mirror of
https://github.com/langbot-app/LangBot.git
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fdc310
1507 lines
63 KiB
Python
1507 lines
63 KiB
Python
from __future__ import annotations
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import typing
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import json
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import time
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import uuid
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import base64
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import mimetypes
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from collections import OrderedDict
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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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from langbot.pkg.utils import image
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import langbot_plugin.api.entities.builtin.pipeline.query as pipeline_query
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from langbot.libs.dify_service_api.v1 import client, errors
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import httpx
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# Module-level store for paused-workflow form state, keyed by session key
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# (launcher_type_value + "_" + launcher_id). Each session holds an
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# insertion-ordered dict of form_token -> form_data, allowing multiple
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# Dify workflows to be paused simultaneously for the same session.
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_PENDING_FORMS: dict[str, 'OrderedDict[str, dict[str, typing.Any]]'] = {}
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_PENDING_FORM_DEFAULT_TTL = 30 * 60 # 30 minutes safety cap
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def _session_key_from_query(query: pipeline_query.Query) -> str:
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return f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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def _prune_pending_forms(now: float | None = None) -> None:
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if now is None:
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now = time.time()
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for session_key in list(_PENDING_FORMS.keys()):
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forms = _PENDING_FORMS[session_key]
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expired_tokens = [token for token, data in forms.items() if data.get('_expires_at', 0) <= now]
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for token in expired_tokens:
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forms.pop(token, None)
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if not forms:
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_PENDING_FORMS.pop(session_key, None)
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def _set_pending_form(session_key: str, form_data: dict[str, typing.Any]) -> None:
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_prune_pending_forms()
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stored = dict(form_data)
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expiration_time = stored.get('expiration_time')
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try:
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expiration_ts = float(expiration_time) if expiration_time is not None else 0.0
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except (TypeError, ValueError):
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expiration_ts = 0.0
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stored['_expires_at'] = expiration_ts or (time.time() + _PENDING_FORM_DEFAULT_TTL)
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form_token = str(stored.get('form_token') or '')
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forms = _PENDING_FORMS.setdefault(session_key, OrderedDict())
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# Re-insert at the end so this becomes the "latest" entry
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forms.pop(form_token, None)
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forms[form_token] = stored
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def _get_pending_form_by_token(session_key: str, form_token: str) -> dict[str, typing.Any] | None:
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_prune_pending_forms()
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forms = _PENDING_FORMS.get(session_key)
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if not forms or not form_token:
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return None
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return forms.get(form_token)
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def _get_pending_form_by_w_suffix(session_key: str, w_suffix: str) -> dict[str, typing.Any] | None:
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"""Look up a pending form whose workflow_run_id ends with the given suffix.
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Used by adapters (e.g. Telegram) whose callback payload is too small to
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carry the full form_token / workflow_run_id.
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"""
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_prune_pending_forms()
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forms = _PENDING_FORMS.get(session_key)
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if not forms or not w_suffix:
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return None
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for token in reversed(forms):
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form = forms[token]
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if str(form.get('workflow_run_id', '')).endswith(w_suffix):
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return form
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return None
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def _get_latest_pending_form(session_key: str) -> dict[str, typing.Any] | None:
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_prune_pending_forms()
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forms = _PENDING_FORMS.get(session_key)
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if not forms:
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return None
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return forms[next(reversed(forms))]
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def _iter_pending_forms(session_key: str) -> typing.Iterator[dict[str, typing.Any]]:
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"""Iterate pending forms for a session, newest-first."""
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_prune_pending_forms()
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forms = _PENDING_FORMS.get(session_key)
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if not forms:
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return
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for token in reversed(list(forms.keys())):
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yield forms[token]
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def _clear_pending_form(session_key: str, form_token: str | None = None) -> None:
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"""Clear one specific pending form (by token) or all forms for the session."""
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forms = _PENDING_FORMS.get(session_key)
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if not forms:
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return
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if form_token is None:
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_PENDING_FORMS.pop(session_key, None)
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return
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forms.pop(form_token, None)
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if not forms:
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_PENDING_FORMS.pop(session_key, None)
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def _format_human_input_text(
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node_title: str,
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form_content: str,
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actions: list[dict[str, typing.Any]],
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) -> str:
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"""Render a paused-workflow human-input prompt as plain text.
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Used by adapters without rich UI (no buttons/cards) so users can reply
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with the option number or the option title to resume the workflow.
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"""
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lines: list[str] = [f'[Human Input Required] {node_title or ""}'.rstrip()]
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if form_content:
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lines.append('')
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lines.append(form_content)
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if actions:
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lines.append('')
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lines.append('Reply with the number or title to continue:')
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for idx, action in enumerate(actions, start=1):
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title = action.get('title') or action.get('id') or ''
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lines.append(f' {idx}. {title}')
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return '\n'.join(lines)
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@runner.runner_class('dify-service-api')
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class DifyServiceAPIRunner(runner.RequestRunner):
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"""Dify Service API 对话请求器"""
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dify_client: client.AsyncDifyServiceClient
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def __init__(self, ap: app.Application, pipeline_config: dict):
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self.ap = ap
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self.pipeline_config = pipeline_config
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valid_app_types = ['chat', 'agent', 'workflow', 'chatflow']
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if self.pipeline_config['ai']['dify-service-api']['app-type'] not in valid_app_types:
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raise errors.DifyAPIError(
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f'不支持的 Dify 应用类型: {self.pipeline_config["ai"]["dify-service-api"]["app-type"]}'
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)
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api_key = self.pipeline_config['ai']['dify-service-api']['api-key']
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self.dify_client = client.AsyncDifyServiceClient(
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api_key=api_key,
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base_url=self.pipeline_config['ai']['dify-service-api']['base-url'],
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)
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def _process_thinking_content(
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self,
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content: str,
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) -> tuple[str, str]:
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"""处理思维链内容
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Args:
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content: 原始内容
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Returns:
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(处理后的内容, 提取的思维链内容)
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"""
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remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
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thinking_content = ''
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# 从 content 中提取 <think> 标签内容
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if content and '<think>' in content and '</think>' in content:
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import re
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think_pattern = r'<think>(.*?)</think>'
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think_matches = re.findall(think_pattern, content, re.DOTALL)
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if think_matches:
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thinking_content = '\n'.join(think_matches)
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# 移除 content 中的 <think> 标签
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content = re.sub(think_pattern, '', content, flags=re.DOTALL).strip()
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# 3. 根据 remove_think 参数决定是否保留思维链
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if remove_think:
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return content, ''
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else:
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# 如果有思维链内容，将其以 <think> 格式添加到 content 开头
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if thinking_content:
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content = f'<think>\n{thinking_content}\n</think>\n{content}'.strip()
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return content, thinking_content
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def _extract_dify_text_output(self, value: typing.Any) -> str:
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"""Extract text content from Dify output payload."""
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if value is None:
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return ''
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if isinstance(value, dict):
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content = value.get('content')
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if isinstance(content, str):
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return content
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return json.dumps(value, ensure_ascii=False)
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if isinstance(value, str):
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text = value.strip()
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if not text:
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return ''
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try:
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parsed = json.loads(text)
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except json.JSONDecodeError:
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return value
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if isinstance(parsed, dict) and isinstance(parsed.get('content'), str):
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return parsed['content']
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return value
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return str(value)
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async def _preprocess_user_message(self, query: pipeline_query.Query) -> tuple[str, list[dict]]:
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"""预处理用户消息，提取纯文本，并将图片/文件上传到 Dify 服务
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Returns:
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tuple[str, list[dict]]: 纯文本和上传后的文件描述（包含 type 与 id）
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"""
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plain_text = ''
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upload_files: list[dict] = []
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user_tag = f'{query.session.launcher_type.value}_{query.session.launcher_id}'
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async def upload_file_bytes(file_name: str, file_bytes: bytes, content_type: str) -> str:
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file_name = file_name or 'file'
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content_type = content_type or 'application/octet-stream'
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file = (file_name, file_bytes, content_type)
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resp = await self.dify_client.upload_file(file, user_tag)
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return resp['id']
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async def download_file(file_url: str) -> tuple[bytes, str]:
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"""Download file from url (supports data url)."""
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async with httpx.AsyncClient() as client_session:
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resp = await client_session.get(file_url)
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resp.raise_for_status()
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content_type = (
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resp.headers.get('content-type') or mimetypes.guess_type(file_url)[0] or 'application/octet-stream'
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)
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return resp.content, content_type
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def _detect_file_type(content_type: str) -> str:
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"""Map MIME to dify file type."""
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if content_type and content_type.startswith('image/'):
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return 'image'
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if content_type and content_type.startswith('audio/'):
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return 'audio'
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if content_type and content_type.startswith('video/'):
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return 'video'
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return 'document'
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if 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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image_b64, image_format = await image.extract_b64_and_format(ce.image_base64)
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file_bytes = base64.b64decode(image_b64)
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image_id = await upload_file_bytes(f'img.{image_format}', file_bytes, f'image/{image_format}')
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upload_files.append({'type': 'image', 'id': image_id})
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elif ce.type == 'file_url':
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file_url = getattr(ce, 'file_url', None)
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file_name = getattr(ce, 'file_name', None) or 'file'
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try:
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file_bytes, content_type = await download_file(file_url)
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file_id = await upload_file_bytes(file_name, file_bytes, content_type)
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file_type = _detect_file_type(content_type)
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upload_files.append({'type': file_type, 'id': file_id})
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except Exception as e:
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self.ap.logger.warning(f'dify file upload failed: {e}')
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elif ce.type == 'file_base64':
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file_name = getattr(ce, 'file_name', None) or 'file'
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header, b64_data = ce.file_base64.split(',', 1)
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content_type = 'application/octet-stream'
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if ';' in header:
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content_type = header.split(';')[0][5:] or content_type
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file_bytes = base64.b64decode(b64_data)
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file_id = await upload_file_bytes(file_name, file_bytes, content_type)
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file_type = _detect_file_type(content_type)
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upload_files.append({'type': file_type, 'id': file_id})
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elif isinstance(query.user_message.content, str):
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plain_text = query.user_message.content
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plain_text = plain_text if plain_text else self.pipeline_config['ai']['dify-service-api']['base-prompt']
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return plain_text, upload_files
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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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"""调用聊天助手"""
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# Check if this is a form action resume (button click or text match)
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form_action_raw = query.variables.get('_dify_form_action')
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session_key = _session_key_from_query(query)
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if form_action_raw:
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form_action = self._merge_pending_form_action(session_key, form_action_raw)
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else:
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form_action = self._match_pending_form_action(session_key, str(query.message_chain))
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if form_action:
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_clear_pending_form(session_key, form_action.get('form_token') or None)
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async for msg in self._submit_workflow_form_blocking(form_action):
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yield msg
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return
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cov_id = query.session.using_conversation.uuid or None
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query.variables['conversation_id'] = cov_id
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plain_text, upload_files = await self._preprocess_user_message(query)
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files = [
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{
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'type': f['type'],
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'transfer_method': 'local_file',
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'upload_file_id': f['id'],
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}
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for f in upload_files
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]
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mode = 'basic' # 标记是基础编排还是工作流编排
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basic_mode_pending_chunk = ''
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inputs = {}
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inputs.update(query.variables)
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chunk = None # 初始化chunk变量，防止在没有响应时引用错误
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async for chunk in self.dify_client.chat_messages(
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inputs=inputs,
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query=plain_text,
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user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
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conversation_id=cov_id,
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files=files,
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timeout=120,
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):
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self.ap.logger.debug('dify-chat-chunk: ' + str(chunk))
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if chunk['event'] == 'workflow_started':
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mode = 'workflow'
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if mode == 'workflow':
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if chunk['event'] == 'workflow_paused':
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reasons = chunk['data'].get('reasons', [])
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workflow_run_id = chunk['data'].get('workflow_run_id', '')
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for reason in reasons:
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if reason.get('TYPE') != 'human_input_required':
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continue
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form_content = reason.get('form_content', '')
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actions = reason.get('actions', [])
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node_title = reason.get('node_title', '')
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_set_pending_form(
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_session_key_from_query(query),
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{
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'workflow_run_id': workflow_run_id,
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'form_id': reason.get('form_id'),
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'form_token': reason.get('form_token'),
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'node_id': reason.get('node_id'),
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'node_title': node_title,
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'form_content': form_content,
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'inputs': reason.get('inputs', {}),
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'actions': actions,
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'expiration_time': reason.get('expiration_time'),
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'user': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
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},
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)
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query.variables['_dify_form_render'] = {
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'form_content': form_content,
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'actions': actions,
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'node_title': node_title,
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}
|
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display_text = _format_human_input_text(node_title, form_content, actions)
|
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yield provider_message.Message(
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role='assistant',
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content=display_text,
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)
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return
|
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|
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if chunk['event'] == 'node_finished':
|
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if chunk['data']['node_type'] == 'answer':
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answer = self._extract_dify_text_output(chunk['data']['outputs'].get('answer'))
|
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content, _ = self._process_thinking_content(answer)
|
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|
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yield provider_message.Message(
|
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role='assistant',
|
||
content=content,
|
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)
|
||
elif mode == 'basic':
|
||
if chunk['event'] == 'message':
|
||
basic_mode_pending_chunk += chunk['answer']
|
||
elif chunk['event'] == 'message_end':
|
||
content, _ = self._process_thinking_content(basic_mode_pending_chunk)
|
||
yield provider_message.Message(
|
||
role='assistant',
|
||
content=content,
|
||
)
|
||
basic_mode_pending_chunk = ''
|
||
|
||
if chunk is None:
|
||
raise errors.DifyAPIError('Dify API 没有返回任何响应，请检查网络连接和API配置')
|
||
|
||
query.session.using_conversation.uuid = chunk['conversation_id']
|
||
|
||
async def _agent_chat_messages(
|
||
self, query: pipeline_query.Query
|
||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||
"""调用聊天助手"""
|
||
cov_id = query.session.using_conversation.uuid or None
|
||
query.variables['conversation_id'] = cov_id
|
||
|
||
plain_text, upload_files = await self._preprocess_user_message(query)
|
||
|
||
files = [
|
||
{
|
||
'type': f['type'],
|
||
'transfer_method': 'local_file',
|
||
'upload_file_id': f['id'],
|
||
}
|
||
for f in upload_files
|
||
]
|
||
|
||
ignored_events = []
|
||
|
||
inputs = {}
|
||
|
||
inputs.update(query.variables)
|
||
|
||
pending_agent_message = ''
|
||
|
||
chunk = None # 初始化chunk变量，防止在没有响应时引用错误
|
||
|
||
async for chunk in self.dify_client.chat_messages(
|
||
inputs=inputs,
|
||
query=plain_text,
|
||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
response_mode='streaming',
|
||
conversation_id=cov_id,
|
||
files=files,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-agent-chunk: ' + str(chunk))
|
||
|
||
if chunk['event'] in ignored_events:
|
||
continue
|
||
|
||
if chunk['event'] == 'agent_message' or chunk['event'] == 'message':
|
||
pending_agent_message += chunk['answer']
|
||
else:
|
||
if pending_agent_message.strip() != '':
|
||
pending_agent_message = pending_agent_message.replace('</details>Action:', '</details>')
|
||
content, _ = self._process_thinking_content(pending_agent_message)
|
||
yield provider_message.Message(
|
||
role='assistant',
|
||
content=content,
|
||
)
|
||
pending_agent_message = ''
|
||
|
||
if chunk['event'] == 'agent_thought':
|
||
if chunk['tool'] != '' and chunk['observation'] != '': # 工具调用结果，跳过
|
||
continue
|
||
|
||
if chunk['tool']:
|
||
msg = provider_message.Message(
|
||
role='assistant',
|
||
tool_calls=[
|
||
provider_message.ToolCall(
|
||
id=chunk['id'],
|
||
type='function',
|
||
function=provider_message.FunctionCall(
|
||
name=chunk['tool'],
|
||
arguments=json.dumps({}),
|
||
),
|
||
)
|
||
],
|
||
)
|
||
yield msg
|
||
if chunk['event'] == 'message_file':
|
||
if chunk['type'] == 'image' and chunk['belongs_to'] == 'assistant':
|
||
# 检查URL是否已经是完整的连接
|
||
if chunk['url'].startswith('http://') or chunk['url'].startswith('https://'):
|
||
image_url = chunk['url']
|
||
else:
|
||
base_url = self.dify_client.base_url
|
||
|
||
if base_url.endswith('/v1'):
|
||
base_url = base_url[:-3]
|
||
|
||
image_url = base_url + chunk['url']
|
||
|
||
yield provider_message.Message(
|
||
role='assistant',
|
||
content=[provider_message.ContentElement.from_image_url(image_url)],
|
||
)
|
||
if chunk['event'] == 'error':
|
||
raise errors.DifyAPIError('dify 服务错误: ' + chunk['message'])
|
||
|
||
if chunk is None:
|
||
raise errors.DifyAPIError('Dify API 没有返回任何响应，请检查网络连接和API配置')
|
||
|
||
query.session.using_conversation.uuid = chunk['conversation_id']
|
||
|
||
async def _submit_workflow_form_blocking(
|
||
self, form_action: dict
|
||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||
"""Submit human input to resume a paused Dify workflow (non-streaming)."""
|
||
|
||
form_token = form_action['form_token']
|
||
workflow_run_id = form_action['workflow_run_id']
|
||
user = form_action['user']
|
||
action_id = form_action.get('action_id', '')
|
||
inputs = form_action.get('inputs', {})
|
||
|
||
async for chunk in self.dify_client.workflow_submit(
|
||
form_token=form_token,
|
||
workflow_run_id=workflow_run_id,
|
||
inputs=inputs,
|
||
user=user,
|
||
action=action_id,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-workflow-submit-chunk: ' + str(chunk))
|
||
|
||
if chunk['event'] == 'workflow_finished':
|
||
if chunk['data'].get('error'):
|
||
raise errors.DifyAPIError(chunk['data']['error'])
|
||
content, _ = self._process_thinking_content(chunk['data']['outputs']['summary'])
|
||
yield provider_message.Message(
|
||
role='assistant',
|
||
content=content,
|
||
)
|
||
return
|
||
|
||
if chunk['event'] == 'workflow_paused':
|
||
reasons = chunk['data'].get('reasons', [])
|
||
new_run_id = chunk['data'].get('workflow_run_id', workflow_run_id)
|
||
for reason in reasons:
|
||
if reason.get('TYPE') != 'human_input_required':
|
||
continue
|
||
form_content = reason.get('form_content', '')
|
||
actions = reason.get('actions', [])
|
||
paused_node_title = reason.get('node_title', '')
|
||
raw_inputs = reason.get('inputs', {})
|
||
|
||
_set_pending_form(
|
||
user,
|
||
{
|
||
'workflow_run_id': new_run_id,
|
||
'form_id': reason.get('form_id'),
|
||
'form_token': reason.get('form_token'),
|
||
'node_id': reason.get('node_id'),
|
||
'node_title': paused_node_title,
|
||
'form_content': form_content,
|
||
'inputs': raw_inputs if isinstance(raw_inputs, dict) else {},
|
||
'actions': actions,
|
||
'expiration_time': reason.get('expiration_time'),
|
||
'user': user,
|
||
},
|
||
)
|
||
|
||
display_text = _format_human_input_text(paused_node_title, form_content, actions)
|
||
yield provider_message.Message(
|
||
role='assistant',
|
||
content=display_text,
|
||
)
|
||
return
|
||
|
||
def _resolve_pending_form(self, session_key: str, form_action: dict) -> dict | None:
|
||
"""Locate the pending form this action targets.
|
||
|
||
Tries identifiers in order of specificity: form_token, full
|
||
workflow_run_id, workflow_run_id suffix (Telegram-style compact id),
|
||
then falls back to the newest pending form for the session.
|
||
"""
|
||
form_token = form_action.get('form_token')
|
||
if form_token:
|
||
form = _get_pending_form_by_token(session_key, form_token)
|
||
if form:
|
||
return form
|
||
|
||
workflow_run_id = form_action.get('workflow_run_id')
|
||
if workflow_run_id:
|
||
for form in _iter_pending_forms(session_key):
|
||
if form.get('workflow_run_id') == workflow_run_id:
|
||
return form
|
||
|
||
w_suffix = form_action.get('w_suffix')
|
||
if w_suffix:
|
||
form = _get_pending_form_by_w_suffix(session_key, w_suffix)
|
||
if form:
|
||
return form
|
||
|
||
return _get_latest_pending_form(session_key)
|
||
|
||
def _merge_pending_form_action(self, session_key: str, form_action: dict | None) -> dict | None:
|
||
"""Backfill resume fields from the matching pending form."""
|
||
if not form_action:
|
||
return None
|
||
|
||
merged_action = dict(form_action)
|
||
merged_action.pop('w_suffix', None)
|
||
pending_form = self._resolve_pending_form(session_key, form_action)
|
||
if pending_form:
|
||
merged_action['form_token'] = merged_action.get('form_token') or pending_form.get('form_token', '')
|
||
merged_action['workflow_run_id'] = merged_action.get('workflow_run_id') or pending_form.get(
|
||
'workflow_run_id', ''
|
||
)
|
||
merged_action.setdefault('inputs', pending_form.get('inputs', {}))
|
||
merged_action.setdefault('user', pending_form.get('user', ''))
|
||
merged_action.setdefault('node_title', pending_form.get('node_title', ''))
|
||
|
||
# Resolve clicked action's display title from the stored actions list
|
||
if 'action_title' not in merged_action:
|
||
clicked_id = merged_action.get('action_id', '')
|
||
for action in pending_form.get('actions', []):
|
||
if str(action.get('id', '')) == str(clicked_id):
|
||
merged_action['action_title'] = action.get('title', clicked_id)
|
||
break
|
||
|
||
return merged_action
|
||
|
||
def _match_pending_form_action(self, session_key: str, user_text: str) -> dict | None:
|
||
"""Match plain text replies against pending Dify form actions.
|
||
|
||
Resolution order:
|
||
1. A pure digit reply (e.g. "1", "2") maps to the 1-indexed action of
|
||
the most recent pending form. Lets users on plain-text platforms
|
||
pick options without retyping titles.
|
||
2. Otherwise, iterate pending forms newest-first and match each
|
||
action's title/id case-insensitively. The first hit wins, so when
|
||
two forms share a button label the newer one resolves.
|
||
"""
|
||
normalized_text = user_text.strip().lower()
|
||
if not normalized_text:
|
||
return None
|
||
|
||
def _build(pending_form: dict, action: dict) -> dict:
|
||
return {
|
||
'form_token': pending_form.get('form_token', ''),
|
||
'workflow_run_id': pending_form.get('workflow_run_id', ''),
|
||
'action_id': action.get('id', ''),
|
||
'action_title': action.get('title', action.get('id', '')),
|
||
'node_title': pending_form.get('node_title', ''),
|
||
'inputs': pending_form.get('inputs', {}),
|
||
'user': pending_form.get('user', ''),
|
||
}
|
||
|
||
if normalized_text.isdigit():
|
||
position = int(normalized_text)
|
||
latest_form = _get_latest_pending_form(session_key)
|
||
if latest_form is not None:
|
||
actions = latest_form.get('actions', [])
|
||
if 1 <= position <= len(actions):
|
||
return _build(latest_form, actions[position - 1])
|
||
|
||
for pending_form in _iter_pending_forms(session_key):
|
||
for action in pending_form.get('actions', []):
|
||
titles = {
|
||
str(action.get('title', '')).strip().lower(),
|
||
str(action.get('id', '')).strip().lower(),
|
||
}
|
||
if normalized_text in titles:
|
||
return _build(pending_form, action)
|
||
|
||
return None
|
||
|
||
async def _workflow_messages(
|
||
self, query: pipeline_query.Query
|
||
) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||
"""调用工作流"""
|
||
|
||
# Check if this is a form action resume (button click or text match)
|
||
form_action_raw = query.variables.get('_dify_form_action')
|
||
session_key = _session_key_from_query(query)
|
||
|
||
if form_action_raw:
|
||
form_action = self._merge_pending_form_action(session_key, form_action_raw)
|
||
else:
|
||
form_action = self._match_pending_form_action(session_key, str(query.message_chain))
|
||
|
||
if form_action:
|
||
_clear_pending_form(session_key, form_action.get('form_token') or None)
|
||
async for msg in self._submit_workflow_form_blocking(form_action):
|
||
yield msg
|
||
return
|
||
|
||
if not query.session.using_conversation.uuid:
|
||
query.session.using_conversation.uuid = str(uuid.uuid4())
|
||
|
||
query.variables['conversation_id'] = query.session.using_conversation.uuid
|
||
|
||
plain_text, upload_files = await self._preprocess_user_message(query)
|
||
|
||
files = [
|
||
{
|
||
'type': f['type'],
|
||
'transfer_method': 'local_file',
|
||
'upload_file_id': f['id'],
|
||
}
|
||
for f in upload_files
|
||
]
|
||
|
||
ignored_events = ['text_chunk', 'workflow_started']
|
||
|
||
inputs = { # these variables are legacy variables, we need to keep them for compatibility
|
||
'langbot_user_message_text': plain_text,
|
||
'langbot_session_id': query.variables['session_id'],
|
||
'langbot_conversation_id': query.variables['conversation_id'],
|
||
'langbot_msg_create_time': query.variables['msg_create_time'],
|
||
}
|
||
|
||
inputs.update(query.variables)
|
||
human_input_yielded = False
|
||
|
||
async for chunk in self.dify_client.workflow_run(
|
||
inputs=inputs,
|
||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
files=files,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-workflow-chunk: ' + str(chunk))
|
||
if chunk['event'] in ignored_events:
|
||
continue
|
||
|
||
if chunk['event'] == 'workflow_paused':
|
||
reasons = chunk['data'].get('reasons', [])
|
||
workflow_run_id = chunk['data'].get('workflow_run_id', '')
|
||
for reason in reasons:
|
||
if reason.get('TYPE') == 'human_input_required':
|
||
form_content = reason.get('form_content', '')
|
||
actions = reason.get('actions', [])
|
||
node_title = reason.get('node_title', '')
|
||
|
||
_set_pending_form(
|
||
_session_key_from_query(query),
|
||
{
|
||
'workflow_run_id': workflow_run_id,
|
||
'form_id': reason.get('form_id'),
|
||
'form_token': reason.get('form_token'),
|
||
'node_id': reason.get('node_id'),
|
||
'node_title': node_title,
|
||
'form_content': form_content,
|
||
'inputs': reason.get('inputs', {}),
|
||
'actions': actions,
|
||
'expiration_time': reason.get('expiration_time'),
|
||
'user': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
},
|
||
)
|
||
|
||
query.variables['_dify_form_render'] = {
|
||
'form_content': form_content,
|
||
'actions': actions,
|
||
'node_title': node_title,
|
||
}
|
||
|
||
display_text = _format_human_input_text(node_title, form_content, actions)
|
||
|
||
human_input_yielded = True
|
||
yield provider_message.Message(
|
||
role='assistant',
|
||
content=display_text,
|
||
)
|
||
|
||
if chunk['event'] == 'node_started':
|
||
if chunk['data']['node_type'] == 'start' or chunk['data']['node_type'] == 'end':
|
||
continue
|
||
|
||
msg = provider_message.Message(
|
||
role='assistant',
|
||
content=None,
|
||
tool_calls=[
|
||
provider_message.ToolCall(
|
||
id=chunk['data']['node_id'],
|
||
type='function',
|
||
function=provider_message.FunctionCall(
|
||
name=chunk['data']['title'],
|
||
arguments=json.dumps({}),
|
||
),
|
||
)
|
||
],
|
||
)
|
||
|
||
yield msg
|
||
|
||
elif chunk['event'] == 'workflow_finished':
|
||
if human_input_yielded:
|
||
break
|
||
if chunk['data']['error']:
|
||
raise errors.DifyAPIError(chunk['data']['error'])
|
||
content, _ = self._process_thinking_content(chunk['data']['outputs']['summary'])
|
||
|
||
msg = provider_message.Message(
|
||
role='assistant',
|
||
content=content,
|
||
)
|
||
|
||
yield msg
|
||
|
||
async def _chat_messages_chunk(
|
||
self, query: pipeline_query.Query
|
||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||
"""调用聊天助手"""
|
||
# Check if this is a form action resume (button click or text match)
|
||
form_action_raw = query.variables.get('_dify_form_action')
|
||
session_key = _session_key_from_query(query)
|
||
|
||
if form_action_raw:
|
||
form_action = self._merge_pending_form_action(session_key, form_action_raw)
|
||
else:
|
||
form_action = self._match_pending_form_action(session_key, str(query.message_chain))
|
||
|
||
if form_action:
|
||
_clear_pending_form(session_key, form_action.get('form_token') or None)
|
||
async for msg in self._submit_workflow_form(form_action):
|
||
yield msg
|
||
return
|
||
|
||
cov_id = query.session.using_conversation.uuid or None
|
||
query.variables['conversation_id'] = cov_id
|
||
|
||
plain_text, upload_files = await self._preprocess_user_message(query)
|
||
|
||
files = [
|
||
{
|
||
'type': f['type'],
|
||
'transfer_method': 'local_file',
|
||
'upload_file_id': f['id'],
|
||
}
|
||
for f in upload_files
|
||
]
|
||
|
||
mode = 'basic'
|
||
basic_mode_pending_chunk = ''
|
||
|
||
inputs = {}
|
||
|
||
inputs.update(query.variables)
|
||
message_idx = 0
|
||
|
||
chunk = None # 初始化chunk变量，防止在没有响应时引用错误
|
||
|
||
is_final = False
|
||
think_start = False
|
||
think_end = False
|
||
yielded_final = False
|
||
human_input_yielded = False
|
||
pending_form_data = None
|
||
display_text = ''
|
||
|
||
remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
|
||
|
||
async for chunk in self.dify_client.chat_messages(
|
||
inputs=inputs,
|
||
query=plain_text,
|
||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
conversation_id=cov_id,
|
||
files=files,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-chat-chunk: ' + str(chunk))
|
||
|
||
if chunk['event'] == 'workflow_started':
|
||
mode = 'workflow'
|
||
elif chunk['event'] in ('node_started', 'node_finished', 'workflow_finished', 'workflow_paused'):
|
||
# Some Dify deployments may omit workflow_started in streamed chunks.
|
||
mode = 'workflow'
|
||
|
||
if chunk['event'] == 'message':
|
||
message_idx += 1
|
||
if remove_think:
|
||
if '<think>' in chunk['answer'] and not think_start:
|
||
think_start = True
|
||
continue
|
||
if '</think>' in chunk['answer'] and not think_end:
|
||
import re
|
||
|
||
content = re.sub(r'^\n</think>', '', chunk['answer'])
|
||
basic_mode_pending_chunk += content
|
||
think_end = True
|
||
elif think_end:
|
||
basic_mode_pending_chunk += chunk['answer']
|
||
if think_start:
|
||
continue
|
||
|
||
else:
|
||
basic_mode_pending_chunk += chunk['answer']
|
||
|
||
if chunk['event'] == 'message_end':
|
||
is_final = True
|
||
elif chunk['event'] == 'workflow_finished':
|
||
is_final = True
|
||
if human_input_yielded:
|
||
break
|
||
if chunk['data'].get('error'):
|
||
raise errors.DifyAPIError(chunk['data']['error'])
|
||
|
||
if mode == 'workflow' and chunk['event'] == 'workflow_paused':
|
||
reasons = chunk['data'].get('reasons', [])
|
||
workflow_run_id = chunk['data'].get('workflow_run_id', '')
|
||
for reason in reasons:
|
||
if reason.get('TYPE') != 'human_input_required':
|
||
continue
|
||
form_content = reason.get('form_content', '')
|
||
actions = reason.get('actions', [])
|
||
node_title = reason.get('node_title', '')
|
||
|
||
raw_inputs = reason.get('inputs', {})
|
||
_set_pending_form(
|
||
_session_key_from_query(query),
|
||
{
|
||
'workflow_run_id': workflow_run_id,
|
||
'form_id': reason.get('form_id'),
|
||
'form_token': reason.get('form_token'),
|
||
'node_id': reason.get('node_id'),
|
||
'node_title': node_title,
|
||
'form_content': form_content,
|
||
'inputs': raw_inputs if isinstance(raw_inputs, dict) else {},
|
||
'actions': actions,
|
||
'expiration_time': reason.get('expiration_time'),
|
||
'user': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
},
|
||
)
|
||
|
||
query.variables['_dify_form_render'] = {
|
||
'form_content': form_content,
|
||
'actions': actions,
|
||
'node_title': node_title,
|
||
}
|
||
|
||
display_text = _format_human_input_text(node_title, form_content, actions)
|
||
# Use a zero-width space so ResponseWrapper lets the chunk
|
||
# propagate to SendResponseBackStage, but the adapter
|
||
# detects _form_data and renders buttons instead of the
|
||
# plain-text prompt (mirrors _workflow_messages_chunk).
|
||
if not basic_mode_pending_chunk:
|
||
basic_mode_pending_chunk = ''
|
||
|
||
pending_form_data = {
|
||
'form_content': form_content,
|
||
'actions': actions,
|
||
'node_title': node_title,
|
||
'workflow_run_id': workflow_run_id,
|
||
'form_token': reason.get('form_token', ''),
|
||
}
|
||
human_input_yielded = True
|
||
|
||
if mode == 'workflow' and chunk['event'] == 'node_finished':
|
||
if chunk['data'].get('node_type') == 'answer':
|
||
answer = self._extract_dify_text_output(chunk['data'].get('outputs', {}).get('answer'))
|
||
if answer:
|
||
basic_mode_pending_chunk = answer
|
||
|
||
if (
|
||
not yielded_final
|
||
and (is_final or message_idx % 8 == 0)
|
||
and (basic_mode_pending_chunk != '' or is_final)
|
||
):
|
||
final_content = basic_mode_pending_chunk if basic_mode_pending_chunk.strip() else ''
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=final_content,
|
||
is_final=is_final,
|
||
)
|
||
if is_final and pending_form_data:
|
||
msg._form_data = pending_form_data
|
||
pending_form_data = None
|
||
yield msg
|
||
if is_final:
|
||
yielded_final = True
|
||
|
||
# If the stream ended after workflow_paused without a
|
||
# workflow_finished event, yield a final chunk so the adapter
|
||
# can update the card and add buttons.
|
||
if human_input_yielded and not yielded_final:
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=basic_mode_pending_chunk or display_text,
|
||
is_final=True,
|
||
)
|
||
msg._form_data = pending_form_data
|
||
yield msg
|
||
|
||
if chunk is None:
|
||
raise errors.DifyAPIError('Dify API 没有返回任何响应，请检查网络连接和API配置')
|
||
|
||
query.session.using_conversation.uuid = chunk['conversation_id']
|
||
|
||
async def _agent_chat_messages_chunk(
|
||
self, query: pipeline_query.Query
|
||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||
"""调用聊天助手"""
|
||
cov_id = query.session.using_conversation.uuid or None
|
||
query.variables['conversation_id'] = cov_id
|
||
|
||
plain_text, upload_files = await self._preprocess_user_message(query)
|
||
|
||
files = [
|
||
{
|
||
'type': f['type'],
|
||
'transfer_method': 'local_file',
|
||
'upload_file_id': f['id'],
|
||
}
|
||
for f in upload_files
|
||
]
|
||
|
||
ignored_events = []
|
||
|
||
inputs = {}
|
||
|
||
inputs.update(query.variables)
|
||
|
||
pending_agent_message = ''
|
||
|
||
chunk = None # 初始化chunk变量，防止在没有响应时引用错误
|
||
message_idx = 0
|
||
is_final = False
|
||
think_start = False
|
||
think_end = False
|
||
|
||
remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
|
||
|
||
async for chunk in self.dify_client.chat_messages(
|
||
inputs=inputs,
|
||
query=plain_text,
|
||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
response_mode='streaming',
|
||
conversation_id=cov_id,
|
||
files=files,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-agent-chunk: ' + str(chunk))
|
||
|
||
if chunk['event'] in ignored_events:
|
||
continue
|
||
|
||
if chunk['event'] == 'agent_message':
|
||
message_idx += 1
|
||
if remove_think:
|
||
if '<think>' in chunk['answer'] and not think_start:
|
||
think_start = True
|
||
continue
|
||
if '</think>' in chunk['answer'] and not think_end:
|
||
import re
|
||
|
||
content = re.sub(r'^\n</think>', '', chunk['answer'])
|
||
pending_agent_message += content
|
||
think_end = True
|
||
elif think_end or not think_start:
|
||
pending_agent_message += chunk['answer']
|
||
if think_start and not think_end:
|
||
continue
|
||
|
||
else:
|
||
pending_agent_message += chunk['answer']
|
||
elif chunk['event'] == 'message_end':
|
||
is_final = True
|
||
else:
|
||
if chunk['event'] == 'agent_thought':
|
||
if chunk['tool'] != '' and chunk['observation'] != '': # 工具调用结果，跳过
|
||
continue
|
||
message_idx += 1
|
||
if chunk['tool']:
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
tool_calls=[
|
||
provider_message.ToolCall(
|
||
id=chunk['id'],
|
||
type='function',
|
||
function=provider_message.FunctionCall(
|
||
name=chunk['tool'],
|
||
arguments=json.dumps({}),
|
||
),
|
||
)
|
||
],
|
||
)
|
||
yield msg
|
||
if chunk['event'] == 'message_file':
|
||
message_idx += 1
|
||
if chunk['type'] == 'image' and chunk['belongs_to'] == 'assistant':
|
||
# 检查URL是否已经是完整的连接
|
||
if chunk['url'].startswith('http://') or chunk['url'].startswith('https://'):
|
||
image_url = chunk['url']
|
||
else:
|
||
base_url = self.dify_client.base_url
|
||
|
||
if base_url.endswith('/v1'):
|
||
base_url = base_url[:-3]
|
||
|
||
image_url = base_url + chunk['url']
|
||
|
||
yield provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=[provider_message.ContentElement.from_image_url(image_url)],
|
||
is_final=is_final,
|
||
)
|
||
|
||
if chunk['event'] == 'error':
|
||
raise errors.DifyAPIError('dify 服务错误: ' + chunk['message'])
|
||
if message_idx % 8 == 0 or is_final:
|
||
yield provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=pending_agent_message,
|
||
is_final=is_final,
|
||
)
|
||
|
||
if chunk is None:
|
||
raise errors.DifyAPIError('Dify API 没有返回任何响应，请检查网络连接和API配置')
|
||
|
||
query.session.using_conversation.uuid = chunk['conversation_id']
|
||
|
||
async def _submit_workflow_form(
|
||
self, form_action: dict
|
||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||
"""Submit human input to resume a paused Dify workflow."""
|
||
|
||
form_token = form_action['form_token']
|
||
workflow_run_id = form_action['workflow_run_id']
|
||
user = form_action['user']
|
||
action_id = form_action.get('action_id', '')
|
||
action_title = form_action.get('action_title', '') or action_id
|
||
node_title = form_action.get('node_title', '')
|
||
inputs = form_action.get('inputs', {})
|
||
|
||
messsage_idx = 0
|
||
is_final = False
|
||
think_start = False
|
||
think_end = False
|
||
workflow_contents = ''
|
||
repause_form_data: dict | None = None
|
||
|
||
remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
|
||
async for chunk in self.dify_client.workflow_submit(
|
||
form_token=form_token,
|
||
workflow_run_id=workflow_run_id,
|
||
inputs=inputs,
|
||
user=user,
|
||
action=action_id,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-workflow-submit-chunk: ' + str(chunk))
|
||
|
||
yield_this_iteration = False
|
||
|
||
if chunk['event'] == 'workflow_finished':
|
||
is_final = True
|
||
yield_this_iteration = True
|
||
if chunk['data'].get('error'):
|
||
raise errors.DifyAPIError(chunk['data']['error'])
|
||
|
||
if chunk['event'] == 'workflow_paused':
|
||
reasons = chunk['data'].get('reasons', [])
|
||
new_run_id = chunk['data'].get('workflow_run_id', workflow_run_id)
|
||
for reason in reasons:
|
||
if reason.get('TYPE') != 'human_input_required':
|
||
continue
|
||
form_content = reason.get('form_content', '')
|
||
actions = reason.get('actions', [])
|
||
# Use a distinct name — `node_title` (the just-resolved step)
|
||
# must keep its value so the resume notice on the previous
|
||
# card still shows which step the user acted on.
|
||
paused_node_title = reason.get('node_title', '')
|
||
raw_inputs = reason.get('inputs', {})
|
||
|
||
_set_pending_form(
|
||
# Use the same session-key format as
|
||
# _session_key_from_query (launcher_type_launcher_id).
|
||
# The 'user' field is set by adapters in this format.
|
||
user,
|
||
{
|
||
'workflow_run_id': new_run_id,
|
||
'form_id': reason.get('form_id'),
|
||
'form_token': reason.get('form_token'),
|
||
'node_id': reason.get('node_id'),
|
||
'node_title': paused_node_title,
|
||
'form_content': form_content,
|
||
'inputs': raw_inputs if isinstance(raw_inputs, dict) else {},
|
||
'actions': actions,
|
||
'expiration_time': reason.get('expiration_time'),
|
||
'user': user,
|
||
},
|
||
)
|
||
|
||
repause_form_data = {
|
||
'form_content': form_content,
|
||
'actions': actions,
|
||
'node_title': paused_node_title,
|
||
'workflow_run_id': new_run_id,
|
||
'form_token': reason.get('form_token', ''),
|
||
}
|
||
# Ensure the final chunk has non-empty content so
|
||
# ResponseWrapper (which skips empty-content chunks) lets it
|
||
# propagate to SendResponseBackStage. Use a zero-width space
|
||
# so neither Lark nor Telegram renders visible noise — the
|
||
# adapter substitutes its own card text from _form_data.
|
||
if not workflow_contents:
|
||
workflow_contents = ''
|
||
is_final = True
|
||
yield_this_iteration = True
|
||
break
|
||
|
||
if chunk['event'] == 'text_chunk':
|
||
messsage_idx += 1
|
||
if remove_think:
|
||
if '<think>' in chunk['data']['text'] and not think_start:
|
||
think_start = True
|
||
continue
|
||
if '</think>' in chunk['data']['text'] and not think_end:
|
||
import re
|
||
|
||
content = re.sub(r'^\n</think>', '', chunk['data']['text'])
|
||
workflow_contents += content
|
||
think_end = True
|
||
elif think_end:
|
||
workflow_contents += chunk['data']['text']
|
||
if think_start:
|
||
continue
|
||
else:
|
||
workflow_contents += chunk['data']['text']
|
||
if messsage_idx % 8 == 0:
|
||
yield_this_iteration = True
|
||
|
||
# Chatflow apps return answers via 'message' events (answer field),
|
||
# not 'text_chunk' events (data.text field).
|
||
if chunk['event'] == 'message':
|
||
answer = chunk.get('answer', '')
|
||
if answer:
|
||
messsage_idx += 1
|
||
if remove_think:
|
||
if '<think>' in answer and not think_start:
|
||
think_start = True
|
||
continue
|
||
if '</think>' in answer and not think_end:
|
||
import re
|
||
|
||
content = re.sub(r'^\n</think>', '', answer)
|
||
workflow_contents += content
|
||
think_end = True
|
||
elif think_end:
|
||
workflow_contents += answer
|
||
if think_start:
|
||
continue
|
||
else:
|
||
workflow_contents += answer
|
||
if messsage_idx % 8 == 0:
|
||
yield_this_iteration = True
|
||
|
||
if yield_this_iteration:
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=workflow_contents,
|
||
is_final=is_final,
|
||
)
|
||
msg._resume_from_form = True
|
||
if action_title:
|
||
msg._resume_action_title = action_title
|
||
if node_title:
|
||
msg._resume_node_title = node_title
|
||
if is_final and repause_form_data:
|
||
msg._form_data = repause_form_data
|
||
msg._open_new_card = True
|
||
yield msg
|
||
if is_final:
|
||
return
|
||
|
||
async def _workflow_messages_chunk(
|
||
self, query: pipeline_query.Query
|
||
) -> typing.AsyncGenerator[provider_message.MessageChunk, None]:
|
||
"""调用工作流"""
|
||
|
||
# Check if this is a form action resume (button click or text match)
|
||
form_action_raw = query.variables.get('_dify_form_action')
|
||
session_key = _session_key_from_query(query)
|
||
|
||
if form_action_raw:
|
||
form_action = self._merge_pending_form_action(session_key, form_action_raw)
|
||
else:
|
||
form_action = self._match_pending_form_action(session_key, str(query.message_chain))
|
||
|
||
if form_action:
|
||
_clear_pending_form(session_key, form_action.get('form_token') or None)
|
||
# Resume paused workflow via submit endpoint
|
||
async for msg in self._submit_workflow_form(form_action):
|
||
yield msg
|
||
return
|
||
|
||
if not query.session.using_conversation.uuid:
|
||
query.session.using_conversation.uuid = str(uuid.uuid4())
|
||
|
||
query.variables['conversation_id'] = query.session.using_conversation.uuid
|
||
|
||
plain_text, upload_files = await self._preprocess_user_message(query)
|
||
|
||
files = [
|
||
{
|
||
'type': f['type'],
|
||
'transfer_method': 'local_file',
|
||
'upload_file_id': f['id'],
|
||
}
|
||
for f in upload_files
|
||
]
|
||
|
||
ignored_events = ['workflow_started']
|
||
|
||
inputs = { # these variables are legacy variables, we need to keep them for compatibility
|
||
'langbot_user_message_text': plain_text,
|
||
'langbot_session_id': query.variables['session_id'],
|
||
'langbot_conversation_id': query.variables['conversation_id'],
|
||
'langbot_msg_create_time': query.variables['msg_create_time'],
|
||
}
|
||
|
||
inputs.update(query.variables)
|
||
messsage_idx = 0
|
||
is_final = False
|
||
think_start = False
|
||
think_end = False
|
||
workflow_contents = ''
|
||
workflow_run_id = ''
|
||
human_input_yielded = False
|
||
|
||
# Saved form data to attach to the final MessageChunk so the adapter
|
||
# can detect it when is_final=True and render buttons.
|
||
pending_form_data = None
|
||
display_text = ''
|
||
|
||
remove_think = self.pipeline_config['output'].get('misc', {}).get('remove-think')
|
||
async for chunk in self.dify_client.workflow_run(
|
||
inputs=inputs,
|
||
user=f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
files=files,
|
||
timeout=120,
|
||
):
|
||
self.ap.logger.debug('dify-workflow-chunk: ' + str(chunk))
|
||
if chunk['event'] in ignored_events:
|
||
if chunk['event'] == 'workflow_started':
|
||
workflow_run_id = chunk['data'].get('workflow_run_id', '')
|
||
continue
|
||
|
||
if chunk['event'] == 'workflow_paused':
|
||
reasons = chunk['data'].get('reasons', [])
|
||
workflow_run_id = chunk['data'].get('workflow_run_id', workflow_run_id)
|
||
for reason in reasons:
|
||
if reason.get('TYPE') == 'human_input_required':
|
||
form_content = reason.get('form_content', '')
|
||
actions = reason.get('actions', [])
|
||
node_title = reason.get('node_title', '')
|
||
|
||
# Persist form state in module-level store keyed by session
|
||
raw_inputs = reason.get('inputs', {})
|
||
_set_pending_form(
|
||
_session_key_from_query(query),
|
||
{
|
||
'workflow_run_id': workflow_run_id,
|
||
'form_id': reason.get('form_id'),
|
||
'form_token': reason.get('form_token'),
|
||
'node_id': reason.get('node_id'),
|
||
'node_title': node_title,
|
||
'form_content': form_content,
|
||
'inputs': raw_inputs if isinstance(raw_inputs, dict) else {},
|
||
'actions': actions,
|
||
'expiration_time': reason.get('expiration_time'),
|
||
'user': f'{query.session.launcher_type.value}_{query.session.launcher_id}',
|
||
},
|
||
)
|
||
|
||
# Pass form render metadata to downstream stages
|
||
query.variables['_dify_form_render'] = {
|
||
'form_content': form_content,
|
||
'actions': actions,
|
||
'node_title': node_title,
|
||
}
|
||
|
||
display_text = _format_human_input_text(node_title, form_content, actions)
|
||
workflow_contents += display_text + '\n'
|
||
|
||
# Save form data to attach to the final chunk later.
|
||
# We do NOT yield here — the form content will be sent
|
||
# as the final MessageChunk (with is_final=True and
|
||
# _form_data) so the adapter can update the card and
|
||
# add buttons in one pass.
|
||
pending_form_data = {
|
||
'form_content': form_content,
|
||
'actions': actions,
|
||
'node_title': node_title,
|
||
'workflow_run_id': workflow_run_id,
|
||
'form_token': reason.get('form_token', ''),
|
||
}
|
||
human_input_yielded = True
|
||
|
||
if chunk['event'] == 'workflow_finished':
|
||
is_final = True
|
||
if chunk['data']['error']:
|
||
raise errors.DifyAPIError(chunk['data']['error'])
|
||
|
||
if chunk['event'] == 'text_chunk':
|
||
messsage_idx += 1
|
||
if remove_think:
|
||
if '<think>' in chunk['data']['text'] and not think_start:
|
||
think_start = True
|
||
continue
|
||
if '</think>' in chunk['data']['text'] and not think_end:
|
||
import re
|
||
|
||
content = re.sub(r'^\n</think>', '', chunk['data']['text'])
|
||
workflow_contents += content
|
||
think_end = True
|
||
elif think_end:
|
||
workflow_contents += chunk['data']['text']
|
||
if think_start:
|
||
continue
|
||
|
||
else:
|
||
workflow_contents += chunk['data']['text']
|
||
|
||
if chunk['event'] == 'node_started':
|
||
if chunk['data']['node_type'] == 'start' or chunk['data']['node_type'] == 'end':
|
||
continue
|
||
messsage_idx += 1
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=None,
|
||
tool_calls=[
|
||
provider_message.ToolCall(
|
||
id=chunk['data']['node_id'],
|
||
type='function',
|
||
function=provider_message.FunctionCall(
|
||
name=chunk['data']['title'],
|
||
arguments=json.dumps({}),
|
||
),
|
||
)
|
||
],
|
||
)
|
||
|
||
yield msg
|
||
|
||
if messsage_idx % 8 == 0 or is_final:
|
||
final_content = workflow_contents if workflow_contents.strip() else ''
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=final_content,
|
||
is_final=is_final,
|
||
)
|
||
# Attach form data to the final chunk for the adapter
|
||
if is_final and pending_form_data:
|
||
msg._form_data = pending_form_data
|
||
pending_form_data = None
|
||
yield msg
|
||
|
||
# If the stream ended after workflow_paused without a
|
||
# workflow_finished event, yield a final chunk so the adapter
|
||
# can update the card and add buttons.
|
||
if human_input_yielded and not is_final:
|
||
msg = provider_message.MessageChunk(
|
||
role='assistant',
|
||
content=workflow_contents or display_text,
|
||
is_final=True,
|
||
)
|
||
msg._form_data = pending_form_data
|
||
yield msg
|
||
|
||
async def run(self, query: pipeline_query.Query) -> typing.AsyncGenerator[provider_message.Message, None]:
|
||
"""运行请求"""
|
||
if await query.adapter.is_stream_output_supported():
|
||
msg_idx = 0
|
||
if self.pipeline_config['ai']['dify-service-api']['app-type'] in ('chat', 'chatflow'):
|
||
async for msg in self._chat_messages_chunk(query):
|
||
msg_idx += 1
|
||
msg.msg_sequence = msg_idx
|
||
yield msg
|
||
elif self.pipeline_config['ai']['dify-service-api']['app-type'] == 'agent':
|
||
async for msg in self._agent_chat_messages_chunk(query):
|
||
msg_idx += 1
|
||
msg.msg_sequence = msg_idx
|
||
yield msg
|
||
elif self.pipeline_config['ai']['dify-service-api']['app-type'] == 'workflow':
|
||
async for msg in self._workflow_messages_chunk(query):
|
||
msg_idx += 1
|
||
msg.msg_sequence = msg_idx
|
||
yield msg
|
||
else:
|
||
raise errors.DifyAPIError(
|
||
f'不支持的 Dify 应用类型: {self.pipeline_config["ai"]["dify-service-api"]["app-type"]}'
|
||
)
|
||
else:
|
||
if self.pipeline_config['ai']['dify-service-api']['app-type'] in ('chat', 'chatflow'):
|
||
async for msg in self._chat_messages(query):
|
||
yield msg
|
||
elif self.pipeline_config['ai']['dify-service-api']['app-type'] == 'agent':
|
||
async for msg in self._agent_chat_messages(query):
|
||
yield msg
|
||
elif self.pipeline_config['ai']['dify-service-api']['app-type'] == 'workflow':
|
||
async for msg in self._workflow_messages(query):
|
||
yield msg
|
||
else:
|
||
raise errors.DifyAPIError(
|
||
f'不支持的 Dify 应用类型: {self.pipeline_config["ai"]["dify-service-api"]["app-type"]}'
|
||
)
|