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...

7 Commits

Author SHA1 Message Date
Thiago Moretto
4ca785eb15 Merge pull request #1597 from crewAIInc/tm-fix-crew-train-test
Fix crew_train_success test
2024-11-13 10:52:49 -03:00
Thiago Moretto
c57cbd8591 Fix crew_train_success test 2024-11-13 10:47:49 -03:00
Thiago Moretto
7fb1289205 Merge pull request #1596 from crewAIInc/tm-recording-cached-prompt-tokens
Add cached prompt tokens info on usage metrics
2024-11-13 10:37:29 -03:00
Thiago Moretto
f02681ae01 Merge branch 'main' into tm-recording-cached-prompt-tokens 2024-11-13 10:19:02 -03:00
Thiago Moretto
c725105b1f do not include cached on total 2024-11-13 10:18:30 -03:00
Thiago Moretto
36aa4bcb46 Cached prompt tokens on usage metrics 2024-11-13 10:16:30 -03:00
Eduardo Chiarotti
b98f8f9fe1 fix: Step callback issue (#1595)
* fix: Step callback issue

* fix: Add empty thought since its required
2024-11-13 10:07:28 -03:00
6 changed files with 171 additions and 133 deletions

View File

@@ -4,6 +4,7 @@ from crewai.types.usage_metrics import UsageMetrics
class TokenProcess:
total_tokens: int = 0
prompt_tokens: int = 0
cached_prompt_tokens: int = 0
completion_tokens: int = 0
successful_requests: int = 0
@@ -15,6 +16,9 @@ class TokenProcess:
self.completion_tokens = self.completion_tokens + tokens
self.total_tokens = self.total_tokens + tokens
def sum_cached_prompt_tokens(self, tokens: int):
self.cached_prompt_tokens = self.cached_prompt_tokens + tokens
def sum_successful_requests(self, requests: int):
self.successful_requests = self.successful_requests + requests
@@ -22,6 +26,7 @@ class TokenProcess:
return UsageMetrics(
total_tokens=self.total_tokens,
prompt_tokens=self.prompt_tokens,
cached_prompt_tokens=self.cached_prompt_tokens,
completion_tokens=self.completion_tokens,
successful_requests=self.successful_requests,
)

View File

@@ -145,25 +145,26 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
formatted_answer.result = action_result
self._show_logs(formatted_answer)
if self.step_callback:
self.step_callback(formatted_answer)
if self.step_callback:
self.step_callback(formatted_answer)
if self._should_force_answer():
if self.have_forced_answer:
return AgentFinish(
output=self._i18n.errors(
"force_final_answer_error"
).format(formatted_answer.text),
text=formatted_answer.text,
)
else:
formatted_answer.text += (
f'\n{self._i18n.errors("force_final_answer")}'
)
self.have_forced_answer = True
self.messages.append(
self._format_msg(formatted_answer.text, role="assistant")
)
if self._should_force_answer():
if self.have_forced_answer:
return AgentFinish(
thought="",
output=self._i18n.errors(
"force_final_answer_error"
).format(formatted_answer.text),
text=formatted_answer.text,
)
else:
formatted_answer.text += (
f'\n{self._i18n.errors("force_final_answer")}'
)
self.have_forced_answer = True
self.messages.append(
self._format_msg(formatted_answer.text, role="assistant")
)
except OutputParserException as e:
self.messages.append({"role": "user", "content": e.error})

View File

@@ -8,6 +8,7 @@ class UsageMetrics(BaseModel):
Attributes:
total_tokens: Total number of tokens used.
prompt_tokens: Number of tokens used in prompts.
cached_prompt_tokens: Number of cached prompt tokens used.
completion_tokens: Number of tokens used in completions.
successful_requests: Number of successful requests made.
"""
@@ -16,6 +17,9 @@ class UsageMetrics(BaseModel):
prompt_tokens: int = Field(
default=0, description="Number of tokens used in prompts."
)
cached_prompt_tokens: int = Field(
default=0, description="Number of cached prompt tokens used."
)
completion_tokens: int = Field(
default=0, description="Number of tokens used in completions."
)
@@ -32,5 +36,6 @@ class UsageMetrics(BaseModel):
"""
self.total_tokens += usage_metrics.total_tokens
self.prompt_tokens += usage_metrics.prompt_tokens
self.cached_prompt_tokens += usage_metrics.cached_prompt_tokens
self.completion_tokens += usage_metrics.completion_tokens
self.successful_requests += usage_metrics.successful_requests

View File

@@ -1,5 +1,5 @@
from litellm.integrations.custom_logger import CustomLogger
from litellm.types.utils import Usage
from crewai.agents.agent_builder.utilities.base_token_process import TokenProcess
@@ -11,8 +11,11 @@ class TokenCalcHandler(CustomLogger):
if self.token_cost_process is None:
return
usage : Usage = response_obj["usage"]
self.token_cost_process.sum_successful_requests(1)
self.token_cost_process.sum_prompt_tokens(response_obj["usage"].prompt_tokens)
self.token_cost_process.sum_completion_tokens(
response_obj["usage"].completion_tokens
)
self.token_cost_process.sum_prompt_tokens(usage.prompt_tokens)
self.token_cost_process.sum_completion_tokens(usage.completion_tokens)
if usage.prompt_tokens_details:
self.token_cost_process.sum_cached_prompt_tokens(
usage.prompt_tokens_details.cached_tokens
)

View File

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version: 1

View File

@@ -564,6 +564,7 @@ def test_crew_kickoff_usage_metrics():
assert result.token_usage.prompt_tokens > 0
assert result.token_usage.completion_tokens > 0
assert result.token_usage.successful_requests > 0
assert result.token_usage.cached_prompt_tokens == 0
def test_agents_rpm_is_never_set_if_crew_max_RPM_is_not_set():
@@ -1284,6 +1285,7 @@ def test_agent_usage_metrics_are_captured_for_hierarchical_process():
prompt_tokens=1562,
completion_tokens=111,
successful_requests=3,
cached_prompt_tokens=0
)
@@ -1777,26 +1779,22 @@ def test_crew_train_success(
]
)
crew_training_handler.assert_has_calls(
[
mock.call("training_data.pkl"),
mock.call().load(),
mock.call("trained_agents_data.pkl"),
mock.call().save_trained_data(
agent_id="Researcher",
trained_data=task_evaluator().evaluate_training_data().model_dump(),
),
mock.call("trained_agents_data.pkl"),
mock.call().save_trained_data(
agent_id="Senior Writer",
trained_data=task_evaluator().evaluate_training_data().model_dump(),
),
mock.call(),
mock.call().load(),
mock.call(),
mock.call().load(),
]
)
crew_training_handler.assert_any_call("training_data.pkl")
crew_training_handler().load.assert_called()
crew_training_handler.assert_any_call("trained_agents_data.pkl")
crew_training_handler().load.assert_called()
crew_training_handler().save_trained_data.assert_has_calls([
mock.call(
agent_id="Researcher",
trained_data=task_evaluator().evaluate_training_data().model_dump(),
),
mock.call(
agent_id="Senior Writer",
trained_data=task_evaluator().evaluate_training_data().model_dump(),
)
])
def test_crew_train_error():