Kris
b018ad25da
实现了完整的Nextcloud Talk聊天适配器，包含以下核心功能： 1. 基础客户端通信与认证 2. 会话路由与聊天类型解析 3. 消息分块与格式转换 4. 用户配对与权限校验 5. 轮询式消息监听 6. 配置验证与诊断工具 7. 安全策略与去重缓存 8. 指标统计与状态快照 新增配套的配对用户缓存文件与模块导出入口。
104 lines
3.5 KiB
Python
104 lines
3.5 KiB
Python
from __future__ import annotations
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import re
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import uuid
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from typing import Any
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from yuxi.channels.models import ChannelResponse, MessageType
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from .chunking import chunk_text
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def convert_markdown_tables(text: str) -> str:
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lines = text.split("\n")
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result: list[str] = []
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i = 0
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while i < len(lines):
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line = lines[i]
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if re.match(r"^\|.*\|$", line.strip()) and i + 1 < len(lines):
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next_line = lines[i + 1]
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if re.match(r"^\|[\s\-:|]+\|$", next_line.strip()):
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header = _parse_table_row(line)
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rows: list[list[str]] = []
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i += 2
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while i < len(lines) and re.match(r"^\|.*\|$", lines[i].strip()):
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rows.append(_parse_table_row(lines[i]))
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i += 1
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result.extend(_table_to_text(header, rows))
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continue
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result.append(line)
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i += 1
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return "\n".join(result)
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def _parse_table_row(line: str) -> list[str]:
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return [cell.strip() for cell in line.strip().strip("|").split("|")]
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def _table_to_text(header: list[str], rows: list[list[str]]) -> list[str]:
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cols = len(header)
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col_widths = [len(h) for h in header]
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for row in rows:
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for c in range(min(cols, len(row))):
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col_widths[c] = max(col_widths[c], len(row[c]))
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header_line = " | ".join(h.ljust(col_widths[i]) for i, h in enumerate(header))
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sep_line = "-+-".join("-" * col_widths[i] for i in range(cols))
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result = [header_line, sep_line]
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for row in rows:
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result.append(" | ".join((row[c] if c < len(row) else "").ljust(col_widths[c]) for c in range(cols)))
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return result
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def format_outbound(
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response: ChannelResponse,
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bot_display_name: str,
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text_chunk_limit: int = 4000,
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markdown_config: dict[str, Any] | None = None,
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response_prefix: str = "",
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) -> list[dict[str, Any]]:
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markdown_config = markdown_config or {}
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table_mode = markdown_config.get("table_mode", markdown_config.get("tableMode", "auto"))
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content = response.content.strip()
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if not content:
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raise ValueError("Empty message content, cannot send")
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if response_prefix:
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content = f"{response_prefix} {content}"
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if table_mode == "always":
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content = convert_markdown_tables(content)
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elif table_mode == "auto":
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content = convert_markdown_tables(content)
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chunks = chunk_text(content, limit=text_chunk_limit)
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results: list[dict[str, Any]] = []
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for idx, chunk in enumerate(chunks):
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payload: dict[str, Any] = {
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"token": response.identity.channel_chat_id,
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"message": chunk,
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"actorDisplayName": bot_display_name,
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}
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if response.reply_to_message_id:
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payload["replyTo"] = response.reply_to_message_id
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ref_id = response.metadata.get("reference_id")
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if ref_id:
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payload["referenceId"] = f"{ref_id}-{idx}" if len(chunks) > 1 else ref_id
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else:
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payload["referenceId"] = f"fp-{uuid.uuid4().hex}-{idx}" if len(chunks) > 1 else f"fp-{uuid.uuid4().hex}"
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if response.message_type in (MessageType.IMAGE, MessageType.FILE, MessageType.AUDIO, MessageType.VIDEO):
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raise ValueError("Media messages must use send_media(), not send()")
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results.append(payload)
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return results
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def format_outbound_single(response: ChannelResponse, bot_display_name: str) -> dict[str, Any]:
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payloads = format_outbound(response, bot_display_name)
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return payloads[0] if payloads else {"token": response.identity.channel_chat_id, "message": ""}
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