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