router_utils.pattern_match_deployments import PatternMatchRouter
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def test_pattern_match_router_initialization():
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router = PatternMatchRouter()
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assert router.patterns == {}
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def test_add_pattern():
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"""
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Tests that openai/* is added to the patterns
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when we try to get the pattern, it should return the deployment
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"""
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router = PatternMatchRouter()
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deployment = Deployment(
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model_name="openai-1",
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litellm_params=LiteLLM_Params(model="gpt-3.5-turbo"),
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model_info=ModelInfo(),
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)
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router.add_pattern("openai/*", deployment.to_json(exclude_none=True))
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assert len(router.patterns) == 1
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assert list(router.patterns.keys())[0] == "openai/(.*)"
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# try getting the pattern
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assert router.route(request="openai/gpt-15") == [
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deployment.to_json(exclude_none=True)
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]
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def test_add_pattern_vertex_ai():
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"""
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Tests that vertex_ai/* is added to the patterns
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when we try to get the pattern, it should return the deployment
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"""
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router = PatternMatchRouter()
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deployment = Deployment(
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model_name="this-can-be-anything",
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litellm_params=LiteLLM_Params(model="vertex_ai/gemini-1.5-flash-latest"),
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model_info=ModelInfo(),
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)
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router.add_pattern("vertex_ai/*", deployment.to_json(exclude_none=True))
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assert len(router.patterns) == 1
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assert list(router.patterns.keys())[0] == "vertex_ai/(.*)"
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# try getting the pattern
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assert router.route(request="vertex_ai/gemini-1.5-flash-latest") == [
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deployment.to_json(exclude_none=True)
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]
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def test_add_multiple_deployments():
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"""
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Tests adding multiple deployments for the same pattern
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when we try to get the pattern, it should return the deployment
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"""
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router = PatternMatchRouter()
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deployment1 = Deployment(
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model_name="openai-1",
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litellm_params=LiteLLM_Params(model="gpt-3.5-turbo"),
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model_info=ModelInfo(),
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)
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deployment2 = Deployment(
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model_name="openai-2",
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litellm_params=LiteLLM_Params(model="gpt-4"),
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model_info=ModelInfo(),
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)
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router.add_pattern("openai/*", deployment1.to_json(exclude_none=True))
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router.add_pattern("openai/*", deployment2.to_json(exclude_none=True))
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assert len(router.route("openai/gpt-4o")) == 2
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def test_pattern_to_regex():
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"""
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Tests that the pattern is converted to a regex
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"""
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router = PatternMatchRouter()
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assert router._pattern_to_regex("openai/*") == "openai/(.*)"
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assert (
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router._pattern_to_regex("openai/fo::*::static::*")
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== "openai/fo::(.*)::static::(.*)"
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)
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def test_route_with_none():
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"""
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Tests that the router returns None when the request is None
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"""
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router = PatternMatchRouter()
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assert router.route(None) is None
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def test_route_with_multiple_matching_patterns():
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"""
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Tests that the router returns the first matching pattern when there are multiple matching patterns
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"""
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router = PatternMatchRouter()
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deployment1 = Deployment(
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model_name="openai-1",
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litellm_params=LiteLLM_Params(model="gpt-3.5-turbo"),
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model_info=ModelInfo(),
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)
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deployment2 = Deployment(
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model_name="openai-2",
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litellm_params=LiteLLM_Params(model="gpt-4"),
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model_info=ModelInfo(),
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)
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router.add_pattern("openai/*", deployment1.to_json(exclude_none=True))
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router.add_pattern("openai/gpt-*", deployment2.to_json(exclude_none=True))
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assert router.route("openai/gpt-3.5-turbo") == [
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deployment1.to_json(exclude_none=True)
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]
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# Add this test to check for exception handling
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def test_route_with_exception():
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"""
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Tests that the router returns None when there is an exception calling router.route()
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"""
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router = PatternMatchRouter()
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deployment = Deployment(
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model_name="openai-1",
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litellm_params=LiteLLM_Params(model="gpt-3.5-turbo"),
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model_info=ModelInfo(),
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)
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router.add_pattern("openai/*", deployment.to_json(exclude_none=True))
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router.patterns = (
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[]
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) # this will cause router.route to raise an exception, since router.patterns should be a dict
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result = router.route("openai/gpt-3.5-turbo")
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assert result is None
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@pytest.mark.asyncio
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async def test_route_with_no_matching_pattern():
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"""
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Tests that the router returns None when there is no matching pattern
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"""
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from litellm.types.router import RouterErrors
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router = Router(
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model_list=[
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{
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"model_name": "*meta.llama3*",
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"litellm_params": {"model": "bedrock/meta.llama3*"},
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}
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]
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)
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## WORKS
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result = await router.acompletion(
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model="bedrock/meta.llama3-70b",
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messages=[{"role": "user", "content": "Hello, world!"}],
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mock_response="Works",
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)
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assert result.choices[0].message.content == "Works"
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## WORKS
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result = await router.acompletion(
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model="meta.llama3-70b-instruct-v1:0",
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messages=[{"role": "user", "content": "Hello, world!"}],
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mock_response="Works",
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)
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assert result.choices[0].message.content == "Works"
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## FAILS
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with pytest.raises(litellm.BadRequestError) as e:
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await router.acompletion(
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model="my-fake-model",
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messages=[{"role": "user", "content": "Hello, world!"}],
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mock_response="Works",
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)
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assert RouterErrors.no_deployments_available.value not in str(e.value)
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with pytest.raises(litellm.BadRequestError):
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await router.aembedding(
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model="my-fake-model",
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input="Hello, world!",
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)
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def test_router_pattern_match_e2e():
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"""
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Tests the end to end flow of the router
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"""
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from litellm.llms.custom_httpx.http_handler import HTTPHandler
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client = HTTPHandler()
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router = Router(
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model_list=[
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{
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"model_name": "llmengine/*",
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"litellm_params": {"model": "anthropic/*", "api_key": "test"},
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}
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]
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)
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with patch.object(client, "post", new=MagicMock()) as mock_post:
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router.completion(
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model="llmengine/my-custom-model",
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messages=[{"role": "user", "content": "Hello, how are you?"}],
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client=client,
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api_key="test",
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)
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mock_post.assert_called_once()
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print(mock_post.call_args.kwargs["data"])
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mock_post.call_args.kwargs["data"] == {
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"model": "gpt-4o",
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"messages": [{"role": "user", "content": "Hello, how are you?"}],
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}
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