from __future__ import annotations import datetime from .. import stage, entities from langbot_plugin.api.entities.builtin.provider import message as provider_message import langbot_plugin.api.entities.events as events import langbot_plugin.api.entities.builtin.platform.message as platform_message import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query import langbot_plugin.api.entities.builtin.platform.events as platform_events @stage.stage_class('PreProcessor') class PreProcessor(stage.PipelineStage): """Request pre-processing stage Check out session, prompt, context, model, and content functions. Rewrite: - session - prompt - messages - user_message - use_model - use_funcs """ async def process( self, query: pipeline_query.Query, stage_inst_name: str, ) -> entities.StageProcessResult: """Process""" selected_runner = query.pipeline_config['ai']['runner']['runner'] include_skill_authoring = ( selected_runner == 'local-agent' and getattr(self.ap, 'skill_service', None) is not None ) session = await self.ap.sess_mgr.get_session(query) # When not local-agent, llm_model is None llm_model = None if selected_runner == 'local-agent': # Read model config — new format is { primary: str, fallbacks: [str] }, # but handle legacy plain string for backward compatibility model_config = query.pipeline_config['ai']['local-agent'].get('model', {}) if isinstance(model_config, str): # Legacy format: plain UUID string primary_uuid = model_config fallback_uuids = [] else: primary_uuid = model_config.get('primary', '') fallback_uuids = model_config.get('fallbacks', []) if primary_uuid: try: llm_model = await self.ap.model_mgr.get_model_by_uuid(primary_uuid) except ValueError: self.ap.logger.warning(f'LLM model {primary_uuid} not found or not configured') # Resolve fallback model UUIDs if fallback_uuids: valid_fallbacks = [] for fb_uuid in fallback_uuids: try: await self.ap.model_mgr.get_model_by_uuid(fb_uuid) valid_fallbacks.append(fb_uuid) except ValueError: self.ap.logger.warning(f'Fallback model {fb_uuid} not found, skipping') if valid_fallbacks: query.variables['_fallback_model_uuids'] = valid_fallbacks conversation = await self.ap.sess_mgr.get_conversation( query, session, query.pipeline_config['ai']['local-agent']['prompt'], query.pipeline_uuid, query.bot_uuid, ) # Expire externally managed conversation ids after the conversation has # been idle for longer than the configured conversation expire time. # The idle window is measured from the last preprocess/update time, not # from the conversation creation time. conversation_expire_time = query.pipeline_config.get('ai', {}).get('runner', {}).get('expire-time', None) now = datetime.datetime.now() if conversation_expire_time is not None and conversation_expire_time > 0: last_update_time = getattr(conversation, 'update_time', None) or getattr(conversation, 'create_time', None) if last_update_time is not None: conversation_idle_time = now.timestamp() - last_update_time.timestamp() if conversation_idle_time > conversation_expire_time: self.ap.logger.info( f'Conversation({query.query_id}) is expired (idle: {conversation_idle_time}s), create new conversation' ) conversation.uuid = None # Treat every preprocess pass as a conversation activity update. This # makes future expiry checks use the latest incoming message/preprocess # time instead of the first message/creation time. conversation.update_time = now # 设置query query.session = session query.prompt = conversation.prompt.copy() query.messages = conversation.messages.copy() if selected_runner == 'local-agent': query.use_funcs = [] if llm_model: query.use_llm_model_uuid = llm_model.model_entity.uuid if 'func_call' in (llm_model.model_entity.abilities or []): # Get bound plugins and MCP servers for filtering tools bound_plugins = query.variables.get('_pipeline_bound_plugins', None) bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None) query.use_funcs = await self.ap.tool_mgr.get_all_tools( bound_plugins, bound_mcp_servers, include_skill_authoring=include_skill_authoring, ) self.ap.logger.debug(f'Bound plugins: {bound_plugins}') self.ap.logger.debug(f'Bound MCP servers: {bound_mcp_servers}') self.ap.logger.debug(f'Use funcs: {query.use_funcs}') # If primary model doesn't support func_call but fallback models exist, # load tools anyway since fallback models may support them if not query.use_funcs and query.variables.get('_fallback_model_uuids'): bound_plugins = query.variables.get('_pipeline_bound_plugins', None) bound_mcp_servers = query.variables.get('_pipeline_bound_mcp_servers', None) query.use_funcs = await self.ap.tool_mgr.get_all_tools( bound_plugins, bound_mcp_servers, include_skill_authoring=include_skill_authoring, ) sender_name = '' if isinstance(query.message_event, platform_events.GroupMessage): sender_name = query.message_event.sender.member_name elif isinstance(query.message_event, platform_events.FriendMessage): sender_name = query.message_event.sender.nickname variables = { 'launcher_type': query.session.launcher_type.value, 'launcher_id': query.session.launcher_id, 'sender_id': query.sender_id, 'session_id': f'{query.session.launcher_type.value}_{query.session.launcher_id}', 'conversation_id': conversation.uuid, 'msg_create_time': ( int(query.message_event.time) if query.message_event.time else int(datetime.datetime.now().timestamp()) ), 'group_name': query.message_event.group.name if isinstance(query.message_event, platform_events.GroupMessage) else '', 'sender_name': sender_name, } query.variables.update(variables) # Check if this model supports vision, if not, remove all images # TODO this checking should be performed in runner, and in this stage, the image should be reserved if selected_runner == 'local-agent' and llm_model and 'vision' not in (llm_model.model_entity.abilities or []): for msg in query.messages: if isinstance(msg.content, list): for me in msg.content: if me.type == 'image_url': msg.content.remove(me) content_list: list[provider_message.ContentElement] = [] plain_text = '' quote_msg = query.pipeline_config['trigger'].get('misc', '').get('combine-quote-message') for me in query.message_chain: if isinstance(me, platform_message.Plain): content_list.append(provider_message.ContentElement.from_text(me.text)) plain_text += me.text elif isinstance(me, platform_message.Image): if selected_runner != 'local-agent' or ( llm_model and 'vision' in (llm_model.model_entity.abilities or []) ): if me.base64 is not None: content_list.append(provider_message.ContentElement.from_image_base64(me.base64)) elif isinstance(me, platform_message.Voice): # 转成文件链接，让下游 runner 上传到目标模型 if me.base64: content_list.append(provider_message.ContentElement.from_file_base64(me.base64, 'voice.silk')) elif me.url: content_list.append(provider_message.ContentElement.from_file_url(me.url, 'voice')) elif isinstance(me, platform_message.File): if me.base64: content_list.append(provider_message.ContentElement.from_file_base64(me.base64, me.name)) elif me.url: content_list.append(provider_message.ContentElement.from_file_url(me.url, me.name)) elif isinstance(me, platform_message.Quote) and quote_msg: for msg in me.origin: if isinstance(msg, platform_message.Plain): content_list.append(provider_message.ContentElement.from_text(msg.text)) elif isinstance(msg, platform_message.Image): if selected_runner != 'local-agent' or ( llm_model and 'vision' in (llm_model.model_entity.abilities or []) ): if msg.base64 is not None: content_list.append(provider_message.ContentElement.from_image_base64(msg.base64)) elif isinstance(msg, platform_message.File): if msg.base64: content_list.append(provider_message.ContentElement.from_file_base64(msg.base64, msg.name)) elif msg.url: content_list.append(provider_message.ContentElement.from_file_url(msg.url, msg.name)) elif isinstance(msg, platform_message.Voice): if msg.base64: content_list.append( provider_message.ContentElement.from_file_base64(msg.base64, 'voice.silk') ) elif msg.url: content_list.append(provider_message.ContentElement.from_file_url(msg.url, 'voice')) query.variables['user_message_text'] = plain_text query.user_message = provider_message.Message(role='user', content=content_list) # Extract knowledge base UUIDs into query variables so plugins can modify them # during PromptPreProcessing before the runner performs retrieval. kb_uuids = query.pipeline_config['ai']['local-agent'].get('knowledge-bases', []) if not kb_uuids: old_kb_uuid = query.pipeline_config['ai']['local-agent'].get('knowledge-base', '') if old_kb_uuid and old_kb_uuid != '__none__': kb_uuids = [old_kb_uuid] query.variables['_knowledge_base_uuids'] = list(kb_uuids) # =========== 触发事件 PromptPreProcessing event = events.PromptPreProcessing( session_name=f'{query.session.launcher_type.value}_{query.session.launcher_id}', default_prompt=query.prompt.messages, prompt=query.messages, query=query, ) # Get bound plugins for filtering bound_plugins = query.variables.get('_pipeline_bound_plugins', None) event_ctx = await self.ap.plugin_connector.emit_event(event, bound_plugins) query.prompt.messages = event_ctx.event.default_prompt query.messages = event_ctx.event.prompt # =========== Skill awareness for the local-agent runner =========== # The actual activation goes through the ``activate`` Tool Call so the # LLM doesn't see full SKILL.md instructions until it commits to a # skill (Claude Code's progressive disclosure). But the LLM still has # to KNOW which skills exist to make that choice, so we: # 1. resolve the pipeline's bound skills and stash them in # ``query.variables['_pipeline_bound_skills']`` for downstream # visibility checks (skill loader, native exec workdir); # 2. inject a short ``Available Skills`` index (name + description # only) into the system prompt. The contributor's original PR # relied on this injection; without it the LLM never discovers # the skills are there and just calls native tools instead. if selected_runner == 'local-agent' and self.ap.skill_mgr: pipeline_data = await self.ap.pipeline_service.get_pipeline(query.pipeline_uuid) extensions_prefs = (pipeline_data or {}).get('extensions_preferences', {}) enable_all_skills = extensions_prefs.get('enable_all_skills', True) if enable_all_skills: bound_skills = None # None = all loaded skills are visible else: bound_skills = extensions_prefs.get('skills', []) query.variables['_pipeline_bound_skills'] = bound_skills skill_addition = self.ap.skill_mgr.build_skill_aware_prompt_addition( bound_skills=bound_skills, ) if skill_addition: # Append to the first system message; create one if the # prompt has none. Handles both plain-string and # content-element (list) message bodies. if query.prompt.messages and query.prompt.messages[0].role == 'system': head = query.prompt.messages[0] if isinstance(head.content, str): head.content = head.content + skill_addition elif isinstance(head.content, list): appended = False for ce in head.content: if getattr(ce, 'type', None) == 'text': ce.text = (ce.text or '') + skill_addition appended = True break if not appended: head.content.append(provider_message.ContentElement(type='text', text=skill_addition)) else: query.prompt.messages.insert( 0, provider_message.Message(role='system', content=skill_addition.strip()), ) self.ap.logger.debug( f'Skill index injected into system prompt: ' f'pipeline={query.pipeline_uuid} ' f'bound_skills={bound_skills or "all"} ' f'loaded_skills={len(self.ap.skill_mgr.skills)}' ) else: self.ap.logger.debug( f'No skills available for prompt injection: ' f'pipeline={query.pipeline_uuid} ' f'loaded_skills={len(self.ap.skill_mgr.skills)} ' f'bound_skills={bound_skills}' ) return entities.StageProcessResult(result_type=entities.ResultType.CONTINUE, new_query=query)