litellm/tests/local_testing/test_router_pattern_matching.py

239 lines
7.1 KiB
Python

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