搭建本地模型，实现聊天机器人

1、使用Ollama安装启动deepseek-r1:7b模型

2、Ollama启动访问地址：http://localhost:11434

3、python程序调用本地模型，实现简单的聊天机器人

实现代码如下app.py：

import chainlit as cl
import requests
from abc import ABC, abstractmethod
from typing import Any, Sequence

# 假设以下类型已经从 llama_index.core.base.llms.types 导入
ChatMessage = dict
ChatResponse = str
CompletionResponse = str

class BaseLLM(ABC):
    @abstractmethod
    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        pass

    @abstractmethod
    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        pass

class DeepSeekLLM(BaseLLM):
    def __init__(self, model="deepseek-r1:7b", api_base="http://localhost:11434/v1", temperature=0.7):
        self.model = model
        self.api_base = api_base
        self.temperature = temperature

    def chat(self, messages: Sequence[ChatMessage], **kwargs: Any) -> ChatResponse:
        url = f"{self.api_base}/v1/chat/completions"
        data = {
            "model": self.model,
            "messages": messages,
            "temperature": self.temperature,
            "stream": False
        }
        response = requests.post(url, json=data)
        response.raise_for_status()
        response_data = response.json()
        return response_data["choices"][0]["message"]["content"]

    def complete(self, prompt: str, **kwargs: Any) -> CompletionResponse:
        url = f"{self.api_base}/chat/completions"
        data = {
            "model": self.model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": self.temperature,
            "stream": False
        }
        response = requests.post(url, json=data)
        response.raise_for_status()
        response_data = response.json()
        return response_data["choices"][0]["message"]["content"]

@cl.on_chat_start
async def start():
    await cl.Message(author="Assistant", content="你好！我是你的聊天助手。").send()

@cl.on_message
async def handle_message(message: cl.Message):
    llm = DeepSeekLLM()
    response = llm.complete(message.content)
    await cl.Message(author="Assistant", content=response).send()

if __name__ == "__main__":
    cl.run()

注意：①安装chainlit前端框架插件：pip install chainlit

           ②页面启动命令：chainlit run app.py -w

           ③访问页面：http://localhost:8000/