xinyin025/LangBot
1
0
Fork 0
mirror of https://github.com/langbot-app/LangBot.git synced 2026-07-18 10:26:07 +00:00
Code Issues Packages Projects Releases Wiki Activity

platfrom

Browse Source
This commit is contained in:
Typer_Body
2026-06-04 03:13:32 +08:00
parent 192b69b0fb
commit 6609bebeec
10 changed files with 223 additions and 45 deletions
Download Patch File Download Diff File Expand all files Collapse all files
src/langbot/pkg/workflow/nodes/llm_call.py
+27 -15
View File
@@ -234,19 +234,25 @@ Respond in the same language as the user's input.
messages: list,
funcs: list | None,
extra_args: dict,
) -> tuple[Any, Any]:
"""Try non-streaming invocation with sequential fallback. Returns (message, model_used)."""
) -> tuple[Any, Any, dict]:
"""Try non-streaming invocation with sequential fallback. Returns (message, model_used, usage_info)."""
last_error = None
for model in candidates:
try:
msg = await model.provider.invoke_llm(
result = await model.provider.invoke_llm(
query=None,
model=model,
messages=messages,
funcs=funcs if model.model_entity.abilities.__contains__('func_call') else [],
extra_args=extra_args,
)
return msg, model
# invoke_llm returns (message, usage_info) tuple
if isinstance(result, tuple) and len(result) == 2:
msg, usage_info = result
else:
msg = result
usage_info = {}
return msg, model, usage_info
except Exception as e:
last_error = e
logger.warning(f'[LLM:{self.node_id}] Model {model.model_entity.name} failed: {e}, trying next...')
@@ -514,7 +520,7 @@ Respond in the same language as the user's input.
# Invoke LLM with fallback
try:
result_message, used_model = await self._invoke_with_fallback(
result_message, used_model, llm_usage = await self._invoke_with_fallback(
candidates=candidates,
messages=messages,
funcs=None,
@@ -579,25 +585,31 @@ Respond in the same language as the user's input.
'blocked_by_filter': True,
}
# Extract usage info
if hasattr(result_message, 'usage') and result_message.usage:
# Extract usage info from LLM call result
# Priority: llm_usage (from _invoke_with_fallback) > result_message.usage > result_message.token_usage
if llm_usage:
usage = {
'prompt_tokens': llm_usage.get('input_tokens', 0) or llm_usage.get('prompt_tokens', 0),
'completion_tokens': llm_usage.get('output_tokens', 0) or llm_usage.get('completion_tokens', 0),
'total_tokens': llm_usage.get('total_tokens', 0),
}
# Check result_message.usage (set by RuntimeProvider.invoke_llm)
elif hasattr(result_message, 'usage') and result_message.usage:
u = result_message.usage
# Handle both object and dict usage
if isinstance(u, dict):
usage = {
'prompt_tokens': u.get('prompt_tokens', 0) or 0,
'completion_tokens': u.get('completion_tokens', 0) or 0,
'total_tokens': u.get('total_tokens', 0) or 0,
'prompt_tokens': u.get('input_tokens', 0) or u.get('prompt_tokens', 0),
'completion_tokens': u.get('output_tokens', 0) or u.get('completion_tokens', 0),
'total_tokens': u.get('total_tokens', 0),
}
else:
usage = {
'prompt_tokens': getattr(u, 'prompt_tokens', 0) or 0,
'completion_tokens': getattr(u, 'completion_tokens', 0) or 0,
'total_tokens': getattr(u, 'total_tokens', 0) or 0,
'prompt_tokens': getattr(u, 'input_tokens', 0) or getattr(u, 'prompt_tokens', 0),
'completion_tokens': getattr(u, 'output_tokens', 0) or getattr(u, 'completion_tokens', 0),
'total_tokens': getattr(u, 'total_tokens', 0),
}
elif hasattr(result_message, 'token_usage') and result_message.token_usage:
u = result_message.token_usage
# Handle both object and dict token_usage
if isinstance(u, dict):
usage = {
'prompt_tokens': u.get('prompt_tokens', 0) or 0,