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 openai.lib.azure import AzureOpenAI
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
from groq import Groq
import os
import json
import requests
import asyncio


class Base(ABC):
    def __init__(self, key, model_name, base_url):
        timeout = int(os.environ.get('LM_TIMEOUT_SECONDS', 600))
        self.client = OpenAI(api_key=key, base_url=base_url, timeout=timeout)
        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.total_tokens
        except openai.APIError as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        ans = ""
        total_tokens = 0
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                stream=True,
                **gen_conf)
            for resp in response:
                if not resp.choices: continue
                if not resp.choices[0].delta.content:
                    resp.choices[0].delta.content = ""
                ans += resp.choices[0].delta.content

                total_tokens += 1
                if not hasattr(resp, "usage") or not resp.usage:
                    total_tokens = (
                                total_tokens
                                + num_tokens_from_string(resp.choices[0].delta.content)
                        )
                elif isinstance(resp.usage, dict):
                    total_tokens = resp.usage.get("total_tokens", total_tokens)
                else: total_tokens = resp.usage.total_tokens

                if resp.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？"
                yield ans

        except openai.APIError as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


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"
        super().__init__(key, model_name, base_url)


class MoonshotChat(Base):
    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"
        super().__init__(key, model_name, base_url)


class XinferenceChat(Base):
    def __init__(self, key=None, model_name="", base_url=""):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        super().__init__(key, model_name, base_url)
class HuggingFaceChat(Base):
    def __init__(self, key=None, model_name="", base_url=""):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        super().__init__(key, model_name, base_url)

class DeepSeekChat(Base):
    def __init__(self, key, model_name="deepseek-chat", base_url="https://api.deepseek.com/v1"):
        if not base_url: base_url = "https://api.deepseek.com/v1"
        super().__init__(key, model_name, base_url)


class AzureChat(Base):
    def __init__(self, key, model_name, **kwargs):
        api_key = json.loads(key).get('api_key', '')
        api_version = json.loads(key).get('api_version', '2024-02-01')
        self.client = AzureOpenAI(api_key=api_key, azure_endpoint=kwargs["base_url"], api_version=api_version)
        self.model_name = model_name


class BaiChuanChat(Base):
    def __init__(self, key, model_name="Baichuan3-Turbo", base_url="https://api.baichuan-ai.com/v1"):
        if not base_url:
            base_url = "https://api.baichuan-ai.com/v1"
        super().__init__(key, model_name, base_url)

    @staticmethod
    def _format_params(params):
        return {
            "temperature": params.get("temperature", 0.3),
            "max_tokens": params.get("max_tokens", 2048),
            "top_p": params.get("top_p", 0.85),
        }

    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,
                extra_body={
                    "tools": [{
                        "type": "web_search",
                        "web_search": {
                            "enable": True,
                            "search_mode": "performance_first"
                        }
                    }]
                },
                **self._format_params(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.total_tokens
        except openai.APIError as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        ans = ""
        total_tokens = 0
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                extra_body={
                    "tools": [{
                        "type": "web_search",
                        "web_search": {
                            "enable": True,
                            "search_mode": "performance_first"
                        }
                    }]
                },
                stream=True,
                **self._format_params(gen_conf))
            for resp in response:
                if not resp.choices: continue
                if not resp.choices[0].delta.content:
                    resp.choices[0].delta.content = ""
                ans += resp.choices[0].delta.content
                total_tokens = (
                    (
                            total_tokens
                            + num_tokens_from_string(resp.choices[0].delta.content)
                    )
                    if not hasattr(resp, "usage")
                    else resp.usage["total_tokens"]
                )
                if resp.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？"
                yield ans

        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


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):
        stream_flag = str(os.environ.get('QWEN_CHAT_BY_STREAM', 'true')).lower() == 'true'
        if not stream_flag:
            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
        else:
            g = self._chat_streamly(system, history, gen_conf, incremental_output=True)
            result_list = list(g)
            error_msg_list = [item for item in result_list if str(item).find("**ERROR**") >= 0]
            if len(error_msg_list) > 0:
                return "**ERROR**: " + "".join(error_msg_list) , 0
            else:
                return "".join(result_list[:-1]), result_list[-1]

    def _chat_streamly(self, system, history, gen_conf, incremental_output=False):
        from http import HTTPStatus
        if system:
            history.insert(0, {"role": "system", "content": system})
        ans = ""
        tk_count = 0
        try:
            response = Generation.call(
                self.model_name,
                messages=history,
                result_format='message',
                stream=True,
                incremental_output=incremental_output,
                **gen_conf
            )
            for resp in response:
                if resp.status_code == HTTPStatus.OK:
                    ans = resp.output.choices[0]['message']['content']
                    tk_count = resp.usage.total_tokens
                    if resp.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？"
                    yield ans
                else:
                    yield ans + "\n**ERROR**: " + resp.message if str(resp.message).find(
                        "Access") < 0 else "Out of credit. Please set the API key in **settings > Model providers.**"
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield tk_count

    def chat_streamly(self, system, history, gen_conf):
        return self._chat_streamly(system, history, gen_conf)


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.total_tokens
        except Exception as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        if "presence_penalty" in gen_conf: del gen_conf["presence_penalty"]
        if "frequency_penalty" in gen_conf: del gen_conf["frequency_penalty"]
        ans = ""
        tk_count = 0
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                stream=True,
                **gen_conf
            )
            for resp in response:
                if not resp.choices[0].delta.content: continue
                delta = resp.choices[0].delta.content
                ans += delta
                if resp.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？"
                    tk_count = resp.usage.total_tokens
                if resp.choices[0].finish_reason == "stop": tk_count = resp.usage.total_tokens
                yield ans
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield tk_count


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 = {}
            if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"]
            if "max_tokens" in gen_conf: options["num_predict"] = gen_conf["max_tokens"]
            if "top_p" in gen_conf: options["top_k"] = gen_conf["top_p"]
            if "presence_penalty" in gen_conf: options["presence_penalty"] = gen_conf["presence_penalty"]
            if "frequency_penalty" in gen_conf: options["frequency_penalty"] = gen_conf["frequency_penalty"]
            response = self.client.chat(
                model=self.model_name,
                messages=history,
                options=options,
                keep_alive=-1
            )
            ans = response["message"]["content"].strip()
            return ans, response["eval_count"] + response.get("prompt_eval_count", 0)
        except Exception as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        options = {}
        if "temperature" in gen_conf: options["temperature"] = gen_conf["temperature"]
        if "max_tokens" in gen_conf: options["num_predict"] = gen_conf["max_tokens"]
        if "top_p" in gen_conf: options["top_k"] = gen_conf["top_p"]
        if "presence_penalty" in gen_conf: options["presence_penalty"] = gen_conf["presence_penalty"]
        if "frequency_penalty" in gen_conf: options["frequency_penalty"] = gen_conf["frequency_penalty"]
        ans = ""
        try:
            response = self.client.chat(
                model=self.model_name,
                messages=history,
                stream=True,
                options=options,
                keep_alive=-1
            )
            for resp in response:
                if resp["done"]:
                    yield resp.get("prompt_eval_count", 0) + resp.get("eval_count", 0)
                ans += resp["message"]["content"]
                yield ans
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)
        yield 0


class LocalAIChat(Base):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        self.client = OpenAI(api_key="empty", base_url=base_url)
        self.model_name = model_name.split("___")[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, key, model_name):
        from jina import Client

        self.client = Client(port=12345, protocol="grpc", asyncio=True)

    def _prepare_prompt(self, system, history, gen_conf):
        from rag.svr.jina_server import Prompt, Generation
        if system:
            history.insert(0, {"role": "system", "content": system})
        if "max_tokens" in gen_conf:
            gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
        return Prompt(message=history, gen_conf=gen_conf)

    def _stream_response(self, endpoint, prompt):
        from rag.svr.jina_server import Prompt, Generation
        answer = ""
        try:
            res = self.client.stream_doc(
                on=endpoint, inputs=prompt, return_type=Generation
            )
            loop = asyncio.get_event_loop()
            try:
                while True:
                    answer = loop.run_until_complete(res.__anext__()).text
                    yield answer
            except StopAsyncIteration:
                pass
        except Exception as e:
            yield answer + "\n**ERROR**: " + str(e)
        yield num_tokens_from_string(answer)

    def chat(self, system, history, gen_conf):
        prompt = self._prepare_prompt(system, history, gen_conf)
        chat_gen = self._stream_response("/chat", prompt)
        ans = next(chat_gen)
        total_tokens = next(chat_gen)
        return ans, total_tokens

    def chat_streamly(self, system, history, gen_conf):
        prompt = self._prepare_prompt(system, history, gen_conf)
        return self._stream_response("/stream", prompt)


class VolcEngineChat(Base):
    def __init__(self, key, model_name, base_url='https://ark.cn-beijing.volces.com/api/v3'):
        """
        Since do not want to modify the original database fields, and the VolcEngine authentication method is quite special,
        Assemble ark_api_key, ep_id into api_key, store it as a dictionary type, and parse it for use
        model_name is for display only
        """
        base_url = base_url if base_url else 'https://ark.cn-beijing.volces.com/api/v3'
        ark_api_key = json.loads(key).get('ark_api_key', '')
        model_name = json.loads(key).get('ep_id', '') + json.loads(key).get('endpoint_id', '')
        super().__init__(ark_api_key, model_name, base_url)


class MiniMaxChat(Base):
    def __init__(
            self,
            key,
            model_name,
            base_url="https://api.minimax.chat/v1/text/chatcompletion_v2",
    ):
        if not base_url:
            base_url = "https://api.minimax.chat/v1/text/chatcompletion_v2"
        self.base_url = base_url
        self.model_name = model_name
        self.api_key = key

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
        }
        payload = json.dumps(
            {"model": self.model_name, "messages": history, **gen_conf}
        )
        try:
            response = requests.request(
                "POST", url=self.base_url, headers=headers, data=payload
            )
            response = response.json()
            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"]["total_tokens"]
        except Exception as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        ans = ""
        total_tokens = 0
        try:
            headers = {
                "Authorization": f"Bearer {self.api_key}",
                "Content-Type": "application/json",
            }
            payload = json.dumps(
                {
                    "model": self.model_name,
                    "messages": history,
                    "stream": True,
                    **gen_conf,
                }
            )
            response = requests.request(
                "POST",
                url=self.base_url,
                headers=headers,
                data=payload,
            )
            for resp in response.text.split("\n\n")[:-1]:
                resp = json.loads(resp[6:])
                text = ""
                if "choices" in resp and "delta" in resp["choices"][0]:
                    text = resp["choices"][0]["delta"]["content"]
                ans += text
                total_tokens = (
                    total_tokens + num_tokens_from_string(text)
                    if "usage" not in resp
                    else resp["usage"]["total_tokens"]
                )
                yield ans

        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


class MistralChat(Base):

    def __init__(self, key, model_name, base_url=None):
        from mistralai.client import MistralClient
        self.client = MistralClient(api_key=key)
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        try:
            response = self.client.chat(
                model=self.model_name,
                messages=history,
                **gen_conf)
            ans = response.choices[0].message.content
            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.total_tokens
        except openai.APIError as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        ans = ""
        total_tokens = 0
        try:
            response = self.client.chat_stream(
                model=self.model_name,
                messages=history,
                **gen_conf)
            for resp in response:
                if not resp.choices or not resp.choices[0].delta.content: continue
                ans += resp.choices[0].delta.content
                total_tokens += 1
                if resp.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？"
                yield ans

        except openai.APIError as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


class BedrockChat(Base):

    def __init__(self, key, model_name, **kwargs):
        import boto3
        self.bedrock_ak = json.loads(key).get('bedrock_ak', '')
        self.bedrock_sk = json.loads(key).get('bedrock_sk', '')
        self.bedrock_region = json.loads(key).get('bedrock_region', '')
        self.model_name = model_name
        self.client = boto3.client(service_name='bedrock-runtime', region_name=self.bedrock_region,
                                   aws_access_key_id=self.bedrock_ak, aws_secret_access_key=self.bedrock_sk)

    def chat(self, system, history, gen_conf):
        from botocore.exceptions import ClientError
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        if "max_tokens" in gen_conf:
            gen_conf["maxTokens"] = gen_conf["max_tokens"]
            _ = gen_conf.pop("max_tokens")
        if "top_p" in gen_conf:
            gen_conf["topP"] = gen_conf["top_p"]
            _ = gen_conf.pop("top_p")
        for item in history:
            if not isinstance(item["content"], list) and not isinstance(item["content"], tuple):
                item["content"] = [{"text": item["content"]}]

        try:
            # Send the message to the model, using a basic inference configuration.
            response = self.client.converse(
                modelId=self.model_name,
                messages=history,
                inferenceConfig=gen_conf,
                system=[{"text": (system if system else "Answer the user's message.")}],
            )

            # Extract and print the response text.
            ans = response["output"]["message"]["content"][0]["text"]
            return ans, num_tokens_from_string(ans)

        except (ClientError, Exception) as e:
            return f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}", 0

    def chat_streamly(self, system, history, gen_conf):
        from botocore.exceptions import ClientError
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        if "max_tokens" in gen_conf:
            gen_conf["maxTokens"] = gen_conf["max_tokens"]
            _ = gen_conf.pop("max_tokens")
        if "top_p" in gen_conf:
            gen_conf["topP"] = gen_conf["top_p"]
            _ = gen_conf.pop("top_p")
        for item in history:
            if not isinstance(item["content"], list) and not isinstance(item["content"], tuple):
                item["content"] = [{"text": item["content"]}]

        if self.model_name.split('.')[0] == 'ai21':
            try:
                response = self.client.converse(
                    modelId=self.model_name,
                    messages=history,
                    inferenceConfig=gen_conf,
                    system=[{"text": (system if system else "Answer the user's message.")}]
                )
                ans = response["output"]["message"]["content"][0]["text"]
                return ans, num_tokens_from_string(ans)

            except (ClientError, Exception) as e:
                return f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}", 0

        ans = ""
        try:
            # Send the message to the model, using a basic inference configuration.
            streaming_response = self.client.converse_stream(
                modelId=self.model_name,
                messages=history,
                inferenceConfig=gen_conf,
                system=[{"text": (system if system else "Answer the user's message.")}]
            )

            # Extract and print the streamed response text in real-time.
            for resp in streaming_response["stream"]:
                if "contentBlockDelta" in resp:
                    ans += resp["contentBlockDelta"]["delta"]["text"]
                    yield ans

        except (ClientError, Exception) as e:
            yield ans + f"ERROR: Can't invoke '{self.model_name}'. Reason: {e}"

        yield num_tokens_from_string(ans)


class GeminiChat(Base):

    def __init__(self, key, model_name, base_url=None):
        from google.generativeai import client, GenerativeModel

        client.configure(api_key=key)
        _client = client.get_default_generative_client()
        self.model_name = 'models/' + model_name
        self.model = GenerativeModel(model_name=self.model_name)
        self.model._client = _client

    def chat(self, system, history, gen_conf):
        from google.generativeai.types import content_types

        if system:
            self.model._system_instruction = content_types.to_content(system)

        if 'max_tokens' in gen_conf:
            gen_conf['max_output_tokens'] = gen_conf['max_tokens']
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_output_tokens"]:
                del gen_conf[k]
        for item in history:
            if 'role' in item and item['role'] == 'assistant':
                item['role'] = 'model'
            if 'role' in item and item['role'] == 'system':
                item['role'] = 'user'
            if 'content' in item:
                item['parts'] = item.pop('content')

        try:
            response = self.model.generate_content(
                history,
                generation_config=gen_conf)
            ans = response.text
            return ans, response.usage_metadata.total_token_count
        except Exception as e:
            return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        from google.generativeai.types import content_types

        if system:
            self.model._system_instruction = content_types.to_content(system)
        if 'max_tokens' in gen_conf:
            gen_conf['max_output_tokens'] = gen_conf['max_tokens']
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_output_tokens"]:
                del gen_conf[k]
        for item in history:
            if 'role' in item and item['role'] == 'assistant':
                item['role'] = 'model'
            if 'content' in item:
                item['parts'] = item.pop('content')
        ans = ""
        try:
            response = self.model.generate_content(
                history,
                generation_config=gen_conf, stream=True)
            for resp in response:
                ans += resp.text
                yield ans

        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield response._chunks[-1].usage_metadata.total_token_count


class GroqChat:
    def __init__(self, key, model_name, base_url=''):
        self.client = Groq(api_key=key)
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        ans = ""
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                **gen_conf
            )
            ans = response.choices[0].message.content
            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.total_tokens
        except Exception as e:
            return ans + "\n**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        ans = ""
        total_tokens = 0
        try:
            response = self.client.chat.completions.create(
                model=self.model_name,
                messages=history,
                stream=True,
                **gen_conf
            )
            for resp in response:
                if not resp.choices or not resp.choices[0].delta.content:
                    continue
                ans += resp.choices[0].delta.content
                total_tokens += 1
                if resp.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？"
                yield ans

        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


## openrouter
class OpenRouterChat(Base):
    def __init__(self, key, model_name, base_url="https://openrouter.ai/api/v1"):
        if not base_url:
            base_url = "https://openrouter.ai/api/v1"
        super().__init__(key, model_name, base_url)


class StepFunChat(Base):
    def __init__(self, key, model_name, base_url="https://api.stepfun.com/v1"):
        if not base_url:
            base_url = "https://api.stepfun.com/v1"
        super().__init__(key, model_name, base_url)


class NvidiaChat(Base):
    def __init__(self, key, model_name, base_url="https://integrate.api.nvidia.com/v1"):
        if not base_url:
            base_url = "https://integrate.api.nvidia.com/v1"
        super().__init__(key, model_name, base_url)


class LmStudioChat(Base):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        self.client = OpenAI(api_key="lm-studio", base_url=base_url)
        self.model_name = model_name


class OpenAI_APIChat(Base):
    def __init__(self, key, model_name, base_url):
        if not base_url:
            raise ValueError("url cannot be None")
        if base_url.split("/")[-1] != "v1":
            base_url = os.path.join(base_url, "v1")
        model_name = model_name.split("___")[0]
        super().__init__(key, model_name, base_url)


class CoHereChat(Base):
    def __init__(self, key, model_name, base_url=""):
        from cohere import Client

        self.client = Client(api_key=key)
        self.model_name = model_name

    def chat(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        if "top_p" in gen_conf:
            gen_conf["p"] = gen_conf.pop("top_p")
        if "frequency_penalty" in gen_conf and "presence_penalty" in gen_conf:
            gen_conf.pop("presence_penalty")
        for item in history:
            if "role" in item and item["role"] == "user":
                item["role"] = "USER"
            if "role" in item and item["role"] == "assistant":
                item["role"] = "CHATBOT"
            if "content" in item:
                item["message"] = item.pop("content")
        mes = history.pop()["message"]
        ans = ""
        try:
            response = self.client.chat(
                model=self.model_name, chat_history=history, message=mes, **gen_conf
            )
            ans = response.text
            if response.finish_reason == "MAX_TOKENS":
                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.meta.tokens.input_tokens + response.meta.tokens.output_tokens,
            )
        except Exception as e:
            return ans + "\n**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            history.insert(0, {"role": "system", "content": system})
        if "top_p" in gen_conf:
            gen_conf["p"] = gen_conf.pop("top_p")
        if "frequency_penalty" in gen_conf and "presence_penalty" in gen_conf:
            gen_conf.pop("presence_penalty")
        for item in history:
            if "role" in item and item["role"] == "user":
                item["role"] = "USER"
            if "role" in item and item["role"] == "assistant":
                item["role"] = "CHATBOT"
            if "content" in item:
                item["message"] = item.pop("content")
        mes = history.pop()["message"]
        ans = ""
        total_tokens = 0
        try:
            response = self.client.chat_stream(
                model=self.model_name, chat_history=history, message=mes, **gen_conf
            )
            for resp in response:
                if resp.event_type == "text-generation":
                    ans += resp.text
                    total_tokens += num_tokens_from_string(resp.text)
                elif resp.event_type == "stream-end":
                    if resp.finish_reason == "MAX_TOKENS":
                        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？"
                        )
                yield ans

        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


class LeptonAIChat(Base):
    def __init__(self, key, model_name, base_url=None):
        if not base_url:
            base_url = os.path.join("https://" + model_name + ".lepton.run", "api", "v1")
        super().__init__(key, model_name, base_url)


class TogetherAIChat(Base):
    def __init__(self, key, model_name, base_url="https://api.together.xyz/v1"):
        if not base_url:
            base_url = "https://api.together.xyz/v1"
        super().__init__(key, model_name, base_url)


class PerfXCloudChat(Base):
    def __init__(self, key, model_name, base_url="https://cloud.perfxlab.cn/v1"):
        if not base_url:
            base_url = "https://cloud.perfxlab.cn/v1"
        super().__init__(key, model_name, base_url)


class UpstageChat(Base):
    def __init__(self, key, model_name, base_url="https://api.upstage.ai/v1/solar"):
        if not base_url:
            base_url = "https://api.upstage.ai/v1/solar"
        super().__init__(key, model_name, base_url)


class NovitaAIChat(Base):
    def __init__(self, key, model_name, base_url="https://api.novita.ai/v3/openai"):
        if not base_url:
            base_url = "https://api.novita.ai/v3/openai"
        super().__init__(key, model_name, base_url)


class SILICONFLOWChat(Base):
    def __init__(self, key, model_name, base_url="https://api.siliconflow.cn/v1"):
        if not base_url:
            base_url = "https://api.siliconflow.cn/v1"
        super().__init__(key, model_name, base_url)


class YiChat(Base):
    def __init__(self, key, model_name, base_url="https://api.lingyiwanwu.com/v1"):
        if not base_url:
            base_url = "https://api.lingyiwanwu.com/v1"
        super().__init__(key, model_name, base_url)


class ReplicateChat(Base):
    def __init__(self, key, model_name, base_url=None):
        from replicate.client import Client

        self.model_name = model_name
        self.client = Client(api_token=key)
        self.system = ""

    def chat(self, system, history, gen_conf):
        if "max_tokens" in gen_conf:
            gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
        if system:
            self.system = system
        prompt = "\n".join(
            [item["role"] + ":" + item["content"] for item in history[-5:]]
        )
        ans = ""
        try:
            response = self.client.run(
                self.model_name,
                input={"system_prompt": self.system, "prompt": prompt, **gen_conf},
            )
            ans = "".join(response)
            return ans, num_tokens_from_string(ans)
        except Exception as e:
            return ans + "\n**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if "max_tokens" in gen_conf:
            gen_conf["max_new_tokens"] = gen_conf.pop("max_tokens")
        if system:
            self.system = system
        prompt = "\n".join(
            [item["role"] + ":" + item["content"] for item in history[-5:]]
        )
        ans = ""
        try:
            response = self.client.run(
                self.model_name,
                input={"system_prompt": self.system, "prompt": prompt, **gen_conf},
            )
            for resp in response:
                ans += resp
                yield ans

        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield num_tokens_from_string(ans)


class HunyuanChat(Base):
    def __init__(self, key, model_name, base_url=None):
        from tencentcloud.common import credential
        from tencentcloud.hunyuan.v20230901 import hunyuan_client

        key = json.loads(key)
        sid = key.get("hunyuan_sid", "")
        sk = key.get("hunyuan_sk", "")
        cred = credential.Credential(sid, sk)
        self.model_name = model_name
        self.client = hunyuan_client.HunyuanClient(cred, "")

    def chat(self, system, history, gen_conf):
        from tencentcloud.hunyuan.v20230901 import models
        from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
            TencentCloudSDKException,
        )

        _gen_conf = {}
        _history = [{k.capitalize(): v for k, v in item.items()} for item in history]
        if system:
            _history.insert(0, {"Role": "system", "Content": system})
        if "temperature" in gen_conf:
            _gen_conf["Temperature"] = gen_conf["temperature"]
        if "top_p" in gen_conf:
            _gen_conf["TopP"] = gen_conf["top_p"]

        req = models.ChatCompletionsRequest()
        params = {"Model": self.model_name, "Messages": _history, **_gen_conf}
        req.from_json_string(json.dumps(params))
        ans = ""
        try:
            response = self.client.ChatCompletions(req)
            ans = response.Choices[0].Message.Content
            return ans, response.Usage.TotalTokens
        except TencentCloudSDKException as e:
            return ans + "\n**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        from tencentcloud.hunyuan.v20230901 import models
        from tencentcloud.common.exception.tencent_cloud_sdk_exception import (
            TencentCloudSDKException,
        )

        _gen_conf = {}
        _history = [{k.capitalize(): v for k, v in item.items()} for item in history]
        if system:
            _history.insert(0, {"Role": "system", "Content": system})

        if "temperature" in gen_conf:
            _gen_conf["Temperature"] = gen_conf["temperature"]
        if "top_p" in gen_conf:
            _gen_conf["TopP"] = gen_conf["top_p"]
        req = models.ChatCompletionsRequest()
        params = {
            "Model": self.model_name,
            "Messages": _history,
            "Stream": True,
            **_gen_conf,
        }
        req.from_json_string(json.dumps(params))
        ans = ""
        total_tokens = 0
        try:
            response = self.client.ChatCompletions(req)
            for resp in response:
                resp = json.loads(resp["data"])
                if not resp["Choices"] or not resp["Choices"][0]["Delta"]["Content"]:
                    continue
                ans += resp["Choices"][0]["Delta"]["Content"]
                total_tokens += 1

                yield ans

        except TencentCloudSDKException as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


class SparkChat(Base):
    def __init__(
            self, key, model_name, base_url="https://spark-api-open.xf-yun.com/v1"
    ):
        if not base_url:
            base_url = "https://spark-api-open.xf-yun.com/v1"
        model2version = {
            "Spark-Max": "generalv3.5",
            "Spark-Lite": "general",
            "Spark-Pro": "generalv3",
            "Spark-Pro-128K": "pro-128k",
            "Spark-4.0-Ultra": "4.0Ultra",
        }
        model_version = model2version[model_name]
        super().__init__(key, model_version, base_url)


class BaiduYiyanChat(Base):
    def __init__(self, key, model_name, base_url=None):
        import qianfan

        key = json.loads(key)
        ak = key.get("yiyan_ak", "")
        sk = key.get("yiyan_sk", "")
        self.client = qianfan.ChatCompletion(ak=ak, sk=sk)
        self.model_name = model_name.lower()
        self.system = ""

    def chat(self, system, history, gen_conf):
        if system:
            self.system = system
        gen_conf["penalty_score"] = (
                                            (gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty",
                                                                                                0)) / 2
                                    ) + 1
        if "max_tokens" in gen_conf:
            gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
        ans = ""

        try:
            response = self.client.do(
                model=self.model_name,
                messages=history,
                system=self.system,
                **gen_conf
            ).body
            ans = response['result']
            return ans, response["usage"]["total_tokens"]

        except Exception as e:
            return ans + "\n**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            self.system = system
        gen_conf["penalty_score"] = (
                                            (gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty",
                                                                                                0)) / 2
                                    ) + 1
        if "max_tokens" in gen_conf:
            gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
        ans = ""
        total_tokens = 0

        try:
            response = self.client.do(
                model=self.model_name,
                messages=history,
                system=self.system,
                stream=True,
                **gen_conf
            )
            for resp in response:
                resp = resp.body
                ans += resp['result']
                total_tokens = resp["usage"]["total_tokens"]

                yield ans

        except Exception as e:
            return ans + "\n**ERROR**: " + str(e), 0

        yield total_tokens


class AnthropicChat(Base):
    def __init__(self, key, model_name, base_url=None):
        import anthropic

        self.client = anthropic.Anthropic(api_key=key)
        self.model_name = model_name
        self.system = ""

    def chat(self, system, history, gen_conf):
        if system:
            self.system = system
        if "max_tokens" not in gen_conf:
            gen_conf["max_tokens"] = 4096

        try:
            response = self.client.messages.create(
                model=self.model_name,
                messages=history,
                system=self.system,
                stream=False,
                **gen_conf,
            ).json()
            ans = response["content"][0]["text"]
            if response["stop_reason"] == "max_tokens":
                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"]["input_tokens"] + response["usage"]["output_tokens"],
            )
        except Exception as e:
            return ans + "\n**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            self.system = system
        if "max_tokens" not in gen_conf:
            gen_conf["max_tokens"] = 4096

        ans = ""
        total_tokens = 0
        try:
            response = self.client.messages.create(
                model=self.model_name,
                messages=history,
                system=self.system,
                stream=True,
                **gen_conf,
            )
            for res in response.iter_lines():
                res = res.decode("utf-8")
                if "content_block_delta" in res and "data" in res:
                    text = json.loads(res[6:])["delta"]["text"]
                    ans += text
                    total_tokens += num_tokens_from_string(text)
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens


class GoogleChat(Base):
    def __init__(self, key, model_name, base_url=None):
        from google.oauth2 import service_account
        import base64

        key = json.load(key)
        access_token = json.loads(
            base64.b64decode(key.get("google_service_account_key", ""))
        )
        project_id = key.get("google_project_id", "")
        region = key.get("google_region", "")

        scopes = ["https://www.googleapis.com/auth/cloud-platform"]
        self.model_name = model_name
        self.system = ""

        if "claude" in self.model_name:
            from anthropic import AnthropicVertex
            from google.auth.transport.requests import Request

            if access_token:
                credits = service_account.Credentials.from_service_account_info(
                    access_token, scopes=scopes
                )
                request = Request()
                credits.refresh(request)
                token = credits.token
                self.client = AnthropicVertex(
                    region=region, project_id=project_id, access_token=token
                )
            else:
                self.client = AnthropicVertex(region=region, project_id=project_id)
        else:
            from google.cloud import aiplatform
            import vertexai.generative_models as glm

            if access_token:
                credits = service_account.Credentials.from_service_account_info(
                    access_token
                )
                aiplatform.init(
                    credentials=credits, project=project_id, location=region
                )
            else:
                aiplatform.init(project=project_id, location=region)
            self.client = glm.GenerativeModel(model_name=self.model_name)

    def chat(self, system, history, gen_conf):
        if system:
            self.system = system

        if "claude" in self.model_name:
            if "max_tokens" not in gen_conf:
                gen_conf["max_tokens"] = 4096
            try:
                response = self.client.messages.create(
                    model=self.model_name,
                    messages=history,
                    system=self.system,
                    stream=False,
                    **gen_conf,
                ).json()
                ans = response["content"][0]["text"]
                if response["stop_reason"] == "max_tokens":
                    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"]["input_tokens"]
                    + response["usage"]["output_tokens"],
                )
            except Exception as e:
                return "\n**ERROR**: " + str(e), 0
        else:
            self.client._system_instruction = self.system
            if "max_tokens" in gen_conf:
                gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
            for k in list(gen_conf.keys()):
                if k not in ["temperature", "top_p", "max_output_tokens"]:
                    del gen_conf[k]
            for item in history:
                if "role" in item and item["role"] == "assistant":
                    item["role"] = "model"
                if "content" in item:
                    item["parts"] = item.pop("content")
            try:
                response = self.client.generate_content(
                    history, generation_config=gen_conf
                )
                ans = response.text
                return ans, response.usage_metadata.total_token_count
            except Exception as e:
                return "**ERROR**: " + str(e), 0

    def chat_streamly(self, system, history, gen_conf):
        if system:
            self.system = system

        if "claude" in self.model_name:
            if "max_tokens" not in gen_conf:
                gen_conf["max_tokens"] = 4096
            ans = ""
            total_tokens = 0
            try:
                response = self.client.messages.create(
                    model=self.model_name,
                    messages=history,
                    system=self.system,
                    stream=True,
                    **gen_conf,
                )
                for res in response.iter_lines():
                    res = res.decode("utf-8")
                    if "content_block_delta" in res and "data" in res:
                        text = json.loads(res[6:])["delta"]["text"]
                        ans += text
                        total_tokens += num_tokens_from_string(text)
            except Exception as e:
                yield ans + "\n**ERROR**: " + str(e)

            yield total_tokens
        else:
            self.client._system_instruction = self.system
            if "max_tokens" in gen_conf:
                gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
            for k in list(gen_conf.keys()):
                if k not in ["temperature", "top_p", "max_output_tokens"]:
                    del gen_conf[k]
            for item in history:
                if "role" in item and item["role"] == "assistant":
                    item["role"] = "model"
                if "content" in item:
                    item["parts"] = item.pop("content")
            ans = ""
            try:
                response = self.model.generate_content(
                    history, generation_config=gen_conf, stream=True
                )
                for resp in response:
                    ans += resp.text
                    yield ans

            except Exception as e:
                yield ans + "\n**ERROR**: " + str(e)

            yield response._chunks[-1].usage_metadata.total_token_count