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refactor: 重构插件系统
This commit is contained in:
RockChinQ
@@ -1,232 +0,0 @@
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import json
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import logging
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import openai
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from openai.types.chat import chat_completion_message
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from .model import RequestBase
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from .. import funcmgr
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from ...plugin import host
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from ...utils import context
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class ChatCompletionRequest(RequestBase):
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"""调用ChatCompletion接口的请求类。
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此类保证每一次返回的角色为assistant的信息的finish_reason一定为stop。
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若有函数调用响应，本类的返回瀑布是：函数调用请求->函数调用结果->...->assistant的信息->stop。
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"""
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model: str
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messages: list[dict[str, str]]
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kwargs: dict
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stopped: bool = False
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pending_func_call: chat_completion_message.FunctionCall = None
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pending_msg: str
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def flush_pending_msg(self):
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self.append_message(
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role="assistant",
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content=self.pending_msg
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)
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self.pending_msg = ""
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def append_message(self, role: str, content: str, name: str=None, function_call: dict=None):
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msg = {
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"role": role,
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"content": content
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}
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if name is not None:
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msg['name'] = name
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if function_call is not None:
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msg['function_call'] = function_call
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self.messages.append(msg)
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def __init__(
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self,
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client: openai.Client,
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model: str,
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messages: list[dict[str, str]],
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**kwargs
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):
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self.client = client
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self.model = model
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self.messages = messages.copy()
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self.kwargs = kwargs
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self.req_func = self.client.chat.completions.create
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self.pending_func_call = None
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self.stopped = False
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self.pending_msg = ""
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def __iter__(self):
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return self
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def __next__(self) -> dict:
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if self.stopped:
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raise StopIteration()
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if self.pending_func_call is None: # 没有待处理的函数调用请求
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args = {
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"model": self.model,
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"messages": self.messages,
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}
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funcs = funcmgr.get_func_schema_list()
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if len(funcs) > 0:
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args['functions'] = funcs
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# 拼接kwargs
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args = {**args, **self.kwargs}
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from openai.types.chat import chat_completion
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resp: chat_completion.ChatCompletion = self._req(**args)
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choice0 = resp.choices[0]
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# 如果不是函数调用，且finish_reason为stop，则停止迭代
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if choice0.finish_reason == 'stop': # and choice0["finish_reason"] == "stop"
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self.stopped = True
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if hasattr(choice0.message, 'function_call') and choice0.message.function_call is not None:
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self.pending_func_call = choice0.message.function_call
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self.append_message(
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role="assistant",
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content=choice0.message.content,
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function_call=choice0.message.function_call
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)
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return {
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"id": resp.id,
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"choices": [
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{
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"index": choice0.index,
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"message": {
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"role": "assistant",
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"type": "function_call",
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"content": choice0.message.content,
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"function_call": {
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"name": choice0.message.function_call.name,
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"arguments": choice0.message.function_call.arguments
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}
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},
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"finish_reason": "function_call"
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}
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],
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"usage": {
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"prompt_tokens": resp.usage.prompt_tokens,
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"completion_tokens": resp.usage.completion_tokens,
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"total_tokens": resp.usage.total_tokens
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}
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}
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else:
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# self.pending_msg += choice0['message']['content']
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# 普通回复一定处于最后方，故不用再追加进内部messages
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return {
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"id": resp.id,
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"choices": [
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{
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"index": choice0.index,
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"message": {
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"role": "assistant",
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"type": "text",
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"content": choice0.message.content
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},
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"finish_reason": choice0.finish_reason
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}
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],
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"usage": {
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"prompt_tokens": resp.usage.prompt_tokens,
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"completion_tokens": resp.usage.completion_tokens,
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"total_tokens": resp.usage.total_tokens
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}
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}
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else: # 处理函数调用请求
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cp_pending_func_call = self.pending_func_call.copy()
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self.pending_func_call = None
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func_name = cp_pending_func_call.name
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arguments = {}
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try:
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try:
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arguments = json.loads(cp_pending_func_call.arguments)
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# 若不是json格式的异常处理
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except json.decoder.JSONDecodeError:
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# 获取函数的参数列表
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func_schema = funcmgr.get_func_schema(func_name)
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arguments = {
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func_schema['parameters']['required'][0]: cp_pending_func_call.arguments
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}
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logging.info("执行函数调用: name={}, arguments={}".format(func_name, arguments))
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# 执行函数调用
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ret = ""
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try:
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ret = funcmgr.execute_function(func_name, arguments)
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logging.info("函数执行完成。")
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except Exception as e:
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ret = "error: execute function failed: {}".format(str(e))
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logging.error("函数执行失败: {}".format(str(e)))
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# 上报数据
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plugin_info = host.get_plugin_info_for_audit(func_name.split('-')[0])
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audit_func_name = func_name.split('-')[1]
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audit_func_desc = funcmgr.get_func_schema(func_name)['description']
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context.get_center_v2_api().usage.post_function_record(
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plugin=plugin_info,
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function_name=audit_func_name,
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function_description=audit_func_desc,
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)
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self.append_message(
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role="function",
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content=json.dumps(ret, ensure_ascii=False),
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name=func_name
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)
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return {
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"id": -1,
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"choices": [
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{
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"index": -1,
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"message": {
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"role": "function",
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"type": "function_return",
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"function_name": func_name,
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"content": json.dumps(ret, ensure_ascii=False)
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},
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"finish_reason": "function_return"
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}
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],
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"usage": {
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"prompt_tokens": 0,
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"completion_tokens": 0,
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"total_tokens": 0
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}
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}
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except funcmgr.ContentFunctionNotFoundError:
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raise Exception("没有找到函数: {}".format(func_name))
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@@ -1,100 +0,0 @@
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import openai
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from openai.types import completion, completion_choice
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from . import model
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class CompletionRequest(model.RequestBase):
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"""调用Completion接口的请求类。
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调用方可以一直next completion直到finish_reason为stop。
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"""
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model: str
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prompt: str
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kwargs: dict
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stopped: bool = False
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def __init__(
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self,
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client: openai.Client,
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model: str,
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messages: list[dict[str, str]],
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**kwargs
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):
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self.client = client
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self.model = model
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self.prompt = ""
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for message in messages:
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self.prompt += message["role"] + ": " + message["content"] + "\n"
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self.prompt += "assistant: "
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self.kwargs = kwargs
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self.req_func = self.client.completions.create
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def __iter__(self):
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return self
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def __next__(self) -> dict:
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"""调用Completion接口，返回生成的文本
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{
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"id": "id",
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"choices": [
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{
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"index": 0,
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"message": {
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"role": "assistant",
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"type": "text",
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"content": "message"
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},
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"finish_reason": "reason"
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}
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],
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"usage": {
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"prompt_tokens": 10,
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"completion_tokens": 20,
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"total_tokens": 30
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}
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}
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"""
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if self.stopped:
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raise StopIteration()
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resp: completion.Completion = self._req(
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model=self.model,
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prompt=self.prompt,
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**self.kwargs
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)
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if resp.choices[0].finish_reason == "stop":
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self.stopped = True
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choice0: completion_choice.CompletionChoice = resp.choices[0]
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self.prompt += choice0.text
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return {
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"id": resp.id,
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"choices": [
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{
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"index": choice0.index,
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"message": {
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"role": "assistant",
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"type": "text",
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"content": choice0.text
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},
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"finish_reason": choice0.finish_reason
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}
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],
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"usage": {
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"prompt_tokens": resp.usage.prompt_tokens,
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"completion_tokens": resp.usage.completion_tokens,
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"total_tokens": resp.usage.total_tokens
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}
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}
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@@ -1,40 +0,0 @@
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# 定义不同接口请求的模型
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import logging
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import openai
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from ...utils import context
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class RequestBase:
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client: openai.Client
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req_func: callable
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def __init__(self, *args, **kwargs):
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raise NotImplementedError
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def _next_key(self):
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switched, name = context.get_openai_manager().key_mgr.auto_switch()
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logging.debug("切换api-key: switched={}, name={}".format(switched, name))
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self.client.api_key = context.get_openai_manager().key_mgr.get_using_key()
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def _req(self, **kwargs):
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"""处理代理问题"""
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logging.debug("请求接口参数: %s", str(kwargs))
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config = context.get_config_manager().data
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ret = self.req_func(**kwargs)
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logging.debug("接口请求返回：%s", str(ret))
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if config['switch_strategy'] == 'active':
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self._next_key()
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return ret
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def __iter__(self):
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raise self
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def __next__(self):
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raise NotImplementedError
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@@ -6,7 +6,6 @@ import typing
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from ...core import app
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from ...core import entities as core_entities
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from .. import entities as llm_entities
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from ..session import entities as session_entities
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class LLMAPIRequester(metaclass=abc.ABCMeta):
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"""LLM API请求器
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@@ -24,7 +23,7 @@ class LLMAPIRequester(metaclass=abc.ABCMeta):
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async def request(
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self,
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query: core_entities.Query,
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conversation: session_entities.Conversation,
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conversation: core_entities.Conversation,
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) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""请求
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"""
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@@ -10,7 +10,6 @@ import openai.types.chat.chat_completion as chat_completion
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from .. import api
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from ....core import entities as core_entities
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from ... import entities as llm_entities
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from ...session import entities as session_entities
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class OpenAIChatCompletion(api.LLMAPIRequester):
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@@ -43,41 +42,18 @@ class OpenAIChatCompletion(api.LLMAPIRequester):
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async def _closure(
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self,
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req_messages: list[dict],
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conversation: session_entities.Conversation,
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user_text: str = None,
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function_ret: str = None,
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conversation: core_entities.Conversation,
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) -> llm_entities.Message:
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self.client.api_key = conversation.use_model.token_mgr.get_token()
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args = self.ap.cfg_mgr.data["completion_api_params"].copy()
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args["model"] = conversation.use_model.name
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tools = await self.ap.tool_mgr.generate_tools_for_openai(conversation)
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# tools = [
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# {
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# "type": "function",
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# "function": {
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# "name": "get_current_weather",
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# "description": "Get the current weather in a given location",
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# "parameters": {
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# "type": "object",
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# "properties": {
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# "location": {
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# "type": "string",
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# "description": "The city and state, e.g. San Francisco, CA",
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# },
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# "unit": {
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# "type": "string",
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# "enum": ["celsius", "fahrenheit"],
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# },
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# },
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# "required": ["location"],
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# },
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# },
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# }
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# ]
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if tools:
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args["tools"] = tools
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if conversation.use_model.tool_call_supported:
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tools = await self.ap.tool_mgr.generate_tools_for_openai(conversation)
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|
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if tools:
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args["tools"] = tools
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# 设置此次请求中的messages
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messages = req_messages
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@@ -92,7 +68,7 @@ class OpenAIChatCompletion(api.LLMAPIRequester):
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return message
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async def request(
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self, query: core_entities.Query, conversation: session_entities.Conversation
|
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self, query: core_entities.Query, conversation: core_entities.Conversation
|
||||
) -> typing.AsyncGenerator[llm_entities.Message, None]:
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"""请求"""
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||||
|
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@@ -1,9 +1,11 @@
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from __future__ import annotations
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||||
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||||
import typing
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import pydantic
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||||
|
||||
from . import api
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from . import token
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from . import token, tokenizer
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|
||||
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class LLMModelInfo(pydantic.BaseModel):
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@@ -17,7 +19,9 @@ class LLMModelInfo(pydantic.BaseModel):
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requester: api.LLMAPIRequester
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function_call_supported: typing.Optional[bool] = False
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tokenizer: 'tokenizer.LLMTokenizer'
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||||
|
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tool_call_supported: typing.Optional[bool] = False
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||||
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class Config:
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arbitrary_types_allowed = True
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@@ -5,6 +5,7 @@ from ...core import app
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||||
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||||
from .apis import chatcmpl
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from . import token
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from .tokenizers import tiktoken
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||||
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class ModelManager:
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||||
@@ -17,25 +18,28 @@ class ModelManager:
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||||
self.ap = ap
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||||
self.model_list = []
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||||
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async def get_model_by_name(self, name: str) -> entities.LLMModelInfo:
|
||||
"""通过名称获取模型
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||||
"""
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||||
for model in self.model_list:
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||||
if model.name == name:
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||||
return model
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||||
raise ValueError(f"Model {name} not found")
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||||
|
||||
async def initialize(self):
|
||||
openai_chat_completion = chatcmpl.OpenAIChatCompletion(self.ap)
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||||
await openai_chat_completion.initialize()
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||||
openai_token_mgr = token.TokenManager(self.ap, list(self.ap.cfg_mgr.data['openai_config']['api_key'].values()))
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||||
|
||||
tiktoken_tokenizer = tiktoken.Tiktoken(self.ap)
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||||
|
||||
self.model_list.append(
|
||||
entities.LLMModelInfo(
|
||||
name="gpt-3.5-turbo",
|
||||
provider="openai",
|
||||
token_mgr=openai_token_mgr,
|
||||
requester=openai_chat_completion,
|
||||
function_call_supported=True
|
||||
tool_call_supported=True,
|
||||
tokenizer=tiktoken_tokenizer
|
||||
)
|
||||
)
|
||||
|
||||
async def get_model_by_name(self, name: str) -> entities.LLMModelInfo:
|
||||
"""通过名称获取模型
|
||||
"""
|
||||
for model in self.model_list:
|
||||
if model.name == name:
|
||||
return model
|
||||
raise ValueError(f"Model {name} not found")
|
||||
@@ -0,0 +1,29 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import abc
|
||||
import typing
|
||||
|
||||
from ...core import app
|
||||
from .. import entities as llm_entities
|
||||
from . import entities
|
||||
|
||||
|
||||
class LLMTokenizer(metaclass=abc.ABCMeta):
|
||||
|
||||
ap: app.Application
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
|
||||
async def initialize(self):
|
||||
"""初始化分词器
|
||||
"""
|
||||
pass
|
||||
|
||||
@abc.abstractmethod
|
||||
async def count_token(
|
||||
self,
|
||||
messages: list[llm_entities.Message],
|
||||
model: entities.LLMModelInfo
|
||||
) -> int:
|
||||
pass
|
||||
@@ -0,0 +1,28 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import tiktoken
|
||||
|
||||
from .. import tokenizer
|
||||
from ... import entities as llm_entities
|
||||
from .. import entities
|
||||
|
||||
|
||||
class Tiktoken(tokenizer.LLMTokenizer):
|
||||
|
||||
async def count_token(
|
||||
self,
|
||||
messages: list[llm_entities.Message],
|
||||
model: entities.LLMModelInfo
|
||||
) -> int:
|
||||
try:
|
||||
encoding = tiktoken.encoding_for_model(model.name)
|
||||
except KeyError:
|
||||
print("Warning: model not found. Using cl100k_base encoding.")
|
||||
encoding = tiktoken.get_encoding("cl100k_base")
|
||||
|
||||
num_tokens = 0
|
||||
for message in messages:
|
||||
num_tokens += len(encoding.encode(message.role))
|
||||
num_tokens += len(encoding.encode(message.content))
|
||||
num_tokens += 3 # every reply is primed with <|start|>assistant<|message|>
|
||||
return num_tokens
|
||||
@@ -1,53 +0,0 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import datetime
|
||||
import asyncio
|
||||
import typing
|
||||
|
||||
import pydantic
|
||||
|
||||
from ..sysprompt import entities as sysprompt_entities
|
||||
from .. import entities as llm_entities
|
||||
from ..requester import entities
|
||||
from ...core import entities as core_entities
|
||||
from ..tools import entities as tools_entities
|
||||
|
||||
|
||||
class Conversation(pydantic.BaseModel):
|
||||
"""对话"""
|
||||
|
||||
prompt: sysprompt_entities.Prompt
|
||||
|
||||
messages: list[llm_entities.Message]
|
||||
|
||||
create_time: typing.Optional[datetime.datetime] = pydantic.Field(default_factory=datetime.datetime.now)
|
||||
|
||||
update_time: typing.Optional[datetime.datetime] = pydantic.Field(default_factory=datetime.datetime.now)
|
||||
|
||||
use_model: entities.LLMModelInfo
|
||||
|
||||
use_funcs: typing.Optional[list[tools_entities.LLMFunction]]
|
||||
|
||||
|
||||
class Session(pydantic.BaseModel):
|
||||
"""会话"""
|
||||
launcher_type: core_entities.LauncherTypes
|
||||
|
||||
launcher_id: int
|
||||
|
||||
sender_id: typing.Optional[int] = 0
|
||||
|
||||
use_prompt_name: typing.Optional[str] = 'default'
|
||||
|
||||
using_conversation: typing.Optional[Conversation] = None
|
||||
|
||||
conversations: typing.Optional[list[Conversation]] = []
|
||||
|
||||
create_time: typing.Optional[datetime.datetime] = pydantic.Field(default_factory=datetime.datetime.now)
|
||||
|
||||
update_time: typing.Optional[datetime.datetime] = pydantic.Field(default_factory=datetime.datetime.now)
|
||||
|
||||
semaphore: typing.Optional[asyncio.Semaphore] = None
|
||||
|
||||
class Config:
|
||||
arbitrary_types_allowed = True
|
||||
@@ -3,14 +3,13 @@ from __future__ import annotations
|
||||
import asyncio
|
||||
|
||||
from ...core import app, entities as core_entities
|
||||
from . import entities
|
||||
|
||||
|
||||
class SessionManager:
|
||||
|
||||
ap: app.Application
|
||||
|
||||
session_list: list[entities.Session]
|
||||
session_list: list[core_entities.Session]
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
@@ -19,14 +18,14 @@ class SessionManager:
|
||||
async def initialize(self):
|
||||
pass
|
||||
|
||||
async def get_session(self, query: core_entities.Query) -> entities.Session:
|
||||
async def get_session(self, query: core_entities.Query) -> core_entities.Session:
|
||||
"""获取会话
|
||||
"""
|
||||
for session in self.session_list:
|
||||
if query.launcher_type == session.launcher_type and query.launcher_id == session.launcher_id:
|
||||
return session
|
||||
|
||||
session = entities.Session(
|
||||
session = core_entities.Session(
|
||||
launcher_type=query.launcher_type,
|
||||
launcher_id=query.launcher_id,
|
||||
semaphore=asyncio.Semaphore(1) if self.ap.cfg_mgr.data['wait_last_done'] else asyncio.Semaphore(10000),
|
||||
@@ -34,12 +33,12 @@ class SessionManager:
|
||||
self.session_list.append(session)
|
||||
return session
|
||||
|
||||
async def get_conversation(self, session: entities.Session) -> entities.Conversation:
|
||||
async def get_conversation(self, session: core_entities.Session) -> core_entities.Conversation:
|
||||
if not session.conversations:
|
||||
session.conversations = []
|
||||
|
||||
if session.using_conversation is None:
|
||||
conversation = entities.Conversation(
|
||||
conversation = core_entities.Conversation(
|
||||
prompt=await self.ap.prompt_mgr.get_prompt(session.use_prompt_name),
|
||||
messages=[],
|
||||
use_model=await self.ap.model_mgr.get_model_by_name(self.ap.cfg_mgr.data['completion_api_params']['model']),
|
||||
|
||||
@@ -6,6 +6,8 @@ import asyncio
|
||||
|
||||
import pydantic
|
||||
|
||||
from ...core import entities as core_entities
|
||||
|
||||
|
||||
class LLMFunction(pydantic.BaseModel):
|
||||
"""函数"""
|
||||
|
||||
@@ -4,7 +4,6 @@ import typing
|
||||
|
||||
from ...core import app, entities as core_entities
|
||||
from . import entities
|
||||
from ..session import entities as session_entities
|
||||
|
||||
|
||||
class ToolManager:
|
||||
@@ -12,8 +11,6 @@ class ToolManager:
|
||||
"""
|
||||
|
||||
ap: app.Application
|
||||
|
||||
all_functions: list[entities.LLMFunction]
|
||||
|
||||
def __init__(self, ap: app.Application):
|
||||
self.ap = ap
|
||||
@@ -22,30 +19,10 @@ class ToolManager:
|
||||
async def initialize(self):
|
||||
pass
|
||||
|
||||
def register_legacy_function(self, name: str, description: str, parameters: dict, func: callable):
|
||||
"""注册函数
|
||||
"""
|
||||
async def wrapper(query, **kwargs):
|
||||
return func(**kwargs)
|
||||
function = entities.LLMFunction(
|
||||
name=name,
|
||||
description=description,
|
||||
human_desc='',
|
||||
enable=True,
|
||||
parameters=parameters,
|
||||
func=wrapper
|
||||
)
|
||||
self.all_functions.append(function)
|
||||
|
||||
async def register_function(self, function: entities.LLMFunction):
|
||||
"""添加函数
|
||||
"""
|
||||
self.all_functions.append(function)
|
||||
|
||||
async def get_function(self, name: str) -> entities.LLMFunction:
|
||||
"""获取函数
|
||||
"""
|
||||
for function in self.all_functions:
|
||||
for function in await self.get_all_functions():
|
||||
if function.name == name:
|
||||
return function
|
||||
return None
|
||||
@@ -53,9 +30,14 @@ class ToolManager:
|
||||
async def get_all_functions(self) -> list[entities.LLMFunction]:
|
||||
"""获取所有函数
|
||||
"""
|
||||
return self.all_functions
|
||||
all_functions: list[entities.LLMFunction] = []
|
||||
|
||||
async def generate_tools_for_openai(self, conversation: session_entities.Conversation) -> str:
|
||||
for plugin in self.ap.plugin_mgr.plugins:
|
||||
all_functions.extend(plugin.content_functions)
|
||||
|
||||
return all_functions
|
||||
|
||||
async def generate_tools_for_openai(self, conversation: core_entities.Conversation) -> str:
|
||||
"""生成函数列表
|
||||
"""
|
||||
tools = []
|
||||
|
||||
Reference in New Issue
Block a user