mirror of
https://github.com/langbot-app/LangBot.git
synced 2026-06-12 16:56:02 +00:00
Merge pull request #2230 from langbot-app/feat/addweknoradeerflow
Add DeerFlow LangGraph API as a Provider Runner
This commit is contained in:
Haoxuan Xing
27
src/langbot/pkg/core/migrations/m042_weknora_api.py
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27
src/langbot/pkg/core/migrations/m042_weknora_api.py
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@@ -0,0 +1,27 @@
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from __future__ import annotations
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from .. import migration
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@migration.migration_class('weknora-api-config', 42)
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class WeKnoraAPICfgMigration(migration.Migration):
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"""WeKnora API 配置迁移"""
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async def need_migrate(self) -> bool:
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"""判断当前环境是否需要运行此迁移"""
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return 'weknora-api' not in self.ap.provider_cfg.data
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async def run(self):
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"""执行迁移"""
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self.ap.provider_cfg.data['weknora-api'] = {
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'base-url': 'http://localhost:8080/api/v1',
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'app-type': 'agent',
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'api-key': '',
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'agent-id': 'builtin-smart-reasoning',
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'knowledge-base-ids': [],
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'web-search-enabled': False,
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'timeout': 120,
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'base-prompt': '请回答用户的问题。',
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}
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await self.ap.provider_cfg.dump_config()
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30
src/langbot/pkg/core/migrations/m043_deerflow_api.py
Normal file
30
src/langbot/pkg/core/migrations/m043_deerflow_api.py
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@@ -0,0 +1,30 @@
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from __future__ import annotations
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from .. import migration
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@migration.migration_class('deerflow-api-config', 43)
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class DeerFlowAPICfgMigration(migration.Migration):
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"""DeerFlow API 配置迁移"""
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async def need_migrate(self) -> bool:
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"""判断当前环境是否需要运行此迁移"""
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return 'deerflow-api' not in self.ap.provider_cfg.data
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async def run(self):
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"""执行迁移"""
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self.ap.provider_cfg.data['deerflow-api'] = {
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'api-base': 'http://127.0.0.1:2026',
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'api-key': '',
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'auth-header': '',
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'assistant-id': 'lead_agent',
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'model-name': '',
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'thinking-enabled': False,
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'plan-mode': False,
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'subagent-enabled': False,
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'max-concurrent-subagents': 3,
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'timeout': 300,
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'recursion-limit': 1000,
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}
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await self.ap.provider_cfg.dump_config()
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511
src/langbot/pkg/provider/runners/deerflowapi.py
Normal file
511
src/langbot/pkg/provider/runners/deerflowapi.py
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@@ -0,0 +1,511 @@
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"""DeerFlow LangGraph API Runner
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参考 astrbot 的 deerflow_agent_runner 实现，适配 LangBot 的 Runner 接口。
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特点：
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- 使用 LangGraph HTTP API 接入 deer-flow 后端
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- 自动管理 thread_id（按 session 隔离）
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- 支持 SSE 流式响应解析
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- 支持 streaming/非流式两种输出
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- 处理 values / messages-tuple / custom 三种事件
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"""
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from __future__ import annotations
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import asyncio
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import hashlib
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import json
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import typing
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from collections import deque
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from dataclasses import dataclass, field
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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.deerflow_api import client, errors, stream_utils
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_MAX_VALUES_HISTORY = 200
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@dataclass
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class _StreamState:
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"""流式状态跟踪"""
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latest_text: str = ''
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prev_text_for_streaming: str = ''
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clarification_text: str = ''
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task_failures: list[str] = field(default_factory=list)
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seen_message_ids: set[str] = field(default_factory=set)
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seen_message_order: deque[str] = field(default_factory=deque)
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no_id_message_fingerprints: dict[int, str] = field(default_factory=dict)
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baseline_initialized: bool = False
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has_values_text: bool = False
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run_values_messages: list[dict[str, typing.Any]] = field(default_factory=list)
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timed_out: bool = False
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@runner.runner_class('deerflow-api')
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class DeerFlowAPIRunner(runner.RequestRunner):
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"""DeerFlow LangGraph API 对话请求器"""
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deerflow_client: client.AsyncDeerFlowClient
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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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cfg = self.pipeline_config['ai']['deerflow-api']
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api_base = cfg.get('api-base', '').strip()
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if not api_base or not api_base.startswith(('http://', 'https://')):
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raise errors.DeerFlowAPIError(
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message='DeerFlow API Base URL 格式错误，必须以 http:// 或 https:// 开头',
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)
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self.api_base = api_base
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self.api_key = cfg.get('api-key', '')
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self.auth_header = cfg.get('auth-header', '')
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self.assistant_id = cfg.get('assistant-id', 'lead_agent')
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self.model_name = cfg.get('model-name', '')
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self.thinking_enabled = bool(cfg.get('thinking-enabled', False))
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self.plan_mode = bool(cfg.get('plan-mode', False))
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self.subagent_enabled = bool(cfg.get('subagent-enabled', False))
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self.max_concurrent_subagents = int(cfg.get('max-concurrent-subagents', 3))
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self.timeout = int(cfg.get('timeout', 300))
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self.recursion_limit = int(cfg.get('recursion-limit', 1000))
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self.deerflow_client = client.AsyncDeerFlowClient(
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api_base=self.api_base,
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api_key=self.api_key,
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auth_header=self.auth_header,
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)
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# ------------------------------------------------------------------
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# 辅助方法
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# ------------------------------------------------------------------
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def _fingerprint_message(self, message: dict[str, typing.Any]) -> str:
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try:
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raw = json.dumps(message, sort_keys=True, ensure_ascii=False, default=str)
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except (TypeError, ValueError):
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raw = repr(message)
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return hashlib.sha1(raw.encode('utf-8', errors='ignore')).hexdigest()
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def _remember_seen_message_id(self, state: _StreamState, msg_id: str) -> None:
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if not msg_id or msg_id in state.seen_message_ids:
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return
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state.seen_message_ids.add(msg_id)
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state.seen_message_order.append(msg_id)
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while len(state.seen_message_order) > _MAX_VALUES_HISTORY:
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dropped = state.seen_message_order.popleft()
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state.seen_message_ids.discard(dropped)
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def _extract_new_messages_from_values(
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self,
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values_messages: list[typing.Any],
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state: _StreamState,
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) -> list[dict[str, typing.Any]]:
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new_messages: list[dict[str, typing.Any]] = []
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no_id_indexes_seen: set[int] = set()
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for idx, msg in enumerate(values_messages):
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if not isinstance(msg, dict):
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continue
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msg_id = stream_utils.get_message_id(msg)
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if msg_id:
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if msg_id in state.seen_message_ids:
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continue
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self._remember_seen_message_id(state, msg_id)
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new_messages.append(msg)
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continue
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no_id_indexes_seen.add(idx)
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fp = self._fingerprint_message(msg)
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if state.no_id_message_fingerprints.get(idx) == fp:
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continue
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state.no_id_message_fingerprints[idx] = fp
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new_messages.append(msg)
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for idx in list(state.no_id_message_fingerprints.keys()):
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if idx not in no_id_indexes_seen:
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state.no_id_message_fingerprints.pop(idx, None)
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return new_messages
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# ------------------------------------------------------------------
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# 用户输入处理
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# ------------------------------------------------------------------
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def _build_user_content(
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self,
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prompt: str,
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image_urls: list[str],
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) -> typing.Any:
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"""构建 LangGraph 兼容的 user content（支持多模态）"""
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if not image_urls:
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return prompt
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content: list[dict[str, typing.Any]] = []
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if prompt:
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content.append({'type': 'text', 'text': prompt})
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for url in image_urls:
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if not isinstance(url, str):
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continue
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url = url.strip()
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if not url:
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continue
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if url.startswith(('http://', 'https://', 'data:')):
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content.append({'type': 'image_url', 'image_url': {'url': url}})
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return content if content else prompt
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def _preprocess_user_message(
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self,
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query: pipeline_query.Query,
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) -> tuple[str, list[str]]:
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"""提取用户消息的纯文本与图片 URL 列表"""
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plain_text = ''
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image_urls: list[str] = []
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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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elif ce.type == 'image_base64':
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# 转换为 data URI 形式
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b64 = getattr(ce, 'image_base64', '')
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if b64:
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if not b64.startswith('data:'):
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b64 = f'data:image/png;base64,{b64}'
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image_urls.append(b64)
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elif ce.type == 'image_url':
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url = getattr(ce, 'image_url', '')
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if url:
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image_urls.append(url)
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return plain_text, image_urls
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# ------------------------------------------------------------------
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# 请求构造
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# ------------------------------------------------------------------
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def _build_messages(
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self,
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prompt: str,
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image_urls: list[str],
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system_prompt: str = '',
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) -> list[dict[str, typing.Any]]:
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messages: list[dict[str, typing.Any]] = []
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if system_prompt:
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messages.append({'role': 'system', 'content': system_prompt})
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messages.append(
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{
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'role': 'user',
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'content': self._build_user_content(prompt, image_urls),
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}
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)
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return messages
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def _build_runtime_configurable(self, thread_id: str) -> dict[str, typing.Any]:
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cfg: dict[str, typing.Any] = {
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'thread_id': thread_id,
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'thinking_enabled': self.thinking_enabled,
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'is_plan_mode': self.plan_mode,
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'subagent_enabled': self.subagent_enabled,
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}
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if self.subagent_enabled:
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cfg['max_concurrent_subagents'] = self.max_concurrent_subagents
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if self.model_name:
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cfg['model_name'] = self.model_name
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return cfg
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def _build_payload(
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self,
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thread_id: str,
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prompt: str,
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image_urls: list[str],
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system_prompt: str = '',
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) -> dict[str, typing.Any]:
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runtime_configurable = self._build_runtime_configurable(thread_id)
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return {
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'assistant_id': self.assistant_id,
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'input': {
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'messages': self._build_messages(prompt, image_urls, system_prompt),
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},
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'stream_mode': ['values', 'messages-tuple', 'custom'],
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# DeerFlow 2.0 从 config.configurable 读取运行时覆盖
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# 同时保留 context 字段做向后兼容
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'context': dict(runtime_configurable),
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'config': {
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'recursion_limit': self.recursion_limit,
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'configurable': runtime_configurable,
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},
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}
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# ------------------------------------------------------------------
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# Session/Thread 管理
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# ------------------------------------------------------------------
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||||
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async def _ensure_thread_id(self, query: pipeline_query.Query) -> str:
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"""从 query.session 取/创建 deerflow thread_id
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LangBot 使用 `query.session.using_conversation.uuid` 持久化 conversation id，
|
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我们复用这个字段存储 deerflow thread_id（与 Dify Runner 同样做法）。
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"""
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thread_id = query.session.using_conversation.uuid or ''
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if thread_id:
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return thread_id
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thread = await self.deerflow_client.create_thread(timeout=min(30, self.timeout))
|
||||
thread_id = thread.get('thread_id', '')
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if not thread_id:
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||||
raise errors.DeerFlowAPIError(message=f'DeerFlow create thread 返回数据缺少 thread_id: {thread}')
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||||
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||||
query.session.using_conversation.uuid = thread_id
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return thread_id
|
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|
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# ------------------------------------------------------------------
|
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# 流式事件处理
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
def _handle_values_event(
|
||||
self,
|
||||
data: typing.Any,
|
||||
state: _StreamState,
|
||||
) -> str | None:
|
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"""处理 values 事件，返回新的完整文本（增量基础上的全量）"""
|
||||
values_messages = stream_utils.extract_messages_from_values_data(data)
|
||||
if not values_messages:
|
||||
return None
|
||||
|
||||
new_messages: list[dict[str, typing.Any]] = []
|
||||
if not state.baseline_initialized:
|
||||
state.baseline_initialized = True
|
||||
for idx, msg in enumerate(values_messages):
|
||||
if not isinstance(msg, dict):
|
||||
continue
|
||||
new_messages.append(msg)
|
||||
msg_id = stream_utils.get_message_id(msg)
|
||||
if msg_id:
|
||||
self._remember_seen_message_id(state, msg_id)
|
||||
continue
|
||||
state.no_id_message_fingerprints[idx] = self._fingerprint_message(msg)
|
||||
else:
|
||||
new_messages = self._extract_new_messages_from_values(values_messages, state)
|
||||
|
||||
latest_text = ''
|
||||
if new_messages:
|
||||
state.run_values_messages.extend(new_messages)
|
||||
if len(state.run_values_messages) > _MAX_VALUES_HISTORY:
|
||||
state.run_values_messages = state.run_values_messages[-_MAX_VALUES_HISTORY:]
|
||||
latest_text = stream_utils.extract_latest_ai_text(state.run_values_messages)
|
||||
if latest_text:
|
||||
state.has_values_text = True
|
||||
latest_clarification = stream_utils.extract_latest_clarification_text(
|
||||
state.run_values_messages,
|
||||
)
|
||||
if latest_clarification:
|
||||
state.clarification_text = latest_clarification
|
||||
|
||||
return latest_text or None
|
||||
|
||||
def _handle_message_event(
|
||||
self,
|
||||
data: typing.Any,
|
||||
state: _StreamState,
|
||||
) -> str | None:
|
||||
"""处理 messages-tuple 事件，返回增量文本
|
||||
|
||||
当 values 事件已经提供完整文本时，跳过 messages-tuple 的增量
|
||||
"""
|
||||
delta = stream_utils.extract_ai_delta_from_event_data(data)
|
||||
if delta and not state.has_values_text:
|
||||
state.latest_text += delta
|
||||
return delta
|
||||
|
||||
maybe_clar = stream_utils.extract_clarification_from_event_data(data)
|
||||
if maybe_clar:
|
||||
state.clarification_text = maybe_clar
|
||||
return None
|
||||
|
||||
def _build_final_text(self, state: _StreamState) -> str:
|
||||
"""构建最终输出文本"""
|
||||
if state.clarification_text:
|
||||
return state.clarification_text
|
||||
|
||||
# 优先使用最后一条 AI message 的文本
|
||||
latest_ai = stream_utils.extract_latest_ai_message(state.run_values_messages)
|
||||
if latest_ai:
|
||||
text = stream_utils.extract_text(latest_ai.get('content'))
|
||||
if text:
|
||||
if state.timed_out:
|
||||
text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
|
||||
return text
|
||||
|
||||
if state.latest_text:
|
||||
text = state.latest_text
|
||||
if state.timed_out:
|
||||
text += f'\n\nDeerFlow stream 在 {self.timeout}s 后超时，返回部分结果。'
|
||||
return text
|
||||
|
||||
# 提取任务失败信息作兜底
|
||||
failure_text = stream_utils.build_task_failure_summary(state.task_failures)
|
||||
if failure_text:
|
||||
return failure_text
|
||||
|
||||
return 'DeerFlow 返回空响应'
|
||||
|
||||
# ------------------------------------------------------------------
|
||||
# 主流程
|
||||
# ------------------------------------------------------------------
|
||||
|
||||
async def _stream_messages_chunk(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
"""流式输出生成器"""
|
||||
plain_text, image_urls = self._preprocess_user_message(query)
|
||||
|
||||
system_prompt = ''
|
||||
# LangBot 的 pipeline 通常通过 prompt-preprocess 已注入 system prompt
|
||||
# 这里保持空，让 prompt-preprocess 的内容作为 user message 一并送给 deerflow
|
||||
|
||||
thread_id = await self._ensure_thread_id(query)
|
||||
payload = self._build_payload(
|
||||
thread_id=thread_id,
|
||||
prompt=plain_text or 'continue',
|
||||
image_urls=image_urls,
|
||||
system_prompt=system_prompt,
|
||||
)
|
||||
|
||||
state = _StreamState()
|
||||
prev_text = ''
|
||||
message_idx = 0
|
||||
|
||||
try:
|
||||
async for event in self.deerflow_client.stream_run(
|
||||
thread_id=thread_id,
|
||||
payload=payload,
|
||||
timeout=self.timeout,
|
||||
):
|
||||
event_type = event.get('event')
|
||||
data = event.get('data')
|
||||
|
||||
if event_type == 'values':
|
||||
new_full = self._handle_values_event(data, state)
|
||||
if new_full and new_full != prev_text:
|
||||
delta = new_full[len(prev_text) :] if new_full.startswith(prev_text) else new_full
|
||||
prev_text = new_full
|
||||
if delta:
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=new_full,
|
||||
is_final=False,
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type in {'messages-tuple', 'messages', 'message'}:
|
||||
delta = self._handle_message_event(data, state)
|
||||
if delta:
|
||||
prev_text = state.latest_text
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=prev_text,
|
||||
is_final=False,
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type == 'custom':
|
||||
state.task_failures.extend(
|
||||
stream_utils.extract_task_failures_from_custom_event(data),
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type == 'error':
|
||||
raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
|
||||
|
||||
if event_type == 'end':
|
||||
break
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
|
||||
state.timed_out = True
|
||||
|
||||
# 最终消息
|
||||
final_text = self._build_final_text(state)
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=final_text,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def _messages(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""非流式聚合输出"""
|
||||
plain_text, image_urls = self._preprocess_user_message(query)
|
||||
|
||||
thread_id = await self._ensure_thread_id(query)
|
||||
payload = self._build_payload(
|
||||
thread_id=thread_id,
|
||||
prompt=plain_text or 'continue',
|
||||
image_urls=image_urls,
|
||||
)
|
||||
|
||||
state = _StreamState()
|
||||
|
||||
try:
|
||||
async for event in self.deerflow_client.stream_run(
|
||||
thread_id=thread_id,
|
||||
payload=payload,
|
||||
timeout=self.timeout,
|
||||
):
|
||||
event_type = event.get('event')
|
||||
data = event.get('data')
|
||||
|
||||
if event_type == 'values':
|
||||
self._handle_values_event(data, state)
|
||||
continue
|
||||
|
||||
if event_type in {'messages-tuple', 'messages', 'message'}:
|
||||
self._handle_message_event(data, state)
|
||||
continue
|
||||
|
||||
if event_type == 'custom':
|
||||
state.task_failures.extend(
|
||||
stream_utils.extract_task_failures_from_custom_event(data),
|
||||
)
|
||||
continue
|
||||
|
||||
if event_type == 'error':
|
||||
raise errors.DeerFlowAPIError(message=f'DeerFlow stream error event: {data}')
|
||||
|
||||
if event_type == 'end':
|
||||
break
|
||||
except (asyncio.TimeoutError, TimeoutError):
|
||||
self.ap.logger.warning(f'DeerFlow stream timed out after {self.timeout}s for thread_id={thread_id}')
|
||||
state.timed_out = True
|
||||
|
||||
final_text = self._build_final_text(state)
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=final_text,
|
||||
)
|
||||
|
||||
async def run(
|
||||
self,
|
||||
query: pipeline_query.Query,
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""主入口：根据 adapter 是否支持流式输出，选择流式或非流式"""
|
||||
if await query.adapter.is_stream_output_supported():
|
||||
msg_idx = 0
|
||||
async for msg in self._stream_messages_chunk(query):
|
||||
msg_idx += 1
|
||||
msg.msg_sequence = msg_idx
|
||||
yield msg
|
||||
else:
|
||||
async for msg in self._messages(query):
|
||||
yield msg
|
||||
351
src/langbot/pkg/provider/runners/weknoraapi.py
Normal file
351
src/langbot/pkg/provider/runners/weknoraapi.py
Normal file
@@ -0,0 +1,351 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import typing
|
||||
import json
|
||||
|
||||
|
||||
from langbot.pkg.provider import runner
|
||||
from langbot.pkg.core import app
|
||||
import langbot_plugin.api.entities.builtin.provider.message as provider_message
|
||||
import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
|
||||
from langbot.libs.weknora_api import client, errors
|
||||
|
||||
|
||||
@runner.runner_class('weknora-api')
|
||||
class WeKnoraAPIRunner(runner.RequestRunner):
|
||||
"""WeKnora API 对话请求器"""
|
||||
|
||||
weknora_client: client.AsyncWeKnoraClient
|
||||
|
||||
def __init__(self, ap: app.Application, pipeline_config: dict):
|
||||
super().__init__(ap, pipeline_config)
|
||||
|
||||
valid_app_types = ['chat', 'agent']
|
||||
if self.pipeline_config['ai']['weknora-api']['app-type'] not in valid_app_types:
|
||||
raise errors.WeKnoraAPIError(
|
||||
f'不支持的 WeKnora 应用类型: {self.pipeline_config["ai"]["weknora-api"]["app-type"]}'
|
||||
)
|
||||
|
||||
api_key = self.pipeline_config['ai']['weknora-api'].get('api-key', '').strip()
|
||||
if not api_key:
|
||||
raise errors.WeKnoraAPIError(
|
||||
'WeKnora API Key 未配置，请在流水线的 WeKnora API 配置中填入 API Key '
|
||||
'(从 WeKnora 前端 设置 → API Keys 生成)'
|
||||
)
|
||||
|
||||
base_url = self.pipeline_config['ai']['weknora-api'].get('base-url', '').strip()
|
||||
if not base_url:
|
||||
raise errors.WeKnoraAPIError('WeKnora Base URL 未配置，请填入服务器地址，例如 http://localhost:8080/api/v1')
|
||||
|
||||
self.weknora_client = client.AsyncWeKnoraClient(
|
||||
api_key=api_key,
|
||||
base_url=base_url,
|
||||
)
|
||||
|
||||
async def _extract_plain_text(self, query: pipeline_query.Query) -> str:
|
||||
"""从用户消息中提取纯文本内容"""
|
||||
plain_text = ''
|
||||
if isinstance(query.user_message.content, str):
|
||||
plain_text = query.user_message.content
|
||||
elif isinstance(query.user_message.content, list):
|
||||
for ce in query.user_message.content:
|
||||
if ce.type == 'text':
|
||||
plain_text += ce.text
|
||||
|
||||
if not plain_text:
|
||||
plain_text = self.pipeline_config['ai']['weknora-api'].get('base-prompt', '')
|
||||
|
||||
return plain_text
|
||||
|
||||
async def _ensure_session(self, query: pipeline_query.Query) -> str:
|
||||
"""确保会话存在，如果不存在则创建"""
|
||||
session_id = query.session.using_conversation.uuid or ''
|
||||
|
||||
if not session_id:
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
session_id = await self.weknora_client.create_session(title=f'IM Chat - {user_tag}')
|
||||
query.session.using_conversation.uuid = session_id
|
||||
|
||||
return session_id
|
||||
|
||||
async def _agent_chat_messages(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""调用 Agent 智能对话（非流式聚合输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-smart-reasoning')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
web_search_enabled = config.get('web-search-enabled', False)
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
full_answer = ''
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.agent_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
web_search_enabled=web_search_enabled,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
|
||||
if response_type == 'tool_call':
|
||||
# 工具调用
|
||||
tool_data = chunk.get('data', {})
|
||||
tool_name = tool_data.get('tool_name', '')
|
||||
if tool_name:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
tool_calls=[
|
||||
provider_message.ToolCall(
|
||||
id=chunk.get('id', ''),
|
||||
type='function',
|
||||
function=provider_message.FunctionCall(
|
||||
name=tool_name,
|
||||
arguments=json.dumps(tool_data.get('arguments', {})),
|
||||
),
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
elif response_type == 'answer':
|
||||
if content:
|
||||
full_answer += content
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
if full_answer:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=full_answer,
|
||||
)
|
||||
|
||||
async def _chat_messages(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""调用知识库 RAG 问答（非流式聚合输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-quick-answer')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
full_answer = ''
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.knowledge_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
|
||||
if response_type == 'answer':
|
||||
if content:
|
||||
full_answer += content
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
if full_answer:
|
||||
yield provider_message.Message(
|
||||
role='assistant',
|
||||
content=full_answer,
|
||||
)
|
||||
|
||||
async def _agent_chat_messages_chunk(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
"""调用 Agent 智能对话（流式输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-smart-reasoning')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
web_search_enabled = config.get('web-search-enabled', False)
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
pending_answer = ''
|
||||
message_idx = 0
|
||||
is_final = False
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.agent_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
web_search_enabled=web_search_enabled,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-agent-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
done = chunk.get('done', False)
|
||||
|
||||
if response_type == 'tool_call':
|
||||
tool_data = chunk.get('data', {})
|
||||
tool_name = tool_data.get('tool_name', '')
|
||||
if tool_name:
|
||||
message_idx += 1
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
tool_calls=[
|
||||
provider_message.ToolCall(
|
||||
id=chunk.get('id', ''),
|
||||
type='function',
|
||||
function=provider_message.FunctionCall(
|
||||
name=tool_name,
|
||||
arguments=json.dumps(tool_data.get('arguments', {})),
|
||||
),
|
||||
)
|
||||
],
|
||||
)
|
||||
|
||||
elif response_type == 'answer':
|
||||
message_idx += 1
|
||||
if content:
|
||||
pending_answer += content
|
||||
|
||||
if done:
|
||||
is_final = True
|
||||
|
||||
# 每 8 个 chunk 输出一次，或最终输出
|
||||
if message_idx % 8 == 0 or is_final:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=is_final,
|
||||
)
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
# 确保最终消息已发出
|
||||
if not is_final and pending_answer:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def _chat_messages_chunk(
|
||||
self, query: pipeline_query.Query
|
||||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||||
"""调用知识库 RAG 问答（流式输出）"""
|
||||
session_id = await self._ensure_session(query)
|
||||
plain_text = await self._extract_plain_text(query)
|
||||
user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
|
||||
|
||||
config = self.pipeline_config['ai']['weknora-api']
|
||||
agent_id = config.get('agent-id', 'builtin-quick-answer')
|
||||
knowledge_base_ids = config.get('knowledge-base-ids', [])
|
||||
timeout = config.get('timeout', 120)
|
||||
|
||||
pending_answer = ''
|
||||
message_idx = 0
|
||||
is_final = False
|
||||
chunk = None
|
||||
|
||||
async for chunk in self.weknora_client.knowledge_chat(
|
||||
session_id=session_id,
|
||||
query=plain_text,
|
||||
user=user_tag,
|
||||
agent_id=agent_id,
|
||||
knowledge_base_ids=knowledge_base_ids,
|
||||
timeout=timeout,
|
||||
):
|
||||
self.ap.logger.debug('weknora-chat-chunk: ' + str(chunk))
|
||||
|
||||
response_type = chunk.get('response_type', '')
|
||||
content = chunk.get('content', '')
|
||||
done = chunk.get('done', False)
|
||||
|
||||
if response_type == 'answer':
|
||||
message_idx += 1
|
||||
if content:
|
||||
pending_answer += content
|
||||
|
||||
if done:
|
||||
is_final = True
|
||||
|
||||
if message_idx % 8 == 0 or is_final:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=is_final,
|
||||
)
|
||||
|
||||
elif response_type == 'error':
|
||||
raise errors.WeKnoraAPIError(f'WeKnora 服务错误: {content}')
|
||||
|
||||
if chunk is None:
|
||||
raise errors.WeKnoraAPIError('WeKnora API 没有返回任何响应，请检查网络连接和API配置')
|
||||
|
||||
if not is_final and pending_answer:
|
||||
yield provider_message.MessageChunk(
|
||||
role='assistant',
|
||||
content=pending_answer,
|
||||
is_final=True,
|
||||
)
|
||||
|
||||
async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||||
"""运行请求"""
|
||||
app_type = self.pipeline_config['ai']['weknora-api']['app-type']
|
||||
|
||||
if await query.adapter.is_stream_output_supported():
|
||||
msg_idx = 0
|
||||
if app_type == 'agent':
|
||||
async for msg in self._agent_chat_messages_chunk(query):
|
||||
msg_idx += 1
|
||||
msg.msg_sequence = msg_idx
|
||||
yield msg
|
||||
elif app_type == 'chat':
|
||||
async for msg in self._chat_messages_chunk(query):
|
||||
msg_idx += 1
|
||||
msg.msg_sequence = msg_idx
|
||||
yield msg
|
||||
else:
|
||||
raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
|
||||
else:
|
||||
if app_type == 'agent':
|
||||
async for msg in self._agent_chat_messages(query):
|
||||
yield msg
|
||||
elif app_type == 'chat':
|
||||
async for msg in self._chat_messages(query):
|
||||
yield msg
|
||||
else:
|
||||
raise errors.WeKnoraAPIError(f'不支持的 WeKnora 应用类型: {app_type}')
|
||||
Reference in New Issue
Block a user