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重新划分目录 (#214)
* 🦄 refactor(web): 修改zhikongtaiweb到web * 🦄 refactor: 重写前端 路由守护尚未写完 * 🦄 refactor: 标准化路由 * update:前端重写，保留后端 * update:添加前端代码 * update:pip转成poetry启动 * update:增加mem0ai包依赖 * update:文档增加mem0ai的描述 * feat: play online mp3 (#181) Co-authored-by: 欣南科技 <huangrongzhuang@xin-nan.com> * 修改前端代码 * update:调整项目目录 * update:优化 * update:配置文件去除8002端口 * update:增加开发说明 * update:更新开发协议 --------- Co-authored-by: kalicyh <34980061+kaliCYH@users.noreply.github.com> Co-authored-by: hrz <1710360675@qq.com> Co-authored-by: freshlife001 <talent@mises.site> Co-authored-by: CGD <3030332422@qq.com>
This commit is contained in:
欣南科技
co-authored by
kalicyh
hrz
freshlife001
CGD
kalicyh
hrz
freshlife001
CGD
parent
8b151d07c2
commit
0e43748fdc
@@ -0,0 +1,8 @@
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from abc import ABC, abstractmethod
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class LLMProviderBase(ABC):
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@abstractmethod
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def response(self, session_id, dialogue):
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"""LLM response generator"""
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pass
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@@ -0,0 +1,37 @@
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from config.logger import setup_logging
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import requests
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import json
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import re
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from core.providers.llm.base import LLMProviderBase
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import os
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# official coze sdk for Python [cozepy](https://github.com/coze-dev/coze-py)
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from cozepy import COZE_CN_BASE_URL
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from cozepy import Coze, TokenAuth, Message, ChatStatus, MessageContentType, ChatEventType # noqa
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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self.personal_access_token = config.get("personal_access_token")
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self.bot_id = config.get("bot_id")
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self.user_id = config.get("user_id")
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def response(self, session_id, dialogue):
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coze_api_token = self.personal_access_token
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coze_api_base = COZE_CN_BASE_URL
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last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
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coze = Coze(auth=TokenAuth(token=coze_api_token), base_url=coze_api_base)
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for event in coze.chat.stream(
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bot_id=self.bot_id,
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user_id=self.user_id,
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additional_messages=[
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Message.build_user_question_text(last_msg["content"]),
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],
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):
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if event.event == ChatEventType.CONVERSATION_MESSAGE_DELTA:
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print(event.message.content, end="", flush=True)
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yield event.message.content
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@@ -0,0 +1,39 @@
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import json
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from config.logger import setup_logging
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import requests
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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self.api_key = config["api_key"]
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self.base_url = config.get("base_url", "https://api.dify.ai/v1").rstrip('/')
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def response(self, session_id, dialogue):
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try:
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# 取最后一条用户消息
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last_msg = next(m for m in reversed(dialogue) if m["role"] == "user")
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# 发起流式请求
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with requests.post(
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f"{self.base_url}/chat-messages",
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headers={"Authorization": f"Bearer {self.api_key}"},
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json={
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"query": last_msg["content"],
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"response_mode": "streaming",
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"user": session_id,
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"inputs": {}
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},
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stream=True
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) as r:
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for line in r.iter_lines():
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if line.startswith(b'data: '):
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event = json.loads(line[6:])
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if event.get('answer'):
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yield event['answer']
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in response generation: {e}")
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yield "【服务响应异常】"
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@@ -0,0 +1,81 @@
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from config.logger import setup_logging
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import google.generativeai as genai
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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"""初始化Gemini LLM Provider"""
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self.model_name = config.get("model_name", "gemini-1.5-pro")
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self.api_key = config.get("api_key")
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if not self.api_key or "你" in self.api_key:
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logger.bind(tag=TAG).error("你还没配置Gemini LLM的密钥，请在配置文件中配置密钥，否则无法正常工作")
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return
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try:
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# 初始化Gemini客户端
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genai.configure(api_key=self.api_key)
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self.model = genai.GenerativeModel(self.model_name)
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# 设置生成参数
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self.generation_config = {
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"temperature": 0.7,
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"top_p": 0.9,
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"top_k": 40,
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"max_output_tokens": 2048,
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}
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self.chat = None
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except Exception as e:
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logger.bind(tag=TAG).error(f"Gemini初始化失败: {e}")
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self.model = None
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def response(self, session_id, dialogue):
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"""生成Gemini对话响应"""
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if not self.model:
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yield "【Gemini服务未正确初始化】"
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return
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try:
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# 处理对话历史
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chat_history = []
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for msg in dialogue[:-1]: # 历史对话
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role = "model" if msg["role"] == "assistant" else "user"
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content = msg["content"].strip()
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if content:
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chat_history.append({
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"role": role,
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"parts": [content]
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})
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# 获取当前消息
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current_msg = dialogue[-1]["content"]
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# 创建新的聊天会话
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chat = self.model.start_chat(history=chat_history)
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# 发送消息并获取流式响应
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response = chat.send_message(
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current_msg,
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stream=True,
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generation_config=self.generation_config
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)
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# 处理流式响应
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for chunk in response:
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if hasattr(chunk, 'text') and chunk.text:
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yield chunk.text
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except Exception as e:
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error_msg = str(e)
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logger.bind(tag=TAG).error(f"Gemini响应生成错误: {error_msg}")
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# 针对不同错误返回友好提示
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if "Rate limit" in error_msg:
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yield "【Gemini服务请求太频繁,请稍后再试】"
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elif "Invalid API key" in error_msg:
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yield "【Gemini API key无效】"
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else:
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yield f"【Gemini服务响应异常: {error_msg}】"
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@@ -0,0 +1,62 @@
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import requests
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from requests.exceptions import RequestException
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from config.logger import setup_logging
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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self.agent_id = config.get("agent_id") # 对应 agent_id
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self.api_key = config.get("api_key")
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self.base_url = config.get("base_url", config.get("url")) # 默认使用 base_url
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self.api_url = f"{self.base_url}/api/conversation/process" # 拼接完整的 API URL
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def response(self, session_id, dialogue):
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print(dialogue)
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try:
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# home assistant语音助手自带意图，无需使用xiaozhi ai自带的，只需要把用户说的话传递给home assistant即可
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# 提取最后一个 role 为 'user' 的 content
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input_text = None
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if isinstance(dialogue, list): # 确保 dialogue 是一个列表
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# 逆序遍历，找到最后一个 role 为 'user' 的消息
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for message in reversed(dialogue):
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if message.get("role") == "user": # 找到 role 为 'user' 的消息
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input_text = message.get("content", "")
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break # 找到后立即退出循环
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# 构造请求数据
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payload = {
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"text": input_text,
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"agent_id": self.agent_id,
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"conversation_id": session_id # 使用 session_id 作为 conversation_id
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}
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# 设置请求头
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headers = {
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"Authorization": f"Bearer {self.api_key}",
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"Content-Type": "application/json"
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}
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# 发起 POST 请求
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response = requests.post(self.api_url, json=payload, headers=headers)
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# 检查请求是否成功
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response.raise_for_status()
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# 解析返回数据
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data = response.json()
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speech = data.get("response", {}).get("speech", {}).get("plain", {}).get("speech", "")
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# 返回生成的内容
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if speech:
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yield speech
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else:
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logger.bind(tag=TAG).warning("API 返回数据中没有 speech 内容")
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except RequestException as e:
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logger.bind(tag=TAG).error(f"HTTP 请求错误: {e}")
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except Exception as e:
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logger.bind(tag=TAG).error(f"生成响应时出错: {e}")
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@@ -0,0 +1,46 @@
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from config.logger import setup_logging
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import requests, json
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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self.model_name = config.get("model_name")
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self.base_url = config.get("base_url", "http://localhost:11434")
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def response(self, session_id, dialogue):
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try:
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# Convert dialogue format to Ollama format
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prompt = ""
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for msg in dialogue:
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if msg["role"] == "system":
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prompt += f"System: {msg['content']}\n"
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elif msg["role"] == "user":
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prompt += f"User: {msg['content']}\n"
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elif msg["role"] == "assistant":
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prompt += f"Assistant: {msg['content']}\n"
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# Make request to Ollama API
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response = requests.post(
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f"{self.base_url}/api/generate",
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json={
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"model": self.model_name,
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"prompt": prompt,
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"stream": True
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},
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stream=True
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)
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for line in response.iter_lines():
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if line:
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json_response = json.loads(line)
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if "response" in json_response:
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yield json_response["response"]
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in Ollama response generation: {e}")
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yield "【Ollama服务响应异常】"
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@@ -0,0 +1,49 @@
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from config.logger import setup_logging
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import openai
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from core.providers.llm.base import LLMProviderBase
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TAG = __name__
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logger = setup_logging()
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class LLMProvider(LLMProviderBase):
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def __init__(self, config):
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self.model_name = config.get("model_name")
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self.api_key = config.get("api_key")
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if 'base_url' in config:
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self.base_url = config.get("base_url")
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else:
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self.base_url = config.get("url")
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if "你" in self.api_key:
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logger.bind(tag=TAG).error("你还没配置LLM的密钥，请在配置文件中配置密钥，否则无法正常工作")
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self.client = openai.OpenAI(api_key=self.api_key, base_url=self.base_url)
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def response(self, session_id, dialogue):
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try:
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responses = self.client.chat.completions.create(
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model=self.model_name,
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messages=dialogue,
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stream=True
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)
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is_active = True
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for chunk in responses:
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try:
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# 检查是否存在有效的choice且content不为空
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delta = chunk.choices[0].delta if getattr(chunk, 'choices', None) else None
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content = delta.content if hasattr(delta, 'content') else ''
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except IndexError:
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content = ''
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if content:
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# 处理标签跨多个chunk的情况
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if '<think>' in content:
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is_active = False
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content = content.split('<think>')[0]
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if '</think>' in content:
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is_active = True
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content = content.split('</think>')[-1]
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if is_active:
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yield content
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except Exception as e:
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logger.bind(tag=TAG).error(f"Error in response generation: {e}")
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