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Configuration error
| import sys, os | |
| import traceback | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| import os, io, asyncio | |
| # this file is to test litellm/proxy | |
| sys.path.insert( | |
| 0, os.path.abspath("../..") | |
| ) # Adds the parent directory to the system path | |
| import pytest, time | |
| import litellm | |
| from litellm import embedding, completion, completion_cost, Timeout | |
| from litellm import RateLimitError | |
| import importlib, inspect | |
| # test /chat/completion request to the proxy | |
| from fastapi.testclient import TestClient | |
| from fastapi import FastAPI | |
| from litellm.proxy.proxy_server import ( | |
| router, | |
| save_worker_config, | |
| initialize, | |
| ) # Replace with the actual module where your FastAPI router is defined | |
| filepath = os.path.dirname(os.path.abspath(__file__)) | |
| python_file_path = f"{filepath}/test_configs/custom_callbacks.py" | |
| def client(): | |
| filepath = os.path.dirname(os.path.abspath(__file__)) | |
| config_fp = f"{filepath}/test_configs/test_custom_logger.yaml" | |
| app = FastAPI() | |
| asyncio.run(initialize(config=config_fp)) | |
| app.include_router(router) # Include your router in the test app | |
| return TestClient(app) | |
| # Your bearer token | |
| token = os.getenv("PROXY_MASTER_KEY") | |
| headers = {"Authorization": f"Bearer {token}"} | |
| print("Testing proxy custom logger") | |
| def test_embedding(client): | |
| try: | |
| litellm.set_verbose = False | |
| from litellm.proxy.types_utils.utils import get_instance_fn | |
| my_custom_logger = get_instance_fn( | |
| value="custom_callbacks.my_custom_logger", config_file_path=python_file_path | |
| ) | |
| print("id of initialized custom logger", id(my_custom_logger)) | |
| litellm.callbacks = [my_custom_logger] | |
| # Your test data | |
| print("initialized proxy") | |
| # import the initialized custom logger | |
| print(litellm.callbacks) | |
| # assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback | |
| print("my_custom_logger", my_custom_logger) | |
| assert my_custom_logger.async_success_embedding is False | |
| test_data = {"model": "azure-embedding-model", "input": ["hello"]} | |
| response = client.post("/embeddings", json=test_data, headers=headers) | |
| print("made request", response.status_code, response.text) | |
| print( | |
| "vars my custom logger /embeddings", | |
| vars(my_custom_logger), | |
| "id", | |
| id(my_custom_logger), | |
| ) | |
| assert ( | |
| my_custom_logger.async_success_embedding is True | |
| ) # checks if the status of async_success is True, only the async_log_success_event can set this to true | |
| assert ( | |
| my_custom_logger.async_embedding_kwargs["model"] == "azure-embedding-model" | |
| ) # checks if kwargs passed to async_log_success_event are correct | |
| kwargs = my_custom_logger.async_embedding_kwargs | |
| litellm_params = kwargs.get("litellm_params") | |
| metadata = litellm_params.get("metadata", None) | |
| print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata")) | |
| assert metadata is not None | |
| assert "user_api_key" in metadata | |
| assert "headers" in metadata | |
| proxy_server_request = litellm_params.get("proxy_server_request") | |
| model_info = litellm_params.get("model_info") | |
| assert proxy_server_request == { | |
| "url": "http://testserver/embeddings", | |
| "method": "POST", | |
| "headers": { | |
| "host": "testserver", | |
| "accept": "*/*", | |
| "accept-encoding": "gzip, deflate, zstd", | |
| "connection": "keep-alive", | |
| "user-agent": "testclient", | |
| "content-length": "51", | |
| "content-type": "application/json", | |
| }, | |
| "body": {"model": "azure-embedding-model", "input": ["hello"]}, | |
| } | |
| assert model_info == { | |
| "input_cost_per_token": 0.002, | |
| "mode": "embedding", | |
| "id": "hello", | |
| "db_model": False, | |
| } | |
| result = response.json() | |
| print(f"Received response: {result}") | |
| print("Passed Embedding custom logger on proxy!") | |
| except Exception as e: | |
| pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") | |
| def test_chat_completion(client): | |
| try: | |
| # Your test data | |
| litellm.set_verbose = False | |
| from litellm.proxy.types_utils.utils import get_instance_fn | |
| my_custom_logger = get_instance_fn( | |
| value="custom_callbacks.my_custom_logger", config_file_path=python_file_path | |
| ) | |
| print("id of initialized custom logger", id(my_custom_logger)) | |
| litellm.callbacks = [my_custom_logger] | |
| # import the initialized custom logger | |
| print(litellm.callbacks) | |
| # assert len(litellm.callbacks) == 1 # assert litellm is initialized with 1 callback | |
| print("LiteLLM Callbacks", litellm.callbacks) | |
| print("my_custom_logger", my_custom_logger) | |
| assert my_custom_logger.async_success == False | |
| test_data = { | |
| "model": "Azure OpenAI GPT-4 Canada", | |
| "messages": [ | |
| {"role": "user", "content": "write a litellm poem"}, | |
| ], | |
| "max_tokens": 10, | |
| } | |
| response = client.post("/chat/completions", json=test_data, headers=headers) | |
| print("made request", response.status_code, response.text) | |
| print("LiteLLM Callbacks", litellm.callbacks) | |
| time.sleep(1) # sleep while waiting for callback to run | |
| print( | |
| "my_custom_logger in /chat/completions", | |
| my_custom_logger, | |
| "id", | |
| id(my_custom_logger), | |
| ) | |
| print("vars my custom logger, ", vars(my_custom_logger)) | |
| assert ( | |
| my_custom_logger.async_success == True | |
| ) # checks if the status of async_success is True, only the async_log_success_event can set this to true | |
| assert ( | |
| my_custom_logger.async_completion_kwargs["model"] == "chatgpt-v-3" | |
| ) # checks if kwargs passed to async_log_success_event are correct | |
| print( | |
| "\n\n Custom Logger Async Completion args", | |
| my_custom_logger.async_completion_kwargs, | |
| ) | |
| litellm_params = my_custom_logger.async_completion_kwargs.get("litellm_params") | |
| metadata = litellm_params.get("metadata", None) | |
| print("\n\n Metadata in custom logger kwargs", litellm_params.get("metadata")) | |
| assert metadata is not None | |
| assert "user_api_key" in metadata | |
| assert "user_api_key_metadata" in metadata | |
| assert "headers" in metadata | |
| config_model_info = litellm_params.get("model_info") | |
| proxy_server_request_object = litellm_params.get("proxy_server_request") | |
| assert config_model_info == { | |
| "id": "gm", | |
| "input_cost_per_token": 0.0002, | |
| "mode": "chat", | |
| "db_model": False, | |
| } | |
| assert "authorization" not in proxy_server_request_object["headers"] | |
| assert proxy_server_request_object == { | |
| "url": "http://testserver/chat/completions", | |
| "method": "POST", | |
| "headers": { | |
| "host": "testserver", | |
| "accept": "*/*", | |
| "accept-encoding": "gzip, deflate, zstd", | |
| "connection": "keep-alive", | |
| "user-agent": "testclient", | |
| "content-length": "115", | |
| "content-type": "application/json", | |
| }, | |
| "body": { | |
| "model": "Azure OpenAI GPT-4 Canada", | |
| "messages": [{"role": "user", "content": "write a litellm poem"}], | |
| "max_tokens": 10, | |
| }, | |
| } | |
| result = response.json() | |
| print(f"Received response: {result}") | |
| print("\nPassed /chat/completions with Custom Logger!") | |
| except Exception as e: | |
| pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") | |
| def test_chat_completion_stream(client): | |
| try: | |
| # Your test data | |
| litellm.set_verbose = False | |
| from litellm.proxy.types_utils.utils import get_instance_fn | |
| my_custom_logger = get_instance_fn( | |
| value="custom_callbacks.my_custom_logger", config_file_path=python_file_path | |
| ) | |
| print("id of initialized custom logger", id(my_custom_logger)) | |
| litellm.callbacks = [my_custom_logger] | |
| import json | |
| print("initialized proxy") | |
| # import the initialized custom logger | |
| print(litellm.callbacks) | |
| print("LiteLLM Callbacks", litellm.callbacks) | |
| print("my_custom_logger", my_custom_logger) | |
| assert ( | |
| my_custom_logger.streaming_response_obj == None | |
| ) # no streaming response obj is set pre call | |
| test_data = { | |
| "model": "Azure OpenAI GPT-4 Canada", | |
| "messages": [ | |
| {"role": "user", "content": "write 1 line poem about LiteLLM"}, | |
| ], | |
| "max_tokens": 40, | |
| "stream": True, # streaming call | |
| } | |
| response = client.post("/chat/completions", json=test_data, headers=headers) | |
| print("made request", response.status_code, response.text) | |
| complete_response = "" | |
| for line in response.iter_lines(): | |
| if line: | |
| # Process the streaming data line here | |
| print("\n\n Line", line) | |
| print(line) | |
| line = str(line) | |
| json_data = line.replace("data: ", "") | |
| if "[DONE]" in json_data: | |
| break | |
| # Parse the JSON string | |
| data = json.loads(json_data) | |
| print("\n\n decode_data", data) | |
| # Access the content of choices[0]['message']['content'] | |
| content = data["choices"][0]["delta"].get("content", None) or "" | |
| # Process the content as needed | |
| print("Content:", content) | |
| complete_response += content | |
| print("\n\nHERE is the complete streaming response string", complete_response) | |
| print("\n\nHERE IS the streaming Response from callback\n\n") | |
| print(my_custom_logger.streaming_response_obj) | |
| import time | |
| time.sleep(0.5) | |
| streamed_response = my_custom_logger.streaming_response_obj | |
| assert ( | |
| complete_response == streamed_response["choices"][0]["message"]["content"] | |
| ) | |
| except Exception as e: | |
| pytest.fail(f"LiteLLM Proxy test failed. Exception {str(e)}") | |