mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-09 07:16:04 +00:00
shit
This commit is contained in:
Typer_Body
@@ -312,6 +312,164 @@ class WorkflowsRouterGroup(group.RouterGroup):
|
||||
except ValueError as e:
|
||||
return self.http_status(404, -1, str(e))
|
||||
|
||||
# LLM Node Performance Test Endpoint
|
||||
# Tests each step of LLM node execution with detailed timing
|
||||
@self.route('/_/test/llm-node', methods=['POST'], auth_type=group.AuthType.USER_TOKEN_OR_API_KEY)
|
||||
async def _() -> str:
|
||||
"""Test LLM node performance with detailed step-by-step timing.
|
||||
|
||||
Request body:
|
||||
{
|
||||
"model_uuid": "uuid-of-model",
|
||||
"system_prompt": "optional system prompt",
|
||||
"user_prompt": "test message",
|
||||
"enable_tools": false,
|
||||
"tools": [],
|
||||
"temperature": 0.7,
|
||||
"max_tokens": 100
|
||||
}
|
||||
|
||||
Response includes timing for each step:
|
||||
- model_fetch: Time to get model from model_mgr
|
||||
- tool_fetch: Time to load tools (if enabled)
|
||||
- prompt_build: Time to build messages
|
||||
- llm_call: Time for actual LLM invocation
|
||||
- total: Total time
|
||||
"""
|
||||
import time
|
||||
|
||||
json_data = await quart.request.json
|
||||
if not json_data:
|
||||
return self.http_status(400, -1, 'Request body is required')
|
||||
|
||||
model_uuid = json_data.get('model_uuid', '')
|
||||
if not model_uuid:
|
||||
return self.http_status(400, -1, 'model_uuid is required')
|
||||
|
||||
user_prompt = json_data.get('user_prompt', 'test')
|
||||
system_prompt = json_data.get('system_prompt', '')
|
||||
enable_tools = json_data.get('enable_tools', False)
|
||||
tools_config = json_data.get('tools', [])
|
||||
temperature = json_data.get('temperature')
|
||||
max_tokens = json_data.get('max_tokens', 0)
|
||||
|
||||
timings = {}
|
||||
errors = []
|
||||
|
||||
# Step 1: Model fetch
|
||||
t_start = time.perf_counter()
|
||||
try:
|
||||
runtime_model = await self.ap.model_mgr.get_model_by_uuid(model_uuid)
|
||||
timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['model_found'] = True
|
||||
timings['model_name'] = runtime_model.model_entity.name if runtime_model else None
|
||||
except Exception as e:
|
||||
timings['model_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['model_found'] = False
|
||||
errors.append(f'Model fetch failed: {str(e)}')
|
||||
return self.http_status(400, -1, {
|
||||
'error': errors[0],
|
||||
'timings': timings,
|
||||
})
|
||||
|
||||
# Step 2: Tool fetch (if enabled)
|
||||
timings['tool_fetch_ms'] = 0
|
||||
timings['tools_loaded'] = 0
|
||||
if enable_tools and tools_config:
|
||||
t_start = time.perf_counter()
|
||||
try:
|
||||
import langbot_plugin.api.entities.builtin.resource.tool as resource_tool
|
||||
all_tools = await self.ap.tool_mgr.get_tools()
|
||||
tool_names = tools_config if isinstance(tools_config, list) else []
|
||||
funcs = [t for t in all_tools if t.name in tool_names]
|
||||
timings['tool_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['tools_loaded'] = len(funcs)
|
||||
timings['tool_names'] = [t.name for t in funcs]
|
||||
except Exception as e:
|
||||
timings['tool_fetch_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
errors.append(f'Tool fetch failed: {str(e)}')
|
||||
|
||||
# Step 3: Build messages
|
||||
t_start = time.perf_counter()
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
messages = []
|
||||
if system_prompt:
|
||||
messages.append(provider_message.Message(role='system', content=system_prompt))
|
||||
messages.append(provider_message.Message(role='user', content=user_prompt))
|
||||
timings['prompt_build_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
|
||||
# Step 4: Build extra args
|
||||
extra_args = {}
|
||||
if temperature is not None:
|
||||
extra_args['temperature'] = float(temperature)
|
||||
if max_tokens and int(max_tokens) > 0:
|
||||
extra_args['max_tokens'] = int(max_tokens)
|
||||
|
||||
# Step 5: LLM call
|
||||
t_start = time.perf_counter()
|
||||
try:
|
||||
result_message = await runtime_model.provider.invoke_llm(
|
||||
query=None,
|
||||
model=runtime_model,
|
||||
messages=messages,
|
||||
funcs=funcs if enable_tools else None,
|
||||
extra_args=extra_args,
|
||||
)
|
||||
timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['llm_call_success'] = True
|
||||
|
||||
# Extract response text
|
||||
response_text = ''
|
||||
if isinstance(result_message.content, str):
|
||||
response_text = result_message.content
|
||||
elif isinstance(result_message.content, list):
|
||||
for elem in result_message.content:
|
||||
if hasattr(elem, 'text') and elem.text:
|
||||
response_text += elem.text
|
||||
elif isinstance(elem, str):
|
||||
response_text += elem
|
||||
|
||||
timings['response_length'] = len(response_text)
|
||||
timings['response_preview'] = response_text[:200]
|
||||
|
||||
# Extract usage
|
||||
usage = {'prompt_tokens': 0, 'completion_tokens': 0, 'total_tokens': 0}
|
||||
if hasattr(result_message, 'usage') and result_message.usage:
|
||||
u = result_message.usage
|
||||
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,
|
||||
}
|
||||
timings['usage'] = usage
|
||||
|
||||
except Exception as e:
|
||||
timings['llm_call_ms'] = round((time.perf_counter() - t_start) * 1000, 2)
|
||||
timings['llm_call_success'] = False
|
||||
errors.append(f'LLM call failed: {str(e)}')
|
||||
|
||||
# Calculate total
|
||||
timings['total_ms'] = round(sum([
|
||||
timings.get('model_fetch_ms', 0),
|
||||
timings.get('tool_fetch_ms', 0),
|
||||
timings.get('prompt_build_ms', 0),
|
||||
timings.get('llm_call_ms', 0),
|
||||
]), 2)
|
||||
|
||||
# Add breakdown percentage
|
||||
if timings['total_ms'] > 0:
|
||||
timings['breakdown'] = {
|
||||
'model_fetch_pct': round(timings.get('model_fetch_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
'tool_fetch_pct': round(timings.get('tool_fetch_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
'prompt_build_pct': round(timings.get('prompt_build_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
'llm_call_pct': round(timings.get('llm_call_ms', 0) / timings['total_ms'] * 100, 1),
|
||||
}
|
||||
|
||||
if errors:
|
||||
timings['errors'] = errors
|
||||
|
||||
return self.success(data={'test_result': timings})
|
||||
|
||||
|
||||
@group.group_class('executions', '/api/v1/executions')
|
||||
class ExecutionsRouterGroup(group.RouterGroup):
|
||||
|
||||
Reference in New Issue
Block a user