Do not raise exception from OpenAIMetricsCalculator

src/promptflow-tracing/promptflow/tracing/_openai_utils.py

@@ -32,7 +32,10 @@ def get_completion_tokens(self, api_call: dict):
  # completion tokens when the output is consumed.
  completion_tokens = 0
  if self._need_collect_metrics(api_call):
- completion_tokens += self._get_completion_tokens_for_signal_api(api_call)
+ try:
+ completion_tokens += self._get_completion_tokens_for_signal_api(api_call)
+ except Exception as ex:
+ self._log_warning(f"Failed to calculate completion tokens due to exception: {ex}.")
 
  children = api_call.get("children")
  if children is not None:
@@ -115,21 +118,26 @@ def _try_get_model(self, inputs, output):
 
  def get_openai_metrics_for_chat_api(self, inputs, output):
  metrics = {}
- enc, tokens_per_message, tokens_per_name = self._get_encoding_for_chat_api(self._try_get_model(inputs, output))
- metrics["prompt_tokens"] = self._get_prompt_tokens_from_messages(
- inputs["messages"], enc, tokens_per_message, tokens_per_name
- )
- if isinstance(output, list):
- if IS_LEGACY_OPENAI:
- metrics["completion_tokens"] = len(output)
+ try:
+ enc, tokens_per_message, tokens_per_name = self._get_encoding_for_chat_api(
+ self._try_get_model(inputs, output)
+ )
+ metrics["prompt_tokens"] = self._get_prompt_tokens_from_messages(
+ inputs["messages"], enc, tokens_per_message, tokens_per_name
+ )
+ if isinstance(output, list):
+ if IS_LEGACY_OPENAI:
+ metrics["completion_tokens"] = len(output)
+ else:
+ metrics["completion_tokens"] = len(
+ [chunk for chunk in output if chunk.choices and chunk.choices[0].delta.content]
+ )
  else:
- metrics["completion_tokens"] = len(
- [chunk for chunk in output if chunk.choices and chunk.choices[0].delta.content]
- )
- else:
- metrics["completion_tokens"] = self._get_completion_tokens_for_chat_api(output, enc)
- metrics["total_tokens"] = metrics["prompt_tokens"] + metrics["completion_tokens"]
- return metrics
+ metrics["completion_tokens"] = self._get_completion_tokens_for_chat_api(output, enc)
+ metrics["total_tokens"] = metrics["prompt_tokens"] + metrics["completion_tokens"]
+ return metrics
+ except Exception as ex:
+ self._log_warning(f"Failed to calculate metrics due to exception: {ex}.")
 
  def _get_encoding_for_chat_api(self, model):
  try:
@@ -143,6 +151,8 @@ def _get_encoding_for_chat_api(self, model):
  tokens_per_message = 3
  tokens_per_name = 1
  else:
+ tokens_per_message = 3
+ tokens_per_name = 1
  self._log_warning(f"Calculating metrics for model {model} is not supported.")
  return enc, tokens_per_message, tokens_per_name
 
@@ -172,25 +182,28 @@ def _get_completion_tokens_for_chat_api(self, output, enc):
 
  def get_openai_metrics_for_completion_api(self, inputs, output):
  metrics = {}
- enc = self._get_encoding_for_completion_api(self._try_get_model(inputs, output))
- metrics["prompt_tokens"] = 0
- prompt = inputs.get("prompt")
- if isinstance(prompt, str):
- metrics["prompt_tokens"] = len(enc.encode(prompt))
- elif isinstance(prompt, list):
- for pro in prompt:
- metrics["prompt_tokens"] += len(enc.encode(pro))
- if isinstance(output, list):
- if IS_LEGACY_OPENAI:
- metrics["completion_tokens"] = len(output)
+ try:
+ enc = self._get_encoding_for_completion_api(self._try_get_model(inputs, output))
+ metrics["prompt_tokens"] = 0
+ prompt = inputs.get("prompt")
+ if isinstance(prompt, str):
+ metrics["prompt_tokens"] = len(enc.encode(prompt))
+ elif isinstance(prompt, list):
+ for pro in prompt:
+ metrics["prompt_tokens"] += len(enc.encode(pro))
+ if isinstance(output, list):
+ if IS_LEGACY_OPENAI:
+ metrics["completion_tokens"] = len(output)
+ else:
+ metrics["completion_tokens"] = len(
+ [chunk for chunk in output if chunk.choices and chunk.choices[0].text]
+ )
  else:
- metrics["completion_tokens"] = len(
- [chunk for chunk in output if chunk.choices and chunk.choices[0].text]
- )
- else:
- metrics["completion_tokens"] = self._get_completion_tokens_for_completion_api(output, enc)
- metrics["total_tokens"] = metrics["prompt_tokens"] + metrics["completion_tokens"]
- return metrics
+ metrics["completion_tokens"] = self._get_completion_tokens_for_completion_api(output, enc)
+ metrics["total_tokens"] = metrics["prompt_tokens"] + metrics["completion_tokens"]
+ return metrics
+ except Exception as ex:
+ self._log_warning(f"Failed to calculate metrics due to exception: {ex}.")
 
  def _get_encoding_for_completion_api(self, model):
  try: