ragflow/rag/llm/chat_model.py

#
#  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 zhipuai import ZhipuAI
from dashscope import Generation
from abc import ABC
from openai import OpenAI
import openai
from ollama import Client
from rag.nlp import is_english
from rag.utils import num_tokens_from_string


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", base_url="https://api.openai.com/v1"):
        if not base_url: base_url="https://api.openai.com/v1"
        self.client = OpenAI(api_key=key, base_url=base_url)
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                **gen_conf)
            ans = response.choices[0].message.content.strip()
            if response.choices[0].finish_reason == "length":
                ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
                    [ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
            return ans, response.usage.completion_tokens
        except openai.APIError as e:
            return "**ERROR**: " + str(e), 0


class MoonshotChat(GptTurbo):
    def __init__(self, key, model_name="moonshot-v1-8k", base_url="https://api.moonshot.cn/v1"):
        if not base_url: base_url="https://api.moonshot.cn/v1"
        self.client = OpenAI(
            api_key=key, base_url=base_url)
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                **gen_conf)
            ans = response.choices[0].message.content.strip()
            if response.choices[0].finish_reason == "length":
                ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
                    [ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
            return ans, response.usage.completion_tokens
        except openai.APIError as e:
            return "**ERROR**: " + str(e), 0


class QWenChat(Base):
    def __init__(self, key, model_name=Generation.Models.qwen_turbo, **kwargs):
        import dashscope
        dashscope.api_key = key
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        from http import HTTPStatus
        if system:
            history.insert(0, {"role": "system", "content": system})
        response = Generation.call(
            self.model_name,
            messages=history,
            result_format='message',
            **gen_conf
        )
        ans = ""
        tk_count = 0
        if response.status_code == HTTPStatus.OK:
            ans += response.output.choices[0]['message']['content']
            tk_count += response.usage.total_tokens
            if response.output.choices[0].get("finish_reason", "") == "length":
                ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
                    [ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
            return ans, tk_count

        return "**ERROR**: " + response.message, tk_count


class ZhipuChat(Base):
    def __init__(self, key, model_name="glm-3-turbo", **kwargs):
        self.client = ZhipuAI(api_key=key)
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        try:
            if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"]
            if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"]
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                **gen_conf
            )
            ans = response.choices[0].message.content.strip()
            if response.choices[0].finish_reason == "length":
                ans += "...\nFor the content length reason, it stopped, continue?" if is_english(
                    [ans]) else "······\n由于长度的原因，回答被截断了，要继续吗？"
            return ans, response.usage.completion_tokens
        except Exception as e:
            return "**ERROR**: " + str(e), 0


class OllamaChat(Base):
    def __init__(self, key, model_name, **kwargs):
        self.client = Client(host=kwargs["base_url"])
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        try:
            options = {"temperature": gen_conf.get("temperature", 0.1),
                       "num_predict": gen_conf.get("max_tokens", 128),
                       "top_k": gen_conf.get("top_p", 0.3),
                       "presence_penalty": gen_conf.get("presence_penalty", 0.4),
                       "frequency_penalty": gen_conf.get("frequency_penalty", 0.7),
                       }
            response = self.client.chat(
                model=self.model_name,
                messages=history,
                options=options
            )
            ans = response["message"]["content"].strip()
            return ans, response["eval_count"]
        except Exception as e:
            return "**ERROR**: " + str(e), 0


class LocalLLM(Base):
    class RPCProxy:
        def __init__(self, host, port):
            self.host = host
            self.port = int(port)
            self.__conn()

        def __conn(self):
            from multiprocessing.connection import Client
            self._connection = Client(
                (self.host, self.port), authkey=b'infiniflow-token4kevinhu')

        def __getattr__(self, name):
            import pickle

            def do_rpc(*args, **kwargs):
                for _ in range(3):
                    try:
                        self._connection.send(
                            pickle.dumps((name, args, kwargs)))
                        return pickle.loads(self._connection.recv())
                    except Exception as e:
                        self.__conn()
                raise Exception("RPC connection lost!")

            return do_rpc

    def __init__(self, *args, **kwargs):
        self.client = LocalLLM.RPCProxy("127.0.0.1", 7860)

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        try:
            ans = self.client.chat(
                history,
                gen_conf
            )
            return ans, num_tokens_from_string(ans)
        except Exception as e:
            return "**ERROR**: " + str(e), 0