ragflow / rag /llm /chat_model.py
KevinHuSh
Test chat API and refine ppt chunker (#42)
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#
# Copyright 2024 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
from abc import ABC
from openai import OpenAI
import os
class Base(ABC):
def __init__(self, key, model_name):
pass
def chat(self, system, history, gen_conf):
raise NotImplementedError("Please implement encode method!")
class GptTurbo(Base):
def __init__(self, key, model_name="gpt-3.5-turbo"):
self.client = OpenAI(api_key=key)
self.model_name = model_name
def chat(self, system, history, gen_conf):
history.insert(0, {"role": "system", "content": system})
res = self.client.chat.completions.create(
model=self.model_name,
messages=history,
**gen_conf)
return res.choices[0].message.content.strip(), res.usage.completion_tokens
from dashscope import Generation
class QWenChat(Base):
def __init__(self, key, model_name=Generation.Models.qwen_turbo):
import dashscope
dashscope.api_key = key
self.model_name = model_name
def chat(self, system, history, gen_conf):
from http import HTTPStatus
history.insert(0, {"role": "system", "content": system})
response = Generation.call(
self.model_name,
messages=history,
result_format='message'
)
if response.status_code == HTTPStatus.OK:
return response.output.choices[0]['message']['content'], response.usage.output_tokens
return response.message, 0