chore: remove legacy coze and dashscope provider
This commit is contained in:
Soulter
@@ -1,650 +0,0 @@
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import base64
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import hashlib
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import json
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import os
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from collections.abc import AsyncGenerator
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import astrbot.core.message.components as Comp
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from astrbot import logger
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from astrbot.api.provider import Provider
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from astrbot.core.message.message_event_result import MessageChain
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from astrbot.core.provider.entities import LLMResponse
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from ..register import register_provider_adapter
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from .coze_api_client import CozeAPIClient
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@register_provider_adapter("coze", "Coze (扣子) 智能体适配器")
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class ProviderCoze(Provider):
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def __init__(
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self,
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provider_config,
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provider_settings,
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) -> None:
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super().__init__(
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provider_config,
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provider_settings,
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)
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self.api_key = provider_config.get("coze_api_key", "")
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if not self.api_key:
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raise Exception("Coze API Key 不能为空。")
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self.bot_id = provider_config.get("bot_id", "")
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if not self.bot_id:
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raise Exception("Coze Bot ID 不能为空。")
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self.api_base: str = provider_config.get("coze_api_base", "https://api.coze.cn")
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if not isinstance(self.api_base, str) or not self.api_base.startswith(
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("http://", "https://"),
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):
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raise Exception(
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"Coze API Base URL 格式不正确，必须以 http:// 或 https:// 开头。",
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)
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self.timeout = provider_config.get("timeout", 120)
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if isinstance(self.timeout, str):
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self.timeout = int(self.timeout)
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self.auto_save_history = provider_config.get("auto_save_history", True)
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self.conversation_ids: dict[str, str] = {}
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self.file_id_cache: dict[str, dict[str, str]] = {}
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# 创建 API 客户端
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self.api_client = CozeAPIClient(api_key=self.api_key, api_base=self.api_base)
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def _generate_cache_key(self, data: str, is_base64: bool = False) -> str:
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"""生成统一的缓存键
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Args:
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data: 图片数据或路径
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is_base64: 是否是 base64 数据
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Returns:
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str: 缓存键
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"""
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try:
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if is_base64 and data.startswith("data:image/"):
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try:
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header, encoded = data.split(",", 1)
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image_bytes = base64.b64decode(encoded)
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cache_key = hashlib.md5(image_bytes).hexdigest()
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return cache_key
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except Exception:
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cache_key = hashlib.md5(encoded.encode("utf-8")).hexdigest()
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return cache_key
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elif data.startswith(("http://", "https://")):
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# URL图片，使用URL作为缓存键
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cache_key = hashlib.md5(data.encode("utf-8")).hexdigest()
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return cache_key
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else:
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clean_path = (
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data.split("_")[0]
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if "_" in data and len(data.split("_")) >= 3
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else data
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)
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if os.path.exists(clean_path):
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with open(clean_path, "rb") as f:
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file_content = f.read()
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cache_key = hashlib.md5(file_content).hexdigest()
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return cache_key
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cache_key = hashlib.md5(clean_path.encode("utf-8")).hexdigest()
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return cache_key
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except Exception as e:
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cache_key = hashlib.md5(data.encode("utf-8")).hexdigest()
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logger.debug(f"[Coze] 异常文件缓存键: {cache_key}, error={e}")
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return cache_key
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async def _upload_file(
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self,
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file_data: bytes,
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session_id: str | None = None,
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cache_key: str | None = None,
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) -> str:
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"""上传文件到 Coze 并返回 file_id"""
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# 使用 API 客户端上传文件
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file_id = await self.api_client.upload_file(file_data)
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# 缓存 file_id
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if session_id and cache_key:
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if session_id not in self.file_id_cache:
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self.file_id_cache[session_id] = {}
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self.file_id_cache[session_id][cache_key] = file_id
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logger.debug(f"[Coze] 图片上传成功并缓存，file_id: {file_id}")
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return file_id
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async def _download_and_upload_image(
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self,
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image_url: str,
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session_id: str | None = None,
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) -> str:
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"""下载图片并上传到 Coze，返回 file_id"""
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# 计算哈希实现缓存
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cache_key = self._generate_cache_key(image_url) if session_id else None
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if session_id and cache_key:
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if session_id not in self.file_id_cache:
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self.file_id_cache[session_id] = {}
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if cache_key in self.file_id_cache[session_id]:
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file_id = self.file_id_cache[session_id][cache_key]
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return file_id
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try:
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image_data = await self.api_client.download_image(image_url)
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file_id = await self._upload_file(image_data, session_id, cache_key)
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if session_id and cache_key:
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self.file_id_cache[session_id][cache_key] = file_id
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return file_id
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except Exception as e:
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logger.error(f"处理图片失败 {image_url}: {e!s}")
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raise Exception(f"处理图片失败: {e!s}")
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async def _process_context_images(
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self,
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content: str | list,
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session_id: str,
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) -> str:
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"""处理上下文中的图片内容，将 base64 图片上传并替换为 file_id"""
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try:
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if isinstance(content, str):
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return content
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processed_content = []
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if session_id not in self.file_id_cache:
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self.file_id_cache[session_id] = {}
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for item in content:
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if not isinstance(item, dict):
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processed_content.append(item)
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continue
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if item.get("type") == "text":
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processed_content.append(item)
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elif item.get("type") == "image_url":
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# 处理图片逻辑
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if "file_id" in item:
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# 已经有 file_id
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logger.debug(f"[Coze] 图片已有file_id: {item['file_id']}")
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processed_content.append(item)
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else:
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# 获取图片数据
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image_data = ""
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if "image_url" in item and isinstance(item["image_url"], dict):
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image_data = item["image_url"].get("url", "")
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elif "data" in item:
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image_data = item.get("data", "")
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elif "url" in item:
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image_data = item.get("url", "")
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if not image_data:
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continue
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# 计算哈希用于缓存
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cache_key = self._generate_cache_key(
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image_data,
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is_base64=image_data.startswith("data:image/"),
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)
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# 检查缓存
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if cache_key in self.file_id_cache[session_id]:
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file_id = self.file_id_cache[session_id][cache_key]
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processed_content.append(
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{"type": "image", "file_id": file_id},
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)
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else:
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# 上传图片并缓存
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if image_data.startswith("data:image/"):
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# base64 处理
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_, encoded = image_data.split(",", 1)
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image_bytes = base64.b64decode(encoded)
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file_id = await self._upload_file(
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image_bytes,
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session_id,
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cache_key,
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)
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elif image_data.startswith(("http://", "https://")):
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# URL 图片
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file_id = await self._download_and_upload_image(
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image_data,
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session_id,
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)
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# 为URL图片也添加缓存
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self.file_id_cache[session_id][cache_key] = file_id
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elif os.path.exists(image_data):
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# 本地文件
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with open(image_data, "rb") as f:
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image_bytes = f.read()
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file_id = await self._upload_file(
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image_bytes,
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session_id,
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cache_key,
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)
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else:
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logger.warning(
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f"无法处理的图片格式: {image_data[:50]}...",
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)
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continue
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processed_content.append(
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{"type": "image", "file_id": file_id},
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)
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result = json.dumps(processed_content, ensure_ascii=False)
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return result
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except Exception as e:
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logger.error(f"处理上下文图片失败: {e!s}")
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if isinstance(content, str):
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return content
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return json.dumps(content, ensure_ascii=False)
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async def text_chat(
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self,
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prompt: str,
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session_id=None,
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image_urls=None,
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func_tool=None,
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contexts=None,
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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) -> LLMResponse:
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"""文本对话, 内部使用流式接口实现非流式
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Args:
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prompt (str): 用户提示词
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session_id (str): 会话ID
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image_urls (List[str]): 图片URL列表
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func_tool (FuncCall): 函数调用工具(不支持)
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contexts (List): 上下文列表
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system_prompt (str): 系统提示语
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tool_calls_result (ToolCallsResult | List[ToolCallsResult]): 工具调用结果(不支持)
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model (str): 模型名称(不支持)
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Returns:
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LLMResponse: LLM响应对象
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"""
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accumulated_content = ""
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final_response = None
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async for llm_response in self.text_chat_stream(
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prompt=prompt,
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session_id=session_id,
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image_urls=image_urls,
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func_tool=func_tool,
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contexts=contexts,
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system_prompt=system_prompt,
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tool_calls_result=tool_calls_result,
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model=model,
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**kwargs,
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):
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if llm_response.is_chunk:
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if llm_response.completion_text:
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accumulated_content += llm_response.completion_text
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else:
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final_response = llm_response
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if final_response:
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return final_response
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if accumulated_content:
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chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
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return LLMResponse(role="assistant", result_chain=chain)
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return LLMResponse(role="assistant", completion_text="")
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async def text_chat_stream(
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self,
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prompt: str,
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session_id=None,
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image_urls=None,
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func_tool=None,
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contexts=None,
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system_prompt=None,
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tool_calls_result=None,
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model=None,
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**kwargs,
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) -> AsyncGenerator[LLMResponse, None]:
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"""流式对话接口"""
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# 用户ID参数(参考文档, 可以自定义)
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user_id = session_id or kwargs.get("user", "default_user")
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# 获取或创建会话ID
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conversation_id = self.conversation_ids.get(user_id)
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# 构建消息
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additional_messages = []
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if system_prompt:
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if not self.auto_save_history or not conversation_id:
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additional_messages.append(
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{
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"role": "system",
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"content": system_prompt,
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"content_type": "text",
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},
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)
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contexts = self._ensure_message_to_dicts(contexts)
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if not self.auto_save_history and contexts:
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# 如果关闭了自动保存历史，传入上下文
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for ctx in contexts:
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if isinstance(ctx, dict) and "role" in ctx and "content" in ctx:
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content = ctx["content"]
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content_type = ctx.get("content_type", "text")
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# 处理可能包含图片的上下文
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if (
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content_type == "object_string"
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or (isinstance(content, str) and content.startswith("["))
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or (
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isinstance(content, list)
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and any(
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isinstance(item, dict)
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and item.get("type") == "image_url"
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for item in content
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)
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)
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):
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processed_content = await self._process_context_images(
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content,
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user_id,
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)
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additional_messages.append(
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{
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"role": ctx["role"],
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"content": processed_content,
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"content_type": "object_string",
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},
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)
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else:
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# 纯文本
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additional_messages.append(
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{
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"role": ctx["role"],
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"content": (
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content
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if isinstance(content, str)
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else json.dumps(content, ensure_ascii=False)
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),
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"content_type": "text",
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},
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)
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else:
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logger.info(f"[Coze] 跳过格式不正确的上下文: {ctx}")
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if prompt or image_urls:
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if image_urls:
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# 多模态
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object_string_content = []
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if prompt:
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object_string_content.append({"type": "text", "text": prompt})
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for url in image_urls:
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try:
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if url.startswith(("http://", "https://")):
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# 网络图片
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file_id = await self._download_and_upload_image(
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url,
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user_id,
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)
|
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else:
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# 本地文件或 base64
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if url.startswith("data:image/"):
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# base64
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_, encoded = url.split(",", 1)
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image_data = base64.b64decode(encoded)
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cache_key = self._generate_cache_key(
|
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url,
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is_base64=True,
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)
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file_id = await self._upload_file(
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image_data,
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user_id,
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cache_key,
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)
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# 本地文件
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elif os.path.exists(url):
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with open(url, "rb") as f:
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image_data = f.read()
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# 用文件路径和修改时间来缓存
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file_stat = os.stat(url)
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cache_key = self._generate_cache_key(
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f"{url}_{file_stat.st_mtime}_{file_stat.st_size}",
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is_base64=False,
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)
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file_id = await self._upload_file(
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image_data,
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user_id,
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cache_key,
|
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)
|
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else:
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logger.warning(f"图片文件不存在: {url}")
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continue
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|
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object_string_content.append(
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{
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"type": "image",
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"file_id": file_id,
|
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},
|
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)
|
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except Exception as e:
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logger.error(f"处理图片失败 {url}: {e!s}")
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continue
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if object_string_content:
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content = json.dumps(object_string_content, ensure_ascii=False)
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additional_messages.append(
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{
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"role": "user",
|
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"content": content,
|
||||
"content_type": "object_string",
|
||||
},
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)
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# 纯文本
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elif prompt:
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additional_messages.append(
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{
|
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"role": "user",
|
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"content": prompt,
|
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"content_type": "text",
|
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},
|
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)
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try:
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accumulated_content = ""
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message_started = False
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||||
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async for chunk in self.api_client.chat_messages(
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bot_id=self.bot_id,
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user_id=user_id,
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additional_messages=additional_messages,
|
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conversation_id=conversation_id,
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auto_save_history=self.auto_save_history,
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stream=True,
|
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timeout=self.timeout,
|
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):
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||||
event_type = chunk.get("event")
|
||||
data = chunk.get("data", {})
|
||||
|
||||
if event_type == "conversation.chat.created":
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||||
if isinstance(data, dict) and "conversation_id" in data:
|
||||
self.conversation_ids[user_id] = data["conversation_id"]
|
||||
|
||||
elif event_type == "conversation.message.delta":
|
||||
if isinstance(data, dict):
|
||||
content = data.get("content", "")
|
||||
if not content and "delta" in data:
|
||||
content = data["delta"].get("content", "")
|
||||
if not content and "text" in data:
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||||
content = data.get("text", "")
|
||||
|
||||
if content:
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||||
message_started = True
|
||||
accumulated_content += content
|
||||
yield LLMResponse(
|
||||
role="assistant",
|
||||
completion_text=content,
|
||||
is_chunk=True,
|
||||
)
|
||||
|
||||
elif event_type == "conversation.message.completed":
|
||||
if isinstance(data, dict):
|
||||
msg_type = data.get("type")
|
||||
if msg_type == "answer" and data.get("role") == "assistant":
|
||||
final_content = data.get("content", "")
|
||||
if not accumulated_content and final_content:
|
||||
chain = MessageChain(chain=[Comp.Plain(final_content)])
|
||||
yield LLMResponse(
|
||||
role="assistant",
|
||||
result_chain=chain,
|
||||
is_chunk=False,
|
||||
)
|
||||
|
||||
elif event_type == "conversation.chat.completed":
|
||||
if accumulated_content:
|
||||
chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
|
||||
yield LLMResponse(
|
||||
role="assistant",
|
||||
result_chain=chain,
|
||||
is_chunk=False,
|
||||
)
|
||||
break
|
||||
|
||||
elif event_type == "done":
|
||||
break
|
||||
|
||||
elif event_type == "error":
|
||||
error_msg = (
|
||||
data.get("message", "未知错误")
|
||||
if isinstance(data, dict)
|
||||
else str(data)
|
||||
)
|
||||
logger.error(f"Coze 流式响应错误: {error_msg}")
|
||||
yield LLMResponse(
|
||||
role="err",
|
||||
completion_text=f"Coze 错误: {error_msg}",
|
||||
is_chunk=False,
|
||||
)
|
||||
break
|
||||
|
||||
if not message_started and not accumulated_content:
|
||||
yield LLMResponse(
|
||||
role="assistant",
|
||||
completion_text="LLM 未响应任何内容。",
|
||||
is_chunk=False,
|
||||
)
|
||||
elif message_started and accumulated_content:
|
||||
chain = MessageChain(chain=[Comp.Plain(accumulated_content)])
|
||||
yield LLMResponse(
|
||||
role="assistant",
|
||||
result_chain=chain,
|
||||
is_chunk=False,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Coze 流式请求失败: {e!s}")
|
||||
yield LLMResponse(
|
||||
role="err",
|
||||
completion_text=f"Coze 流式请求失败: {e!s}",
|
||||
is_chunk=False,
|
||||
)
|
||||
|
||||
async def forget(self, session_id: str):
|
||||
"""清空指定会话的上下文"""
|
||||
user_id = session_id
|
||||
conversation_id = self.conversation_ids.get(user_id)
|
||||
|
||||
if user_id in self.file_id_cache:
|
||||
self.file_id_cache.pop(user_id, None)
|
||||
|
||||
if not conversation_id:
|
||||
return True
|
||||
|
||||
try:
|
||||
response = await self.api_client.clear_context(conversation_id)
|
||||
|
||||
if "code" in response and response["code"] == 0:
|
||||
self.conversation_ids.pop(user_id, None)
|
||||
return True
|
||||
logger.warning(f"清空 Coze 会话上下文失败: {response}")
|
||||
return False
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"清空 Coze 会话失败: {e!s}")
|
||||
return False
|
||||
|
||||
async def get_current_key(self):
|
||||
"""获取当前API Key"""
|
||||
return self.api_key
|
||||
|
||||
async def set_key(self, key: str):
|
||||
"""设置新的API Key"""
|
||||
raise NotImplementedError("Coze 适配器不支持设置 API Key。")
|
||||
|
||||
async def get_models(self):
|
||||
"""获取可用模型列表"""
|
||||
return [f"bot_{self.bot_id}"]
|
||||
|
||||
def get_model(self):
|
||||
"""获取当前模型"""
|
||||
return f"bot_{self.bot_id}"
|
||||
|
||||
def set_model(self, model: str):
|
||||
"""设置模型（在Coze中是Bot ID）"""
|
||||
if model.startswith("bot_"):
|
||||
self.bot_id = model[4:]
|
||||
else:
|
||||
self.bot_id = model
|
||||
|
||||
async def get_human_readable_context(
|
||||
self,
|
||||
session_id: str,
|
||||
page: int = 1,
|
||||
page_size: int = 10,
|
||||
):
|
||||
"""获取人类可读的上下文历史"""
|
||||
user_id = session_id
|
||||
conversation_id = self.conversation_ids.get(user_id)
|
||||
|
||||
if not conversation_id:
|
||||
return []
|
||||
|
||||
try:
|
||||
data = await self.api_client.get_message_list(
|
||||
conversation_id=conversation_id,
|
||||
order="desc",
|
||||
limit=page_size,
|
||||
offset=(page - 1) * page_size,
|
||||
)
|
||||
|
||||
if data.get("code") != 0:
|
||||
logger.warning(f"获取 Coze 消息历史失败: {data}")
|
||||
return []
|
||||
|
||||
messages = data.get("data", {}).get("messages", [])
|
||||
|
||||
readable_history = []
|
||||
for msg in messages:
|
||||
role = msg.get("role", "unknown")
|
||||
content = msg.get("content", "")
|
||||
msg_type = msg.get("type", "")
|
||||
|
||||
if role == "user":
|
||||
readable_history.append(f"用户: {content}")
|
||||
elif role == "assistant" and msg_type == "answer":
|
||||
readable_history.append(f"助手: {content}")
|
||||
|
||||
return readable_history
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"获取 Coze 消息历史失败: {e!s}")
|
||||
return []
|
||||
|
||||
async def terminate(self):
|
||||
"""清理资源"""
|
||||
await self.api_client.close()
|
||||
@@ -1,207 +0,0 @@
|
||||
import asyncio
|
||||
import functools
|
||||
import re
|
||||
|
||||
from dashscope import Application
|
||||
from dashscope.app.application_response import ApplicationResponse
|
||||
|
||||
from astrbot.core import logger, sp
|
||||
from astrbot.core.message.message_event_result import MessageChain
|
||||
|
||||
from .. import Provider
|
||||
from ..entities import LLMResponse
|
||||
from ..register import register_provider_adapter
|
||||
from .openai_source import ProviderOpenAIOfficial
|
||||
|
||||
|
||||
@register_provider_adapter("dashscope", "Dashscope APP 适配器。")
|
||||
class ProviderDashscope(ProviderOpenAIOfficial):
|
||||
def __init__(
|
||||
self,
|
||||
provider_config: dict,
|
||||
provider_settings: dict,
|
||||
) -> None:
|
||||
Provider.__init__(
|
||||
self,
|
||||
provider_config,
|
||||
provider_settings,
|
||||
)
|
||||
self.api_key = provider_config.get("dashscope_api_key", "")
|
||||
if not self.api_key:
|
||||
raise Exception("阿里云百炼 API Key 不能为空。")
|
||||
self.app_id = provider_config.get("dashscope_app_id", "")
|
||||
if not self.app_id:
|
||||
raise Exception("阿里云百炼 APP ID 不能为空。")
|
||||
self.dashscope_app_type = provider_config.get("dashscope_app_type", "")
|
||||
if not self.dashscope_app_type:
|
||||
raise Exception("阿里云百炼 APP 类型不能为空。")
|
||||
self.model_name = "dashscope"
|
||||
self.variables: dict = provider_config.get("variables", {})
|
||||
self.rag_options: dict = provider_config.get("rag_options", {})
|
||||
self.output_reference = self.rag_options.get("output_reference", False)
|
||||
self.rag_options = self.rag_options.copy()
|
||||
self.rag_options.pop("output_reference", None)
|
||||
|
||||
self.timeout = provider_config.get("timeout", 120)
|
||||
if isinstance(self.timeout, str):
|
||||
self.timeout = int(self.timeout)
|
||||
|
||||
def has_rag_options(self):
|
||||
"""判断是否有 RAG 选项
|
||||
|
||||
Returns:
|
||||
bool: 是否有 RAG 选项
|
||||
|
||||
"""
|
||||
if self.rag_options and (
|
||||
len(self.rag_options.get("pipeline_ids", [])) > 0
|
||||
or len(self.rag_options.get("file_ids", [])) > 0
|
||||
):
|
||||
return True
|
||||
return False
|
||||
|
||||
async def text_chat(
|
||||
self,
|
||||
prompt: str,
|
||||
session_id=None,
|
||||
image_urls=None,
|
||||
func_tool=None,
|
||||
contexts=None,
|
||||
system_prompt=None,
|
||||
model=None,
|
||||
**kwargs,
|
||||
) -> LLMResponse:
|
||||
if image_urls is None:
|
||||
image_urls = []
|
||||
if contexts is None:
|
||||
contexts = []
|
||||
# 获得会话变量
|
||||
payload_vars = self.variables.copy()
|
||||
# 动态变量
|
||||
session_var = await sp.session_get(session_id, "session_variables", default={})
|
||||
payload_vars.update(session_var)
|
||||
|
||||
if (
|
||||
self.dashscope_app_type in ["agent", "dialog-workflow"]
|
||||
and not self.has_rag_options()
|
||||
):
|
||||
# 支持多轮对话的
|
||||
new_record = {"role": "user", "content": prompt}
|
||||
if image_urls:
|
||||
logger.warning("阿里云百炼暂不支持图片输入，将自动忽略图片内容。")
|
||||
contexts_no_img = await self._remove_image_from_context(contexts)
|
||||
context_query = [*contexts_no_img, new_record]
|
||||
if system_prompt:
|
||||
context_query.insert(0, {"role": "system", "content": system_prompt})
|
||||
for part in context_query:
|
||||
if "_no_save" in part:
|
||||
del part["_no_save"]
|
||||
# 调用阿里云百炼 API
|
||||
payload = {
|
||||
"app_id": self.app_id,
|
||||
"api_key": self.api_key,
|
||||
"messages": context_query,
|
||||
"biz_params": payload_vars or None,
|
||||
}
|
||||
partial = functools.partial(
|
||||
Application.call,
|
||||
**payload,
|
||||
)
|
||||
response = await asyncio.get_event_loop().run_in_executor(None, partial)
|
||||
else:
|
||||
# 不支持多轮对话的
|
||||
# 调用阿里云百炼 API
|
||||
payload = {
|
||||
"app_id": self.app_id,
|
||||
"prompt": prompt,
|
||||
"api_key": self.api_key,
|
||||
"biz_params": payload_vars or None,
|
||||
}
|
||||
if self.rag_options:
|
||||
payload["rag_options"] = self.rag_options
|
||||
partial = functools.partial(
|
||||
Application.call,
|
||||
**payload,
|
||||
)
|
||||
response = await asyncio.get_event_loop().run_in_executor(None, partial)
|
||||
|
||||
assert isinstance(response, ApplicationResponse)
|
||||
|
||||
logger.debug(f"dashscope resp: {response}")
|
||||
|
||||
if response.status_code != 200:
|
||||
logger.error(
|
||||
f"阿里云百炼请求失败: request_id={response.request_id}, code={response.status_code}, message={response.message}, 请参考文档：https://help.aliyun.com/zh/model-studio/developer-reference/error-code",
|
||||
)
|
||||
return LLMResponse(
|
||||
role="err",
|
||||
result_chain=MessageChain().message(
|
||||
f"阿里云百炼请求失败: message={response.message} code={response.status_code}",
|
||||
),
|
||||
)
|
||||
|
||||
output_text = response.output.get("text", "") or ""
|
||||
# RAG 引用脚标格式化
|
||||
output_text = re.sub(r"<ref>\[(\d+)\]</ref>", r"[\1]", output_text)
|
||||
if self.output_reference and response.output.get("doc_references", None):
|
||||
ref_parts = []
|
||||
for ref in response.output.get("doc_references", []) or []:
|
||||
ref_title = (
|
||||
ref.get("title", "")
|
||||
if ref.get("title")
|
||||
else ref.get("doc_name", "")
|
||||
)
|
||||
ref_parts.append(f"{ref['index_id']}. {ref_title}\n")
|
||||
ref_str = "".join(ref_parts)
|
||||
output_text += f"\n\n回答来源:\n{ref_str}"
|
||||
|
||||
llm_response = LLMResponse("assistant")
|
||||
llm_response.result_chain = MessageChain().message(output_text)
|
||||
|
||||
return llm_response
|
||||
|
||||
async def text_chat_stream(
|
||||
self,
|
||||
prompt,
|
||||
session_id=None,
|
||||
image_urls=...,
|
||||
func_tool=None,
|
||||
contexts=...,
|
||||
system_prompt=None,
|
||||
tool_calls_result=None,
|
||||
model=None,
|
||||
**kwargs,
|
||||
):
|
||||
# raise NotImplementedError("This method is not implemented yet.")
|
||||
# 调用 text_chat 模拟流式
|
||||
llm_response = await self.text_chat(
|
||||
prompt=prompt,
|
||||
session_id=session_id,
|
||||
image_urls=image_urls,
|
||||
func_tool=func_tool,
|
||||
contexts=contexts,
|
||||
system_prompt=system_prompt,
|
||||
tool_calls_result=tool_calls_result,
|
||||
)
|
||||
llm_response.is_chunk = True
|
||||
yield llm_response
|
||||
llm_response.is_chunk = False
|
||||
yield llm_response
|
||||
|
||||
async def forget(self, session_id):
|
||||
return True
|
||||
|
||||
async def get_current_key(self):
|
||||
return self.api_key
|
||||
|
||||
async def set_key(self, key):
|
||||
raise Exception("阿里云百炼 适配器不支持设置 API Key。")
|
||||
|
||||
async def get_models(self):
|
||||
return [self.get_model()]
|
||||
|
||||
async def get_human_readable_context(self, session_id, page, page_size):
|
||||
raise Exception("暂不支持获得 阿里云百炼 的历史消息记录。")
|
||||
|
||||
async def terminate(self):
|
||||
pass
|
||||
Reference in New Issue
Block a user