mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-07-17 09:56:06 +00:00
feat(agent-runner): add plugin runner host integration
This commit is contained in:
huanghuoguoguo
@@ -10,30 +10,36 @@ from datetime import datetime
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from .. import handler
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from ... import entities
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from ... import plugin_diagnostics
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from ....provider import runner as runner_module
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import langbot_plugin.api.entities.events as events
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from ....utils import importutil, constants, runner as runner_utils
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from ....agent.runner.config_migration import ConfigMigration
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from ....agent.runner import config_schema
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from ....utils import constants, runner as runner_utils
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from ....telemetry import features as telemetry_features
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from ....provider import runners
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import langbot_plugin.api.entities.builtin.provider.session as provider_session
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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import langbot_plugin.api.entities.builtin.provider.message as provider_message
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importutil.import_modules_in_pkg(runners)
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DEFAULT_PROMPT_CONFIG = [
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{'role': 'system', 'content': 'You are a helpful assistant.'},
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]
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class ChatMessageHandler(handler.MessageHandler):
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"""Chat message handler using AgentRunOrchestrator.
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This handler delegates all runner execution to the agent_run_orchestrator,
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which resolves runner ID, builds context, invokes plugin runtime,
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and normalizes results.
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"""
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async def handle(
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self,
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query: pipeline_query.Query,
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) -> typing.AsyncGenerator[entities.StageProcessResult, None]:
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"""处理"""
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# 调API
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# 生成器
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# 触发插件事件
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"""Handle chat message by delegating to AgentRunOrchestrator."""
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# Trigger plugin event
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event_class = (
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events.PersonNormalMessageReceived
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if query.launcher_type == provider_session.LauncherTypes.PERSON
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@@ -54,7 +60,7 @@ class ChatMessageHandler(handler.MessageHandler):
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bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
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event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins)
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is_create_card = False # 判断下是否需要创建流式卡片
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is_create_card = False # Track if streaming card was created
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if event_ctx.is_prevented_default():
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if event_ctx.event.reply_message_chain is not None:
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@@ -87,40 +93,51 @@ class ChatMessageHandler(handler.MessageHandler):
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text_length = 0
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try:
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is_stream = await query.adapter.is_stream_output_supported()
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except AttributeError:
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is_stream = False
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try:
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for r in runner_module.preregistered_runners:
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if r.name == query.pipeline_config['ai']['runner']['runner']:
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runner = r(self.ap, query.pipeline_config)
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break
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else:
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raise ValueError(f'Request Runner not found: {query.pipeline_config["ai"]["runner"]["runner"]}')
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# Mark start time for telemetry
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start_ts = time.time()
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if is_stream:
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resp_message_id = uuid.uuid4()
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chunk_count = 0 # Track streaming chunks to reduce excessive logging
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try_claim_steering = getattr(
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self.ap.agent_run_orchestrator,
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'try_claim_steering_from_query',
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None,
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)
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if try_claim_steering and await try_claim_steering(query):
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yield entities.StageProcessResult(result_type=entities.ResultType.INTERRUPT, new_query=query)
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return
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async for result in runner.run(query):
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result.resp_message_id = str(resp_message_id)
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try:
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is_stream = await query.adapter.is_stream_output_supported()
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except AttributeError:
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is_stream = False
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# Create a single resp_message_id for the entire streaming response
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resp_message_id = uuid.uuid4()
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chunk_count = 0
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# Use AgentRunOrchestrator to run the agent
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# This replaces direct runner lookup and PluginAgentRunnerWrapper
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async for result in self.ap.agent_run_orchestrator.run_from_query(query):
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result.resp_message_id = str(resp_message_id)
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# For streaming mode, pop previous response before adding new chunk
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# This allows incremental card updates
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if is_stream:
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if query.resp_messages:
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query.resp_messages.pop()
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if query.resp_message_chain:
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query.resp_message_chain.pop()
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# 此时连接外部 AI 服务正常,创建卡片
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if not is_create_card: # 只有不是第一次才创建卡片
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# Create streaming card on first result (connection established)
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if not is_create_card:
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await query.adapter.create_message_card(str(resp_message_id), query.message_event)
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is_create_card = True
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query.resp_messages.append(result)
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query.resp_messages.append(result)
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if is_stream:
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chunk_count += 1
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# Only log every 10th chunk to reduce excessive logging during streaming
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# This prevents memory overflow from thousands of log entries per conversation
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# First chunk uses INFO level to confirm connection establishment
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# Only log every 10th chunk to reduce excessive logging during streaming.
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# First chunk uses INFO level to confirm connection establishment.
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if chunk_count == 1:
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summary = self.format_result_log(result)
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if summary is not None:
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@@ -131,46 +148,59 @@ class ChatMessageHandler(handler.MessageHandler):
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self.ap.logger.debug(
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f'Conversation({query.query_id}) Streaming chunk {chunk_count}: {self.cut_str(result.readable_str())}'
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)
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if result.content is not None:
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text_length += len(result.content)
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yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
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# Log final summary after streaming completes
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self.ap.logger.info(
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f'Conversation({query.query_id}) Streaming completed: {chunk_count} chunks, {text_length} chars'
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)
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else:
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async for result in runner.run(query):
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query.resp_messages.append(result)
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else:
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summary = self.format_result_log(result)
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if summary is not None:
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self.ap.logger.info(f'Conversation({query.query_id}) Response: {summary}')
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if result.content is not None:
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text_length += len(result.content)
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if result.content is not None:
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text_length += len(result.content)
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yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
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yield entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)
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query.session.using_conversation.messages.append(query.user_message)
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# Log final summary after streaming completes
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if is_stream:
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self.ap.logger.info(
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f'Conversation({query.query_id}) Streaming completed: {chunk_count} chunks, {text_length} chars'
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)
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# Keep a conversation object available for downstream legacy
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# readers, but do not mirror AgentRunner history into
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# conversation.messages. TranscriptStore is the canonical
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# history source for this path.
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await self._ensure_conversation_for_history(query)
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query.session.using_conversation.messages.extend(query.resp_messages)
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except Exception as e:
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# Import orchestrator errors for specific handling
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from ....agent.runner.errors import (
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RunnerNotFoundError,
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RunnerNotAuthorizedError,
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RunnerExecutionError,
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)
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error_info = f'{traceback.format_exc()}'
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self.ap.logger.error(f'Conversation({query.query_id}) Request Failed: {error_info}')
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traceback.print_exc()
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exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
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# Handle specific runner errors with appropriate messages
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if isinstance(e, RunnerNotFoundError):
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user_notice = f'Agent runner not found: {e.runner_id}'
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elif isinstance(e, RunnerNotAuthorizedError):
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user_notice = 'Agent runner not authorized for this pipeline'
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elif isinstance(e, RunnerExecutionError):
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if e.retryable:
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user_notice = 'Agent runner temporarily unavailable. Please try again.'
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else:
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user_notice = 'Agent runner execution failed.'
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else:
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# Use existing exception handling
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exception_handling = query.pipeline_config['output']['misc'].get('exception-handling', 'show-hint')
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if exception_handling == 'show-error':
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user_notice = f'{e}'
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elif exception_handling == 'show-hint':
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user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
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else: # hide
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user_notice = None
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if exception_handling == 'show-error':
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user_notice = f'{e}'
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elif exception_handling == 'show-hint':
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user_notice = query.pipeline_config['output']['misc'].get('failure-hint', 'Request failed.')
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else: # hide
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user_notice = None
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yield entities.StageProcessResult(
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result_type=entities.ResultType.INTERRUPT,
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@@ -180,7 +210,7 @@ class ChatMessageHandler(handler.MessageHandler):
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debug_notice=traceback.format_exc(),
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)
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finally:
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# Telemetry reporting: collect minimal per-query execution info and send asynchronously
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# Telemetry reporting
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try:
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end_ts = time.time()
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duration_ms = None
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@@ -188,16 +218,14 @@ class ChatMessageHandler(handler.MessageHandler):
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duration_ms = int((end_ts - start_ts) * 1000)
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adapter_name = query.adapter.__class__.__name__ if hasattr(query, 'adapter') else None
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runner_name = (
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query.pipeline_config.get('ai', {}).get('runner', {}).get('runner')
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if query.pipeline_config
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else None
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)
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# Model name if using localagent
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# Use orchestrator to resolve runner ID for telemetry
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runner_name = self.ap.agent_run_orchestrator.resolve_runner_id_for_telemetry(query)
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# Model name if available
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model_name = None
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try:
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if runner_name == 'local-agent' and getattr(query, 'use_llm_model_uuid', None):
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if getattr(query, 'use_llm_model_uuid', None):
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m = await self.ap.model_mgr.get_model_by_uuid(query.use_llm_model_uuid)
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if m and getattr(m, 'model_entity', None):
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model_name = getattr(m.model_entity, 'name', None)
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@@ -207,7 +235,7 @@ class ChatMessageHandler(handler.MessageHandler):
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pipeline_plugins = query.variables.get('_pipeline_bound_plugins', None)
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runner_category = runner_utils.get_runner_category_from_runner(
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runner_name, runner, query.pipeline_config
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runner_name, None, query.pipeline_config
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)
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# Feature usage collected during query processing (tool calls,
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@@ -231,7 +259,6 @@ class ChatMessageHandler(handler.MessageHandler):
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'timestamp': datetime.utcnow().isoformat(),
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}
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# Send telemetry asynchronously and do not block pipeline via app's telemetry manager
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await self.ap.telemetry.start_send_task(payload)
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# Trigger survey events on successful non-WebSocket responses
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@@ -241,5 +268,70 @@ class ChatMessageHandler(handler.MessageHandler):
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# Counts toward the bot_response_success_100 milestone event
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await self.ap.survey.record_bot_response_success()
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except Exception as ex:
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# Ensure telemetry issues do not affect normal flow
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self.ap.logger.warning(f'Failed to send telemetry: {ex}')
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async def _ensure_conversation_for_history(
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self,
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query: pipeline_query.Query,
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) -> provider_session.Conversation:
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session = getattr(query, 'session', None)
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conversation = getattr(session, 'using_conversation', None)
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if conversation is not None:
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return conversation
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if session is None or getattr(self.ap, 'sess_mgr', None) is None:
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raise RuntimeError('Conversation is not available for history update')
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prompt_config = await self._build_history_prompt_config(query)
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conversation = await self.ap.sess_mgr.get_conversation(
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query,
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session,
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prompt_config,
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query.pipeline_uuid,
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query.bot_uuid,
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)
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if conversation is None:
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raise RuntimeError('Conversation manager did not return a conversation')
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if getattr(session, 'using_conversation', None) is None:
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session.using_conversation = conversation
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return conversation
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async def _build_history_prompt_config(
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self,
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query: pipeline_query.Query,
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) -> list[dict[str, typing.Any]]:
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prompt_messages = getattr(getattr(query, 'prompt', None), 'messages', None)
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if prompt_messages:
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prompt_config = []
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for message in prompt_messages:
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if hasattr(message, 'model_dump'):
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prompt_config.append(message.model_dump(mode='python'))
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elif isinstance(message, dict):
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prompt_config.append(message)
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if prompt_config:
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return prompt_config
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runner_id = ConfigMigration.resolve_runner_id(query.pipeline_config)
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runner_config = ConfigMigration.resolve_runner_config(query.pipeline_config, runner_id) if runner_id else {}
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bound_plugins = query.variables.get('_pipeline_bound_plugins', None)
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descriptor = await self._get_runner_descriptor(runner_id, bound_plugins)
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return config_schema.extract_prompt_config(descriptor, runner_config, DEFAULT_PROMPT_CONFIG)
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async def _get_runner_descriptor(
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self,
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runner_id: str | None,
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bound_plugins: list[str] | None,
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) -> typing.Any | None:
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if not runner_id:
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return None
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registry = getattr(self.ap, 'agent_runner_registry', None)
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if registry is None:
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return None
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try:
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return await registry.get(runner_id, bound_plugins)
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except Exception as e:
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self.ap.logger.debug(f'Unable to load AgentRunner descriptor for {runner_id}: {e}')
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return None
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