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Typer_Body
352 lines
14 KiB
Python
352 lines
14 KiB
Python
from __future__ import annotations
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import typing
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import json
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from langbot.pkg.provider import runner
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from langbot.pkg.core import app
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import langbot_plugin.api.entities.builtin.provider.message as provider_message
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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from langbot.libs.weknora_api import client, errors
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@runner.runner_class('weknora-api')
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class WeKnoraAPIRunner(runner.RequestRunner):
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"""WeKnora API 对话请求器"""
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weknora_client: client.AsyncWeKnoraClient
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def __init__(self, ap: app.Application, pipeline_config: dict):
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super().__init__(ap, pipeline_config)
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valid_app_types = ['chat', 'agent']
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if self.pipeline_config['ai']['weknora-api']['app-type'] not in valid_app_types:
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raise errors.WeKnoraAPIError(
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f'不支持的 WeKnora 应用类型: {self.pipeline_config["ai"]["weknora-api"]["app-type"]}'
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)
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api_key = self.pipeline_config['ai']['weknora-api'].get('api-key', '').strip()
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if not api_key:
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raise errors.WeKnoraAPIError(
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'WeKnora API Key 未配置，请在流水线的 WeKnora API 配置中填入 API Key '
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'(从 WeKnora 前端 设置 → API Keys 生成)'
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)
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base_url = self.pipeline_config['ai']['weknora-api'].get('base-url', '').strip()
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if not base_url:
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raise errors.WeKnoraAPIError('WeKnora Base URL 未配置，请填入服务器地址，例如 http://localhost:8080/api/v1')
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self.weknora_client = client.AsyncWeKnoraClient(
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api_key=api_key,
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base_url=base_url,
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)
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async def _extract_plain_text(self, query: pipeline_query.Query) -> str:
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"""从用户消息中提取纯文本内容"""
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plain_text = ''
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if isinstance(query.user_message.content, str):
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plain_text = query.user_message.content
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elif isinstance(query.user_message.content, list):
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for ce in query.user_message.content:
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if ce.type == 'text':
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plain_text += ce.text
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if not plain_text:
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plain_text = self.pipeline_config['ai']['weknora-api'].get('base-prompt', '')
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return plain_text
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async def _ensure_session(self, query: pipeline_query.Query) -> str:
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"""确保会话存在，如果不存在则创建"""
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session_id = query.session.using_conversation.uuid or ''
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if not session_id:
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user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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session_id = await self.weknora_client.create_session(title=f'IM Chat - {user_tag}')
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query.session.using_conversation.uuid = session_id
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return session_id
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async def _agent_chat_messages(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.Message, None]:
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"""调用 Agent 智能对话（非流式聚合输出）"""
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session_id = await self._ensure_session(query)
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plain_text = await self._extract_plain_text(query)
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user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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config = self.pipeline_config['ai']['weknora-api']
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agent_id = config.get('agent-id', 'builtin-smart-reasoning')
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knowledge_base_ids = config.get('knowledge-base-ids', [])
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web_search_enabled = config.get('web-search-enabled', False)
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timeout = config.get('timeout', 120)
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full_answer = ''
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chunk = None
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async for chunk in self.weknora_client.agent_chat(
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session_id=session_id,
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query=plain_text,
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user=user_tag,
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agent_id=agent_id,
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knowledge_base_ids=knowledge_base_ids,
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web_search_enabled=web_search_enabled,
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timeout=timeout,
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):
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self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
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response_type = chunk.get('response_type', '')
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content = chunk.get('content', '')
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if response_type == 'tool_call':
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# 工具调用
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tool_data = chunk.get('data', {})
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tool_name = tool_data.get('tool_name', '')
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if tool_name:
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yield provider_message.Message(
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role='assistant',
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tool_calls=[
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provider_message.ToolCall(
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id=chunk.get('id', ''),
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type='function',
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function=provider_message.FunctionCall(
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name=tool_name,
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arguments=json.dumps(tool_data.get('arguments', {})),
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),
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)
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],
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)
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elif response_type == 'answer':
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if content:
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full_answer += content
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elif response_type == 'error':
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raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
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if chunk is None:
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raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
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if full_answer:
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yield provider_message.Message(
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role='assistant',
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content=full_answer,
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)
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async def _chat_messages(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.Message, None]:
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"""调用知识库 RAG 问答（非流式聚合输出）"""
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session_id = await self._ensure_session(query)
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plain_text = await self._extract_plain_text(query)
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user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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config = self.pipeline_config['ai']['weknora-api']
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agent_id = config.get('agent-id', 'builtin-quick-answer')
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knowledge_base_ids = config.get('knowledge-base-ids', [])
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timeout = config.get('timeout', 120)
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full_answer = ''
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chunk = None
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async for chunk in self.weknora_client.knowledge_chat(
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session_id=session_id,
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query=plain_text,
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user=user_tag,
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agent_id=agent_id,
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knowledge_base_ids=knowledge_base_ids,
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timeout=timeout,
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):
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self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
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response_type = chunk.get('response_type', '')
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content = chunk.get('content', '')
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if response_type == 'answer':
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if content:
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full_answer += content
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elif response_type == 'error':
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raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
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if chunk is None:
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raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
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if full_answer:
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yield provider_message.Message(
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role='assistant',
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content=full_answer,
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)
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async def _agent_chat_messages_chunk(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
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"""调用 Agent 智能对话（流式输出）"""
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session_id = await self._ensure_session(query)
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plain_text = await self._extract_plain_text(query)
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user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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config = self.pipeline_config['ai']['weknora-api']
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agent_id = config.get('agent-id', 'builtin-smart-reasoning')
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knowledge_base_ids = config.get('knowledge-base-ids', [])
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web_search_enabled = config.get('web-search-enabled', False)
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timeout = config.get('timeout', 120)
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pending_answer = ''
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message_idx = 0
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is_final = False
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chunk = None
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async for chunk in self.weknora_client.agent_chat(
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session_id=session_id,
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query=plain_text,
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user=user_tag,
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agent_id=agent_id,
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knowledge_base_ids=knowledge_base_ids,
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web_search_enabled=web_search_enabled,
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timeout=timeout,
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):
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self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
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response_type = chunk.get('response_type', '')
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content = chunk.get('content', '')
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done = chunk.get('done', False)
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if response_type == 'tool_call':
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tool_data = chunk.get('data', {})
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tool_name = tool_data.get('tool_name', '')
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if tool_name:
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message_idx += 1
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yield provider_message.MessageChunk(
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role='assistant',
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tool_calls=[
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provider_message.ToolCall(
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id=chunk.get('id', ''),
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type='function',
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function=provider_message.FunctionCall(
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name=tool_name,
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arguments=json.dumps(tool_data.get('arguments', {})),
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),
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)
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],
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)
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elif response_type == 'answer':
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message_idx += 1
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if content:
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pending_answer += content
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if done:
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is_final = True
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# 每 8 个 chunk 输出一次，或最终输出
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if message_idx % 8 == 0 or is_final:
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yield provider_message.MessageChunk(
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role='assistant',
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content=pending_answer,
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is_final=is_final,
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)
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elif response_type == 'error':
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raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
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if chunk is None:
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raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
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# 确保最终消息已发出
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if not is_final and pending_answer:
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yield provider_message.MessageChunk(
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role='assistant',
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content=pending_answer,
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is_final=True,
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)
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async def _chat_messages_chunk(
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self, query: pipeline_query.Query
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) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
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"""调用知识库 RAG 问答（流式输出）"""
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session_id = await self._ensure_session(query)
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plain_text = await self._extract_plain_text(query)
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user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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config = self.pipeline_config['ai']['weknora-api']
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agent_id = config.get('agent-id', 'builtin-quick-answer')
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knowledge_base_ids = config.get('knowledge-base-ids', [])
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timeout = config.get('timeout', 120)
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pending_answer = ''
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message_idx = 0
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is_final = False
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chunk = None
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async for chunk in self.weknora_client.knowledge_chat(
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session_id=session_id,
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query=plain_text,
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user=user_tag,
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agent_id=agent_id,
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knowledge_base_ids=knowledge_base_ids,
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timeout=timeout,
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):
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self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
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response_type = chunk.get('response_type', '')
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content = chunk.get('content', '')
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done = chunk.get('done', False)
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if response_type == 'answer':
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message_idx += 1
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if content:
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pending_answer += content
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if done:
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is_final = True
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if message_idx % 8 == 0 or is_final:
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yield provider_message.MessageChunk(
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role='assistant',
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content=pending_answer,
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is_final=is_final,
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)
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elif response_type == 'error':
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raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
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if chunk is None:
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raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
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if not is_final and pending_answer:
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yield provider_message.MessageChunk(
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role='assistant',
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content=pending_answer,
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is_final=True,
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)
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async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
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"""运行请求"""
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app_type = self.pipeline_config['ai']['weknora-api']['app-type']
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if await query.adapter.is_stream_output_supported():
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msg_idx = 0
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if app_type == 'agent':
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async for msg in self._agent_chat_messages_chunk(query):
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msg_idx += 1
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msg.msg_sequence = msg_idx
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yield msg
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elif app_type == 'chat':
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async for msg in self._chat_messages_chunk(query):
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msg_idx += 1
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msg.msg_sequence = msg_idx
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yield msg
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else:
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raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
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else:
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if app_type == 'agent':
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async for msg in self._agent_chat_messages(query):
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yield msg
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elif app_type == 'chat':
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async for msg in self._chat_messages(query):
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yield msg
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else:
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raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
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