forked from phoenix/litellm-mirror
fix error tracking
This commit is contained in:
Vince Lwt
parent
9a43b23876
commit
61afceece1
4 changed files with 50 additions and 71 deletions
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@ -4,15 +4,16 @@
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## Use LLMonitor to log requests across all LLM Providers (OpenAI, Azure, Anthropic, Cohere, Replicate, PaLM)
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liteLLM provides `success_callbacks` and `failure_callbacks`, making it easy for you to send data to a particular provider depending on the status of your responses.
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liteLLM provides `callbacks`, making it easy for you to log data depending on the status of your responses.
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### Using Callbacks
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Use just 2 lines of code, to instantly log your responses **across all providers** with llmonitor:
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```
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litellm.success_callback=["llmonitor"]
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litellm.error_callback=["llmonitor"]
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litellm.success_callback = ["llmonitor"]
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litellm.failure_callback = ["llmonitor"]
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```
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Complete code
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@ -27,8 +28,8 @@ os.environ["LLMONITOR_APP_ID"] = "your-llmonitor-app-id"
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os.environ["OPENAI_API_KEY"], os.environ["COHERE_API_KEY"] = "", ""
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# set callbacks
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litellm.success_callback=["llmonitor"]
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litellm.error_callback=["llmonitor"]
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litellm.success_callback = ["llmonitor"]
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litellm.failure_callback = ["llmonitor"]
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#openai call
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response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
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@ -57,14 +57,14 @@ class LLMonitorLogger:
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messages=None,
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user_id=None,
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response_obj=None,
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time=datetime.datetime.now(),
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start_time=datetime.datetime.now(),
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end_time=datetime.datetime.now(),
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error=None,
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):
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# Method definition
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try:
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print_verbose(
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f"LLMonitor Logging - Enters logging function for model {model}"
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)
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f"LLMonitor Logging - Logging request for model {model}")
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if response_obj:
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usage = parse_usage(response_obj['usage'])
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@ -73,27 +73,41 @@ class LLMonitorLogger:
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usage = None
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output = None
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print(type, run_id, model, messages, usage, output, time, user_id,
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error)
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if error:
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error_obj = {'stack': error}
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headers = {'Content-Type': 'application/json'}
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else:
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error_obj = None
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data = {
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data = [{
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"type": "llm",
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"name": model,
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"runId": run_id,
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"app": self.app_id,
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"error": error,
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"event": type,
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"timestamp": time.isoformat(),
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'event': 'start',
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"timestamp": start_time.isoformat(),
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"userId": user_id,
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"input": parse_messages(messages),
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"usage": usage,
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}, {
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"type": "llm",
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"runId": run_id,
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"app": self.app_id,
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"event": type,
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"error": error_obj,
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"timestamp": end_time.isoformat(),
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"userId": user_id,
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"output": parse_messages(output),
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}
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"tokensUsage": usage,
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}]
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print_verbose(f"LLMonitor Logging - final data object: {data}")
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# response = requests.post(url, headers=headers, json=data)
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# print_verbose(f"LLMonitor Logging - final data object: {data}")
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response = requests.post(
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self.api_url + '/api/report',
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headers={'Content-Type': 'application/json'},
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json={'events': data})
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print_verbose(f"LLMonitor Logging - response: {response}")
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except:
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# traceback.print_exc()
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print_verbose(
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@ -9,37 +9,16 @@ sys.path.insert(0, os.path.abspath('../..'))
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from litellm import completion
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import litellm
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litellm.input_callback = ["llmonitor"]
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litellm.success_callback = ["llmonitor"]
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litellm.error_callback = ["llmonitor"]
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litellm.failure_callback = ["llmonitor"]
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litellm.set_verbose = True
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os.environ[
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"OPENAI_API_KEY"] = "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"
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# openai call
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first_success_test = completion(model="gpt-3.5-turbo",
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messages=[{
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"role": "user",
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"content": "Hi 👋 - i'm openai"
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}])
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print(os.environ["OPENAI_API_KEY"])
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# def my_custom_logging_fn(model_call_dict):
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# print(f"model call details: {model_call_dict}")
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# # openai call
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# response = completion(model="gpt-3.5-turbo",
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# messages=[{
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# "role": "user",
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# "content": "Hi 👋 - i'm openai"
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# }],
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# logger_fn=my_custom_logging_fn)
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# print(response)
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# #bad request call
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# response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad request"}])
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# cohere call
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response = completion(model="command-nightly",
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messages=[{
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"role": "user",
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"content": "Hi 👋 - i'm cohere"
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}])
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print(response)
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print(first_success_test)
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@ -209,19 +209,7 @@ class Logging:
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litellm_params["litellm_call_id"],
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print_verbose=print_verbose,
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)
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elif callback == "llmonitor":
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print_verbose("reaches llmonitor for logging!")
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model = self.model
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messages = self.messages
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print(f"liteDebuggerClient: {liteDebuggerClient}")
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llmonitorLogger.log_event(
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type="start",
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model=model,
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messages=messages,
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user_id=litellm._thread_context.user,
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run_id=self.litellm_params["litellm_call_id"],
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print_verbose=print_verbose,
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)
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elif callback == "lite_debugger":
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print_verbose("reaches litedebugger for logging!")
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model = self.model
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@ -426,6 +414,7 @@ def client(original_function):
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add_cache(result, *args, **kwargs)
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# LOG SUCCESS
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crash_reporting(*args, **kwargs)
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my_thread = threading.Thread(
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target=handle_success,
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args=(args, kwargs, result, start_time,
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@ -433,6 +422,7 @@ def client(original_function):
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my_thread.start()
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return result
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except Exception as e:
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traceback_exception = traceback.format_exc()
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crash_reporting(*args, **kwargs, exception=traceback_exception)
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end_time = datetime.datetime.now()
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@ -855,21 +845,15 @@ def handle_failure(exception, traceback_exception, start_time, end_time, args,
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print_verbose("reaches llmonitor for logging!")
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model = args[0] if len(args) > 0 else kwargs["model"]
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messages = args[1] if len(args) > 1 else kwargs["messages"]
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usage = {
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"prompt_tokens":
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prompt_token_calculator(model, messages=messages),
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"completion_tokens":
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0,
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}
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llmonitorLogger.log_event(
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type="error",
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user_id=litellm._thread_context.user,
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model=model,
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error=traceback_exception,
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response_obj=result,
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run_id=kwargs["litellm_call_id"],
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timestamp=end_time,
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usage=usage,
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start_time=start_time,
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end_time=end_time,
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print_verbose=print_verbose,
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)
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elif callback == "supabase":
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@ -992,7 +976,8 @@ def handle_success(args, kwargs, result, start_time, end_time):
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messages=messages,
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user_id=litellm._thread_context.user,
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response_obj=result,
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time=end_time,
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start_time=start_time,
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end_time=end_time,
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run_id=kwargs["litellm_call_id"],
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print_verbose=print_verbose,
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)
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