ragflow/rag/llm/chat_model.py

#
#  Copyright 2025 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.
#
import asyncio
import json
import logging
import os
import random
import re
import time
from abc import ABC
from copy import deepcopy
from urllib.parse import urljoin

import json_repair
import litellm
import openai
import requests
from openai import OpenAI
from openai.lib.azure import AzureOpenAI
from strenum import StrEnum
from zhipuai import ZhipuAI

from rag.llm import FACTORY_DEFAULT_BASE_URL, LITELLM_PROVIDER_PREFIX, SupportedLiteLLMProvider
from rag.nlp import is_chinese, is_english
from common.token_utils import num_tokens_from_string, total_token_count_from_response


# Error message constants
class LLMErrorCode(StrEnum):
    ERROR_RATE_LIMIT = "RATE_LIMIT_EXCEEDED"
    ERROR_AUTHENTICATION = "AUTH_ERROR"
    ERROR_INVALID_REQUEST = "INVALID_REQUEST"
    ERROR_SERVER = "SERVER_ERROR"
    ERROR_TIMEOUT = "TIMEOUT"
    ERROR_CONNECTION = "CONNECTION_ERROR"
    ERROR_MODEL = "MODEL_ERROR"
    ERROR_MAX_ROUNDS = "ERROR_MAX_ROUNDS"
    ERROR_CONTENT_FILTER = "CONTENT_FILTERED"
    ERROR_QUOTA = "QUOTA_EXCEEDED"
    ERROR_MAX_RETRIES = "MAX_RETRIES_EXCEEDED"
    ERROR_GENERIC = "GENERIC_ERROR"


class ReActMode(StrEnum):
    FUNCTION_CALL = "function_call"
    REACT = "react"


ERROR_PREFIX = "**ERROR**"
LENGTH_NOTIFICATION_CN = "······\n由于大模型的上下文窗口大小限制，回答已经被大模型截断。"
LENGTH_NOTIFICATION_EN = "...\nThe answer is truncated by your chosen LLM due to its limitation on context length."


class Base(ABC):
    def __init__(self, key, model_name, base_url, **kwargs):
        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
        # Configure retry parameters
        self.max_retries = kwargs.get("max_retries", int(os.environ.get("LLM_MAX_RETRIES", 5)))
        self.base_delay = kwargs.get("retry_interval", float(os.environ.get("LLM_BASE_DELAY", 2.0)))
        self.max_rounds = kwargs.get("max_rounds", 5)
        self.is_tools = False
        self.tools = []
        self.toolcall_sessions = {}

    def _get_delay(self):
        """Calculate retry delay time"""
        return self.base_delay * random.uniform(10, 150)

    def _classify_error(self, error):
        """Classify error based on error message content"""
        error_str = str(error).lower()

        keywords_mapping = [
            (["quota", "capacity", "credit", "billing", "balance", "欠费"], LLMErrorCode.ERROR_QUOTA),
            (["rate limit", "429", "tpm limit", "too many requests", "requests per minute"], LLMErrorCode.ERROR_RATE_LIMIT),
            (["auth", "key", "apikey", "401", "forbidden", "permission"], LLMErrorCode.ERROR_AUTHENTICATION),
            (["invalid", "bad request", "400", "format", "malformed", "parameter"], LLMErrorCode.ERROR_INVALID_REQUEST),
            (["server", "503", "502", "504", "500", "unavailable"], LLMErrorCode.ERROR_SERVER),
            (["timeout", "timed out"], LLMErrorCode.ERROR_TIMEOUT),
            (["connect", "network", "unreachable", "dns"], LLMErrorCode.ERROR_CONNECTION),
            (["filter", "content", "policy", "blocked", "safety", "inappropriate"], LLMErrorCode.ERROR_CONTENT_FILTER),
            (["model", "not found", "does not exist", "not available"], LLMErrorCode.ERROR_MODEL),
            (["max rounds"], LLMErrorCode.ERROR_MODEL),
        ]
        for words, code in keywords_mapping:
            if re.search("({})".format("|".join(words)), error_str):
                return code

        return LLMErrorCode.ERROR_GENERIC

    def _clean_conf(self, gen_conf):
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]

        allowed_conf = {
            "temperature",
            "max_completion_tokens",
            "top_p",
            "stream",
            "stream_options",
            "stop",
            "n",
            "presence_penalty",
            "frequency_penalty",
            "functions",
            "function_call",
            "logit_bias",
            "user",
            "response_format",
            "seed",
            "tools",
            "tool_choice",
            "logprobs",
            "top_logprobs",
            "extra_headers"
        }

        gen_conf = {k: v for k, v in gen_conf.items() if k in allowed_conf}

        return gen_conf

    def _chat(self, history, gen_conf, **kwargs):
        logging.info("[HISTORY]" + json.dumps(history, ensure_ascii=False, indent=2))
        if self.model_name.lower().find("qwq") >= 0:
            logging.info(f"[INFO] {self.model_name} detected as reasoning model, using _chat_streamly")

            final_ans = ""
            tol_token = 0
            for delta, tol in self._chat_streamly(history, gen_conf, with_reasoning=False, **kwargs):
                if delta.startswith("<think>") or delta.endswith("</think>"):
                    continue
                final_ans += delta
                tol_token = tol

            if len(final_ans.strip()) == 0:
                final_ans = "**ERROR**: Empty response from reasoning model"

            return final_ans.strip(), tol_token

        if self.model_name.lower().find("qwen3") >= 0:
            kwargs["extra_body"] = {"enable_thinking": False}

        response = self.client.chat.completions.create(model=self.model_name, messages=history, **gen_conf, **kwargs)

        if not response.choices or not response.choices[0].message or not response.choices[0].message.content:
            return "", 0
        ans = response.choices[0].message.content.strip()
        if response.choices[0].finish_reason == "length":
            ans = self._length_stop(ans)
        return ans, total_token_count_from_response(response)

    def _chat_streamly(self, history, gen_conf, **kwargs):
        logging.info("[HISTORY STREAMLY]" + json.dumps(history, ensure_ascii=False, indent=4))
        reasoning_start = False

        if kwargs.get("stop") or "stop" in gen_conf:
            response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, **gen_conf, stop=kwargs.get("stop"))
        else:
            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 = ""
            if kwargs.get("with_reasoning", True) and hasattr(resp.choices[0].delta, "reasoning_content") and resp.choices[0].delta.reasoning_content:
                ans = ""
                if not reasoning_start:
                    reasoning_start = True
                    ans = "<think>"
                ans += resp.choices[0].delta.reasoning_content + "</think>"
            else:
                reasoning_start = False
                ans = resp.choices[0].delta.content

            tol = total_token_count_from_response(resp)
            if not tol:
                tol = num_tokens_from_string(resp.choices[0].delta.content)

            if resp.choices[0].finish_reason == "length":
                if is_chinese(ans):
                    ans += LENGTH_NOTIFICATION_CN
                else:
                    ans += LENGTH_NOTIFICATION_EN
            yield ans, tol

    def _length_stop(self, ans):
        if is_chinese([ans]):
            return ans + LENGTH_NOTIFICATION_CN
        return ans + LENGTH_NOTIFICATION_EN

    @property
    def _retryable_errors(self) -> set[str]:
        return {
            LLMErrorCode.ERROR_RATE_LIMIT,
            LLMErrorCode.ERROR_SERVER,
        }

    def _should_retry(self, error_code: str) -> bool:
        return error_code in self._retryable_errors

    def _exceptions(self, e, attempt) -> str | None:
        logging.exception("OpenAI chat_with_tools")
        # Classify the error
        error_code = self._classify_error(e)
        if attempt == self.max_retries:
            error_code = LLMErrorCode.ERROR_MAX_RETRIES

        if self._should_retry(error_code):
            delay = self._get_delay()
            logging.warning(f"Error: {error_code}. Retrying in {delay:.2f} seconds... (Attempt {attempt + 1}/{self.max_retries})")
            time.sleep(delay)
            return None

        return f"{ERROR_PREFIX}: {error_code} - {str(e)}"

    def _verbose_tool_use(self, name, args, res):
        return "<tool_call>" + json.dumps({"name": name, "args": args, "result": res}, ensure_ascii=False, indent=2) + "</tool_call>"

    def _append_history(self, hist, tool_call, tool_res):
        hist.append(
            {
                "role": "assistant",
                "tool_calls": [
                    {
                        "index": tool_call.index,
                        "id": tool_call.id,
                        "function": {
                            "name": tool_call.function.name,
                            "arguments": tool_call.function.arguments,
                        },
                        "type": "function",
                    },
                ],
            }
        )
        try:
            if isinstance(tool_res, dict):
                tool_res = json.dumps(tool_res, ensure_ascii=False)
        finally:
            hist.append({"role": "tool", "tool_call_id": tool_call.id, "content": str(tool_res)})
        return hist

    def bind_tools(self, toolcall_session, tools):
        if not (toolcall_session and tools):
            return
        self.is_tools = True
        self.toolcall_session = toolcall_session
        self.tools = tools

    def chat_with_tools(self, system: str, history: list, gen_conf: dict = {}):
        gen_conf = self._clean_conf(gen_conf)
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})

        ans = ""
        tk_count = 0
        hist = deepcopy(history)
        # Implement exponential backoff retry strategy
        for attempt in range(self.max_retries + 1):
            history = hist
            try:
                for _ in range(self.max_rounds + 1):
                    logging.info(f"{self.tools=}")
                    response = self.client.chat.completions.create(model=self.model_name, messages=history, tools=self.tools, tool_choice="auto", **gen_conf)
                    tk_count += total_token_count_from_response(response)
                    if any([not response.choices, not response.choices[0].message]):
                        raise Exception(f"500 response structure error. Response: {response}")

                    if not hasattr(response.choices[0].message, "tool_calls") or not response.choices[0].message.tool_calls:
                        if hasattr(response.choices[0].message, "reasoning_content") and response.choices[0].message.reasoning_content:
                            ans += "<think>" + response.choices[0].message.reasoning_content + "</think>"

                        ans += response.choices[0].message.content
                        if response.choices[0].finish_reason == "length":
                            ans = self._length_stop(ans)

                        return ans, tk_count

                    for tool_call in response.choices[0].message.tool_calls:
                        logging.info(f"Response {tool_call=}")
                        name = tool_call.function.name
                        try:
                            args = json_repair.loads(tool_call.function.arguments)
                            tool_response = self.toolcall_session.tool_call(name, args)
                            history = self._append_history(history, tool_call, tool_response)
                            ans += self._verbose_tool_use(name, args, tool_response)
                        except Exception as e:
                            logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}")
                            history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Tool call error: \n{tool_call}\nException:\n" + str(e)})
                            ans += self._verbose_tool_use(name, {}, str(e))

                logging.warning(f"Exceed max rounds: {self.max_rounds}")
                history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"})
                response, token_count = self._chat(history, gen_conf)
                ans += response
                tk_count += token_count
                return ans, tk_count
            except Exception as e:
                e = self._exceptions(e, attempt)
                if e:
                    return e, tk_count

        assert False, "Shouldn't be here."

    def chat(self, system, history, gen_conf={}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        gen_conf = self._clean_conf(gen_conf)

        # Implement exponential backoff retry strategy
        for attempt in range(self.max_retries + 1):
            try:
                return self._chat(history, gen_conf, **kwargs)
            except Exception as e:
                e = self._exceptions(e, attempt)
                if e:
                    return e, 0
        assert False, "Shouldn't be here."

    def _wrap_toolcall_message(self, stream):
        final_tool_calls = {}

        for chunk in stream:
            for tool_call in chunk.choices[0].delta.tool_calls or []:
                index = tool_call.index

                if index not in final_tool_calls:
                    final_tool_calls[index] = tool_call

                final_tool_calls[index].function.arguments += tool_call.function.arguments

        return final_tool_calls

    def chat_streamly_with_tools(self, system: str, history: list, gen_conf: dict = {}):
        gen_conf = self._clean_conf(gen_conf)
        tools = self.tools
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})

        total_tokens = 0
        hist = deepcopy(history)
        # Implement exponential backoff retry strategy
        for attempt in range(self.max_retries + 1):
            history = hist
            try:
                for _ in range(self.max_rounds + 1):
                    reasoning_start = False
                    logging.info(f"{tools=}")
                    response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, tools=tools, tool_choice="auto", **gen_conf)
                    final_tool_calls = {}
                    answer = ""
                    for resp in response:
                        if resp.choices[0].delta.tool_calls:
                            for tool_call in resp.choices[0].delta.tool_calls or []:
                                index = tool_call.index

                                if index not in final_tool_calls:
                                    if not tool_call.function.arguments:
                                        tool_call.function.arguments = ""
                                    final_tool_calls[index] = tool_call
                                else:
                                    final_tool_calls[index].function.arguments += tool_call.function.arguments if tool_call.function.arguments else ""
                            continue

                        if any([not resp.choices, not resp.choices[0].delta, not hasattr(resp.choices[0].delta, "content")]):
                            raise Exception("500 response structure error.")

                        if not resp.choices[0].delta.content:
                            resp.choices[0].delta.content = ""

                        if hasattr(resp.choices[0].delta, "reasoning_content") and resp.choices[0].delta.reasoning_content:
                            ans = ""
                            if not reasoning_start:
                                reasoning_start = True
                                ans = "<think>"
                            ans += resp.choices[0].delta.reasoning_content + "</think>"
                            yield ans
                        else:
                            reasoning_start = False
                            answer += resp.choices[0].delta.content
                            yield resp.choices[0].delta.content

                        tol = total_token_count_from_response(resp)
                        if not tol:
                            total_tokens += num_tokens_from_string(resp.choices[0].delta.content)
                        else:
                            total_tokens = tol

                        finish_reason = resp.choices[0].finish_reason if hasattr(resp.choices[0], "finish_reason") else ""
                        if finish_reason == "length":
                            yield self._length_stop("")

                    if answer:
                        yield total_tokens
                        return

                    for tool_call in final_tool_calls.values():
                        name = tool_call.function.name
                        try:
                            args = json_repair.loads(tool_call.function.arguments)
                            yield self._verbose_tool_use(name, args, "Begin to call...")
                            tool_response = self.toolcall_session.tool_call(name, args)
                            history = self._append_history(history, tool_call, tool_response)
                            yield self._verbose_tool_use(name, args, tool_response)
                        except Exception as e:
                            logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}")
                            history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Tool call error: \n{tool_call}\nException:\n" + str(e)})
                            yield self._verbose_tool_use(name, {}, str(e))

                logging.warning(f"Exceed max rounds: {self.max_rounds}")
                history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"})
                response = self.client.chat.completions.create(model=self.model_name, messages=history, stream=True, **gen_conf)
                for resp in response:
                    if any([not resp.choices, not resp.choices[0].delta, not hasattr(resp.choices[0].delta, "content")]):
                        raise Exception("500 response structure error.")
                    if not resp.choices[0].delta.content:
                        resp.choices[0].delta.content = ""
                        continue
                    tol = total_token_count_from_response(resp)
                    if not tol:
                        total_tokens += num_tokens_from_string(resp.choices[0].delta.content)
                    else:
                        total_tokens = tol
                    answer += resp.choices[0].delta.content
                    yield resp.choices[0].delta.content

                yield total_tokens
                return

            except Exception as e:
                e = self._exceptions(e, attempt)
                if e:
                    yield e
                    yield total_tokens
                    return

        assert False, "Shouldn't be here."

    def chat_streamly(self, system, history, gen_conf: dict = {}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        gen_conf = self._clean_conf(gen_conf)
        ans = ""
        total_tokens = 0
        try:
            for delta_ans, tol in self._chat_streamly(history, gen_conf, **kwargs):
                yield delta_ans
                total_tokens += tol
        except openai.APIError as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens

    def _calculate_dynamic_ctx(self, history):
        """Calculate dynamic context window size"""

        def count_tokens(text):
            """Calculate token count for text"""
            # Simple calculation: 1 token per ASCII character
            # 2 tokens for non-ASCII characters (Chinese, Japanese, Korean, etc.)
            total = 0
            for char in text:
                if ord(char) < 128:  # ASCII characters
                    total += 1
                else:  # Non-ASCII characters (Chinese, Japanese, Korean, etc.)
                    total += 2
            return total

        # Calculate total tokens for all messages
        total_tokens = 0
        for message in history:
            content = message.get("content", "")
            # Calculate content tokens
            content_tokens = count_tokens(content)
            # Add role marker token overhead
            role_tokens = 4
            total_tokens += content_tokens + role_tokens

        # Apply 1.2x buffer ratio
        total_tokens_with_buffer = int(total_tokens * 1.2)

        if total_tokens_with_buffer <= 8192:
            ctx_size = 8192
        else:
            ctx_multiplier = (total_tokens_with_buffer // 8192) + 1
            ctx_size = ctx_multiplier * 8192

        return ctx_size


class GptTurbo(Base):
    _FACTORY_NAME = "OpenAI"

    def __init__(self, key, model_name="gpt-3.5-turbo", base_url="https://api.openai.com/v1", **kwargs):
        if not base_url:
            base_url = "https://api.openai.com/v1"
        super().__init__(key, model_name, base_url, **kwargs)


class XinferenceChat(Base):
    _FACTORY_NAME = "Xinference"

    def __init__(self, key=None, model_name="", base_url="", **kwargs):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        super().__init__(key, model_name, base_url, **kwargs)


class HuggingFaceChat(Base):
    _FACTORY_NAME = "HuggingFace"

    def __init__(self, key=None, model_name="", base_url="", **kwargs):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        super().__init__(key, model_name.split("___")[0], base_url, **kwargs)


class ModelScopeChat(Base):
    _FACTORY_NAME = "ModelScope"

    def __init__(self, key=None, model_name="", base_url="", **kwargs):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        super().__init__(key, model_name.split("___")[0], base_url, **kwargs)


class AzureChat(Base):
    _FACTORY_NAME = "Azure-OpenAI"

    def __init__(self, key, model_name, base_url, **kwargs):
        api_key = json.loads(key).get("api_key", "")
        api_version = json.loads(key).get("api_version", "2024-02-01")
        super().__init__(key, model_name, base_url, **kwargs)
        self.client = AzureOpenAI(api_key=api_key, azure_endpoint=base_url, api_version=api_version)
        self.model_name = model_name

    @property
    def _retryable_errors(self) -> set[str]:
        return {
            LLMErrorCode.ERROR_RATE_LIMIT,
            LLMErrorCode.ERROR_SERVER,
            LLMErrorCode.ERROR_QUOTA,
        }


class BaiChuanChat(Base):
    _FACTORY_NAME = "BaiChuan"

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

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

    def _clean_conf(self, gen_conf):
        return {
            "temperature": gen_conf.get("temperature", 0.3),
            "top_p": gen_conf.get("top_p", 0.85),
        }

    def _chat(self, history, gen_conf={}, **kwargs):
        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"}}]},
            **gen_conf,
        )
        ans = response.choices[0].message.content.strip()
        if response.choices[0].finish_reason == "length":
            if is_chinese([ans]):
                ans += LENGTH_NOTIFICATION_CN
            else:
                ans += LENGTH_NOTIFICATION_EN
        return ans, total_token_count_from_response(response)

    def chat_streamly(self, system, history, gen_conf={}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        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
                tol = total_token_count_from_response(resp)
                if not tol:
                    total_tokens += num_tokens_from_string(resp.choices[0].delta.content)
                else:
                    total_tokens = tol
                if resp.choices[0].finish_reason == "length":
                    if is_chinese([ans]):
                        ans += LENGTH_NOTIFICATION_CN
                    else:
                        ans += LENGTH_NOTIFICATION_EN
                yield ans

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

        yield total_tokens


class ZhipuChat(Base):
    _FACTORY_NAME = "ZHIPU-AI"

    def __init__(self, key, model_name="glm-3-turbo", base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

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

    def _clean_conf(self, gen_conf):
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        gen_conf = self._clean_conf_plealty(gen_conf)
        return gen_conf

    def _clean_conf_plealty(self, gen_conf):
        if "presence_penalty" in gen_conf:
            del gen_conf["presence_penalty"]
        if "frequency_penalty" in gen_conf:
            del gen_conf["frequency_penalty"]
        return gen_conf

    def chat_with_tools(self, system: str, history: list, gen_conf: dict):
        gen_conf = self._clean_conf_plealty(gen_conf)

        return super().chat_with_tools(system, history, gen_conf)

    def chat_streamly(self, system, history, gen_conf={}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        gen_conf = self._clean_conf(gen_conf)
        ans = ""
        tk_count = 0
        try:
            logging.info(json.dumps(history, ensure_ascii=False, indent=2))
            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":
                    if is_chinese(ans):
                        ans += LENGTH_NOTIFICATION_CN
                    else:
                        ans += LENGTH_NOTIFICATION_EN
                    tk_count = total_token_count_from_response(resp)
                if resp.choices[0].finish_reason == "stop":
                    tk_count = total_token_count_from_response(resp)
                yield ans
        except Exception as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield tk_count

    def chat_streamly_with_tools(self, system: str, history: list, gen_conf: dict):
        gen_conf = self._clean_conf_plealty(gen_conf)
        return super().chat_streamly_with_tools(system, history, gen_conf)


class LocalAIChat(Base):
    _FACTORY_NAME = "LocalAI"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        self.client = OpenAI(api_key="empty", base_url=base_url)
        self.model_name = model_name.split("___")[0]


class LocalLLM(Base):
    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)
        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

        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        return Prompt(message=history, gen_conf=gen_conf)

    def _stream_response(self, endpoint, prompt):
        from rag.svr.jina_server import 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={}, **kwargs):
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        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={}, **kwargs):
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        prompt = self._prepare_prompt(system, history, gen_conf)
        return self._stream_response("/stream", prompt)


class VolcEngineChat(Base):
    _FACTORY_NAME = "VolcEngine"

    def __init__(self, key, model_name, base_url="https://ark.cn-beijing.volces.com/api/v3", **kwargs):
        """
        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, **kwargs)


class MiniMaxChat(Base):
    _FACTORY_NAME = "MiniMax"

    def __init__(self, key, model_name, base_url="https://api.minimax.chat/v1/text/chatcompletion_v2", **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        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 _clean_conf(self, gen_conf):
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        return gen_conf

    def _chat(self, history, gen_conf):
        headers = {
            "Authorization": f"Bearer {self.api_key}",
            "Content-Type": "application/json",
        }
        payload = json.dumps({"model": self.model_name, "messages": history, **gen_conf})
        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":
            if is_chinese(ans):
                ans += LENGTH_NOTIFICATION_CN
            else:
                ans += LENGTH_NOTIFICATION_EN
        return ans, total_token_count_from_response(response)

    def chat_streamly(self, system, history, gen_conf):
        if system and history and history[0].get("role") != "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:
            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
                tol = total_token_count_from_response(resp)
                if not tol:
                    total_tokens += num_tokens_from_string(text)
                else:
                    total_tokens = tol
                yield ans

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

        yield total_tokens


class MistralChat(Base):
    _FACTORY_NAME = "Mistral"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        from mistralai.client import MistralClient

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

    def _clean_conf(self, gen_conf):
        for k in list(gen_conf.keys()):
            if k not in ["temperature", "top_p", "max_tokens"]:
                del gen_conf[k]
        return gen_conf

    def _chat(self, history, gen_conf={}, **kwargs):
        gen_conf = self._clean_conf(gen_conf)
        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":
            if is_chinese(ans):
                ans += LENGTH_NOTIFICATION_CN
            else:
                ans += LENGTH_NOTIFICATION_EN
        return ans, total_token_count_from_response(response)

    def chat_streamly(self, system, history, gen_conf={}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        gen_conf = self._clean_conf(gen_conf)
        ans = ""
        total_tokens = 0
        try:
            response = self.client.chat_stream(model=self.model_name, messages=history, **gen_conf, **kwargs)
            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":
                    if is_chinese(ans):
                        ans += LENGTH_NOTIFICATION_CN
                    else:
                        ans += LENGTH_NOTIFICATION_EN
                yield ans

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

        yield total_tokens


class LmStudioChat(Base):
    _FACTORY_NAME = "LM-Studio"

    def __init__(self, key, model_name, base_url, **kwargs):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        super().__init__(key, model_name, base_url, **kwargs)
        self.client = OpenAI(api_key="lm-studio", base_url=base_url)
        self.model_name = model_name


class OpenAI_APIChat(Base):
    _FACTORY_NAME = ["VLLM", "OpenAI-API-Compatible"]

    def __init__(self, key, model_name, base_url, **kwargs):
        if not base_url:
            raise ValueError("url cannot be None")
        model_name = model_name.split("___")[0]
        super().__init__(key, model_name, base_url, **kwargs)


class LeptonAIChat(Base):
    _FACTORY_NAME = "LeptonAI"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        if not base_url:
            base_url = urljoin("https://" + model_name + ".lepton.run", "api/v1")
        super().__init__(key, model_name, base_url, **kwargs)


class ReplicateChat(Base):
    _FACTORY_NAME = "Replicate"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        from replicate.client import Client

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

    def _chat(self, history, gen_conf={}, **kwargs):
        system = history[0]["content"] if history and history[0]["role"] == "system" else ""
        prompt = "\n".join([item["role"] + ":" + item["content"] for item in history[-5:] if item["role"] != "system"])
        response = self.client.run(
            self.model_name,
            input={"system_prompt": system, "prompt": prompt, **gen_conf},
        )
        ans = "".join(response)
        return ans, num_tokens_from_string(ans)

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

    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        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 _clean_conf(self, gen_conf):
        _gen_conf = {}
        if "temperature" in gen_conf:
            _gen_conf["Temperature"] = gen_conf["temperature"]
        if "top_p" in gen_conf:
            _gen_conf["TopP"] = gen_conf["top_p"]
        return _gen_conf

    def _chat(self, history, gen_conf={}, **kwargs):
        from tencentcloud.hunyuan.v20230901 import models

        hist = [{k.capitalize(): v for k, v in item.items()} for item in history]
        req = models.ChatCompletionsRequest()
        params = {"Model": self.model_name, "Messages": hist, **gen_conf}
        req.from_json_string(json.dumps(params))
        response = self.client.ChatCompletions(req)
        ans = response.Choices[0].Message.Content
        return ans, response.Usage.TotalTokens

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

        _gen_conf = {}
        _history = [{k.capitalize(): v for k, v in item.items()} for item in history]
        if system and history and history[0].get("role") != "system":
            _history.insert(0, {"Role": "system", "Content": system})
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        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):
    _FACTORY_NAME = "XunFei Spark"

    def __init__(self, key, model_name, base_url="https://spark-api-open.xf-yun.com/v1", **kwargs):
        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",
        }
        version2model = {v: k for k, v in model2version.items()}
        assert model_name in model2version or model_name in version2model, f"The given model name is not supported yet. Support: {list(model2version.keys())}"
        if model_name in model2version:
            model_version = model2version[model_name]
        else:
            model_version = model_name
        super().__init__(key, model_version, base_url, **kwargs)


class BaiduYiyanChat(Base):
    _FACTORY_NAME = "BaiduYiyan"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        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()

    def _clean_conf(self, gen_conf):
        gen_conf["penalty_score"] = ((gen_conf.get("presence_penalty", 0) + gen_conf.get("frequency_penalty", 0)) / 2) + 1
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        return gen_conf

    def _chat(self, history, gen_conf):
        system = history[0]["content"] if history and history[0]["role"] == "system" else ""
        response = self.client.do(model=self.model_name, messages=[h for h in history if h["role"] != "system"], system=system, **gen_conf).body
        ans = response["result"]
        return ans, total_token_count_from_response(response)

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

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

                yield ans

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

        yield total_tokens


class GoogleChat(Base):
    _FACTORY_NAME = "Google Cloud"

    def __init__(self, key, model_name, base_url=None, **kwargs):
        super().__init__(key, model_name, base_url=base_url, **kwargs)

        import base64

        from google.oauth2 import service_account

        key = json.loads(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

        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 import genai

            if access_token:
                credits = service_account.Credentials.from_service_account_info(access_token, scopes=scopes)
                self.client = genai.Client(vertexai=True, project=project_id, location=region, credentials=credits)
            else:
                self.client = genai.Client(vertexai=True, project=project_id, location=region)

    def _clean_conf(self, gen_conf):
        if "claude" in self.model_name:
            if "max_tokens" in gen_conf:
                del gen_conf["max_tokens"]
        else:
            if "max_tokens" in gen_conf:
                gen_conf["max_output_tokens"] = gen_conf["max_tokens"]
                del 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]
        return gen_conf

    def _chat(self, history, gen_conf={}, **kwargs):
        system = history[0]["content"] if history and history[0]["role"] == "system" else ""

        if "claude" in self.model_name:
            gen_conf = self._clean_conf(gen_conf)
            response = self.client.messages.create(
                model=self.model_name,
                messages=[h for h in history if h["role"] != "system"],
                system=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"],
            )

        # Gemini models with google-genai SDK
        # Set default thinking_budget=0 if not specified
        if "thinking_budget" not in gen_conf:
            gen_conf["thinking_budget"] = 0

        thinking_budget = gen_conf.pop("thinking_budget", 0)
        gen_conf = self._clean_conf(gen_conf)

        # Build GenerateContentConfig
        try:
            from google.genai.types import GenerateContentConfig, ThinkingConfig, Content, Part
        except ImportError as e:
            logging.error(f"[GoogleChat] Failed to import google-genai: {e}. Please install: pip install google-genai>=1.41.0")
            raise

        config_dict = {}
        if system:
            config_dict["system_instruction"] = system
        if "temperature" in gen_conf:
            config_dict["temperature"] = gen_conf["temperature"]
        if "top_p" in gen_conf:
            config_dict["top_p"] = gen_conf["top_p"]
        if "max_output_tokens" in gen_conf:
            config_dict["max_output_tokens"] = gen_conf["max_output_tokens"]

        # Add ThinkingConfig
        config_dict["thinking_config"] = ThinkingConfig(thinking_budget=thinking_budget)

        config = GenerateContentConfig(**config_dict)

        # Convert history to google-genai Content format
        contents = []
        for item in history:
            if item["role"] == "system":
                continue
            # google-genai uses 'model' instead of 'assistant'
            role = "model" if item["role"] == "assistant" else item["role"]
            content = Content(
                role=role,
                parts=[Part(text=item["content"])]
            )
            contents.append(content)

        response = self.client.models.generate_content(
            model=self.model_name,
            contents=contents,
            config=config
        )

        ans = response.text
        # Get token count from response
        try:
            total_tokens = response.usage_metadata.total_token_count
        except Exception:
            total_tokens = 0

        return ans, total_tokens

    def chat_streamly(self, system, history, gen_conf={}, **kwargs):
        if "claude" in self.model_name:
            if "max_tokens" in gen_conf:
                del gen_conf["max_tokens"]
            ans = ""
            total_tokens = 0
            try:
                response = self.client.messages.create(
                    model=self.model_name,
                    messages=history,
                    system=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:
            # Gemini models with google-genai SDK
            ans = ""
            total_tokens = 0

            # Set default thinking_budget=0 if not specified
            if "thinking_budget" not in gen_conf:
                gen_conf["thinking_budget"] = 0

            thinking_budget = gen_conf.pop("thinking_budget", 0)
            gen_conf = self._clean_conf(gen_conf)

            # Build GenerateContentConfig
            try:
                from google.genai.types import GenerateContentConfig, ThinkingConfig, Content, Part
            except ImportError as e:
                logging.error(f"[GoogleChat] Failed to import google-genai: {e}. Please install: pip install google-genai>=1.41.0")
                raise

            config_dict = {}
            if system:
                config_dict["system_instruction"] = system
            if "temperature" in gen_conf:
                config_dict["temperature"] = gen_conf["temperature"]
            if "top_p" in gen_conf:
                config_dict["top_p"] = gen_conf["top_p"]
            if "max_output_tokens" in gen_conf:
                config_dict["max_output_tokens"] = gen_conf["max_output_tokens"]

            # Add ThinkingConfig
            config_dict["thinking_config"] = ThinkingConfig(thinking_budget=thinking_budget)

            config = GenerateContentConfig(**config_dict)

            # Convert history to google-genai Content format
            contents = []
            for item in history:
                # google-genai uses 'model' instead of 'assistant'
                role = "model" if item["role"] == "assistant" else item["role"]
                content = Content(
                    role=role,
                    parts=[Part(text=item["content"])]
                )
                contents.append(content)

            try:
                for chunk in self.client.models.generate_content_stream(
                    model=self.model_name,
                    contents=contents,
                    config=config
                ):
                    text = chunk.text
                    ans = text
                    total_tokens += num_tokens_from_string(text)
                    yield ans

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

            yield total_tokens


class GPUStackChat(Base):
    _FACTORY_NAME = "GPUStack"

    def __init__(self, key=None, model_name="", base_url="", **kwargs):
        if not base_url:
            raise ValueError("Local llm url cannot be None")
        base_url = urljoin(base_url, "v1")
        super().__init__(key, model_name, base_url, **kwargs)


class TokenPonyChat(Base):
    _FACTORY_NAME = "TokenPony"

    def __init__(self, key, model_name, base_url="https://ragflow.vip-api.tokenpony.cn/v1", **kwargs):
        if not base_url:
            base_url = "https://ragflow.vip-api.tokenpony.cn/v1"
        super().__init__(key, model_name, base_url, **kwargs)


class DeerAPIChat(Base):
    _FACTORY_NAME = "DeerAPI"

    def __init__(self, key, model_name, base_url="https://api.deerapi.com/v1", **kwargs):
        if not base_url:
            base_url = "https://api.deerapi.com/v1"
        super().__init__(key, model_name, base_url, **kwargs)


class LiteLLMBase(ABC):
    _FACTORY_NAME = [
        "Tongyi-Qianwen",
        "Bedrock",
        "Moonshot",
        "xAI",
        "DeepInfra",
        "Groq",
        "Cohere",
        "Gemini",
        "DeepSeek",
        "NVIDIA",
        "TogetherAI",
        "Anthropic",
        "Ollama",
        "LongCat",
        "CometAPI",
        "SILICONFLOW",
        "OpenRouter",
        "StepFun",
        "PPIO",
        "PerfXCloud",
        "Upstage",
        "NovitaAI",
        "01.AI",
        "GiteeAI",
        "302.AI",
        "Jiekou.AI",
    ]

    def __init__(self, key, model_name, base_url=None, **kwargs):
        self.timeout = int(os.environ.get("LM_TIMEOUT_SECONDS", 600))
        self.provider = kwargs.get("provider", "")
        self.prefix = LITELLM_PROVIDER_PREFIX.get(self.provider, "")
        self.model_name = f"{self.prefix}{model_name}"
        self.api_key = key
        self.base_url = (base_url or FACTORY_DEFAULT_BASE_URL.get(self.provider, "")).rstrip("/")
        # Configure retry parameters
        self.max_retries = kwargs.get("max_retries", int(os.environ.get("LLM_MAX_RETRIES", 5)))
        self.base_delay = kwargs.get("retry_interval", float(os.environ.get("LLM_BASE_DELAY", 2.0)))
        self.max_rounds = kwargs.get("max_rounds", 5)
        self.is_tools = False
        self.tools = []
        self.toolcall_sessions = {}

        # Factory specific fields
        if self.provider == SupportedLiteLLMProvider.Bedrock:
            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", "")
        elif self.provider == SupportedLiteLLMProvider.OpenRouter:
            self.api_key = json.loads(key).get("api_key", "")
            self.provider_order = json.loads(key).get("provider_order", "")

    def _get_delay(self):
        """Calculate retry delay time"""
        return self.base_delay * random.uniform(10, 150)

    def _classify_error(self, error):
        """Classify error based on error message content"""
        error_str = str(error).lower()

        keywords_mapping = [
            (["quota", "capacity", "credit", "billing", "balance", "欠费"], LLMErrorCode.ERROR_QUOTA),
            (["rate limit", "429", "tpm limit", "too many requests", "requests per minute"], LLMErrorCode.ERROR_RATE_LIMIT),
            (["auth", "key", "apikey", "401", "forbidden", "permission"], LLMErrorCode.ERROR_AUTHENTICATION),
            (["invalid", "bad request", "400", "format", "malformed", "parameter"], LLMErrorCode.ERROR_INVALID_REQUEST),
            (["server", "503", "502", "504", "500", "unavailable"], LLMErrorCode.ERROR_SERVER),
            (["timeout", "timed out"], LLMErrorCode.ERROR_TIMEOUT),
            (["connect", "network", "unreachable", "dns"], LLMErrorCode.ERROR_CONNECTION),
            (["filter", "content", "policy", "blocked", "safety", "inappropriate"], LLMErrorCode.ERROR_CONTENT_FILTER),
            (["model", "not found", "does not exist", "not available"], LLMErrorCode.ERROR_MODEL),
            (["max rounds"], LLMErrorCode.ERROR_MODEL),
        ]
        for words, code in keywords_mapping:
            if re.search("({})".format("|".join(words)), error_str):
                return code

        return LLMErrorCode.ERROR_GENERIC

    def _clean_conf(self, gen_conf):
        if "max_tokens" in gen_conf:
            del gen_conf["max_tokens"]
        return gen_conf

    def _chat(self, history, gen_conf, **kwargs):
        logging.info("[HISTORY]" + json.dumps(history, ensure_ascii=False, indent=2))
        if self.model_name.lower().find("qwen3") >= 0:
            kwargs["extra_body"] = {"enable_thinking": False}

        completion_args = self._construct_completion_args(history=history, stream=False, tools=False, **gen_conf)
        response = litellm.completion(
            **completion_args,
            drop_params=True,
            timeout=self.timeout,
        )
        # response = self.client.chat.completions.create(model=self.model_name, messages=history, **gen_conf, **kwargs)
        if any([not response.choices, not response.choices[0].message, not response.choices[0].message.content]):
            return "", 0
        ans = response.choices[0].message.content.strip()
        if response.choices[0].finish_reason == "length":
            ans = self._length_stop(ans)

        return ans, total_token_count_from_response(response)

    def _chat_streamly(self, history, gen_conf, **kwargs):
        logging.info("[HISTORY STREAMLY]" + json.dumps(history, ensure_ascii=False, indent=4))
        reasoning_start = False

        completion_args = self._construct_completion_args(history=history, stream=True, tools=False, **gen_conf)
        stop = kwargs.get("stop")
        if stop:
            completion_args["stop"] = stop
        response = litellm.completion(
            **completion_args,
            drop_params=True,
            timeout=self.timeout,
        )

        for resp in response:
            if not hasattr(resp, "choices") or not resp.choices:
                continue

            delta = resp.choices[0].delta
            if not hasattr(delta, "content") or delta.content is None:
                delta.content = ""

            if kwargs.get("with_reasoning", True) and hasattr(delta, "reasoning_content") and delta.reasoning_content:
                ans = ""
                if not reasoning_start:
                    reasoning_start = True
                    ans = "<think>"
                ans += delta.reasoning_content + "</think>"
            else:
                reasoning_start = False
                ans = delta.content

            tol = total_token_count_from_response(resp)
            if not tol:
                tol = num_tokens_from_string(delta.content)

            finish_reason = resp.choices[0].finish_reason if hasattr(resp.choices[0], "finish_reason") else ""
            if finish_reason == "length":
                if is_chinese(ans):
                    ans += LENGTH_NOTIFICATION_CN
                else:
                    ans += LENGTH_NOTIFICATION_EN

            yield ans, tol

    def _length_stop(self, ans):
        if is_chinese([ans]):
            return ans + LENGTH_NOTIFICATION_CN
        return ans + LENGTH_NOTIFICATION_EN

    @property
    def _retryable_errors(self) -> set[str]:
        return {
            LLMErrorCode.ERROR_RATE_LIMIT,
            LLMErrorCode.ERROR_SERVER,
        }

    def _should_retry(self, error_code: str) -> bool:
        return error_code in self._retryable_errors

    def _exceptions(self, e, attempt) -> str | None:
        logging.exception("OpenAI chat_with_tools")
        # Classify the error
        error_code = self._classify_error(e)
        if attempt == self.max_retries:
            error_code = LLMErrorCode.ERROR_MAX_RETRIES

        if self._should_retry(error_code):
            delay = self._get_delay()
            logging.warning(f"Error: {error_code}. Retrying in {delay:.2f} seconds... (Attempt {attempt + 1}/{self.max_retries})")
            time.sleep(delay)
            return None

        return f"{ERROR_PREFIX}: {error_code} - {str(e)}"

    def _verbose_tool_use(self, name, args, res):
        return "<tool_call>" + json.dumps({"name": name, "args": args, "result": res}, ensure_ascii=False, indent=2) + "</tool_call>"

    def _append_history(self, hist, tool_call, tool_res):
        hist.append(
            {
                "role": "assistant",
                "tool_calls": [
                    {
                        "index": tool_call.index,
                        "id": tool_call.id,
                        "function": {
                            "name": tool_call.function.name,
                            "arguments": tool_call.function.arguments,
                        },
                        "type": "function",
                    },
                ],
            }
        )
        try:
            if isinstance(tool_res, dict):
                tool_res = json.dumps(tool_res, ensure_ascii=False)
        finally:
            hist.append({"role": "tool", "tool_call_id": tool_call.id, "content": str(tool_res)})
        return hist

    def bind_tools(self, toolcall_session, tools):
        if not (toolcall_session and tools):
            return
        self.is_tools = True
        self.toolcall_session = toolcall_session
        self.tools = tools

    def _construct_completion_args(self, history, stream: bool, tools: bool, **kwargs):
        completion_args = {
            "model": self.model_name,
            "messages": history,
            "api_key": self.api_key,
            "num_retries": self.max_retries,
            **kwargs,
        }
        if stream:
            completion_args.update(
                {
                    "stream": stream,
                }
            )
        if tools and self.tools:
            completion_args.update(
                {
                    "tools": self.tools,
                    "tool_choice": "auto",
                }
            )
        if self.provider in FACTORY_DEFAULT_BASE_URL:
            completion_args.update({"api_base": self.base_url})
        elif self.provider == SupportedLiteLLMProvider.Bedrock:
            completion_args.pop("api_key", None)
            completion_args.pop("api_base", None)
            completion_args.update(
                {
                    "aws_access_key_id": self.bedrock_ak,
                    "aws_secret_access_key": self.bedrock_sk,
                    "aws_region_name": self.bedrock_region,
                }
            )

        if self.provider == SupportedLiteLLMProvider.OpenRouter:
            if self.provider_order:
                def _to_order_list(x):
                    if x is None:
                        return []
                    if isinstance(x, str):
                        return [s.strip() for s in x.split(",") if s.strip()]
                    if isinstance(x, (list, tuple)):
                        return [str(s).strip() for s in x if str(s).strip()]
                    return []
                extra_body = {}
                provider_cfg = {}
                provider_order = _to_order_list(self.provider_order)
                provider_cfg["order"] = provider_order
                provider_cfg["allow_fallbacks"] = False
                extra_body["provider"] = provider_cfg
                completion_args.update({"extra_body": extra_body})
        return completion_args

    def chat_with_tools(self, system: str, history: list, gen_conf: dict = {}):
        gen_conf = self._clean_conf(gen_conf)
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})

        ans = ""
        tk_count = 0
        hist = deepcopy(history)

        # Implement exponential backoff retry strategy
        for attempt in range(self.max_retries + 1):
            history = deepcopy(hist)  # deepcopy is required here
            try:
                for _ in range(self.max_rounds + 1):
                    logging.info(f"{self.tools=}")

                    completion_args = self._construct_completion_args(history=history, stream=False, tools=True, **gen_conf)
                    response = litellm.completion(
                        **completion_args,
                        drop_params=True,
                        timeout=self.timeout,
                    )

                    tk_count += total_token_count_from_response(response)

                    if not hasattr(response, "choices") or not response.choices or not response.choices[0].message:
                        raise Exception(f"500 response structure error. Response: {response}")

                    message = response.choices[0].message

                    if not hasattr(message, "tool_calls") or not message.tool_calls:
                        if hasattr(message, "reasoning_content") and message.reasoning_content:
                            ans += f"<think>{message.reasoning_content}</think>"
                        ans += message.content or ""
                        if response.choices[0].finish_reason == "length":
                            ans = self._length_stop(ans)
                        return ans, tk_count

                    for tool_call in message.tool_calls:
                        logging.info(f"Response {tool_call=}")
                        name = tool_call.function.name
                        try:
                            args = json_repair.loads(tool_call.function.arguments)
                            tool_response = self.toolcall_session.tool_call(name, args)
                            history = self._append_history(history, tool_call, tool_response)
                            ans += self._verbose_tool_use(name, args, tool_response)
                        except Exception as e:
                            logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}")
                            history.append({"role": "tool", "tool_call_id": tool_call.id, "content": f"Tool call error: \n{tool_call}\nException:\n" + str(e)})
                            ans += self._verbose_tool_use(name, {}, str(e))

                logging.warning(f"Exceed max rounds: {self.max_rounds}")
                history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"})

                response, token_count = self._chat(history, gen_conf)
                ans += response
                tk_count += token_count
                return ans, tk_count

            except Exception as e:
                e = self._exceptions(e, attempt)
                if e:
                    return e, tk_count

        assert False, "Shouldn't be here."

    def chat(self, system, history, gen_conf={}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        gen_conf = self._clean_conf(gen_conf)

        # Implement exponential backoff retry strategy
        for attempt in range(self.max_retries + 1):
            try:
                response = self._chat(history, gen_conf, **kwargs)
                return response
            except Exception as e:
                e = self._exceptions(e, attempt)
                if e:
                    return e, 0
        assert False, "Shouldn't be here."

    def _wrap_toolcall_message(self, stream):
        final_tool_calls = {}

        for chunk in stream:
            for tool_call in chunk.choices[0].delta.tool_calls or []:
                index = tool_call.index

                if index not in final_tool_calls:
                    final_tool_calls[index] = tool_call

                final_tool_calls[index].function.arguments += tool_call.function.arguments

        return final_tool_calls

    def chat_streamly_with_tools(self, system: str, history: list, gen_conf: dict = {}):
        gen_conf = self._clean_conf(gen_conf)
        tools = self.tools
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})

        total_tokens = 0
        hist = deepcopy(history)

        # Implement exponential backoff retry strategy
        for attempt in range(self.max_retries + 1):
            history = deepcopy(hist)  # deepcopy is required here
            try:
                for _ in range(self.max_rounds + 1):
                    reasoning_start = False
                    logging.info(f"{tools=}")

                    completion_args = self._construct_completion_args(history=history, stream=True, tools=True, **gen_conf)
                    response = litellm.completion(
                        **completion_args,
                        drop_params=True,
                        timeout=self.timeout,
                    )

                    final_tool_calls = {}
                    answer = ""

                    for resp in response:
                        if not hasattr(resp, "choices") or not resp.choices:
                            continue

                        delta = resp.choices[0].delta

                        if hasattr(delta, "tool_calls") and delta.tool_calls:
                            for tool_call in delta.tool_calls:
                                index = tool_call.index
                                if index not in final_tool_calls:
                                    if not tool_call.function.arguments:
                                        tool_call.function.arguments = ""
                                    final_tool_calls[index] = tool_call
                                else:
                                    final_tool_calls[index].function.arguments += tool_call.function.arguments or ""
                            continue

                        if not hasattr(delta, "content") or delta.content is None:
                            delta.content = ""

                        if hasattr(delta, "reasoning_content") and delta.reasoning_content:
                            ans = ""
                            if not reasoning_start:
                                reasoning_start = True
                                ans = "<think>"
                            ans += delta.reasoning_content + "</think>"
                            yield ans
                        else:
                            reasoning_start = False
                            answer += delta.content
                            yield delta.content

                        tol = total_token_count_from_response(resp)
                        if not tol:
                            total_tokens += num_tokens_from_string(delta.content)
                        else:
                            total_tokens += tol

                        finish_reason = getattr(resp.choices[0], "finish_reason", "")
                        if finish_reason == "length":
                            yield self._length_stop("")

                    if answer:
                        yield total_tokens
                        return

                    for tool_call in final_tool_calls.values():
                        name = tool_call.function.name
                        try:
                            args = json_repair.loads(tool_call.function.arguments)
                            yield self._verbose_tool_use(name, args, "Begin to call...")
                            tool_response = self.toolcall_session.tool_call(name, args)
                            history = self._append_history(history, tool_call, tool_response)
                            yield self._verbose_tool_use(name, args, tool_response)
                        except Exception as e:
                            logging.exception(msg=f"Wrong JSON argument format in LLM tool call response: {tool_call}")
                            history.append(
                                {
                                    "role": "tool",
                                    "tool_call_id": tool_call.id,
                                    "content": f"Tool call error: \n{tool_call}\nException:\n{str(e)}",
                                }
                            )
                            yield self._verbose_tool_use(name, {}, str(e))

                logging.warning(f"Exceed max rounds: {self.max_rounds}")
                history.append({"role": "user", "content": f"Exceed max rounds: {self.max_rounds}"})

                completion_args = self._construct_completion_args(history=history, stream=True, tools=True, **gen_conf)
                response = litellm.completion(
                    **completion_args,
                    drop_params=True,
                    timeout=self.timeout,
                )

                for resp in response:
                    if not hasattr(resp, "choices") or not resp.choices:
                        continue
                    delta = resp.choices[0].delta
                    if not hasattr(delta, "content") or delta.content is None:
                        continue
                    tol = total_token_count_from_response(resp)
                    if not tol:
                        total_tokens += num_tokens_from_string(delta.content)
                    else:
                        total_tokens += tol
                    yield delta.content

                yield total_tokens
                return

            except Exception as e:
                e = self._exceptions(e, attempt)
                if e:
                    yield e
                    yield total_tokens
                    return

        assert False, "Shouldn't be here."

    def chat_streamly(self, system, history, gen_conf: dict = {}, **kwargs):
        if system and history and history[0].get("role") != "system":
            history.insert(0, {"role": "system", "content": system})
        gen_conf = self._clean_conf(gen_conf)
        ans = ""
        total_tokens = 0
        try:
            for delta_ans, tol in self._chat_streamly(history, gen_conf, **kwargs):
                yield delta_ans
                total_tokens += tol
        except openai.APIError as e:
            yield ans + "\n**ERROR**: " + str(e)

        yield total_tokens

    def _calculate_dynamic_ctx(self, history):
        """Calculate dynamic context window size"""

        def count_tokens(text):
            """Calculate token count for text"""
            # Simple calculation: 1 token per ASCII character
            # 2 tokens for non-ASCII characters (Chinese, Japanese, Korean, etc.)
            total = 0
            for char in text:
                if ord(char) < 128:  # ASCII characters
                    total += 1
                else:  # Non-ASCII characters (Chinese, Japanese, Korean, etc.)
                    total += 2
            return total

        # Calculate total tokens for all messages
        total_tokens = 0
        for message in history:
            content = message.get("content", "")
            # Calculate content tokens
            content_tokens = count_tokens(content)
            # Add role marker token overhead
            role_tokens = 4
            total_tokens += content_tokens + role_tokens

        # Apply 1.2x buffer ratio
        total_tokens_with_buffer = int(total_tokens * 1.2)

        if total_tokens_with_buffer <= 8192:
            ctx_size = 8192
        else:
            ctx_multiplier = (total_tokens_with_buffer // 8192) + 1
            ctx_size = ctx_multiplier * 8192

        return ctx_size