refactor: remove unused codes, move core/agent module into dataset retrieval feature (#2614)

2025-08-13 05:19:00 +08:00 · 2024-02-28 23:32:47 +08:00 · 2024-02-28 23:32:47 +08:00 · dd961985f0
commit dd961985f0
parent d44b05a9e5
29 changed files with 41 additions and 2016 deletions
--- a/api/core/agent/agent/calc_token_mixin.py
+++ b/api/core/agent/agent/calc_token_mixin.py
@ -1,49 +0,0 @@
 from typing import cast
 from core.entities.application_entities import ModelConfigEntity
 from core.model_runtime.entities.message_entities import PromptMessage
 from core.model_runtime.entities.model_entities import ModelPropertyKey
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 class CalcTokenMixin:
    def get_message_rest_tokens(self, model_config: ModelConfigEntity, messages: list[PromptMessage], **kwargs) -> int:
        """
        Got the rest tokens available for the model after excluding messages tokens and completion max tokens
        :param model_config:
        :param messages:
        :return:
        """
        model_type_instance = model_config.provider_model_bundle.model_type_instance
        model_type_instance = cast(LargeLanguageModel, model_type_instance)
        model_context_tokens = model_config.model_schema.model_properties.get(ModelPropertyKey.CONTEXT_SIZE)
        max_tokens = 0
        for parameter_rule in model_config.model_schema.parameter_rules:
            if (parameter_rule.name == 'max_tokens'
                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
                max_tokens = (model_config.parameters.get(parameter_rule.name)
                              or model_config.parameters.get(parameter_rule.use_template)) or 0
        if model_context_tokens is None:
            return 0
        if max_tokens is None:
            max_tokens = 0
        prompt_tokens = model_type_instance.get_num_tokens(
            model_config.model,
            model_config.credentials,
            messages
        )
        rest_tokens = model_context_tokens - max_tokens - prompt_tokens
        return rest_tokens
 class ExceededLLMTokensLimitError(Exception):
    pass
--- a/api/core/agent/agent/openai_function_call.py
+++ b/api/core/agent/agent/openai_function_call.py
@ -1,361 +0,0 @@
 from collections.abc import Sequence
 from typing import Any, Optional, Union
 from langchain.agents import BaseSingleActionAgent, OpenAIFunctionsAgent
 from langchain.agents.openai_functions_agent.base import _format_intermediate_steps, _parse_ai_message
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.chat_models.openai import _convert_message_to_dict, _import_tiktoken
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts.chat import BaseMessagePromptTemplate
 from langchain.schema import (
    AgentAction,
    AgentFinish,
    AIMessage,
    BaseMessage,
    HumanMessage,
    SystemMessage,
    get_buffer_string,
 )
 from langchain.tools import BaseTool
 from pydantic import root_validator
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
 from core.chain.llm_chain import LLMChain
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessage, PromptMessageTool
 from core.third_party.langchain.llms.fake import FakeLLM
 class AutoSummarizingOpenAIFunctionCallAgent(OpenAIFunctionsAgent, CalcTokenMixin):
    moving_summary_buffer: str = ""
    moving_summary_index: int = 0
    summary_model_config: ModelConfigEntity = None
    model_config: ModelConfigEntity
    agent_llm_callback: Optional[AgentLLMCallback] = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    @root_validator
    def validate_llm(cls, values: dict) -> dict:
        return values
    @classmethod
    def from_llm_and_tools(
            cls,
            model_config: ModelConfigEntity,
            tools: Sequence[BaseTool],
            callback_manager: Optional[BaseCallbackManager] = None,
            extra_prompt_messages: Optional[list[BaseMessagePromptTemplate]] = None,
            system_message: Optional[SystemMessage] = SystemMessage(
                content="You are a helpful AI assistant."
            ),
            agent_llm_callback: Optional[AgentLLMCallback] = None,
            **kwargs: Any,
    ) -> BaseSingleActionAgent:
        prompt = cls.create_prompt(
            extra_prompt_messages=extra_prompt_messages,
            system_message=system_message,
        )
        return cls(
            model_config=model_config,
            llm=FakeLLM(response=''),
            prompt=prompt,
            tools=tools,
            callback_manager=callback_manager,
            agent_llm_callback=agent_llm_callback,
            **kwargs,
        )
    def should_use_agent(self, query: str):
        """
        return should use agent
        :param query:
        :return:
        """
        original_max_tokens = 0
        for parameter_rule in self.model_config.model_schema.parameter_rules:
            if (parameter_rule.name == 'max_tokens'
                    or (parameter_rule.use_template and parameter_rule.use_template == 'max_tokens')):
                original_max_tokens = (self.model_config.parameters.get(parameter_rule.name)
                              or self.model_config.parameters.get(parameter_rule.use_template)) or 0
        self.model_config.parameters['max_tokens'] = 40
        prompt = self.prompt.format_prompt(input=query, agent_scratchpad=[])
        messages = prompt.to_messages()
        try:
            prompt_messages = lc_messages_to_prompt_messages(messages)
            model_instance = ModelInstance(
                provider_model_bundle=self.model_config.provider_model_bundle,
                model=self.model_config.model,
            )
            tools = []
            for function in self.functions:
                tool = PromptMessageTool(
                    **function
                )
                tools.append(tool)
            result = model_instance.invoke_llm(
                prompt_messages=prompt_messages,
                tools=tools,
                stream=False,
                model_parameters={
                    'temperature': 0.2,
                    'top_p': 0.3,
                    'max_tokens': 1500
                }
            )
        except Exception as e:
            raise e
        self.model_config.parameters['max_tokens'] = original_max_tokens
        return True if result.message.tool_calls else False
    def plan(
            self,
            intermediate_steps: list[tuple[AgentAction, str]],
            callbacks: Callbacks = None,
            **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date, along with observations
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        agent_scratchpad = _format_intermediate_steps(intermediate_steps)
        selected_inputs = {
            k: kwargs[k] for k in self.prompt.input_variables if k != "agent_scratchpad"
        }
        full_inputs = dict(**selected_inputs, agent_scratchpad=agent_scratchpad)
        prompt = self.prompt.format_prompt(**full_inputs)
        messages = prompt.to_messages()
        prompt_messages = lc_messages_to_prompt_messages(messages)
        # summarize messages if rest_tokens < 0
        try:
            prompt_messages = self.summarize_messages_if_needed(prompt_messages, functions=self.functions)
        except ExceededLLMTokensLimitError as e:
            return AgentFinish(return_values={"output": str(e)}, log=str(e))
        model_instance = ModelInstance(
            provider_model_bundle=self.model_config.provider_model_bundle,
            model=self.model_config.model,
        )
        tools = []
        for function in self.functions:
            tool = PromptMessageTool(
                **function
            )
            tools.append(tool)
        result = model_instance.invoke_llm(
            prompt_messages=prompt_messages,
            tools=tools,
            stream=False,
            callbacks=[self.agent_llm_callback] if self.agent_llm_callback else [],
            model_parameters={
                'temperature': 0.2,
                'top_p': 0.3,
                'max_tokens': 1500
            }
        )
        ai_message = AIMessage(
            content=result.message.content or "",
            additional_kwargs={
                'function_call': {
                    'id': result.message.tool_calls[0].id,
                    **result.message.tool_calls[0].function.dict()
                } if result.message.tool_calls else None
            }
        )
        agent_decision = _parse_ai_message(ai_message)
        if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
            tool_inputs = agent_decision.tool_input
            if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
                tool_inputs['query'] = kwargs['input']
                agent_decision.tool_input = tool_inputs
        return agent_decision
    @classmethod
    def get_system_message(cls):
        return SystemMessage(content="You are a helpful AI assistant.\n"
                                     "The current date or current time you know is wrong.\n"
                                     "Respond directly if appropriate.")
    def return_stopped_response(
            self,
            early_stopping_method: str,
            intermediate_steps: list[tuple[AgentAction, str]],
            **kwargs: Any,
    ) -> AgentFinish:
        try:
            return super().return_stopped_response(early_stopping_method, intermediate_steps, **kwargs)
        except ValueError:
            return AgentFinish({"output": "I'm sorry, I don't know how to respond to that."}, "")
    def summarize_messages_if_needed(self, messages: list[PromptMessage], **kwargs) -> list[PromptMessage]:
        # calculate rest tokens and summarize previous function observation messages if rest_tokens < 0
        rest_tokens = self.get_message_rest_tokens(
            self.model_config,
            messages,
            **kwargs
        )
        rest_tokens = rest_tokens - 20  # to deal with the inaccuracy of rest_tokens
        if rest_tokens >= 0:
            return messages
        system_message = None
        human_message = None
        should_summary_messages = []
        for message in messages:
            if isinstance(message, SystemMessage):
                system_message = message
            elif isinstance(message, HumanMessage):
                human_message = message
            else:
                should_summary_messages.append(message)
        if len(should_summary_messages) > 2:
            ai_message = should_summary_messages[-2]
            function_message = should_summary_messages[-1]
            should_summary_messages = should_summary_messages[self.moving_summary_index:-2]
            self.moving_summary_index = len(should_summary_messages)
        else:
            error_msg = "Exceeded LLM tokens limit, stopped."
            raise ExceededLLMTokensLimitError(error_msg)
        new_messages = [system_message, human_message]
        if self.moving_summary_index == 0:
            should_summary_messages.insert(0, human_message)
        self.moving_summary_buffer = self.predict_new_summary(
            messages=should_summary_messages,
            existing_summary=self.moving_summary_buffer
        )
        new_messages.append(AIMessage(content=self.moving_summary_buffer))
        new_messages.append(ai_message)
        new_messages.append(function_message)
        return new_messages
    def predict_new_summary(
        self, messages: list[BaseMessage], existing_summary: str
    ) -> str:
        new_lines = get_buffer_string(
            messages,
            human_prefix="Human",
            ai_prefix="AI",
        )
        chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
        return chain.predict(summary=existing_summary, new_lines=new_lines)
    def get_num_tokens_from_messages(self, model_config: ModelConfigEntity, messages: list[BaseMessage], **kwargs) -> int:
        """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
        Official documentation: https://github.com/openai/openai-cookbook/blob/
        main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
        if model_config.provider == 'azure_openai':
            model = model_config.model
            model = model.replace("gpt-35", "gpt-3.5")
        else:
            model = model_config.credentials.get("base_model_name")
        tiktoken_ = _import_tiktoken()
        try:
            encoding = tiktoken_.encoding_for_model(model)
        except KeyError:
            model = "cl100k_base"
            encoding = tiktoken_.get_encoding(model)
        if model.startswith("gpt-3.5-turbo"):
            # every message follows <im_start>{role/name}\n{content}<im_end>\n
            tokens_per_message = 4
            # if there's a name, the role is omitted
            tokens_per_name = -1
        elif model.startswith("gpt-4"):
            tokens_per_message = 3
            tokens_per_name = 1
        else:
            raise NotImplementedError(
                f"get_num_tokens_from_messages() is not presently implemented "
                f"for model {model}."
                "See https://github.com/openai/openai-python/blob/main/chatml.md for "
                "information on how messages are converted to tokens."
            )
        num_tokens = 0
        for m in messages:
            message = _convert_message_to_dict(m)
            num_tokens += tokens_per_message
            for key, value in message.items():
                if key == "function_call":
                    for f_key, f_value in value.items():
                        num_tokens += len(encoding.encode(f_key))
                        num_tokens += len(encoding.encode(f_value))
                else:
                    num_tokens += len(encoding.encode(value))
                if key == "name":
                    num_tokens += tokens_per_name
        # every reply is primed with <im_start>assistant
        num_tokens += 3
        if kwargs.get('functions'):
            for function in kwargs.get('functions'):
                num_tokens += len(encoding.encode('name'))
                num_tokens += len(encoding.encode(function.get("name")))
                num_tokens += len(encoding.encode('description'))
                num_tokens += len(encoding.encode(function.get("description")))
                parameters = function.get("parameters")
                num_tokens += len(encoding.encode('parameters'))
                if 'title' in parameters:
                    num_tokens += len(encoding.encode('title'))
                    num_tokens += len(encoding.encode(parameters.get("title")))
                num_tokens += len(encoding.encode('type'))
                num_tokens += len(encoding.encode(parameters.get("type")))
                if 'properties' in parameters:
                    num_tokens += len(encoding.encode('properties'))
                    for key, value in parameters.get('properties').items():
                        num_tokens += len(encoding.encode(key))
                        for field_key, field_value in value.items():
                            num_tokens += len(encoding.encode(field_key))
                            if field_key == 'enum':
                                for enum_field in field_value:
                                    num_tokens += 3
                                    num_tokens += len(encoding.encode(enum_field))
                            else:
                                num_tokens += len(encoding.encode(field_key))
                                num_tokens += len(encoding.encode(str(field_value)))
                if 'required' in parameters:
                    num_tokens += len(encoding.encode('required'))
                    for required_field in parameters['required']:
                        num_tokens += 3
                        num_tokens += len(encoding.encode(required_field))
        return num_tokens
--- a/api/core/agent/agent/structured_chat.py
+++ b/api/core/agent/agent/structured_chat.py
@ -1,306 +0,0 @@
 import re
 from collections.abc import Sequence
 from typing import Any, Optional, Union, cast
 from langchain import BasePromptTemplate, PromptTemplate
 from langchain.agents import Agent, AgentOutputParser, StructuredChatAgent
 from langchain.agents.structured_chat.base import HUMAN_MESSAGE_TEMPLATE
 from langchain.agents.structured_chat.prompt import PREFIX, SUFFIX
 from langchain.callbacks.base import BaseCallbackManager
 from langchain.callbacks.manager import Callbacks
 from langchain.memory.prompt import SUMMARY_PROMPT
 from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, SystemMessagePromptTemplate
 from langchain.schema import (
    AgentAction,
    AgentFinish,
    AIMessage,
    BaseMessage,
    HumanMessage,
    OutputParserException,
    get_buffer_string,
 )
 from langchain.tools import BaseTool
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.agent.agent.calc_token_mixin import CalcTokenMixin, ExceededLLMTokensLimitError
 from core.chain.llm_chain import LLMChain
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
 Valid "action" values: "Final Answer" or {tool_names}
 Provide only ONE action per $JSON_BLOB, as shown:
 ```
 {{{{
  "action": $TOOL_NAME,
  "action_input": $INPUT
 }}}}
 ```
 Follow this format:
 Question: input question to answer
 Thought: consider previous and subsequent steps
 Action:
 ```
 $JSON_BLOB
 ```
 Observation: action result
 ... (repeat Thought/Action/Observation N times)
 Thought: I know what to respond
 Action:
 ```
 {{{{
  "action": "Final Answer",
  "action_input": "Final response to human"
 }}}}
 ```"""
 class AutoSummarizingStructuredChatAgent(StructuredChatAgent, CalcTokenMixin):
    moving_summary_buffer: str = ""
    moving_summary_index: int = 0
    summary_model_config: ModelConfigEntity = None
    class Config:
        """Configuration for this pydantic object."""
        arbitrary_types_allowed = True
    def should_use_agent(self, query: str):
        """
        return should use agent
        Using the ReACT mode to determine whether an agent is needed is costly,
        so it's better to just use an Agent for reasoning, which is cheaper.
        :param query:
        :return:
        """
        return True
    def plan(
        self,
        intermediate_steps: list[tuple[AgentAction, str]],
        callbacks: Callbacks = None,
        **kwargs: Any,
    ) -> Union[AgentAction, AgentFinish]:
        """Given input, decided what to do.
        Args:
            intermediate_steps: Steps the LLM has taken to date,
                along with observatons
            callbacks: Callbacks to run.
            **kwargs: User inputs.
        Returns:
            Action specifying what tool to use.
        """
        full_inputs = self.get_full_inputs(intermediate_steps, **kwargs)
        prompts, _ = self.llm_chain.prep_prompts(input_list=[self.llm_chain.prep_inputs(full_inputs)])
        messages = []
        if prompts:
            messages = prompts[0].to_messages()
        prompt_messages = lc_messages_to_prompt_messages(messages)
        rest_tokens = self.get_message_rest_tokens(self.llm_chain.model_config, prompt_messages)
        if rest_tokens < 0:
            full_inputs = self.summarize_messages(intermediate_steps, **kwargs)
        try:
            full_output = self.llm_chain.predict(callbacks=callbacks, **full_inputs)
        except Exception as e:
            raise e
        try:
            agent_decision = self.output_parser.parse(full_output)
            if isinstance(agent_decision, AgentAction) and agent_decision.tool == 'dataset':
                tool_inputs = agent_decision.tool_input
                if isinstance(tool_inputs, dict) and 'query' in tool_inputs:
                    tool_inputs['query'] = kwargs['input']
                    agent_decision.tool_input = tool_inputs
            return agent_decision
        except OutputParserException:
            return AgentFinish({"output": "I'm sorry, the answer of model is invalid, "
                                          "I don't know how to respond to that."}, "")
    def summarize_messages(self, intermediate_steps: list[tuple[AgentAction, str]], **kwargs):
        if len(intermediate_steps) >= 2 and self.summary_model_config:
            should_summary_intermediate_steps = intermediate_steps[self.moving_summary_index:-1]
            should_summary_messages = [AIMessage(content=observation)
                                       for _, observation in should_summary_intermediate_steps]
            if self.moving_summary_index == 0:
                should_summary_messages.insert(0, HumanMessage(content=kwargs.get("input")))
            self.moving_summary_index = len(intermediate_steps)
        else:
            error_msg = "Exceeded LLM tokens limit, stopped."
            raise ExceededLLMTokensLimitError(error_msg)
        if self.moving_summary_buffer and 'chat_history' in kwargs:
            kwargs["chat_history"].pop()
        self.moving_summary_buffer = self.predict_new_summary(
            messages=should_summary_messages,
            existing_summary=self.moving_summary_buffer
        )
        if 'chat_history' in kwargs:
            kwargs["chat_history"].append(AIMessage(content=self.moving_summary_buffer))
        return self.get_full_inputs([intermediate_steps[-1]], **kwargs)
    def predict_new_summary(
        self, messages: list[BaseMessage], existing_summary: str
    ) -> str:
        new_lines = get_buffer_string(
            messages,
            human_prefix="Human",
            ai_prefix="AI",
        )
        chain = LLMChain(model_config=self.summary_model_config, prompt=SUMMARY_PROMPT)
        return chain.predict(summary=existing_summary, new_lines=new_lines)
    @classmethod
    def create_prompt(
            cls,
            tools: Sequence[BaseTool],
            prefix: str = PREFIX,
            suffix: str = SUFFIX,
            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
            format_instructions: str = FORMAT_INSTRUCTIONS,
            input_variables: Optional[list[str]] = None,
            memory_prompts: Optional[list[BasePromptTemplate]] = None,
    ) -> BasePromptTemplate:
        tool_strings = []
        for tool in tools:
            args_schema = re.sub("}", "}}}}", re.sub("{", "{{{{", str(tool.args)))
            tool_strings.append(f"{tool.name}: {tool.description}, args: {args_schema}")
        formatted_tools = "\n".join(tool_strings)
        tool_names = ", ".join([('"' + tool.name + '"') for tool in tools])
        format_instructions = format_instructions.format(tool_names=tool_names)
        template = "\n\n".join([prefix, formatted_tools, format_instructions, suffix])
        if input_variables is None:
            input_variables = ["input", "agent_scratchpad"]
        _memory_prompts = memory_prompts or []
        messages = [
            SystemMessagePromptTemplate.from_template(template),
            *_memory_prompts,
            HumanMessagePromptTemplate.from_template(human_message_template),
        ]
        return ChatPromptTemplate(input_variables=input_variables, messages=messages)
    @classmethod
    def create_completion_prompt(
            cls,
            tools: Sequence[BaseTool],
            prefix: str = PREFIX,
            format_instructions: str = FORMAT_INSTRUCTIONS,
            input_variables: Optional[list[str]] = None,
    ) -> PromptTemplate:
        """Create prompt in the style of the zero shot agent.
        Args:
            tools: List of tools the agent will have access to, used to format the
                prompt.
            prefix: String to put before the list of tools.
            input_variables: List of input variables the final prompt will expect.
        Returns:
            A PromptTemplate with the template assembled from the pieces here.
        """
        suffix = """Begin! Reminder to ALWAYS respond with a valid json blob of a single action. Use tools if necessary. Respond directly if appropriate. Format is Action:```$JSON_BLOB```then Observation:.
 Question: {input}
 Thought: {agent_scratchpad}
 """
        tool_strings = "\n".join([f"{tool.name}: {tool.description}" for tool in tools])
        tool_names = ", ".join([tool.name for tool in tools])
        format_instructions = format_instructions.format(tool_names=tool_names)
        template = "\n\n".join([prefix, tool_strings, format_instructions, suffix])
        if input_variables is None:
            input_variables = ["input", "agent_scratchpad"]
        return PromptTemplate(template=template, input_variables=input_variables)
    def _construct_scratchpad(
        self, intermediate_steps: list[tuple[AgentAction, str]]
    ) -> str:
        agent_scratchpad = ""
        for action, observation in intermediate_steps:
            agent_scratchpad += action.log
            agent_scratchpad += f"\n{self.observation_prefix}{observation}\n{self.llm_prefix}"
        if not isinstance(agent_scratchpad, str):
            raise ValueError("agent_scratchpad should be of type string.")
        if agent_scratchpad:
            llm_chain = cast(LLMChain, self.llm_chain)
            if llm_chain.model_config.mode == "chat":
                return (
                    f"This was your previous work "
                    f"(but I haven't seen any of it! I only see what "
                    f"you return as final answer):\n{agent_scratchpad}"
                )
            else:
                return agent_scratchpad
        else:
            return agent_scratchpad
    @classmethod
    def from_llm_and_tools(
            cls,
            model_config: ModelConfigEntity,
            tools: Sequence[BaseTool],
            callback_manager: Optional[BaseCallbackManager] = None,
            output_parser: Optional[AgentOutputParser] = None,
            prefix: str = PREFIX,
            suffix: str = SUFFIX,
            human_message_template: str = HUMAN_MESSAGE_TEMPLATE,
            format_instructions: str = FORMAT_INSTRUCTIONS,
            input_variables: Optional[list[str]] = None,
            memory_prompts: Optional[list[BasePromptTemplate]] = None,
            agent_llm_callback: Optional[AgentLLMCallback] = None,
            **kwargs: Any,
    ) -> Agent:
        """Construct an agent from an LLM and tools."""
        cls._validate_tools(tools)
        if model_config.mode == "chat":
            prompt = cls.create_prompt(
                tools,
                prefix=prefix,
                suffix=suffix,
                human_message_template=human_message_template,
                format_instructions=format_instructions,
                input_variables=input_variables,
                memory_prompts=memory_prompts,
            )
        else:
            prompt = cls.create_completion_prompt(
                tools,
                prefix=prefix,
                format_instructions=format_instructions,
                input_variables=input_variables,
            )
        llm_chain = LLMChain(
            model_config=model_config,
            prompt=prompt,
            callback_manager=callback_manager,
            agent_llm_callback=agent_llm_callback,
            parameters={
                'temperature': 0.2,
                'top_p': 0.3,
                'max_tokens': 1500
            }
        )
        tool_names = [tool.name for tool in tools]
        _output_parser = output_parser
        return cls(
            llm_chain=llm_chain,
            allowed_tools=tool_names,
            output_parser=_output_parser,
            **kwargs,
        )
--- a/api/core/app_runner/assistant_app_runner.py
+++ b/api/core/app_runner/assistant_app_runner.py
@ -1,4 +1,3 @@
 import json
 import logging
 from typing import cast
@ -15,7 +14,7 @@ from core.model_runtime.model_providers.__base.large_language_model import Large
 from core.moderation.base import ModerationException
 from core.tools.entities.tool_entities import ToolRuntimeVariablePool
 from extensions.ext_database import db
-from models.model import App, Conversation, Message, MessageAgentThought, MessageChain
+from models.model import App, Conversation, Message, MessageAgentThought
 from models.tools import ToolConversationVariables
 logger = logging.getLogger(__name__)
@ -174,11 +173,6 @@ class AssistantApplicationRunner(AppRunner):
        # convert db variables to tool variables
        tool_variables = self._convert_db_variables_to_tool_variables(tool_conversation_variables)
        message_chain = self._init_message_chain(
            message=message,
            query=query
        )
        # init model instance
        model_instance = ModelInstance(
            provider_model_bundle=app_orchestration_config.model_config.provider_model_bundle,
@ -290,38 +284,6 @@ class AssistantApplicationRunner(AppRunner):
            'pool': db_variables.variables
        })
    def _init_message_chain(self, message: Message, query: str) -> MessageChain:
        """
        Init MessageChain
        :param message: message
        :param query: query
        :return:
        """
        message_chain = MessageChain(
            message_id=message.id,
            type="AgentExecutor",
            input=json.dumps({
                "input": query
            })
        )
        db.session.add(message_chain)
        db.session.commit()
        return message_chain
    def _save_message_chain(self, message_chain: MessageChain, output_text: str) -> None:
        """
        Save MessageChain
        :param message_chain: message chain
        :param output_text: output text
        :return:
        """
        message_chain.output = json.dumps({
            "output": output_text
        })
        db.session.commit()
    def _get_usage_of_all_agent_thoughts(self, model_config: ModelConfigEntity,
                                         message: Message) -> LLMUsage:
        """
--- a/api/core/app_runner/basic_app_runner.py
+++ b/api/core/app_runner/basic_app_runner.py
@ -5,7 +5,7 @@ from core.app_runner.app_runner import AppRunner
 from core.application_queue_manager import ApplicationQueueManager, PublishFrom
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.entities.application_entities import ApplicationGenerateEntity, DatasetEntity, InvokeFrom, ModelConfigEntity
-from core.features.dataset_retrieval import DatasetRetrievalFeature
+from core.features.dataset_retrieval.dataset_retrieval import DatasetRetrievalFeature
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_manager import ModelInstance
 from core.moderation.base import ModerationException
--- a/api/core/entities/agent_entities.py
+++ b/api/core/entities/agent_entities.py
@ -0,0 +1,8 @@
 from enum import Enum
 class PlanningStrategy(Enum):
    ROUTER = 'router'
    REACT_ROUTER = 'react_router'
    REACT = 'react'
    FUNCTION_CALL = 'function_call'
--- a/api/core/features/agent_runner.py
+++ b/api/core/features/agent_runner.py
@ -1,199 +0,0 @@
 import logging
 from typing import Optional, cast
 from langchain.tools import BaseTool
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
 from core.application_queue_manager import ApplicationQueueManager
 from core.callback_handler.agent_loop_gather_callback_handler import AgentLoopGatherCallbackHandler
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.callback_handler.std_out_callback_handler import DifyStdOutCallbackHandler
 from core.entities.application_entities import (
    AgentEntity,
    AppOrchestrationConfigEntity,
    InvokeFrom,
    ModelConfigEntity,
 )
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities.model_entities import ModelFeature, ModelType
 from core.model_runtime.model_providers import model_provider_factory
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
 from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
 from extensions.ext_database import db
 from models.dataset import Dataset
 from models.model import Message
 logger = logging.getLogger(__name__)
 class AgentRunnerFeature:
    def __init__(self, tenant_id: str,
                 app_orchestration_config: AppOrchestrationConfigEntity,
                 model_config: ModelConfigEntity,
                 config: AgentEntity,
                 queue_manager: ApplicationQueueManager,
                 message: Message,
                 user_id: str,
                 agent_llm_callback: AgentLLMCallback,
                 callback: AgentLoopGatherCallbackHandler,
                 memory: Optional[TokenBufferMemory] = None,) -> None:
        """
        Agent runner
        :param tenant_id: tenant id
        :param app_orchestration_config: app orchestration config
        :param model_config: model config
        :param config: dataset config
        :param queue_manager: queue manager
        :param message: message
        :param user_id: user id
        :param agent_llm_callback: agent llm callback
        :param callback: callback
        :param memory: memory
        """
        self.tenant_id = tenant_id
        self.app_orchestration_config = app_orchestration_config
        self.model_config = model_config
        self.config = config
        self.queue_manager = queue_manager
        self.message = message
        self.user_id = user_id
        self.agent_llm_callback = agent_llm_callback
        self.callback = callback
        self.memory = memory
    def run(self, query: str,
            invoke_from: InvokeFrom) -> Optional[str]:
        """
        Retrieve agent loop result.
        :param query: query
        :param invoke_from: invoke from
        :return:
        """
        provider = self.config.provider
        model = self.config.model
        tool_configs = self.config.tools
        # check model is support tool calling
        provider_instance = model_provider_factory.get_provider_instance(provider=provider)
        model_type_instance = provider_instance.get_model_instance(ModelType.LLM)
        model_type_instance = cast(LargeLanguageModel, model_type_instance)
        # get model schema
        model_schema = model_type_instance.get_model_schema(
            model=model,
            credentials=self.model_config.credentials
        )
        if not model_schema:
            return None
        planning_strategy = PlanningStrategy.REACT
        features = model_schema.features
        if features:
            if ModelFeature.TOOL_CALL in features \
                    or ModelFeature.MULTI_TOOL_CALL in features:
                planning_strategy = PlanningStrategy.FUNCTION_CALL
        tools = self.to_tools(
            tool_configs=tool_configs,
            invoke_from=invoke_from,
            callbacks=[self.callback, DifyStdOutCallbackHandler()],
        )
        if len(tools) == 0:
            return None
        agent_configuration = AgentConfiguration(
            strategy=planning_strategy,
            model_config=self.model_config,
            tools=tools,
            memory=self.memory,
            max_iterations=10,
            max_execution_time=400.0,
            early_stopping_method="generate",
            agent_llm_callback=self.agent_llm_callback,
            callbacks=[self.callback, DifyStdOutCallbackHandler()]
        )
        agent_executor = AgentExecutor(agent_configuration)
        try:
            # check if should use agent
            should_use_agent = agent_executor.should_use_agent(query)
            if not should_use_agent:
                return None
            result = agent_executor.run(query)
            return result.output
        except Exception as ex:
            logger.exception("agent_executor run failed")
            return None
    def to_dataset_retriever_tool(self, tool_config: dict,
                                  invoke_from: InvokeFrom) \
            -> Optional[BaseTool]:
        """
        A dataset tool is a tool that can be used to retrieve information from a dataset
        :param tool_config: tool config
        :param invoke_from: invoke from
        """
        show_retrieve_source = self.app_orchestration_config.show_retrieve_source
        hit_callback = DatasetIndexToolCallbackHandler(
            queue_manager=self.queue_manager,
            app_id=self.message.app_id,
            message_id=self.message.id,
            user_id=self.user_id,
            invoke_from=invoke_from
        )
        # get dataset from dataset id
        dataset = db.session.query(Dataset).filter(
            Dataset.tenant_id == self.tenant_id,
            Dataset.id == tool_config.get("id")
        ).first()
        # pass if dataset is not available
        if not dataset:
            return None
        # pass if dataset is not available
        if (dataset and dataset.available_document_count == 0
                and dataset.available_document_count == 0):
            return None
        # get retrieval model config
        default_retrieval_model = {
            'search_method': 'semantic_search',
            'reranking_enable': False,
            'reranking_model': {
                'reranking_provider_name': '',
                'reranking_model_name': ''
            },
            'top_k': 2,
            'score_threshold_enabled': False
        }
        retrieval_model_config = dataset.retrieval_model \
            if dataset.retrieval_model else default_retrieval_model
        # get top k
        top_k = retrieval_model_config['top_k']
        # get score threshold
        score_threshold = None
        score_threshold_enabled = retrieval_model_config.get("score_threshold_enabled")
        if score_threshold_enabled:
            score_threshold = retrieval_model_config.get("score_threshold")
        tool = DatasetRetrieverTool.from_dataset(
            dataset=dataset,
            top_k=top_k,
            score_threshold=score_threshold,
            hit_callbacks=[hit_callback],
            return_resource=show_retrieve_source,
            retriever_from=invoke_from.to_source()
        )
        return tool
--- a/api/core/features/dataset_retrieval/init.py
+++ b/api/core/features/dataset_retrieval/init.py
--- a/api/core/features/dataset_retrieval/agent/init.py
+++ b/api/core/features/dataset_retrieval/agent/init.py
--- a/api/core/features/dataset_retrieval/agent/agent_llm_callback.py
+++ b/api/core/features/dataset_retrieval/agent/agent_llm_callback.py
--- a/api/core/features/dataset_retrieval/agent/fake_llm.py
+++ b/api/core/features/dataset_retrieval/agent/fake_llm.py
--- a/api/core/features/dataset_retrieval/agent/llm_chain.py
+++ b/api/core/features/dataset_retrieval/agent/llm_chain.py
@ -5,11 +5,11 @@ from langchain.callbacks.manager import CallbackManagerForChainRun
 from langchain.schema import Generation, LLMResult
 from langchain.schema.language_model import BaseLanguageModel
 from core.agent.agent.agent_llm_callback import AgentLLMCallback
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
 from core.features.dataset_retrieval.agent.agent_llm_callback import AgentLLMCallback
 from core.features.dataset_retrieval.agent.fake_llm import FakeLLM
 from core.model_manager import ModelInstance
 from core.third_party.langchain.llms.fake import FakeLLM
 class LLMChain(LCLLMChain):
--- a/api/core/features/dataset_retrieval/agent/multi_dataset_router_agent.py
+++ b/api/core/features/dataset_retrieval/agent/multi_dataset_router_agent.py
@ -12,9 +12,9 @@ from pydantic import root_validator
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import lc_messages_to_prompt_messages
 from core.features.dataset_retrieval.agent.fake_llm import FakeLLM
 from core.model_manager import ModelInstance
 from core.model_runtime.entities.message_entities import PromptMessageTool
 from core.third_party.langchain.llms.fake import FakeLLM
 class MultiDatasetRouterAgent(OpenAIFunctionsAgent):
--- a/api/core/features/dataset_retrieval/agent/output_parser/init.py
+++ b/api/core/features/dataset_retrieval/agent/output_parser/init.py
--- a/api/core/features/dataset_retrieval/agent/output_parser/structured_chat.py
+++ b/api/core/features/dataset_retrieval/agent/output_parser/structured_chat.py
--- a/api/core/features/dataset_retrieval/agent/structed_multi_dataset_router_agent.py
+++ b/api/core/features/dataset_retrieval/agent/structed_multi_dataset_router_agent.py
@ -12,8 +12,8 @@ from langchain.prompts import ChatPromptTemplate, HumanMessagePromptTemplate, Sy
 from langchain.schema import AgentAction, AgentFinish, OutputParserException
 from langchain.tools import BaseTool
 from core.chain.llm_chain import LLMChain
 from core.entities.application_entities import ModelConfigEntity
 from core.features.dataset_retrieval.agent.llm_chain import LLMChain
 FORMAT_INSTRUCTIONS = """Use a json blob to specify a tool by providing an action key (tool name) and an action_input key (tool input).
 The nouns in the format of "Thought", "Action", "Action Input", "Final Answer" must be expressed in English.
--- a/api/core/features/dataset_retrieval/agent_based_dataset_executor.py
+++ b/api/core/features/dataset_retrieval/agent_based_dataset_executor.py
@ -1,4 +1,3 @@
 import enum
 import logging
 from typing import Optional, Union
@ -8,14 +7,13 @@ from langchain.callbacks.manager import Callbacks
 from langchain.tools import BaseTool
 from pydantic import BaseModel, Extra
-from core.agent.agent.agent_llm_callback import AgentLLMCallback
+from core.entities.agent_entities import PlanningStrategy
 from core.agent.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
 from core.agent.agent.openai_function_call import AutoSummarizingOpenAIFunctionCallAgent
 from core.agent.agent.output_parser.structured_chat import StructuredChatOutputParser
 from core.agent.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
 from core.agent.agent.structured_chat import AutoSummarizingStructuredChatAgent
 from core.entities.application_entities import ModelConfigEntity
 from core.entities.message_entities import prompt_messages_to_lc_messages
 from core.features.dataset_retrieval.agent.agent_llm_callback import AgentLLMCallback
 from core.features.dataset_retrieval.agent.multi_dataset_router_agent import MultiDatasetRouterAgent
 from core.features.dataset_retrieval.agent.output_parser.structured_chat import StructuredChatOutputParser
 from core.features.dataset_retrieval.agent.structed_multi_dataset_router_agent import StructuredMultiDatasetRouterAgent
 from core.helper import moderation
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.errors.invoke import InvokeError
@ -23,13 +21,6 @@ from core.tools.tool.dataset_retriever.dataset_multi_retriever_tool import Datas
 from core.tools.tool.dataset_retriever.dataset_retriever_tool import DatasetRetrieverTool
 class PlanningStrategy(str, enum.Enum):
    ROUTER = 'router'
    REACT_ROUTER = 'react_router'
    REACT = 'react'
    FUNCTION_CALL = 'function_call'
 class AgentConfiguration(BaseModel):
    strategy: PlanningStrategy
    model_config: ModelConfigEntity
@ -62,28 +53,7 @@ class AgentExecutor:
        self.agent = self._init_agent()
    def _init_agent(self) -> Union[BaseSingleActionAgent, BaseMultiActionAgent]:
-        if self.configuration.strategy == PlanningStrategy.REACT:
+        if self.configuration.strategy == PlanningStrategy.ROUTER:
            agent = AutoSummarizingStructuredChatAgent.from_llm_and_tools(
                model_config=self.configuration.model_config,
                tools=self.configuration.tools,
                output_parser=StructuredChatOutputParser(),
                summary_model_config=self.configuration.summary_model_config
                if self.configuration.summary_model_config else None,
                agent_llm_callback=self.configuration.agent_llm_callback,
                verbose=True
            )
        elif self.configuration.strategy == PlanningStrategy.FUNCTION_CALL:
            agent = AutoSummarizingOpenAIFunctionCallAgent.from_llm_and_tools(
                model_config=self.configuration.model_config,
                tools=self.configuration.tools,
                extra_prompt_messages=prompt_messages_to_lc_messages(self.configuration.memory.get_history_prompt_messages())
                if self.configuration.memory else None,  # used for read chat histories memory
                summary_model_config=self.configuration.summary_model_config
                if self.configuration.summary_model_config else None,
                agent_llm_callback=self.configuration.agent_llm_callback,
                verbose=True
            )
        elif self.configuration.strategy == PlanningStrategy.ROUTER:
            self.configuration.tools = [t for t in self.configuration.tools
                                        if isinstance(t, DatasetRetrieverTool)
                                        or isinstance(t, DatasetMultiRetrieverTool)]
--- a/api/core/features/dataset_retrieval/dataset_retrieval.py
+++ b/api/core/features/dataset_retrieval/dataset_retrieval.py
@ -2,9 +2,10 @@ from typing import Optional, cast
 from langchain.tools import BaseTool
 from core.agent.agent_executor import AgentConfiguration, AgentExecutor, PlanningStrategy
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.entities.agent_entities import PlanningStrategy
 from core.entities.application_entities import DatasetEntity, DatasetRetrieveConfigEntity, InvokeFrom, ModelConfigEntity
 from core.features.dataset_retrieval.agent_based_dataset_executor import AgentConfiguration, AgentExecutor
 from core.memory.token_buffer_memory import TokenBufferMemory
 from core.model_runtime.entities.model_entities import ModelFeature
 from core.model_runtime.model_providers.__base.large_language_model import LargeLanguageModel
--- a/api/core/third_party/spark/spark_llm.py
+++ b/api/core/third_party/spark/spark_llm.py
@ -1,189 +0,0 @@
 import base64
 import hashlib
 import hmac
 import json
 import queue
 import ssl
 from datetime import datetime
 from time import mktime
 from typing import Optional
 from urllib.parse import urlencode, urlparse
 from wsgiref.handlers import format_date_time
 import websocket
 class SparkLLMClient:
    def __init__(self, model_name: str, app_id: str, api_key: str, api_secret: str, api_domain: Optional[str] = None):
        domain = 'spark-api.xf-yun.com'
        endpoint = 'chat'
        if api_domain:
            domain = api_domain
            if model_name == 'spark-v3':
                endpoint = 'multimodal'
        model_api_configs = {
            'spark': {
                'version': 'v1.1',
                'chat_domain': 'general'
            },
            'spark-v2': {
                'version': 'v2.1',
                'chat_domain': 'generalv2'
            },
            'spark-v3': {
                'version': 'v3.1',
                'chat_domain': 'generalv3'
            },
            'spark-v3.5': {
                'version': 'v3.5',
                'chat_domain': 'generalv3.5'
            }
        }
        api_version = model_api_configs[model_name]['version']
        self.chat_domain = model_api_configs[model_name]['chat_domain']
        self.api_base = f"wss://{domain}/{api_version}/{endpoint}"
        self.app_id = app_id
        self.ws_url = self.create_url(
            urlparse(self.api_base).netloc,
            urlparse(self.api_base).path,
            self.api_base,
            api_key,
            api_secret
        )
        self.queue = queue.Queue()
        self.blocking_message = ''
    def create_url(self, host: str, path: str, api_base: str, api_key: str, api_secret: str) -> str:
        # generate timestamp by RFC1123
        now = datetime.now()
        date = format_date_time(mktime(now.timetuple()))
        signature_origin = "host: " + host + "\n"
        signature_origin += "date: " + date + "\n"
        signature_origin += "GET " + path + " HTTP/1.1"
        # encrypt using hmac-sha256
        signature_sha = hmac.new(api_secret.encode('utf-8'), signature_origin.encode('utf-8'),
                                 digestmod=hashlib.sha256).digest()
        signature_sha_base64 = base64.b64encode(signature_sha).decode(encoding='utf-8')
        authorization_origin = f'api_key="{api_key}", algorithm="hmac-sha256", headers="host date request-line", signature="{signature_sha_base64}"'
        authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
        v = {
            "authorization": authorization,
            "date": date,
            "host": host
        }
        # generate url
        url = api_base + '?' + urlencode(v)
        return url
    def run(self, messages: list, user_id: str,
            model_kwargs: Optional[dict] = None, streaming: bool = False):
        websocket.enableTrace(False)
        ws = websocket.WebSocketApp(
            self.ws_url,
            on_message=self.on_message,
            on_error=self.on_error,
            on_close=self.on_close,
            on_open=self.on_open
        )
        ws.messages = messages
        ws.user_id = user_id
        ws.model_kwargs = model_kwargs
        ws.streaming = streaming
        ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
    def on_error(self, ws, error):
        self.queue.put({
            'status_code': error.status_code,
            'error': error.resp_body.decode('utf-8')
        })
        ws.close()
    def on_close(self, ws, close_status_code, close_reason):
        self.queue.put({'done': True})
    def on_open(self, ws):
        self.blocking_message = ''
        data = json.dumps(self.gen_params(
            messages=ws.messages,
            user_id=ws.user_id,
            model_kwargs=ws.model_kwargs
        ))
        ws.send(data)
    def on_message(self, ws, message):
        data = json.loads(message)
        code = data['header']['code']
        if code != 0:
            self.queue.put({
                'status_code': 400,
                'error': f"Code: {code}, Error: {data['header']['message']}"
            })
            ws.close()
        else:
            choices = data["payload"]["choices"]
            status = choices["status"]
            content = choices["text"][0]["content"]
            if ws.streaming:
                self.queue.put({'data': content})
            else:
                self.blocking_message += content
            if status == 2:
                if not ws.streaming:
                    self.queue.put({'data': self.blocking_message})
                ws.close()
    def gen_params(self, messages: list, user_id: str,
                   model_kwargs: Optional[dict] = None) -> dict:
        data = {
            "header": {
                "app_id": self.app_id,
                "uid": user_id
            },
            "parameter": {
                "chat": {
                    "domain": self.chat_domain
                }
            },
            "payload": {
                "message": {
                    "text": messages
                }
            }
        }
        if model_kwargs:
            data['parameter']['chat'].update(model_kwargs)
        return data
    def subscribe(self):
        while True:
            content = self.queue.get()
            if 'error' in content:
                if content['status_code'] == 401:
                    raise SparkError('[Spark] The credentials you provided are incorrect. '
                                     'Please double-check and fill them in again.')
                elif content['status_code'] == 403:
                    raise SparkError("[Spark] Sorry, the credentials you provided are access denied. "
                                     "Please try again after obtaining the necessary permissions.")
                else:
                    raise SparkError(f"[Spark] code: {content['status_code']}, error: {content['error']}")
            if 'data' not in content:
                break
            yield content
 class SparkError(Exception):
    pass
--- a/api/core/tool/current_datetime_tool.py
+++ b/api/core/tool/current_datetime_tool.py
@ -1,24 +0,0 @@
 from datetime import datetime
 from langchain.tools import BaseTool
 from pydantic import BaseModel, Field
 class DatetimeToolInput(BaseModel):
    type: str = Field(..., description="Type for current time, must be: datetime.")
 class DatetimeTool(BaseTool):
    """Tool for querying current datetime."""
    name: str = "current_datetime"
    args_schema: type[BaseModel] = DatetimeToolInput
    description: str = "A tool when you want to get the current date, time, week, month or year, " \
                       "and the time zone is UTC. Result is \"<date> <time> <timezone> <week>\"."
    def _run(self, type: str) -> str:
        # get current time
        current_time = datetime.utcnow()
        return current_time.strftime("%Y-%m-%d %H:%M:%S UTC+0000 %A")
    async def _arun(self, tool_input: str) -> str:
        raise NotImplementedError()
--- a/api/core/tool/provider/base.py
+++ b/api/core/tool/provider/base.py
@ -1,63 +0,0 @@
 import base64
 from abc import ABC, abstractmethod
 from typing import Optional
 from extensions.ext_database import db
 from libs import rsa
 from models.account import Tenant
 from models.tool import ToolProvider, ToolProviderName
 class BaseToolProvider(ABC):
    def __init__(self, tenant_id: str):
        self.tenant_id = tenant_id
    @abstractmethod
    def get_provider_name(self) -> ToolProviderName:
        raise NotImplementedError
    @abstractmethod
    def encrypt_credentials(self, credentials: dict) -> Optional[dict]:
        raise NotImplementedError
    @abstractmethod
    def get_credentials(self, obfuscated: bool = False) -> Optional[dict]:
        raise NotImplementedError
    @abstractmethod
    def credentials_to_func_kwargs(self) -> Optional[dict]:
        raise NotImplementedError
    @abstractmethod
    def credentials_validate(self, credentials: dict):
        raise NotImplementedError
    def get_provider(self, must_enabled: bool = False) -> Optional[ToolProvider]:
        """
        Returns the Provider instance for the given tenant_id and tool_name.
        """
        query = db.session.query(ToolProvider).filter(
            ToolProvider.tenant_id == self.tenant_id,
            ToolProvider.tool_name == self.get_provider_name().value
        )
        if must_enabled:
            query = query.filter(ToolProvider.is_enabled == True)
        return query.first()
    def encrypt_token(self, token) -> str:
        tenant = db.session.query(Tenant).filter(Tenant.id == self.tenant_id).first()
        encrypted_token = rsa.encrypt(token, tenant.encrypt_public_key)
        return base64.b64encode(encrypted_token).decode()
    def decrypt_token(self, token: str, obfuscated: bool = False) -> str:
        token = rsa.decrypt(base64.b64decode(token), self.tenant_id)
        if obfuscated:
            return self._obfuscated_token(token)
        return token
    def _obfuscated_token(self, token: str) -> str:
        return token[:6] + '*' * (len(token) - 8) + token[-2:]
--- a/api/core/tool/provider/errors.py
+++ b/api/core/tool/provider/errors.py
@ -1,2 +0,0 @@
 class ToolValidateFailedError(Exception):
    description = "Tool Provider Validate failed"
--- a/api/core/tool/provider/serpapi_provider.py
+++ b/api/core/tool/provider/serpapi_provider.py
@ -1,77 +0,0 @@
 from typing import Optional
 from core.tool.provider.base import BaseToolProvider
 from core.tool.provider.errors import ToolValidateFailedError
 from core.tool.serpapi_wrapper import OptimizedSerpAPIWrapper
 from models.tool import ToolProviderName
 class SerpAPIToolProvider(BaseToolProvider):
    def get_provider_name(self) -> ToolProviderName:
        """
        Returns the name of the provider.
        :return:
        """
        return ToolProviderName.SERPAPI
    def get_credentials(self, obfuscated: bool = False) -> Optional[dict]:
        """
        Returns the credentials for SerpAPI as a dictionary.
        :param obfuscated: obfuscate credentials if True
        :return:
        """
        tool_provider = self.get_provider(must_enabled=True)
        if not tool_provider:
            return None
        credentials = tool_provider.credentials
        if not credentials:
            return None
        if credentials.get('api_key'):
            credentials['api_key'] = self.decrypt_token(credentials.get('api_key'), obfuscated)
        return credentials
    def credentials_to_func_kwargs(self) -> Optional[dict]:
        """
        Returns the credentials function kwargs as a dictionary.
        :return:
        """
        credentials = self.get_credentials()
        if not credentials:
            return None
        return {
            'serpapi_api_key': credentials.get('api_key')
        }
    def credentials_validate(self, credentials: dict):
        """
        Validates the given credentials.
        :param credentials:
        :return:
        """
        if 'api_key' not in credentials or not credentials.get('api_key'):
            raise ToolValidateFailedError("SerpAPI api_key is required.")
        api_key = credentials.get('api_key')
        try:
            OptimizedSerpAPIWrapper(serpapi_api_key=api_key).run(query='test')
        except Exception as e:
            raise ToolValidateFailedError("SerpAPI api_key is invalid. {}".format(e))
    def encrypt_credentials(self, credentials: dict) -> Optional[dict]:
        """
        Encrypts the given credentials.
        :param credentials:
        :return:
        """
        credentials['api_key'] = self.encrypt_token(credentials.get('api_key'))
        return credentials
--- a/api/core/tool/provider/tool_provider_service.py
+++ b/api/core/tool/provider/tool_provider_service.py
@ -1,43 +0,0 @@
 from typing import Optional
 from core.tool.provider.base import BaseToolProvider
 from core.tool.provider.serpapi_provider import SerpAPIToolProvider
 class ToolProviderService:
    def __init__(self, tenant_id: str, provider_name: str):
        self.provider = self._init_provider(tenant_id, provider_name)
    def _init_provider(self, tenant_id: str, provider_name: str) -> BaseToolProvider:
        if provider_name == 'serpapi':
            return SerpAPIToolProvider(tenant_id)
        else:
            raise Exception('tool provider {} not found'.format(provider_name))
    def get_credentials(self, obfuscated: bool = False) -> Optional[dict]:
        """
        Returns the credentials for Tool as a dictionary.
        :param obfuscated:
        :return:
        """
        return self.provider.get_credentials(obfuscated)
    def credentials_validate(self, credentials: dict):
        """
        Validates the given credentials.
        :param credentials:
        :raises: ValidateFailedError
        """
        return self.provider.credentials_validate(credentials)
    def encrypt_credentials(self, credentials: dict):
        """
        Encrypts the given credentials.
        :param credentials:
        :return:
        """
        return self.provider.encrypt_credentials(credentials)
--- a/api/core/tool/serpapi_wrapper.py
+++ b/api/core/tool/serpapi_wrapper.py
@ -1,51 +0,0 @@
 from langchain import SerpAPIWrapper
 from pydantic import BaseModel, Field
 class OptimizedSerpAPIInput(BaseModel):
    query: str = Field(..., description="search query.")
 class OptimizedSerpAPIWrapper(SerpAPIWrapper):
    @staticmethod
    def _process_response(res: dict, num_results: int = 5) -> str:
        """Process response from SerpAPI."""
        if "error" in res.keys():
            raise ValueError(f"Got error from SerpAPI: {res['error']}")
        if "answer_box" in res.keys() and type(res["answer_box"]) == list:
            res["answer_box"] = res["answer_box"][0]
        if "answer_box" in res.keys() and "answer" in res["answer_box"].keys():
            toret = res["answer_box"]["answer"]
        elif "answer_box" in res.keys() and "snippet" in res["answer_box"].keys():
            toret = res["answer_box"]["snippet"]
        elif (
            "answer_box" in res.keys()
            and "snippet_highlighted_words" in res["answer_box"].keys()
        ):
            toret = res["answer_box"]["snippet_highlighted_words"][0]
        elif (
            "sports_results" in res.keys()
            and "game_spotlight" in res["sports_results"].keys()
        ):
            toret = res["sports_results"]["game_spotlight"]
        elif (
            "shopping_results" in res.keys()
            and "title" in res["shopping_results"][0].keys()
        ):
            toret = res["shopping_results"][:3]
        elif (
            "knowledge_graph" in res.keys()
            and "description" in res["knowledge_graph"].keys()
        ):
            toret = res["knowledge_graph"]["description"]
        elif 'organic_results' in res.keys() and len(res['organic_results']) > 0:
            toret = ""
            for result in res["organic_results"][:num_results]:
                if "link" in result:
                    toret += "----------------\nlink: " + result["link"] + "\n"
                if "snippet" in result:
                    toret += "snippet: " + result["snippet"] + "\n"
        else:
            toret = "No good search result found"
        return "search result:\n" + toret
--- a/api/core/tool/web_reader_tool.py
+++ b/api/core/tool/web_reader_tool.py
@ -1,443 +0,0 @@
 import hashlib
 import json
 import os
 import re
 import site
 import subprocess
 import tempfile
 import unicodedata
 from contextlib import contextmanager
 from typing import Any
 import requests
 from bs4 import BeautifulSoup, CData, Comment, NavigableString
 from langchain.chains import RefineDocumentsChain
 from langchain.chains.summarize import refine_prompts
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.tools.base import BaseTool
 from newspaper import Article
 from pydantic import BaseModel, Field
 from regex import regex
 from core.chain.llm_chain import LLMChain
 from core.entities.application_entities import ModelConfigEntity
 from core.rag.extractor import extract_processor
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.models.document import Document
 FULL_TEMPLATE = """
 TITLE: {title}
 AUTHORS: {authors}
 PUBLISH DATE: {publish_date}
 TOP_IMAGE_URL: {top_image}
 TEXT:
 {text}
 """
 class WebReaderToolInput(BaseModel):
    url: str = Field(..., description="URL of the website to read")
    summary: bool = Field(
        default=False,
        description="When the user's question requires extracting the summarizing content of the webpage, "
                    "set it to true."
    )
    cursor: int = Field(
        default=0,
        description="Start reading from this character."
        "Use when the first response was truncated"
        "and you want to continue reading the page."
        "The value cannot exceed 24000.",
    )
 class WebReaderTool(BaseTool):
    """Reader tool for getting website title and contents. Gives more control than SimpleReaderTool."""
    name: str = "web_reader"
    args_schema: type[BaseModel] = WebReaderToolInput
    description: str = "use this to read a website. " \
                       "If you can answer the question based on the information provided, " \
                       "there is no need to use."
    page_contents: str = None
    url: str = None
    max_chunk_length: int = 4000
    summary_chunk_tokens: int = 4000
    summary_chunk_overlap: int = 0
    summary_separators: list[str] = ["\n\n", "。", ".", " ", ""]
    continue_reading: bool = True
    model_config: ModelConfigEntity
    model_parameters: dict[str, Any]
    def _run(self, url: str, summary: bool = False, cursor: int = 0) -> str:
        try:
            if not self.page_contents or self.url != url:
                page_contents = get_url(url)
                self.page_contents = page_contents
                self.url = url
            else:
                page_contents = self.page_contents
        except Exception as e:
            return f'Read this website failed, caused by: {str(e)}.'
        if summary:
            character_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
                chunk_size=self.summary_chunk_tokens,
                chunk_overlap=self.summary_chunk_overlap,
                separators=self.summary_separators
            )
            texts = character_splitter.split_text(page_contents)
            docs = [Document(page_content=t) for t in texts]
            if len(docs) == 0 or docs[0].page_content.endswith('TEXT:'):
                return "No content found."
            # only use first 5 docs
            if len(docs) > 5:
                docs = docs[:5]
            chain = self.get_summary_chain()
            try:
                page_contents = chain.run(docs)
            except Exception as e:
                return f'Read this website failed, caused by: {str(e)}.'
        else:
            page_contents = page_result(page_contents, cursor, self.max_chunk_length)
            if self.continue_reading and len(page_contents) >= self.max_chunk_length:
                page_contents += f"\nPAGE WAS TRUNCATED. IF YOU FIND INFORMATION THAT CAN ANSWER QUESTION " \
                                 f"THEN DIRECT ANSWER AND STOP INVOKING web_reader TOOL, OTHERWISE USE " \
                                 f"CURSOR={cursor+len(page_contents)} TO CONTINUE READING."
        return page_contents
    async def _arun(self, url: str) -> str:
        raise NotImplementedError
    def get_summary_chain(self) -> RefineDocumentsChain:
        initial_chain = LLMChain(
            model_config=self.model_config,
            prompt=refine_prompts.PROMPT,
            parameters=self.model_parameters
        )
        refine_chain = LLMChain(
            model_config=self.model_config,
            prompt=refine_prompts.REFINE_PROMPT,
            parameters=self.model_parameters
        )
        return RefineDocumentsChain(
            initial_llm_chain=initial_chain,
            refine_llm_chain=refine_chain,
            document_variable_name="text",
            initial_response_name="existing_answer",
            callbacks=self.callbacks
        )
 def page_result(text: str, cursor: int, max_length: int) -> str:
    """Page through `text` and return a substring of `max_length` characters starting from `cursor`."""
    return text[cursor: cursor + max_length]
 def get_url(url: str) -> str:
    """Fetch URL and return the contents as a string."""
    headers = {
        "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36"
    }
    supported_content_types = extract_processor.SUPPORT_URL_CONTENT_TYPES + ["text/html"]
    head_response = requests.head(url, headers=headers, allow_redirects=True, timeout=(5, 10))
    if head_response.status_code != 200:
        return "URL returned status code {}.".format(head_response.status_code)
    # check content-type
    main_content_type = head_response.headers.get('Content-Type').split(';')[0].strip()
    if main_content_type not in supported_content_types:
        return "Unsupported content-type [{}] of URL.".format(main_content_type)
    if main_content_type in extract_processor.SUPPORT_URL_CONTENT_TYPES:
        return ExtractProcessor.load_from_url(url, return_text=True)
    response = requests.get(url, headers=headers, allow_redirects=True, timeout=(5, 30))
    a = extract_using_readabilipy(response.text)
    if not a['plain_text'] or not a['plain_text'].strip():
        return get_url_from_newspaper3k(url)
    res = FULL_TEMPLATE.format(
        title=a['title'],
        authors=a['byline'],
        publish_date=a['date'],
        top_image="",
        text=a['plain_text'] if a['plain_text'] else "",
    )
    return res
 def get_url_from_newspaper3k(url: str) -> str:
    a = Article(url)
    a.download()
    a.parse()
    res = FULL_TEMPLATE.format(
        title=a.title,
        authors=a.authors,
        publish_date=a.publish_date,
        top_image=a.top_image,
        text=a.text,
    )
    return res
 def extract_using_readabilipy(html):
    with tempfile.NamedTemporaryFile(delete=False, mode='w+') as f_html:
        f_html.write(html)
        f_html.close()
    html_path = f_html.name
    # Call Mozilla's Readability.js Readability.parse() function via node, writing output to a temporary file
    article_json_path = html_path + ".json"
    jsdir = os.path.join(find_module_path('readabilipy'), 'javascript')
    with chdir(jsdir):
        subprocess.check_call(["node", "ExtractArticle.js", "-i", html_path, "-o", article_json_path])
    # Read output of call to Readability.parse() from JSON file and return as Python dictionary
    with open(article_json_path, encoding="utf-8") as json_file:
        input_json = json.loads(json_file.read())
    # Deleting files after processing
    os.unlink(article_json_path)
    os.unlink(html_path)
    article_json = {
        "title": None,
        "byline": None,
        "date": None,
        "content": None,
        "plain_content": None,
        "plain_text": None
    }
    # Populate article fields from readability fields where present
    if input_json:
        if "title" in input_json and input_json["title"]:
            article_json["title"] = input_json["title"]
        if "byline" in input_json and input_json["byline"]:
            article_json["byline"] = input_json["byline"]
        if "date" in input_json and input_json["date"]:
            article_json["date"] = input_json["date"]
        if "content" in input_json and input_json["content"]:
            article_json["content"] = input_json["content"]
            article_json["plain_content"] = plain_content(article_json["content"], False, False)
            article_json["plain_text"] = extract_text_blocks_as_plain_text(article_json["plain_content"])
        if "textContent" in input_json and input_json["textContent"]:
            article_json["plain_text"] = input_json["textContent"]
            article_json["plain_text"] = re.sub(r'\n\s*\n', '\n', article_json["plain_text"])
    return article_json
 def find_module_path(module_name):
    for package_path in site.getsitepackages():
        potential_path = os.path.join(package_path, module_name)
        if os.path.exists(potential_path):
            return potential_path
    return None
@contextmanager
 def chdir(path):
    """Change directory in context and return to original on exit"""
    # From https://stackoverflow.com/a/37996581, couldn't find a built-in
    original_path = os.getcwd()
    os.chdir(path)
    try:
        yield
    finally:
        os.chdir(original_path)
 def extract_text_blocks_as_plain_text(paragraph_html):
    # Load article as DOM
    soup = BeautifulSoup(paragraph_html, 'html.parser')
    # Select all lists
    list_elements = soup.find_all(['ul', 'ol'])
    # Prefix text in all list items with "* " and make lists paragraphs
    for list_element in list_elements:
        plain_items = "".join(list(filter(None, [plain_text_leaf_node(li)["text"] for li in list_element.find_all('li')])))
        list_element.string = plain_items
        list_element.name = "p"
    # Select all text blocks
    text_blocks = [s.parent for s in soup.find_all(string=True)]
    text_blocks = [plain_text_leaf_node(block) for block in text_blocks]
    # Drop empty paragraphs
    text_blocks = list(filter(lambda p: p["text"] is not None, text_blocks))
    return text_blocks
 def plain_text_leaf_node(element):
    # Extract all text, stripped of any child HTML elements and normalise it
    plain_text = normalise_text(element.get_text())
    if plain_text != "" and element.name == "li":
        plain_text = "* {}, ".format(plain_text)
    if plain_text == "":
        plain_text = None
    if "data-node-index" in element.attrs:
        plain = {"node_index": element["data-node-index"], "text": plain_text}
    else:
        plain = {"text": plain_text}
    return plain
 def plain_content(readability_content, content_digests, node_indexes):
    # Load article as DOM
    soup = BeautifulSoup(readability_content, 'html.parser')
    # Make all elements plain
    elements = plain_elements(soup.contents, content_digests, node_indexes)
    if node_indexes:
        # Add node index attributes to nodes
        elements = [add_node_indexes(element) for element in elements]
    # Replace article contents with plain elements
    soup.contents = elements
    return str(soup)
 def plain_elements(elements, content_digests, node_indexes):
    # Get plain content versions of all elements
    elements = [plain_element(element, content_digests, node_indexes)
                for element in elements]
    if content_digests:
        # Add content digest attribute to nodes
        elements = [add_content_digest(element) for element in elements]
    return elements
 def plain_element(element, content_digests, node_indexes):
    # For lists, we make each item plain text
    if is_leaf(element):
        # For leaf node elements, extract the text content, discarding any HTML tags
        # 1. Get element contents as text
        plain_text = element.get_text()
        # 2. Normalise the extracted text string to a canonical representation
        plain_text = normalise_text(plain_text)
        # 3. Update element content to be plain text
        element.string = plain_text
    elif is_text(element):
        if is_non_printing(element):
            # The simplified HTML may have come from Readability.js so might
            # have non-printing text (e.g. Comment or CData). In this case, we
            # keep the structure, but ensure that the string is empty.
            element = type(element)("")
        else:
            plain_text = element.string
            plain_text = normalise_text(plain_text)
            element = type(element)(plain_text)
    else:
        # If not a leaf node or leaf type call recursively on child nodes, replacing
        element.contents = plain_elements(element.contents, content_digests, node_indexes)
    return element
 def add_node_indexes(element, node_index="0"):
    # Can't add attributes to string types
    if is_text(element):
        return element
    # Add index to current element
    element["data-node-index"] = node_index
    # Add index to child elements
    for local_idx, child in enumerate(
            [c for c in element.contents if not is_text(c)], start=1):
        # Can't add attributes to leaf string types
        child_index = "{stem}.{local}".format(
            stem=node_index, local=local_idx)
        add_node_indexes(child, node_index=child_index)
    return element
 def normalise_text(text):
    """Normalise unicode and whitespace."""
    # Normalise unicode first to try and standardise whitespace characters as much as possible before normalising them
    text = strip_control_characters(text)
    text = normalise_unicode(text)
    text = normalise_whitespace(text)
    return text
 def strip_control_characters(text):
    """Strip out unicode control characters which might break the parsing."""
    # Unicode control characters
    #   [Cc]: Other, Control [includes new lines]
    #   [Cf]: Other, Format
    #   [Cn]: Other, Not Assigned
    #   [Co]: Other, Private Use
    #   [Cs]: Other, Surrogate
    control_chars = set(['Cc', 'Cf', 'Cn', 'Co', 'Cs'])
    retained_chars = ['\t', '\n', '\r', '\f']
    # Remove non-printing control characters
    return "".join(["" if (unicodedata.category(char) in control_chars) and (char not in retained_chars) else char for char in text])
 def normalise_unicode(text):
    """Normalise unicode such that things that are visually equivalent map to the same unicode string where possible."""
    normal_form = "NFKC"
    text = unicodedata.normalize(normal_form, text)
    return text
 def normalise_whitespace(text):
    """Replace runs of whitespace characters with a single space as this is what happens when HTML text is displayed."""
    text = regex.sub(r"\s+", " ", text)
    # Remove leading and trailing whitespace
    text = text.strip()
    return text
 def is_leaf(element):
    return (element.name in ['p', 'li'])
 def is_text(element):
    return isinstance(element, NavigableString)
 def is_non_printing(element):
    return any(isinstance(element, _e) for _e in [Comment, CData])
 def add_content_digest(element):
    if not is_text(element):
        element["data-content-digest"] = content_digest(element)
    return element
 def content_digest(element):
    if is_text(element):
        # Hash
        trimmed_string = element.string.strip()
        if trimmed_string == "":
            digest = ""
        else:
            digest = hashlib.sha256(trimmed_string.encode('utf-8')).hexdigest()
    else:
        contents = element.contents
        num_contents = len(contents)
        if num_contents == 0:
            # No hash when no child elements exist
            digest = ""
        elif num_contents == 1:
            # If single child, use digest of child
            digest = content_digest(contents[0])
        else:
            # Build content digest from the "non-empty" digests of child nodes
            digest = hashlib.sha256()
            child_digests = list(
                filter(lambda x: x != "", [content_digest(content) for content in contents]))
            for child in child_digests:
                digest.update(child.encode('utf-8'))
            digest = digest.hexdigest()
    return digest
--- a/api/core/tools/tool/dataset_retriever_tool.py
+++ b/api/core/tools/tool/dataset_retriever_tool.py
@ -4,7 +4,7 @@ from langchain.tools import BaseTool
 from core.callback_handler.index_tool_callback_handler import DatasetIndexToolCallbackHandler
 from core.entities.application_entities import DatasetRetrieveConfigEntity, InvokeFrom
-from core.features.dataset_retrieval import DatasetRetrievalFeature
+from core.features.dataset_retrieval.dataset_retrieval import DatasetRetrievalFeature
 from core.tools.entities.common_entities import I18nObject
 from core.tools.entities.tool_entities import ToolDescription, ToolIdentity, ToolInvokeMessage, ToolParameter
 from core.tools.tool.tool import Tool
--- a/api/core/tools/utils/web_reader_tool.py
+++ b/api/core/tools/utils/web_reader_tool.py
@ -7,23 +7,14 @@ import subprocess
 import tempfile
 import unicodedata
 from contextlib import contextmanager
 from typing import Any
 import requests
 from bs4 import BeautifulSoup, CData, Comment, NavigableString
 from langchain.chains import RefineDocumentsChain
 from langchain.chains.summarize import refine_prompts
 from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.tools.base import BaseTool
 from newspaper import Article
 from pydantic import BaseModel, Field
 from regex import regex
 from core.chain.llm_chain import LLMChain
 from core.entities.application_entities import ModelConfigEntity
 from core.rag.extractor import extract_processor
 from core.rag.extractor.extract_processor import ExtractProcessor
 from core.rag.models.document import Document
 FULL_TEMPLATE = """
 TITLE: {title}
@ -36,106 +27,6 @@ TEXT:
 """
 class WebReaderToolInput(BaseModel):
    url: str = Field(..., description="URL of the website to read")
    summary: bool = Field(
        default=False,
        description="When the user's question requires extracting the summarizing content of the webpage, "
                    "set it to true."
    )
    cursor: int = Field(
        default=0,
        description="Start reading from this character."
        "Use when the first response was truncated"
        "and you want to continue reading the page."
        "The value cannot exceed 24000.",
    )
 class WebReaderTool(BaseTool):
    """Reader tool for getting website title and contents. Gives more control than SimpleReaderTool."""
    name: str = "web_reader"
    args_schema: type[BaseModel] = WebReaderToolInput
    description: str = "use this to read a website. " \
                       "If you can answer the question based on the information provided, " \
                       "there is no need to use."
    page_contents: str = None
    url: str = None
    max_chunk_length: int = 4000
    summary_chunk_tokens: int = 4000
    summary_chunk_overlap: int = 0
    summary_separators: list[str] = ["\n\n", "。", ".", " ", ""]
    continue_reading: bool = True
    model_config: ModelConfigEntity
    model_parameters: dict[str, Any]
    def _run(self, url: str, summary: bool = False, cursor: int = 0) -> str:
        try:
            if not self.page_contents or self.url != url:
                page_contents = get_url(url)
                self.page_contents = page_contents
                self.url = url
            else:
                page_contents = self.page_contents
        except Exception as e:
            return f'Read this website failed, caused by: {str(e)}.'
        if summary:
            character_splitter = RecursiveCharacterTextSplitter.from_tiktoken_encoder(
                chunk_size=self.summary_chunk_tokens,
                chunk_overlap=self.summary_chunk_overlap,
                separators=self.summary_separators
            )
            texts = character_splitter.split_text(page_contents)
            docs = [Document(page_content=t) for t in texts]
            if len(docs) == 0 or docs[0].page_content.endswith('TEXT:'):
                return "No content found."
            # only use first 5 docs
            if len(docs) > 5:
                docs = docs[:5]
            chain = self.get_summary_chain()
            try:
                page_contents = chain.run(docs)
            except Exception as e:
                return f'Read this website failed, caused by: {str(e)}.'
        else:
            page_contents = page_result(page_contents, cursor, self.max_chunk_length)
            if self.continue_reading and len(page_contents) >= self.max_chunk_length:
                page_contents += f"\nPAGE WAS TRUNCATED. IF YOU FIND INFORMATION THAT CAN ANSWER QUESTION " \
                                 f"THEN DIRECT ANSWER AND STOP INVOKING web_reader TOOL, OTHERWISE USE " \
                                 f"CURSOR={cursor+len(page_contents)} TO CONTINUE READING."
        return page_contents
    async def _arun(self, url: str) -> str:
        raise NotImplementedError
    def get_summary_chain(self) -> RefineDocumentsChain:
        initial_chain = LLMChain(
            model_config=self.model_config,
            prompt=refine_prompts.PROMPT,
            parameters=self.model_parameters
        )
        refine_chain = LLMChain(
            model_config=self.model_config,
            prompt=refine_prompts.REFINE_PROMPT,
            parameters=self.model_parameters
        )
        return RefineDocumentsChain(
            initial_llm_chain=initial_chain,
            refine_llm_chain=refine_chain,
            document_variable_name="text",
            initial_response_name="existing_answer",
            callbacks=self.callbacks
        )
 def page_result(text: str, cursor: int, max_length: int) -> str:
    """Page through `text` and return a substring of `max_length` characters starting from `cursor`."""
    return text[cursor: cursor + max_length]
--- a/api/services/app_model_config_service.py
+++ b/api/services/app_model_config_service.py
@ -1,7 +1,7 @@
 import re
 import uuid
-from core.agent.agent_executor import PlanningStrategy
+from core.entities.agent_entities import PlanningStrategy
 from core.external_data_tool.factory import ExternalDataToolFactory
 from core.model_runtime.entities.model_entities import ModelPropertyKey, ModelType
 from core.model_runtime.model_providers import model_provider_factory
		`@ -1,2 +0,0 @@`
			`class ToolValidateFailedError(Exception):`
			`description = "Tool Provider Validate failed"`