Merge pull request #19 from yanlang0123/chatglm3
chatglm3接入，可实现函数调用，代码执行等功能
This commit is contained in:
Changhua
GitHub
41
chatglm/README.MD
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41
chatglm/README.MD
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# ChatGLM3集成使用说明
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* 1.需要取消配置中 chatglm 的注释， 并配置对应信息，使用ChatGLM3,启用最新版ChatGLM3根目录下openai_api.py获取api地址：
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```yaml
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# 如果要使用 chatglm，取消下面的注释并填写相关内容
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chatglm:
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key: xxx #根据需要自己做key校验
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api: http://localhost:8000/v1 # 根据自己的chatglm地址修改
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proxy: # 如果你在国内，你可能需要魔法，大概长这样：http://域名或者IP地址:端口号
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prompt: 你是智能聊天机器人，你叫小薇 # 根据需要对角色进行设定
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file_path: F:/Pictures/temp #设定生成图片和代码使用的文件夹路径
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```
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* 2.修改chatglm/tool_registry.py工具里面的一下配置，comfyUI地址或者根据需要自己配置一些工具,函数名上需要加@register_tool,函数里面需要叫'''函数描述'''，参数需要用Annotated[str,'',True]修饰，分别是类型，参数说明，是否必填，再加->加上对应的返回类型
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```python
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@register_tool
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def get_confyui_image(prompt: Annotated[str, '要生成图片的提示词,注意必须是英文', True]) -> dict:
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'''
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生成图片
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'''
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with open("func_chatglm\\base.json", "r", encoding="utf-8") as f:
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data2 = json.load(f)
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data2['prompt']['3']['inputs']['seed'] = ''.join(
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random.sample('123456789012345678901234567890', 14))
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# 模型名称
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data2['prompt']['4']['inputs']['ckpt_name'] = 'chilloutmix_NiPrunedFp32Fix.safetensors'
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data2['prompt']['6']['inputs']['text'] = prompt # 正向提示词
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# data2['prompt']['7']['inputs']['text']='' #反向提示词
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cfui = ComfyUIApi(server_address="127.0.0.1:8188") # 根据自己comfyUI地址修改
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images = cfui.get_images(data2['prompt'])
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return {'res': images[0]['image'], 'res_type': 'image', 'filename': images[0]['filename']}
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```
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* 3 使用 Code Interpreter 还需要安装 Jupyter 内核,默认名称叫chatglm3：
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```
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ipython kernel install --name chatglm3 --user
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```
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如果名称需要自定义，可以配置系统环境变量：IPYKERNEL 或者修改 chatglm/code_kernel.py
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```
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IPYKERNEL = os.environ.get('IPYKERNEL', 'chatglm3')
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```
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* 4 启动后，发送 #帮助 可以查看 模式和常用指令
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0
chatglm/__init__.py
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0
chatglm/__init__.py
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88
chatglm/base.json
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88
chatglm/base.json
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@@ -0,0 +1,88 @@
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{
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"prompt": {
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"3": {
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"inputs": {
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"seed": 1000573256060686,
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"steps": 20,
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"cfg": 8,
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"sampler_name": "euler",
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"scheduler": "normal",
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"denoise": 1,
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"model": [
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"4",
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0
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],
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"positive": [
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"6",
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0
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],
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"negative": [
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"7",
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0
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],
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"latent_image": [
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"5",
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0
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]
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},
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"class_type": "KSampler"
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},
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"4": {
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"inputs": {
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"ckpt_name": "(修复)512-inpainting-ema.safetensors"
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},
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"class_type": "CheckpointLoaderSimple"
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},
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"5": {
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"inputs": {
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"width": 512,
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"height": 512,
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"batch_size": 1
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},
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"class_type": "EmptyLatentImage"
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},
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"6": {
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"inputs": {
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"text": "beautiful scenery nature glass bottle landscape, , purple galaxy bottle,dress, ",
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"clip": [
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"4",
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1
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]
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},
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"class_type": "CLIPTextEncode"
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},
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"7": {
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"inputs": {
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"text": "text, watermark",
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"clip": [
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"4",
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1
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]
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},
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"class_type": "CLIPTextEncode"
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},
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"8": {
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"inputs": {
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"samples": [
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"3",
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0
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],
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"vae": [
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"4",
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2
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]
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},
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"class_type": "VAEDecode"
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},
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"9": {
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"inputs": {
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"filename_prefix": "ComfyUI",
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"images": [
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"8",
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0
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]
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},
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"class_type": "SaveImage"
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}
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}
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}
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201
chatglm/code_kernel.py
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201
chatglm/code_kernel.py
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import base64
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import os
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import queue
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import re
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import time
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from io import BytesIO
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from pprint import pprint
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from subprocess import PIPE
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from typing import Dict, Optional, Tuple, Union
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import jupyter_client
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from PIL import Image
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IPYKERNEL = os.environ.get('IPYKERNEL', 'chatglm3')
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class CodeKernel(object):
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def __init__(self,
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kernel_name='kernel',
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kernel_id=None,
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kernel_config_path="",
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python_path=None,
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ipython_path=None,
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init_file_path="./startup.py",
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verbose=1):
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self.kernel_name = kernel_name
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self.kernel_id = kernel_id
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self.kernel_config_path = kernel_config_path
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self.python_path = python_path
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self.ipython_path = ipython_path
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self.init_file_path = init_file_path
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self.verbose = verbose
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if python_path is None and ipython_path is None:
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env = None
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else:
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env = {"PATH": self.python_path + ":$PATH",
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"PYTHONPATH": self.python_path}
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# Initialize the backend kernel
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self.kernel_manager = jupyter_client.KernelManager(kernel_name=IPYKERNEL,
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connection_file=self.kernel_config_path,
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exec_files=[
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self.init_file_path],
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env=env)
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if self.kernel_config_path:
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self.kernel_manager.load_connection_file()
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self.kernel_manager.start_kernel(stdout=PIPE, stderr=PIPE)
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print("Backend kernel started with the configuration: {}".format(
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self.kernel_config_path))
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else:
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self.kernel_manager.start_kernel(stdout=PIPE, stderr=PIPE)
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print("Backend kernel started with the configuration: {}".format(
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self.kernel_manager.connection_file))
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if verbose:
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print(self.kernel_manager.get_connection_info())
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# Initialize the code kernel
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self.kernel = self.kernel_manager.blocking_client()
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# self.kernel.load_connection_file()
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self.kernel.start_channels()
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print("Code kernel started.")
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def execute(self, code):
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self.kernel.execute(code)
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try:
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shell_msg = self.kernel.get_shell_msg(timeout=40)
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io_msg_content = self.kernel.get_iopub_msg(timeout=40)['content']
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while True:
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msg_out = io_msg_content
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# Poll the message
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try:
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io_msg_content = self.kernel.get_iopub_msg(timeout=40)[
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'content']
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if 'execution_state' in io_msg_content and io_msg_content['execution_state'] == 'idle':
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break
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except queue.Empty:
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break
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return shell_msg, msg_out
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except Exception as e:
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print(e)
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return None
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def execute_interactive(self, code, verbose=False):
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shell_msg = self.kernel.execute_interactive(code)
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if shell_msg is queue.Empty:
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if verbose:
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print("Timeout waiting for shell message.")
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self.check_msg(shell_msg, verbose=verbose)
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return shell_msg
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def inspect(self, code, verbose=False):
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msg_id = self.kernel.inspect(code)
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shell_msg = self.kernel.get_shell_msg(timeout=30)
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if shell_msg is queue.Empty:
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if verbose:
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print("Timeout waiting for shell message.")
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self.check_msg(shell_msg, verbose=verbose)
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return shell_msg
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def get_error_msg(self, msg, verbose=False) -> Optional[str]:
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if msg['content']['status'] == 'error':
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try:
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error_msg = msg['content']['traceback']
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except BaseException:
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try:
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error_msg = msg['content']['traceback'][-1].strip()
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except BaseException:
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error_msg = "Traceback Error"
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if verbose:
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print("Error: ", error_msg)
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return error_msg
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return None
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def check_msg(self, msg, verbose=False):
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status = msg['content']['status']
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if status == 'ok':
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if verbose:
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print("Execution succeeded.")
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elif status == 'error':
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for line in msg['content']['traceback']:
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if verbose:
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print(line)
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def shutdown(self):
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# Shutdown the backend kernel
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self.kernel_manager.shutdown_kernel()
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print("Backend kernel shutdown.")
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# Shutdown the code kernel
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self.kernel.shutdown()
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print("Code kernel shutdown.")
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def restart(self):
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# Restart the backend kernel
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self.kernel_manager.restart_kernel()
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# print("Backend kernel restarted.")
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def interrupt(self):
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# Interrupt the backend kernel
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self.kernel_manager.interrupt_kernel()
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# print("Backend kernel interrupted.")
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def is_alive(self):
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return self.kernel.is_alive()
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def b64_2_img(data):
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buff = BytesIO(base64.b64decode(data))
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return Image.open(buff)
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def clean_ansi_codes(input_string):
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ansi_escape = re.compile(r'(\x9B|\x1B\[|\u001b\[)[0-?]*[ -/]*[@-~]')
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return ansi_escape.sub('', input_string)
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def execute(code, kernel: CodeKernel) -> tuple[str, Union[str, Image.Image]]:
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res = ""
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res_type = None
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code = code.replace("<|observation|>", "")
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code = code.replace("<|assistant|>interpreter", "")
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code = code.replace("<|assistant|>", "")
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code = code.replace("<|user|>", "")
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code = code.replace("<|system|>", "")
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msg, output = kernel.execute(code)
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if msg['metadata']['status'] == "timeout":
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return res_type, 'Timed out'
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elif msg['metadata']['status'] == 'error':
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return res_type, clean_ansi_codes('\n'.join(kernel.get_error_msg(msg, verbose=True)))
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if 'text' in output:
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res_type = "text"
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res = output['text']
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elif 'data' in output:
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for key in output['data']:
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if 'image/png' in key:
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res_type = "image"
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res = output['data'][key]
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break
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elif 'text/plain' in key:
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res_type = "text"
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res = output['data'][key]
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if res_type == "image":
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return res_type, b64_2_img(res)
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elif res_type == "text" or res_type == "traceback":
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res = res
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return res_type, res
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def extract_code(text: str) -> str:
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pattern = r'```([^\n]*)\n(.*?)```'
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matches = re.findall(pattern, text, re.DOTALL)
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return matches[-1][1]
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186
chatglm/comfyUI_api.py
Normal file
186
chatglm/comfyUI_api.py
Normal file
@@ -0,0 +1,186 @@
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# This is an example that uses the websockets api to know when a prompt execution is done
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# Once the prompt execution is done it downloads the images using the /history endpoint
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import io
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import json
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import random
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import urllib
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import uuid
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import requests
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# NOTE: websocket-client (https://github.com/websocket-client/websocket-client)
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import websocket
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from PIL import Image
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class ComfyUIApi():
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def __init__(self, server_address="127.0.0.1:8188"):
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self.server_address = server_address
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self.client_id = str(uuid.uuid4())
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self.ws = websocket.WebSocket()
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self.ws.connect(
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"ws://{}/ws?clientId={}".format(server_address, self.client_id))
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def queue_prompt(self, prompt):
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p = {"prompt": prompt, "client_id": self.client_id}
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data = json.dumps(p).encode('utf-8')
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req = requests.post(
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"http://{}/prompt".format(self.server_address), data=data)
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print(req.text)
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return json.loads(req.text)
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def get_image(self, filename, subfolder, folder_type):
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data = {"filename": filename,
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"subfolder": subfolder, "type": folder_type}
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url_values = urllib.parse.urlencode(data)
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with requests.get("http://{}/view?{}".format(self.server_address, url_values)) as response:
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image = Image.open(io.BytesIO(response.content))
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return image
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def get_image_url(self, filename, subfolder, folder_type):
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data = {"filename": filename,
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"subfolder": subfolder, "type": folder_type}
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url_values = urllib.parse.urlencode(data)
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return "http://{}/view?{}".format(self.server_address, url_values)
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def get_history(self, prompt_id):
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with requests.get("http://{}/history/{}".format(self.server_address, prompt_id)) as response:
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return json.loads(response.text)
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def get_images(self, prompt, isUrl=False):
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prompt_id = self.queue_prompt(prompt)['prompt_id']
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output_images = []
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while True:
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out = self.ws.recv()
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if isinstance(out, str):
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message = json.loads(out)
|
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if message['type'] == 'executing':
|
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data = message['data']
|
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if data['node'] is None and data['prompt_id'] == prompt_id:
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break # Execution is done
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else:
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continue # previews are binary data
|
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history = self.get_history(prompt_id)[prompt_id]
|
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for o in history['outputs']:
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for node_id in history['outputs']:
|
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node_output = history['outputs'][node_id]
|
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if 'images' in node_output:
|
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for image in node_output['images']:
|
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image_data = self.get_image_url(image['filename'], image['subfolder'], image['type']) if isUrl else self.get_image(
|
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image['filename'], image['subfolder'], image['type'])
|
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image['image'] = image_data
|
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output_images.append(image)
|
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|
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return output_images
|
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|
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|
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prompt_text = """
|
||||
{
|
||||
"3": {
|
||||
"class_type": "KSampler",
|
||||
"inputs": {
|
||||
"cfg": 8,
|
||||
"denoise": 1,
|
||||
"latent_image": [
|
||||
"5",
|
||||
0
|
||||
],
|
||||
"model": [
|
||||
"4",
|
||||
0
|
||||
],
|
||||
"negative": [
|
||||
"7",
|
||||
0
|
||||
],
|
||||
"positive": [
|
||||
"6",
|
||||
0
|
||||
],
|
||||
"sampler_name": "euler",
|
||||
"scheduler": "normal",
|
||||
"seed": 8566257,
|
||||
"steps": 20
|
||||
}
|
||||
},
|
||||
"4": {
|
||||
"class_type": "CheckpointLoaderSimple",
|
||||
"inputs": {
|
||||
"ckpt_name": "chilloutmix_NiPrunedFp32Fix.safetensors"
|
||||
}
|
||||
},
|
||||
"5": {
|
||||
"class_type": "EmptyLatentImage",
|
||||
"inputs": {
|
||||
"batch_size": 1,
|
||||
"height": 512,
|
||||
"width": 512
|
||||
}
|
||||
},
|
||||
"6": {
|
||||
"class_type": "CLIPTextEncode",
|
||||
"inputs": {
|
||||
"clip": [
|
||||
"4",
|
||||
1
|
||||
],
|
||||
"text": "masterpiece best quality girl"
|
||||
}
|
||||
},
|
||||
"7": {
|
||||
"class_type": "CLIPTextEncode",
|
||||
"inputs": {
|
||||
"clip": [
|
||||
"4",
|
||||
1
|
||||
],
|
||||
"text": "bad hands"
|
||||
}
|
||||
},
|
||||
"8": {
|
||||
"class_type": "VAEDecode",
|
||||
"inputs": {
|
||||
"samples": [
|
||||
"3",
|
||||
0
|
||||
],
|
||||
"vae": [
|
||||
"4",
|
||||
2
|
||||
]
|
||||
}
|
||||
},
|
||||
"9": {
|
||||
"class_type": "SaveImage",
|
||||
"inputs": {
|
||||
"filename_prefix": "ComfyUI",
|
||||
"images": [
|
||||
"8",
|
||||
0
|
||||
]
|
||||
}
|
||||
}
|
||||
}
|
||||
"""
|
||||
if __name__ == '__main__':
|
||||
prompt = json.loads(prompt_text)
|
||||
# set the text prompt for our positive CLIPTextEncode
|
||||
prompt["6"]["inputs"]["text"] = "masterpiece best quality man"
|
||||
|
||||
# set the seed for our KSampler node
|
||||
prompt["3"]["inputs"]["seed"] = ''.join(
|
||||
random.sample('123456789012345678901234567890', 14))
|
||||
|
||||
cfui = ComfyUIApi()
|
||||
images = cfui.get_images(prompt)
|
||||
|
||||
# Commented out code to display the output images:
|
||||
|
||||
for node_id in images:
|
||||
for image_data in images[node_id]:
|
||||
import io
|
||||
|
||||
from PIL import Image
|
||||
image = Image.open(io.BytesIO(image_data))
|
||||
image.show()
|
||||
171
chatglm/tool_registry.py
Normal file
171
chatglm/tool_registry.py
Normal file
@@ -0,0 +1,171 @@
|
||||
import inspect
|
||||
import json
|
||||
import random
|
||||
import re
|
||||
import time
|
||||
import traceback
|
||||
from copy import deepcopy
|
||||
from datetime import datetime
|
||||
from pprint import pformat
|
||||
from types import GenericAlias
|
||||
from typing import Annotated, get_origin
|
||||
|
||||
import requests
|
||||
from func_news import News
|
||||
from zhdate import ZhDate
|
||||
|
||||
from chatglm.comfyUI_api import ComfyUIApi
|
||||
|
||||
_TOOL_HOOKS = {}
|
||||
_TOOL_DESCRIPTIONS = {}
|
||||
|
||||
|
||||
def extract_code(text: str) -> str:
|
||||
pattern = r'```([^\n]*)\n(.*?)```'
|
||||
matches = re.findall(pattern, text, re.DOTALL)
|
||||
return matches[-1][1]
|
||||
|
||||
|
||||
def register_tool(func: callable):
|
||||
tool_name = func.__name__
|
||||
tool_description = inspect.getdoc(func).strip()
|
||||
python_params = inspect.signature(func).parameters
|
||||
tool_params = []
|
||||
for name, param in python_params.items():
|
||||
annotation = param.annotation
|
||||
if annotation is inspect.Parameter.empty:
|
||||
raise TypeError(f"Parameter `{name}` missing type annotation")
|
||||
if get_origin(annotation) != Annotated:
|
||||
raise TypeError(
|
||||
f"Annotation type for `{name}` must be typing.Annotated")
|
||||
|
||||
typ, (description, required) = annotation.__origin__, annotation.__metadata__
|
||||
typ: str = str(typ) if isinstance(typ, GenericAlias) else typ.__name__
|
||||
if not isinstance(description, str):
|
||||
raise TypeError(f"Description for `{name}` must be a string")
|
||||
if not isinstance(required, bool):
|
||||
raise TypeError(f"Required for `{name}` must be a bool")
|
||||
|
||||
tool_params.append({
|
||||
"name": name,
|
||||
"description": description,
|
||||
"type": typ,
|
||||
"required": required
|
||||
})
|
||||
tool_def = {
|
||||
"name": tool_name,
|
||||
"description": tool_description,
|
||||
"params": tool_params
|
||||
}
|
||||
|
||||
# print("[registered tool] " + pformat(tool_def))
|
||||
_TOOL_HOOKS[tool_name] = func
|
||||
_TOOL_DESCRIPTIONS[tool_name] = tool_def
|
||||
|
||||
return func
|
||||
|
||||
|
||||
def dispatch_tool(tool_name: str, tool_params: dict) -> str:
|
||||
if tool_name not in _TOOL_HOOKS:
|
||||
return f"Tool `{tool_name}` not found. Please use a provided tool."
|
||||
tool_call = _TOOL_HOOKS[tool_name]
|
||||
try:
|
||||
ret = tool_call(**tool_params)
|
||||
except BaseException:
|
||||
ret = traceback.format_exc()
|
||||
return ret
|
||||
|
||||
|
||||
def get_tools() -> dict:
|
||||
return deepcopy(_TOOL_DESCRIPTIONS)
|
||||
|
||||
# Tool Definitions
|
||||
|
||||
# @register_tool
|
||||
# def random_number_generator(
|
||||
# seed: Annotated[int, 'The random seed used by the generator', True],
|
||||
# range: Annotated[tuple[int, int], 'The range of the generated numbers', True],
|
||||
# ) -> int:
|
||||
# """
|
||||
# Generates a random number x, s.t. range[0] <= x < range[1]
|
||||
# """
|
||||
# if not isinstance(seed, int):
|
||||
# raise TypeError("Seed must be an integer")
|
||||
# if not isinstance(range, tuple):
|
||||
# raise TypeError("Range must be a tuple")
|
||||
# if not isinstance(range[0], int) or not isinstance(range[1], int):
|
||||
# raise TypeError("Range must be a tuple of integers")
|
||||
|
||||
# import random
|
||||
# return random.Random(seed).randint(*range)
|
||||
|
||||
|
||||
@register_tool
|
||||
def get_weather(
|
||||
city_name: Annotated[str, 'The name of the city to be queried', True],
|
||||
) -> str:
|
||||
"""
|
||||
Get the current weather for `city_name`
|
||||
"""
|
||||
if not isinstance(city_name, str):
|
||||
raise TypeError("City name must be a string")
|
||||
|
||||
key_selection = {
|
||||
"current_condition": ["temp_C", "FeelsLikeC", "humidity", "weatherDesc", "observation_time"],
|
||||
}
|
||||
import requests
|
||||
try:
|
||||
resp = requests.get(f"https://wttr.in/{city_name}?format=j1")
|
||||
resp.raise_for_status()
|
||||
resp = resp.json()
|
||||
ret = {k: {_v: resp[k][0][_v] for _v in v}
|
||||
for k, v in key_selection.items()}
|
||||
except BaseException:
|
||||
import traceback
|
||||
ret = "Error encountered while fetching weather data!\n" + traceback.format_exc()
|
||||
|
||||
return str(ret)
|
||||
|
||||
|
||||
@register_tool
|
||||
def get_confyui_image(prompt: Annotated[str, '要生成图片的提示词,注意必须是英文', True]) -> dict:
|
||||
'''
|
||||
生成图片
|
||||
'''
|
||||
with open("chatglm\\base.json", "r", encoding="utf-8") as f:
|
||||
data2 = json.load(f)
|
||||
data2['prompt']['3']['inputs']['seed'] = ''.join(
|
||||
random.sample('123456789012345678901234567890', 14))
|
||||
# 模型名称
|
||||
data2['prompt']['4']['inputs']['ckpt_name'] = 'chilloutmix_NiPrunedFp32Fix.safetensors'
|
||||
data2['prompt']['6']['inputs']['text'] = prompt # 正向提示词
|
||||
# data2['prompt']['7']['inputs']['text']='' #反向提示词
|
||||
cfui = ComfyUIApi(server_address="127.0.0.1:8188") # 根据自己comfyUI地址修改
|
||||
images = cfui.get_images(data2['prompt'])
|
||||
return {'res': images[0]['image'], 'res_type': 'image', 'filename': images[0]['filename']}
|
||||
|
||||
|
||||
@register_tool
|
||||
def get_news() -> str:
|
||||
'''
|
||||
获取最新新闻
|
||||
'''
|
||||
news = News()
|
||||
return news.get_important_news()
|
||||
|
||||
|
||||
@register_tool
|
||||
def get_time() -> str:
|
||||
'''
|
||||
获取当前日期，时间，农历日期，星期几
|
||||
'''
|
||||
time = datetime.now()
|
||||
date2 = ZhDate.from_datetime(time)
|
||||
week_list = ["星期一", "星期二", "星期三", "星期四", "星期五", "星期六", "星期日"]
|
||||
|
||||
return '{} {} {}'.format(time.strftime("%Y年%m月%d日 %H:%M:%S"), week_list[time.weekday()], '农历:' + date2.chinese())
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
print(dispatch_tool("get_weather", {"city_name": "beijing"}))
|
||||
print(get_tools())
|
||||
@@ -54,6 +54,13 @@ report_reminder:
|
||||
# proxy: # 如果你在国内，你可能需要魔法，大概长这样：http://域名或者IP地址:端口号
|
||||
# prompt: 你是智能聊天机器人，你叫wcferry # 根据需要对角色进行设定
|
||||
|
||||
#chatglm:
|
||||
# key: key #暂时没有
|
||||
# api: http://localhost:8000/v1 # 根据自己的chatglm地址修改
|
||||
# proxy: # 如果你在国内，你可能需要魔法，大概长这样：http://域名或者IP地址:端口号
|
||||
# prompt: 你是智能聊天机器人，你叫小薇 # 根据需要对角色进行设定
|
||||
# file_path: F:/Pictures/temp #设定生成图片和代码使用的文件夹路径
|
||||
|
||||
tigerbot:
|
||||
key: key
|
||||
model: tigerbot-7b-sft
|
||||
|
||||
@@ -33,3 +33,4 @@ class Config(object):
|
||||
self.REPORT_REMINDERS = yconfig["report_reminder"]["receivers"]
|
||||
self.TIGERBOT = yconfig.get("tigerbot")
|
||||
self.XINGHUO_WEB = yconfig.get("xinghuo_web")
|
||||
self.CHATGLM = yconfig.get("chatglm")
|
||||
|
||||
181
func_chatglm.py
Normal file
181
func_chatglm.py
Normal file
@@ -0,0 +1,181 @@
|
||||
#! /usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
|
||||
import json
|
||||
import os
|
||||
import random
|
||||
from datetime import datetime
|
||||
from typing import Dict, Optional, Tuple, Union
|
||||
|
||||
import openai
|
||||
from wcferry import Wcf
|
||||
|
||||
from chatglm.code_kernel import CodeKernel, execute
|
||||
from chatglm.tool_registry import dispatch_tool, extract_code, get_tools
|
||||
|
||||
functions = get_tools()
|
||||
|
||||
|
||||
class ChatGLM():
|
||||
|
||||
def __init__(self, config={}, wcf: Optional[Wcf] = None, max_retry=5) -> None:
|
||||
openai.api_key = config.get('key', 'XXX')
|
||||
# 自己搭建或第三方代理的接口
|
||||
openai.api_base = config.get('api', 'http://localhost:8000/v1')
|
||||
if config.get('proxy', None):
|
||||
openai.proxy = {"http": config.get(
|
||||
'proxy', None), "https": config.get('proxy', None)}
|
||||
self.conversation_list = {}
|
||||
self.chat_type = {}
|
||||
self.max_retry = max_retry
|
||||
self.wcf = wcf
|
||||
self.filePath = config.get('file_path', 'temp')
|
||||
self.kernel = CodeKernel()
|
||||
self.system_content_msg = {"chat": [{"role": "system", "content": config.get('prompt', '你是智能聊天机器人，你叫小薇')}],
|
||||
"tool": [{"role": "system", "content": "Answer the following questions as best as you can. You have access to the following tools:"}],
|
||||
"code": [{"role": "system", "content": "你是一位智能AI助手，你叫ChatGLM，你连接着一台电脑，但请注意不能联网。在使用Python解决任务时，你可以运行代码并得到结果，如果运行结果有错误，你需要尽可能对代码进行改进。你可以处理用户上传到电脑上的文件，文件默认存储路径是{}。".format(self.filePath)}]}
|
||||
|
||||
def get_answer(self, question: str, wxid: str) -> str:
|
||||
# wxid或者roomid,个人时为微信id，群消息时为群id
|
||||
if '#帮助' == question:
|
||||
return '本助手有三种模式，#聊天模式 = #1 ，#工具模式 = #2 ，#代码模式 = #3 , #清除模式会话 = #4 , #清除全部会话 = #5 可用发送#对应模式 或者 #编号 进行切换'
|
||||
elif '#聊天模式' == question or '#1' == question:
|
||||
self.chat_type[wxid] = 'chat'
|
||||
return '已切换#聊天模式'
|
||||
elif '#工具模式' == question or '#2' == question:
|
||||
self.chat_type[wxid] = 'tool'
|
||||
return '已切换#工具模式 \n工具有：查看天气，日期，新闻,comfyUI文生图。例如：\n帮我生成一张小鸟的图片，提示词必须是英文'
|
||||
elif '#代码模式' == question or '#3' == question:
|
||||
self.chat_type[wxid] = 'code'
|
||||
return '已切换#代码模式 \n代码模式可以用于写python代码，例如：\n用python画一个爱心'
|
||||
elif '#清除模式会话' == question or '#4' == question:
|
||||
self.conversation_list[wxid][self.chat_type[wxid]
|
||||
] = self.system_content_msg[self.chat_type[wxid]]
|
||||
return '已清除'
|
||||
elif '#清除全部会话' == question or '#5' == question:
|
||||
self.conversation_list[wxid] = self.system_content_msg
|
||||
return '已清除'
|
||||
|
||||
self.updateMessage(wxid, question, "user")
|
||||
|
||||
try:
|
||||
params = dict(model="chatglm3", temperature=1.0,
|
||||
messages=self.conversation_list[wxid][self.chat_type[wxid]], stream=False)
|
||||
if 'tool' == self.chat_type[wxid]:
|
||||
params["functions"] = functions
|
||||
response = openai.ChatCompletion.create(**params)
|
||||
for _ in range(self.max_retry):
|
||||
if response.choices[0].message.get("function_call"):
|
||||
function_call = response.choices[0].message.function_call
|
||||
print(
|
||||
f"Function Call Response: {function_call.to_dict_recursive()}")
|
||||
|
||||
function_args = json.loads(function_call.arguments)
|
||||
observation = dispatch_tool(
|
||||
function_call.name, function_args)
|
||||
if isinstance(observation, dict):
|
||||
res_type = observation['res_type'] if 'res_type' in observation else 'text'
|
||||
res = observation['res'] if 'res_type' in observation else str(
|
||||
observation)
|
||||
if res_type == 'image':
|
||||
filename = observation['filename']
|
||||
filePath = os.path.join(self.filePath, filename)
|
||||
res.save(filePath)
|
||||
self.wcf and self.wcf.send_image(filePath, wxid)
|
||||
tool_response = '[Image]' if res_type == 'image' else res
|
||||
else:
|
||||
tool_response = observation if isinstance(
|
||||
observation, str) else str(observation)
|
||||
print(f"Tool Call Response: {tool_response}")
|
||||
|
||||
params["messages"].append(response.choices[0].message)
|
||||
params["messages"].append(
|
||||
{
|
||||
"role": "function",
|
||||
"name": function_call.name,
|
||||
"content": tool_response, # 调用函数返回结果
|
||||
}
|
||||
)
|
||||
self.updateMessage(wxid, tool_response, "function")
|
||||
response = openai.ChatCompletion.create(**params)
|
||||
elif response.choices[0].message.content.find('interpreter') != -1:
|
||||
output_text = response.choices[0].message.content
|
||||
code = extract_code(output_text)
|
||||
self.wcf and self.wcf.send_text('代码如下：\n' + code, wxid)
|
||||
self.wcf and self.wcf.send_text('执行代码...', wxid)
|
||||
try:
|
||||
res_type, res = execute(code, self.kernel)
|
||||
except Exception as e:
|
||||
rsp = f'代码执行错误: {e}'
|
||||
break
|
||||
if res_type == 'image':
|
||||
filename = '{}.png'.format(''.join(random.sample(
|
||||
'abcdefghijklmnopqrstuvwxyz1234567890', 8)))
|
||||
filePath = os.path.join(self.filePath, filename)
|
||||
res.save(filePath)
|
||||
self.wcf and self.wcf.send_image(filePath, wxid)
|
||||
else:
|
||||
self.wcf and self.wcf.send_text("执行结果:\n" + res, wxid)
|
||||
tool_response = '[Image]' if res_type == 'image' else res
|
||||
print("Received:", res_type, res)
|
||||
params["messages"].append(response.choices[0].message)
|
||||
params["messages"].append(
|
||||
{
|
||||
"role": "function",
|
||||
"name": "interpreter",
|
||||
"content": tool_response, # 调用函数返回结果
|
||||
}
|
||||
)
|
||||
self.updateMessage(wxid, tool_response, "function")
|
||||
response = openai.ChatCompletion.create(**params)
|
||||
else:
|
||||
rsp = response.choices[0].message.content
|
||||
break
|
||||
|
||||
self.updateMessage(wxid, rsp, "assistant")
|
||||
except Exception as e0:
|
||||
rsp = "发生未知错误：" + str(e0)
|
||||
|
||||
return rsp
|
||||
|
||||
def updateMessage(self, wxid: str, question: str, role: str) -> None:
|
||||
now_time = str(datetime.now().strftime("%Y-%m-%d %H:%M:%S"))
|
||||
|
||||
# 初始化聊天记录,组装系统信息
|
||||
if wxid not in self.conversation_list.keys():
|
||||
self.conversation_list[wxid] = self.system_content_msg
|
||||
if wxid not in self.chat_type.keys():
|
||||
self.chat_type[wxid] = 'chat'
|
||||
|
||||
# 当前问题
|
||||
content_question_ = {"role": role, "content": question}
|
||||
self.conversation_list[wxid][self.chat_type[wxid]].append(
|
||||
content_question_)
|
||||
|
||||
# 只存储10条记录，超过滚动清除
|
||||
i = len(self.conversation_list[wxid][self.chat_type[wxid]])
|
||||
if i > 10:
|
||||
print("滚动清除微信记录：" + wxid)
|
||||
# 删除多余的记录，倒着删，且跳过第一个的系统消息
|
||||
del self.conversation_list[wxid][self.chat_type[wxid]][1]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
from configuration import Config
|
||||
config = Config().CHATGLM
|
||||
if not config:
|
||||
exit(0)
|
||||
|
||||
chat = ChatGLM(config)
|
||||
|
||||
while True:
|
||||
q = input(">>> ")
|
||||
try:
|
||||
time_start = datetime.now() # 记录开始时间
|
||||
print(chat.get_answer(q, "wxid"))
|
||||
time_end = datetime.now() # 记录结束时间
|
||||
|
||||
# 计算的时间差为程序的执行时间，单位为秒/s
|
||||
print(f"{round((time_end - time_start).total_seconds(), 2)}s")
|
||||
except Exception as e:
|
||||
print(e)
|
||||
@@ -8,3 +8,8 @@ schedule
|
||||
pyhandytools
|
||||
sparkdesk-api==1.3.0
|
||||
wcferry>=39.0.3.0
|
||||
websocket
|
||||
pillow
|
||||
jupyter_client
|
||||
zhdate
|
||||
ipykernel
|
||||
3
robot.py
3
robot.py
@@ -11,6 +11,7 @@ from wcferry import Wcf, WxMsg
|
||||
|
||||
from configuration import Config
|
||||
from func_chatgpt import ChatGPT
|
||||
from func_chatglm import ChatGLM
|
||||
from func_chengyu import cy
|
||||
from func_news import News
|
||||
from func_tigerbot import TigerBot
|
||||
@@ -36,6 +37,8 @@ class Robot(Job):
|
||||
self.chat = ChatGPT(cgpt.get("key"), cgpt.get("api"), cgpt.get("proxy"), cgpt.get("prompt"))
|
||||
elif self.config.XINGHUO_WEB:
|
||||
self.chat = XinghuoWeb(self.config.XINGHUO_WEB)
|
||||
elif self.config.CHATGLM:
|
||||
self.chat = ChatGLM(self.config.CHATGLM,wcf)
|
||||
else:
|
||||
self.chat = None
|
||||
|
||||
|
||||
Reference in New Issue
Block a user