forked from phoenix/litellm-mirror
27e18358ab
* fix(pattern_match_deployments.py): default to user input if unable to map based on wildcards * test: fix test * test: reset test name * test: update conftest to reload proxy server module between tests * ci(config.yml): move langfuse out of local_testing reduce ci/cd time * ci(config.yml): cleanup langfuse ci/cd tests * fix: update test to not use global proxy_server app module * ci: move caching to a separate test pipeline speed up ci pipeline * test: update conftest to check if proxy_server attr exists before reloading * build(conftest.py): don't block on inability to reload proxy_server * ci(config.yml): update caching unit test filter to work on 'cache' keyword as well * fix(encrypt_decrypt_utils.py): use function to get salt key * test: mark flaky test * test: handle anthropic overloaded errors * refactor: create separate ci/cd pipeline for proxy unit tests make ci/cd faster * ci(config.yml): add litellm_proxy_unit_testing to build_and_test jobs * ci(config.yml): generate prisma binaries for proxy unit tests * test: readd vertex_key.json * ci(config.yml): remove `-s` from proxy_unit_test cmd speed up test * ci: remove any 'debug' logging flag speed up ci pipeline * test: fix test * test(test_braintrust.py): rerun * test: add delay for braintrust test
299 lines
10 KiB
Python
299 lines
10 KiB
Python
import sys, os
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import traceback
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from dotenv import load_dotenv
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load_dotenv()
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import os, io, asyncio
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# this file is to test litellm/proxy
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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 pytest, time
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import litellm
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from litellm import embedding, completion, completion_cost, Timeout
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from litellm import RateLimitError
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import importlib, inspect
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# test /chat/completion request to the proxy
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from fastapi.testclient import TestClient
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from fastapi import FastAPI
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from litellm.proxy.proxy_server import (
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router,
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save_worker_config,
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initialize,
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startup_event,
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) # Replace with the actual module where your FastAPI router is defined
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filepath = os.path.dirname(os.path.abspath(__file__))
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python_file_path = f"{filepath}/test_configs/custom_callbacks.py"
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# @app.on_event("startup")
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# async def wrapper_startup_event():
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# initialize(config=config_fp)
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# Use the app fixture in your client fixture
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@pytest.fixture
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def client():
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filepath = os.path.dirname(os.path.abspath(__file__))
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config_fp = f"{filepath}/test_configs/test_custom_logger.yaml"
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app = FastAPI()
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asyncio.run(initialize(config=config_fp))
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app.include_router(router) # Include your router in the test app
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return TestClient(app)
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# Your bearer token
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token = os.getenv("PROXY_MASTER_KEY")
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headers = {"Authorization": f"Bearer {token}"}
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print("Testing proxy custom logger")
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def test_embedding(client):
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try:
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litellm.set_verbose = False
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from litellm.proxy.utils import get_instance_fn
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my_custom_logger = get_instance_fn(
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value="custom_callbacks.my_custom_logger", config_file_path=python_file_path
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)
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print("id of initialized custom logger", id(my_custom_logger))
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litellm.callbacks = [my_custom_logger]
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# Your test data
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print("initialized proxy")
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# import the initialized custom logger
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print(litellm.callbacks)
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# assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback
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print("my_custom_logger", my_custom_logger)
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assert my_custom_logger.async_success_embedding is False
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test_data = {"model": "azure-embedding-model", "input": ["hello"]}
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response = client.post("/embeddings", json=test_data, headers=headers)
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print("made request", response.status_code, response.text)
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print(
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"vars my custom logger /embeddings",
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vars(my_custom_logger),
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"id",
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id(my_custom_logger),
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)
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assert (
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my_custom_logger.async_success_embedding is True
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) # checks if the status of async_success is True, only the async_log_success_event can set this to true
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assert (
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my_custom_logger.async_embedding_kwargs["model"] == "azure-embedding-model"
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) # checks if kwargs passed to async_log_success_event are correct
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kwargs = my_custom_logger.async_embedding_kwargs
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litellm_params = kwargs.get("litellm_params")
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metadata = litellm_params.get("metadata", None)
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print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata"))
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assert metadata is not None
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assert "user_api_key" in metadata
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assert "headers" in metadata
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proxy_server_request = litellm_params.get("proxy_server_request")
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model_info = litellm_params.get("model_info")
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assert proxy_server_request == {
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"url": "http://testserver/embeddings",
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"method": "POST",
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"headers": {
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"host": "testserver",
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"accept": "*/*",
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"accept-encoding": "gzip, deflate",
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"connection": "keep-alive",
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"user-agent": "testclient",
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"content-length": "54",
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"content-type": "application/json",
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},
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"body": {"model": "azure-embedding-model", "input": ["hello"]},
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}
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assert model_info == {
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"input_cost_per_token": 0.002,
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"mode": "embedding",
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"id": "hello",
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"db_model": False,
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}
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result = response.json()
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print(f"Received response: {result}")
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print("Passed Embedding custom logger on proxy!")
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except Exception as e:
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pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}")
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def test_chat_completion(client):
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try:
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# Your test data
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litellm.set_verbose = False
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from litellm.proxy.utils import get_instance_fn
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my_custom_logger = get_instance_fn(
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value="custom_callbacks.my_custom_logger", config_file_path=python_file_path
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)
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print("id of initialized custom logger", id(my_custom_logger))
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litellm.callbacks = [my_custom_logger]
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# import the initialized custom logger
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print(litellm.callbacks)
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# assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback
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print("LiteLLM Callbacks", litellm.callbacks)
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print("my_custom_logger", my_custom_logger)
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assert my_custom_logger.async_success == False
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test_data = {
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"model": "Azure OpenAI GPT-4 Canada",
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"messages": [
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{"role": "user", "content": "write a litellm poem"},
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],
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"max_tokens": 10,
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}
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response = client.post("/chat/completions", json=test_data, headers=headers)
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print("made request", response.status_code, response.text)
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print("LiteLLM Callbacks", litellm.callbacks)
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time.sleep(1) # sleep while waiting for callback to run
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print(
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"my_custom_logger in /chat/completions",
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my_custom_logger,
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"id",
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id(my_custom_logger),
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)
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print("vars my custom logger, ", vars(my_custom_logger))
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assert (
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my_custom_logger.async_success == True
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) # checks if the status of async_success is True, only the async_log_success_event can set this to true
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assert (
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my_custom_logger.async_completion_kwargs["model"] == "chatgpt-v-2"
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) # checks if kwargs passed to async_log_success_event are correct
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print(
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"\n\n Custom Logger Async Completion args",
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my_custom_logger.async_completion_kwargs,
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)
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litellm_params = my_custom_logger.async_completion_kwargs.get("litellm_params")
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metadata = litellm_params.get("metadata", None)
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print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata"))
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assert metadata is not None
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assert "user_api_key" in metadata
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assert "user_api_key_metadata" in metadata
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assert "headers" in metadata
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config_model_info = litellm_params.get("model_info")
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proxy_server_request_object = litellm_params.get("proxy_server_request")
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assert config_model_info == {
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"id": "gm",
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"input_cost_per_token": 0.0002,
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"mode": "chat",
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"db_model": False,
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}
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assert "authorization" not in proxy_server_request_object["headers"]
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assert proxy_server_request_object == {
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"url": "http://testserver/chat/completions",
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"method": "POST",
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"headers": {
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"host": "testserver",
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"accept": "*/*",
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"accept-encoding": "gzip, deflate",
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"connection": "keep-alive",
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"user-agent": "testclient",
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"content-length": "123",
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"content-type": "application/json",
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},
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"body": {
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"model": "Azure OpenAI GPT-4 Canada",
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"messages": [{"role": "user", "content": "write a litellm poem"}],
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"max_tokens": 10,
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},
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}
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result = response.json()
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print(f"Received response: {result}")
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print("\nPassed /chat/completions with Custom Logger!")
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except Exception as e:
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pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}")
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def test_chat_completion_stream(client):
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try:
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# Your test data
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litellm.set_verbose = False
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from litellm.proxy.utils import get_instance_fn
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my_custom_logger = get_instance_fn(
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value="custom_callbacks.my_custom_logger", config_file_path=python_file_path
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)
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print("id of initialized custom logger", id(my_custom_logger))
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litellm.callbacks = [my_custom_logger]
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import json
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print("initialized proxy")
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# import the initialized custom logger
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print(litellm.callbacks)
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print("LiteLLM Callbacks", litellm.callbacks)
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print("my_custom_logger", my_custom_logger)
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assert (
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my_custom_logger.streaming_response_obj == None
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) # no streaming response obj is set pre call
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test_data = {
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"model": "Azure OpenAI GPT-4 Canada",
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"messages": [
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{"role": "user", "content": "write 1 line poem about LiteLLM"},
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],
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"max_tokens": 40,
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"stream": True, # streaming call
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}
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response = client.post("/chat/completions", json=test_data, headers=headers)
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print("made request", response.status_code, response.text)
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complete_response = ""
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for line in response.iter_lines():
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if line:
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# Process the streaming data line here
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print("\n\n Line", line)
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print(line)
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line = str(line)
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json_data = line.replace("data: ", "")
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if "[DONE]" in json_data:
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break
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# Parse the JSON string
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data = json.loads(json_data)
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print("\n\n decode_data", data)
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# Access the content of choices[0]['message']['content']
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content = data["choices"][0]["delta"].get("content", None) or ""
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# Process the content as needed
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print("Content:", content)
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complete_response += content
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print("\n\nHERE is the complete streaming response string", complete_response)
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print("\n\nHERE IS the streaming Response from callback\n\n")
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print(my_custom_logger.streaming_response_obj)
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import time
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time.sleep(0.5)
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streamed_response = my_custom_logger.streaming_response_obj
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assert (
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complete_response == streamed_response["choices"][0]["message"]["content"]
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)
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except Exception as e:
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pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}")
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