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llm log (#3414)

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This commit is contained in:
Shuchang Zheng
2025-09-11 18:10:05 -07:00
committed by GitHub
parent 7a8974f6c7
commit 0e2aecc75d
11 changed files with 137 additions and 113 deletions
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2
skyvern/core/script_generations/skyvern_page.py
View File

@@ -330,6 +330,7 @@ class SkyvernPage:
json_response = await app.SINGLE_CLICK_AGENT_LLM_API_HANDLER(
prompt=single_click_prompt,
prompt_name="single-click-action",
organization_id=context.organization_id,
)
actions = json_response.get("actions", [])
if actions:
@@ -404,6 +405,7 @@ class SkyvernPage:
json_response = await app.SINGLE_INPUT_AGENT_LLM_API_HANDLER(
prompt=script_generation_input_text_prompt,
prompt_name="script-generation-input-text-generatiion",
organization_id=context.organization_id if context else None,
)
value = json_response.get("answer", value)
except Exception:

220
skyvern/forge/sdk/api/llm/api_handler_factory.py
View File

@@ -100,6 +100,7 @@ class LLMAPIHandlerFactory:
ai_suggestion: AISuggestion | None = None,
screenshots: list[bytes] | None = None,
parameters: dict[str, Any] | None = None,
organization_id: str | None = None,
) -> dict[str, Any]:
"""
Custom LLM API handler that utilizes the LiteLLM router and fallbacks to OpenAI GPT-4 Vision.
@@ -204,45 +205,43 @@ class LLMAPIHandlerFactory:
cached_tokens = 0
completion_token_detail = None
cached_token_detail = None
llm_cost = 0
if step or thought:
try:
# FIXME: volcengine doesn't support litellm cost calculation.
llm_cost = litellm.completion_cost(completion_response=response)
except Exception as e:
LOG.debug("Failed to calculate LLM cost", error=str(e), exc_info=True)
llm_cost = 0
prompt_tokens = response.get("usage", {}).get("prompt_tokens", 0)
completion_tokens = response.get("usage", {}).get("completion_tokens", 0)
reasoning_tokens = 0
completion_token_detail = response.get("usage", {}).get("completion_tokens_details")
if completion_token_detail:
reasoning_tokens = completion_token_detail.reasoning_tokens or 0
cached_tokens = 0
cached_token_detail = response.get("usage", {}).get("prompt_tokens_details")
if cached_token_detail:
cached_tokens = cached_token_detail.cached_tokens or 0
if step:
await app.DATABASE.update_step(
task_id=step.task_id,
step_id=step.step_id,
organization_id=step.organization_id,
incremental_cost=llm_cost,
incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
incremental_reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
incremental_cached_tokens=cached_tokens if cached_tokens > 0 else None,
)
if thought:
await app.DATABASE.update_thought(
thought_id=thought.observer_thought_id,
organization_id=thought.organization_id,
input_token_count=prompt_tokens if prompt_tokens > 0 else None,
output_token_count=completion_tokens if completion_tokens > 0 else None,
thought_cost=llm_cost,
reasoning_token_count=reasoning_tokens if reasoning_tokens > 0 else None,
cached_token_count=cached_tokens if cached_tokens > 0 else None,
)
try:
# FIXME: volcengine doesn't support litellm cost calculation.
llm_cost = litellm.completion_cost(completion_response=response)
except Exception as e:
LOG.info("Failed to calculate LLM cost", error=str(e), exc_info=True)
llm_cost = 0
prompt_tokens = response.get("usage", {}).get("prompt_tokens", 0)
completion_tokens = response.get("usage", {}).get("completion_tokens", 0)
reasoning_tokens = 0
completion_token_detail = response.get("usage", {}).get("completion_tokens_details")
if completion_token_detail:
reasoning_tokens = completion_token_detail.reasoning_tokens or 0
cached_tokens = 0
cached_token_detail = response.get("usage", {}).get("prompt_tokens_details")
if cached_token_detail:
cached_tokens = cached_token_detail.cached_tokens or 0
if step:
await app.DATABASE.update_step(
task_id=step.task_id,
step_id=step.step_id,
organization_id=step.organization_id,
incremental_cost=llm_cost,
incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
incremental_reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
incremental_cached_tokens=cached_tokens if cached_tokens > 0 else None,
)
if thought:
await app.DATABASE.update_thought(
thought_id=thought.observer_thought_id,
organization_id=thought.organization_id,
input_token_count=prompt_tokens if prompt_tokens > 0 else None,
output_token_count=completion_tokens if completion_tokens > 0 else None,
thought_cost=llm_cost,
reasoning_token_count=reasoning_tokens if reasoning_tokens > 0 else None,
cached_token_count=cached_tokens if cached_tokens > 0 else None,
)
parsed_response = parse_api_response(response, llm_config.add_assistant_prefix)
await app.ARTIFACT_MANAGER.create_llm_artifact(
data=json.dumps(parsed_response, indent=2).encode("utf-8"),
@@ -267,6 +266,9 @@ class LLMAPIHandlerFactory:
)
# Track LLM API handler duration, token counts, and cost
organization_id = organization_id or (
step.organization_id if step else (thought.organization_id if thought else None)
)
duration_seconds = time.time() - start_time
LOG.info(
"LLM API handler duration metrics",
@@ -276,7 +278,7 @@ class LLMAPIHandlerFactory:
duration_seconds=duration_seconds,
step_id=step.step_id if step else None,
thought_id=thought.observer_thought_id if thought else None,
organization_id=step.organization_id if step else (thought.organization_id if thought else None),
organization_id=organization_id,
input_tokens=prompt_tokens if prompt_tokens > 0 else None,
output_tokens=completion_tokens if completion_tokens > 0 else None,
reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
@@ -310,6 +312,7 @@ class LLMAPIHandlerFactory:
ai_suggestion: AISuggestion | None = None,
screenshots: list[bytes] | None = None,
parameters: dict[str, Any] | None = None,
organization_id: str | None = None,
) -> dict[str, Any]:
start_time = time.time()
active_parameters = base_parameters or {}
@@ -421,45 +424,44 @@ class LLMAPIHandlerFactory:
cached_tokens = 0
completion_token_detail = None
cached_token_detail = None
llm_cost = 0
if step or thought:
try:
# FIXME: volcengine doesn't support litellm cost calculation.
llm_cost = litellm.completion_cost(completion_response=response)
except Exception as e:
LOG.debug("Failed to calculate LLM cost", error=str(e), exc_info=True)
llm_cost = 0
prompt_tokens = response.get("usage", {}).get("prompt_tokens", 0)
completion_tokens = response.get("usage", {}).get("completion_tokens", 0)
reasoning_tokens = 0
completion_token_detail = response.get("usage", {}).get("completion_tokens_details")
if completion_token_detail:
reasoning_tokens = completion_token_detail.reasoning_tokens or 0
cached_tokens = 0
cached_token_detail = response.get("usage", {}).get("prompt_tokens_details")
if cached_token_detail:
cached_tokens = cached_token_detail.cached_tokens or 0
if step:
await app.DATABASE.update_step(
task_id=step.task_id,
step_id=step.step_id,
organization_id=step.organization_id,
incremental_cost=llm_cost,
incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
incremental_reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
incremental_cached_tokens=cached_tokens if cached_tokens > 0 else None,
)
if thought:
await app.DATABASE.update_thought(
thought_id=thought.observer_thought_id,
organization_id=thought.organization_id,
input_token_count=prompt_tokens if prompt_tokens > 0 else None,
output_token_count=completion_tokens if completion_tokens > 0 else None,
reasoning_token_count=reasoning_tokens if reasoning_tokens > 0 else None,
cached_token_count=cached_tokens if cached_tokens > 0 else None,
thought_cost=llm_cost,
)
try:
# FIXME: volcengine doesn't support litellm cost calculation.
llm_cost = litellm.completion_cost(completion_response=response)
except Exception as e:
LOG.info("Failed to calculate LLM cost", error=str(e), exc_info=True)
llm_cost = 0
prompt_tokens = response.get("usage", {}).get("prompt_tokens", 0)
completion_tokens = response.get("usage", {}).get("completion_tokens", 0)
reasoning_tokens = 0
completion_token_detail = response.get("usage", {}).get("completion_tokens_details")
if completion_token_detail:
reasoning_tokens = completion_token_detail.reasoning_tokens or 0
cached_tokens = 0
cached_token_detail = response.get("usage", {}).get("prompt_tokens_details")
if cached_token_detail:
cached_tokens = cached_token_detail.cached_tokens or 0
if step:
await app.DATABASE.update_step(
task_id=step.task_id,
step_id=step.step_id,
organization_id=step.organization_id,
incremental_cost=llm_cost,
incremental_input_tokens=prompt_tokens if prompt_tokens > 0 else None,
incremental_output_tokens=completion_tokens if completion_tokens > 0 else None,
incremental_reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
incremental_cached_tokens=cached_tokens if cached_tokens > 0 else None,
)
if thought:
await app.DATABASE.update_thought(
thought_id=thought.observer_thought_id,
organization_id=thought.organization_id,
input_token_count=prompt_tokens if prompt_tokens > 0 else None,
output_token_count=completion_tokens if completion_tokens > 0 else None,
reasoning_token_count=reasoning_tokens if reasoning_tokens > 0 else None,
cached_token_count=cached_tokens if cached_tokens > 0 else None,
thought_cost=llm_cost,
)
parsed_response = parse_api_response(response, llm_config.add_assistant_prefix)
await app.ARTIFACT_MANAGER.create_llm_artifact(
data=json.dumps(parsed_response, indent=2).encode("utf-8"),
@@ -484,6 +486,9 @@ class LLMAPIHandlerFactory:
)
# Track LLM API handler duration, token counts, and cost
organization_id = organization_id or (
step.organization_id if step else (thought.organization_id if thought else None)
)
duration_seconds = time.time() - start_time
LOG.info(
"LLM API handler duration metrics",
@@ -493,7 +498,7 @@ class LLMAPIHandlerFactory:
duration_seconds=duration_seconds,
step_id=step.step_id if step else None,
thought_id=thought.observer_thought_id if thought else None,
organization_id=step.organization_id if step else (thought.organization_id if thought else None),
organization_id=organization_id,
input_tokens=prompt_tokens if prompt_tokens > 0 else None,
output_tokens=completion_tokens if completion_tokens > 0 else None,
reasoning_tokens=reasoning_tokens if reasoning_tokens > 0 else None,
@@ -572,6 +577,7 @@ class LLMCaller:
use_message_history: bool = False,
raw_response: bool = False,
window_dimension: Resolution | None = None,
organization_id: str | None = None,
**extra_parameters: Any,
) -> dict[str, Any]:
start_time = time.perf_counter()
@@ -702,30 +708,32 @@ class LLMCaller:
ai_suggestion=ai_suggestion,
)
call_stats = None
if step or thought:
call_stats = await self.get_call_stats(response)
if step:
await app.DATABASE.update_step(
task_id=step.task_id,
step_id=step.step_id,
organization_id=step.organization_id,
incremental_cost=call_stats.llm_cost,
incremental_input_tokens=call_stats.input_tokens,
incremental_output_tokens=call_stats.output_tokens,
incremental_reasoning_tokens=call_stats.reasoning_tokens,
incremental_cached_tokens=call_stats.cached_tokens,
)
if thought:
await app.DATABASE.update_thought(
thought_id=thought.observer_thought_id,
organization_id=thought.organization_id,
input_token_count=call_stats.input_tokens,
output_token_count=call_stats.output_tokens,
reasoning_token_count=call_stats.reasoning_tokens,
cached_token_count=call_stats.cached_tokens,
thought_cost=call_stats.llm_cost,
)
call_stats = await self.get_call_stats(response)
if step:
await app.DATABASE.update_step(
task_id=step.task_id,
step_id=step.step_id,
organization_id=step.organization_id,
incremental_cost=call_stats.llm_cost,
incremental_input_tokens=call_stats.input_tokens,
incremental_output_tokens=call_stats.output_tokens,
incremental_reasoning_tokens=call_stats.reasoning_tokens,
incremental_cached_tokens=call_stats.cached_tokens,
)
if thought:
await app.DATABASE.update_thought(
thought_id=thought.observer_thought_id,
organization_id=thought.organization_id,
input_token_count=call_stats.input_tokens,
output_token_count=call_stats.output_tokens,
reasoning_token_count=call_stats.reasoning_tokens,
cached_token_count=call_stats.cached_tokens,
thought_cost=call_stats.llm_cost,
)
organization_id = organization_id or (
step.organization_id if step else (thought.organization_id if thought else None)
)
# Track LLM API handler duration, token counts, and cost
duration_seconds = time.perf_counter() - start_time
LOG.info(
@@ -736,7 +744,7 @@ class LLMCaller:
duration_seconds=duration_seconds,
step_id=step.step_id if step else None,
thought_id=thought.observer_thought_id if thought else None,
organization_id=step.organization_id if step else (thought.organization_id if thought else None),
organization_id=organization_id,
input_tokens=call_stats.input_tokens if call_stats and call_stats.input_tokens else None,
output_tokens=call_stats.output_tokens if call_stats and call_stats.output_tokens else None,
reasoning_tokens=call_stats.reasoning_tokens if call_stats and call_stats.reasoning_tokens else None,
@@ -920,7 +928,7 @@ class LLMCaller:
try:
llm_cost = litellm.completion_cost(completion_response=response)
except Exception as e:
LOG.debug("Failed to calculate LLM cost", error=str(e), exc_info=True)
LOG.info("Failed to calculate LLM cost", error=str(e), exc_info=True)
llm_cost = 0
input_tokens = response.get("usage", {}).get("prompt_tokens", 0)
output_tokens = response.get("usage", {}).get("completion_tokens", 0)

2
skyvern/forge/sdk/api/llm/models.py
View File

@@ -95,6 +95,7 @@ class LLMAPIHandler(Protocol):
ai_suggestion: AISuggestion | None = None,
screenshots: list[bytes] | None = None,
parameters: dict[str, Any] | None = None,
organization_id: str | None = None,
) -> Awaitable[dict[str, Any]]: ...
@@ -107,5 +108,6 @@ async def dummy_llm_api_handler(
ai_suggestion: AISuggestion | None = None,
screenshots: list[bytes] | None = None,
parameters: dict[str, Any] | None = None,
organization_id: str | None = None,
) -> dict[str, Any]:
raise NotImplementedError("Your LLM provider is not configured. Please configure it in the .env file.")

5
skyvern/forge/sdk/routes/agent_protocol.py
View File

@@ -1863,7 +1863,10 @@ async def suggest(
)
llm_response = await app.LLM_API_HANDLER(
prompt=llm_prompt, ai_suggestion=new_ai_suggestion, prompt_name="suggest-data-schema"
prompt=llm_prompt,
ai_suggestion=new_ai_suggestion,
prompt_name="suggest-data-schema",
organization_id=current_org.organization_id,
)
parsed_ai_suggestion = AISuggestionBase.model_validate(llm_response)

11
skyvern/forge/sdk/routes/credentials.py
View File

@@ -32,9 +32,11 @@ from skyvern.forge.sdk.services.bitwarden import BitwardenService
LOG = structlog.get_logger()
async def parse_totp_code(content: str) -> str | None:
async def parse_totp_code(content: str, organization_id: str) -> str | None:
prompt = prompt_engine.load_prompt("parse-verification-code", content=content)
code_resp = await app.SECONDARY_LLM_API_HANDLER(prompt=prompt, prompt_name="parse-verification-code")
code_resp = await app.SECONDARY_LLM_API_HANDLER(
prompt=prompt, prompt_name="parse-verification-code", organization_id=organization_id
)
LOG.info("TOTP Code Parser Response", code_resp=code_resp)
return code_resp.get("code", None)
@@ -58,7 +60,8 @@ async def parse_totp_code(content: str) -> str | None:
include_in_schema=False,
)
async def send_totp_code(
data: TOTPCodeCreate, curr_org: Organization = Depends(org_auth_service.get_current_org)
data: TOTPCodeCreate,
curr_org: Organization = Depends(org_auth_service.get_current_org),
) -> TOTPCode:
LOG.info(
"Saving TOTP code",
@@ -72,7 +75,7 @@ async def send_totp_code(
code: str | None = content
# We assume the user is sending the code directly when the length of code is less than or equal to 10
if len(content) > 10:
code = await parse_totp_code(content)
code = await parse_totp_code(content, curr_org.organization_id)
if not code:
LOG.error(
"Failed to parse totp code",

1
skyvern/forge/sdk/workflow/service.py
View File

@@ -696,6 +696,7 @@ class WorkflowService:
metadata_response = await app.LLM_API_HANDLER(
prompt=metadata_prompt,
prompt_name="conversational_ui_goal",
organization_id=organization.organization_id,
)
block_label: str = metadata_response.get("block_label", DEFAULT_FIRST_BLOCK_LABEL)

1
skyvern/services/script_service.py
View File

@@ -1360,6 +1360,7 @@ async def generate_text(
json_response = await app.SINGLE_INPUT_AGENT_LLM_API_HANDLER(
prompt=script_generation_input_text_prompt,
prompt_name="script-generation-input-text-generatiion",
organization_id=context.organization_id,
)
new_text = json_response.get("answer", new_text)
except Exception:

4
skyvern/services/task_v1_service.py
View File

@@ -47,7 +47,9 @@ async def generate_task(user_prompt: str, organization: Organization) -> TaskGen
llm_prompt = prompt_engine.load_prompt("generate-task", user_prompt=user_prompt)
try:
llm_response = await app.LLM_API_HANDLER(prompt=llm_prompt, prompt_name="generate-task")
llm_response = await app.LLM_API_HANDLER(
prompt=llm_prompt, prompt_name="generate-task", organization_id=organization.organization_id
)
parsed_task_generation_obj = TaskGenerationBase.model_validate(llm_response)
# generate a TaskGenerationModel

1
skyvern/services/task_v2_service.py
View File

@@ -1284,6 +1284,7 @@ async def _generate_extraction_task(
generate_extraction_task_prompt,
task_v2=task_v2,
prompt_name="task_v2_generate_extraction_task",
organization_id=task_v2.organization_id,
)
LOG.info("Data extraction response", data_extraction_response=generate_extraction_task_response)

2
skyvern/webeye/actions/handler.py
View File

@@ -3800,7 +3800,7 @@ async def _get_input_or_select_context(
starter=element_handle,
frame=skyvern_element.get_frame_id(),
)
clean_up_func = app.AGENT_FUNCTION.cleanup_element_tree_factory()
clean_up_func = app.AGENT_FUNCTION.cleanup_element_tree_factory(step=step)
element_tree = await clean_up_func(skyvern_element.get_frame(), "", copy.deepcopy(element_tree))
element_tree_trimmed = trim_element_tree(copy.deepcopy(element_tree))
element_tree_builder = ScrapedPage(

1
skyvern/webeye/actions/parse_actions.py
View File

@@ -741,6 +741,7 @@ async def generate_cua_fallback_actions(
action_response = await app.LLM_API_HANDLER(
prompt=fallback_action_prompt,
prompt_name="cua-fallback-action",
step=step,
)
LOG.info("Fallback action response", action_response=action_response)
skyvern_action_type = action_response.get("action")