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from abc import ABC
import requests
from typing import Optional, List
from langchain.llms.base import LLM
from models.loader import LoaderCheckPoint
from models.base import (RemoteRpcModel,
AnswerResult)
from typing import (
Collection,
Dict
)
def _build_message_template() -> Dict[str, str]:
"""
:return: 结构
"""
return {
"role": "",
"content": "",
}
class FastChatOpenAILLM(RemoteRpcModel, LLM, ABC):
api_base_url: str = "http://localhost:8000/v1"
model_name: str = "chatglm-6b"
max_token: int = 10000
temperature: float = 0.01
top_p = 0.9
checkPoint: LoaderCheckPoint = None
history = []
history_len: int = 10
def __init__(self, checkPoint: LoaderCheckPoint = None):
super().__init__()
self.checkPoint = checkPoint
@property
def _llm_type(self) -> str:
return "FastChat"
@property
def _check_point(self) -> LoaderCheckPoint:
return self.checkPoint
@property
def _history_len(self) -> int:
return self.history_len
def set_history_len(self, history_len: int = 10) -> None:
self.history_len = history_len
@property
def _api_key(self) -> str:
pass
@property
def _api_base_url(self) -> str:
return self.api_base_url
def set_api_key(self, api_key: str):
pass
def set_api_base_url(self, api_base_url: str):
self.api_base_url = api_base_url
def call_model_name(self, model_name):
self.model_name = model_name
def _call(self, prompt: str, stop: Optional[List[str]] = None) -> str:
print(f"__call:{prompt}")
try:
import openai
# Not support yet
openai.api_key = "EMPTY"
openai.api_base = self.api_base_url
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
# create a chat completion
completion = openai.ChatCompletion.create(
model=self.model_name,
messages=self.build_message_list(prompt)
)
print(f"response:{completion.choices[0].message.content}")
print(f"+++++++++++++++++++++++++++++++++++")
return completion.choices[0].message.content
# 将历史对话数组转换为文本格式
def build_message_list(self, query) -> Collection[Dict[str, str]]:
build_message_list: Collection[Dict[str, str]] = []
history = self.history[-self.history_len:] if self.history_len > 0 else []
for i, (old_query, response) in enumerate(history):
user_build_message = _build_message_template()
user_build_message['role'] = 'user'
user_build_message['content'] = old_query
system_build_message = _build_message_template()
system_build_message['role'] = 'system'
system_build_message['content'] = response
build_message_list.append(user_build_message)
build_message_list.append(system_build_message)
user_build_message = _build_message_template()
user_build_message['role'] = 'user'
user_build_message['content'] = query
build_message_list.append(user_build_message)
return build_message_list
def generatorAnswer(self, prompt: str,
history: List[List[str]] = [],
streaming: bool = False):
try:
import openai
# Not support yet
openai.api_key = "EMPTY"
openai.api_base = self.api_base_url
except ImportError:
raise ValueError(
"Could not import openai python package. "
"Please install it with `pip install openai`."
)
# create a chat completion
completion = openai.ChatCompletion.create(
model=self.model_name,
messages=self.build_message_list(prompt)
)
history += [[prompt, completion.choices[0].message.content]]
answer_result = AnswerResult()
answer_result.history = history
answer_result.llm_output = {"answer": completion.choices[0].message.content}
yield answer_result
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