refactor: Update with camel version 0.2.23

.pre-commit-config.yaml

@@ -0,0 +1,29 @@
+repos:
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: 'v0.7.4'
+    hooks:
+      - id: ruff
+        args: [--fix, --exit-non-zero-on-fix, --show-fixes]
+        exclude: ^docs/cookbooks/  # Ignore files under docs/cookbooks
+      - id: ruff-format
+        exclude: ^docs/cookbooks/  # Ignore files under docs/cookbooks
+
+  - repo: local
+    hooks:
+      - id: mypy
+        name: Check mypy
+        entry: mypy --namespace-packages -p owl
+        language: python
+        types: [python]
+        pass_filenames: false
+        require_serial: true
+        exclude: ^docs/cookbooks/  # Ignore files under docs/cookbooks
+
+  - repo: local
+    hooks:
+    - id: check-license
+      name: Check License
+      entry: python licenses/update_license.py . licenses/license_template.txt 
+      language: system
+      types: [python]
+      exclude: ^docs/cookbooks/  # Ignore files under docs/cookbooks

README.md

@@ -102,36 +102,31 @@ https://private-user-images.githubusercontent.com/55657767/420212194-e813fc05-13
 
 # 🛠️ Installation
 
-## **Clone the Github repository**
-
 ```bash
+# Clone github repo
 git clone https://github.com/camel-ai/owl.git
+
+# Change directory into project directory
 cd owl
-```
 
-## **Set up Environment**
+# Install uv if you don't have it already
+pip install uv
 
-Using Conda (recommended):
-```bash
-conda create -n owl python=3.11
-conda activate owl
-```
+# Create a virtual environment and install dependencies
+# We support using Python 3.10, 3.11, 3.12
+uv venv .venv --python=3.10
 
-Using venv (alternative):
-```bash
-python -m venv owl_env
-# On Windows
-owl_env\Scripts\activate
-# On Unix or MacOS
-source owl_env/bin/activate
-```
+# Activate the virtual environment
+# For macOS/Linux
+source .venv/bin/activate
+# For Windows
+.venv\Scripts\activate
 
+# Install CAMEL with all dependencies
+uv pip install -e .
 
-## **Install Dependencies**
-
-```bash
-python -m pip install -r requirements.txt
-playwright install
+# Exit the virtual environment when done
+deactivate
 ```
 
 ## **Setup Environment Variables** 
@@ -210,7 +205,7 @@ question = "Task description here."
 society = construct_society(question)
 answer, chat_history, token_count = run_society(society)
 
-print(f"Answer: {answer}")
+print(f"\033[94mAnswer: {answer}\033[0m")
 ```
 
 For uploading files, simply provide the file path along with your question:
@@ -221,7 +216,7 @@ question = "What is in the given DOCX file? Here is the file path: tmp/example.d
 
 society = construct_society(question)
 answer, chat_history, token_count = run_society(society)
-print(f"Answer: {answer}")
+print(f"\033[94mAnswer: {answer}\033[0m")
 ```
 
 OWL will then automatically invoke document-related tools to process the file and extract the answer.

README_zh.md

@@ -104,31 +104,30 @@ https://private-user-images.githubusercontent.com/55657767/420212194-e813fc05-13
 ## **克隆 Github 仓库**
 
 ```bash
+# 克隆 GitHub 仓库
 git clone https://github.com/camel-ai/owl.git
+
+# 进入项目目录
 cd owl
-```
 
-## **设置环境**
+# 如果你还没有安装 uv，请先安装
+pip install uv
 
-使用 Conda（推荐）：
-```bash
-conda create -n owl python=3.11
-conda activate owl
-```
+# 创建虚拟环境并安装依赖
+# 我们支持使用 Python 3.10、3.11、3.12
+uv venv .venv --python=3.10
 
-使用 venv（备用）：
-```bash
-python -m venv owl_env
-# Windows 系统
-owl_env\Scripts\activate
-# Unix 或 MacOS 系统
-source owl_env/bin/activate
-```
+# 激活虚拟环境
+# 对于 macOS/Linux
+source .venv/bin/activate
+# 对于 Windows
+.venv\Scripts\activate
 
-## **安装依赖**
+# 安装 CAMEL 及其所有依赖
+uv pip install -e .
 
-```bash
-python -m pip install -r requirements.txt
+# 完成后退出虚拟环境
+deactivate
 ```
 
 ## **设置环境变量**  
@@ -201,7 +200,7 @@ question = "Task description here."
 society = construct_society(question)
 answer, chat_history, token_count = run_society(society)
 
-print(f"Answer: {answer}")
+print(f"\033[94mAnswer: {answer}\033[0m")
 ```
 
 上传文件时，只需提供文件路径和问题：

licenses/update_license.py

@@ -39,43 +39,37 @@ def update_license_in_file(
     start_line_start_with: str,
     end_line_start_with: str,
 ) -> bool:
-    with open(
-        file_path, 'r', encoding='utf-8'
-    ) as f:  # for windows compatibility
+    with open(file_path, "r", encoding="utf-8") as f:  # for windows compatibility
         content = f.read()
 
-    with open(license_template_path, 'r', encoding='utf-8') as f:
+    with open(license_template_path, "r", encoding="utf-8") as f:
         new_license = f.read().strip()
 
     maybe_existing_licenses = re.findall(
-        r'^#.*?(?=\n)', content, re.MULTILINE | re.DOTALL
+        r"^#.*?(?=\n)", content, re.MULTILINE | re.DOTALL
     )
     start_index = fine_license_start_line(
         maybe_existing_licenses, start_line_start_with
     )
-    end_index = find_license_end_line(
-        maybe_existing_licenses, end_line_start_with
-    )
+    end_index = find_license_end_line(maybe_existing_licenses, end_line_start_with)
     if start_index is not None and end_index is not None:
-        maybe_existing_licenses = maybe_existing_licenses[
-            start_index : end_index + 1
-        ]
+        maybe_existing_licenses = maybe_existing_licenses[start_index : end_index + 1]
     else:
         maybe_existing_licenses = None
     if maybe_existing_licenses:
-        maybe_old_licenses = '\n'.join(maybe_existing_licenses)
+        maybe_old_licenses = "\n".join(maybe_existing_licenses)
         if maybe_old_licenses.strip() != new_license.strip():
             replaced_content = content.replace(maybe_old_licenses, new_license)
-            with open(file_path, 'w') as f:
+            with open(file_path, "w") as f:
                 f.write(replaced_content)
-            print(f'Replaced license in {file_path}')
+            print(f"Replaced license in {file_path}")
             return True
         else:
             return False
     else:
-        with open(file_path, 'w') as f:
-            f.write(new_license + '\n' + content)
-        print(f'Added license to {file_path}')
+        with open(file_path, "w") as f:
+            f.write(new_license + "\n" + content)
+        print(f"Added license to {file_path}")
         return True
 
 
@@ -87,16 +81,16 @@ def update_license_in_directory(
 ) -> None:
     # Check if directory exists
     if not os.path.isdir(directory_path):
-        raise NotADirectoryError(f'{directory_path} is not a directory')
+        raise NotADirectoryError(f"{directory_path} is not a directory")
     # Check if license template exists
     if not os.path.isfile(license_template_path):
-        raise FileNotFoundError(f'{license_template_path} not found')
+        raise FileNotFoundError(f"{license_template_path} not found")
 
     file_count = 0
     for py_files in Path(directory_path).rglob("*.py"):
-        if py_files.name.startswith('.'):
+        if py_files.name.startswith("."):
             continue
-        if any(part.startswith('.') for part in py_files.parts):
+        if any(part.startswith(".") for part in py_files.parts):
             continue
         if update_license_in_file(
             py_files,
@@ -106,10 +100,10 @@ def update_license_in_directory(
         ):
             file_count += 1
 
-    print(f'License updated in {file_count} files')
+    print(f"License updated in {file_count} files")
 
 
-if __name__ == '__main__':
+if __name__ == "__main__":
     if len(sys.argv) < 3:
         print(
             "Usage from command line: "

owl/camel/__init__.py

@@ -1,25 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-
-from camel.logger import disable_logging, enable_logging, set_log_level
-
-__version__ = '0.2.11'
-
-__all__ = [
-    '__version__',
-    'camel',
-    'disable_logging',
-    'enable_logging',
-    'set_log_level',
-]

owl/camel/agents/__init__.py

@@ -1,44 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from .base import BaseAgent
-from .chat_agent import ChatAgent
-from .critic_agent import CriticAgent
-from .embodied_agent import EmbodiedAgent
-from .knowledge_graph_agent import KnowledgeGraphAgent
-from .role_assignment_agent import RoleAssignmentAgent
-from .search_agent import SearchAgent
-from .task_agent import (
-    TaskCreationAgent,
-    TaskPlannerAgent,
-    TaskPrioritizationAgent,
-    TaskSpecifyAgent,
-)
-from .tool_agents.base import BaseToolAgent
-from .tool_agents.hugging_face_tool_agent import HuggingFaceToolAgent
-
-__all__ = [
-    'BaseAgent',
-    'ChatAgent',
-    'TaskSpecifyAgent',
-    'TaskPlannerAgent',
-    'TaskCreationAgent',
-    'TaskPrioritizationAgent',
-    'CriticAgent',
-    'BaseToolAgent',
-    'HuggingFaceToolAgent',
-    'EmbodiedAgent',
-    'RoleAssignmentAgent',
-    'SearchAgent',
-    'KnowledgeGraphAgent',
-]

owl/camel/agents/base.py

@@ -1,29 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from abc import ABC, abstractmethod
-from typing import Any
-
-
-class BaseAgent(ABC):
-    r"""An abstract base class for all CAMEL agents."""
-
-    @abstractmethod
-    def reset(self, *args: Any, **kwargs: Any) -> Any:
-        r"""Resets the agent to its initial state."""
-        pass
-
-    @abstractmethod
-    def step(self, *args: Any, **kwargs: Any) -> Any:
-        r"""Performs a single step of the agent."""
-        pass

owl/camel/agents/chat_agent.py

@@ -1,1411 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-import json
-# import logging
-import re
-import uuid
-from collections import defaultdict
-from typing import (
-    TYPE_CHECKING,
-    Any,
-    Callable,
-    Dict,
-    List,
-    Optional,
-    Tuple,
-    Type,
-    Union,
-)
-
-from loguru import logger
-
-from openai.types.chat import ChatCompletionMessageToolCall
-from openai.types.chat.chat_completion_message_tool_call import Function
-from pydantic import BaseModel
-
-from camel.agents.base import BaseAgent
-from camel.memories import (
-    AgentMemory,
-    ChatHistoryMemory,
-    MemoryRecord,
-    ScoreBasedContextCreator,
-)
-from camel.messages import BaseMessage, FunctionCallingMessage, OpenAIMessage
-from camel.models import (
-    BaseModelBackend,
-    ModelFactory,
-    ModelManager,
-    ModelProcessingError,
-)
-from camel.responses import ChatAgentResponse
-from camel.types import (
-    ChatCompletion,
-    ChatCompletionChunk,
-    ModelPlatformType,
-    ModelType,
-    OpenAIBackendRole,
-    RoleType,
-)
-from camel.utils import (
-    func_string_to_callable,
-    get_model_encoding,
-    get_pydantic_object_schema,
-    json_to_function_code,
-)
-
-if TYPE_CHECKING:
-    from openai import Stream
-
-    from camel.terminators import ResponseTerminator
-    from camel.toolkits import FunctionTool
-
-
-# logger = logging.getLogger(__name__)
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-class FunctionCallingRecord(BaseModel):
-    r"""Historical records of functions called in the conversation.
-
-    Attributes:
-        func_name (str): The name of the function being called.
-        args (Dict[str, Any]): The dictionary of arguments passed to
-            the function.
-        result (Any): The execution result of calling this function.
-    """
-
-    func_name: str
-    args: Dict[str, Any]
-    result: Any
-
-    def __str__(self) -> str:
-        r"""Overridden version of the string function.
-
-        Returns:
-            str: Modified string to represent the function calling.
-        """
-        return (
-            f"Function Execution: {self.func_name}\n"
-            f"\tArgs: {self.args}\n"
-            f"\tResult: {self.result}"
-        )
-
-    def as_dict(self) -> dict[str, Any]:
-        r"""Returns the function calling record as a dictionary.
-
-        Returns:
-            dict[str, Any]: The function calling record as a dictionary.
-        """
-        return self.model_dump()
-
-
-@track_agent(name="ChatAgent")
-class ChatAgent(BaseAgent):
-    r"""Class for managing conversations of CAMEL Chat Agents.
-
-    Args:
-        system_message (Union[BaseMessage, str], optional): The system message
-            for the chat agent.
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`ModelPlatformType.DEFAULT`
-            with `ModelType.DEFAULT`)
-        memory (AgentMemory, optional): The agent memory for managing chat
-            messages. If `None`, a :obj:`ChatHistoryMemory` will be used.
-            (default: :obj:`None`)
-        message_window_size (int, optional): The maximum number of previous
-            messages to include in the context window. If `None`, no windowing
-            is performed. (default: :obj:`None`)
-        token_limit (int, optional): The maximum number of tokens in a context.
-            The context will be automatically pruned to fulfill the limitation.
-            If `None`, it will be set according to the backend model.
-            (default: :obj:`None`)
-        output_language (str, optional): The language to be output by the
-            agent. (default: :obj:`None`)
-        tools (List[FunctionTool], optional): List of available
-            :obj:`FunctionTool`. (default: :obj:`None`)
-        external_tools (List[FunctionTool], optional): List of external tools
-            (:obj:`FunctionTool`) bind to one chat agent. When these tools
-            are called, the agent will directly return the request instead of
-            processing it. (default: :obj:`None`)
-        response_terminators (List[ResponseTerminator], optional): List of
-            :obj:`ResponseTerminator` bind to one chat agent.
-            (default: :obj:`None`)
-        scheduling_strategy (str): name of function that defines how to select
-            the next model in ModelManager. (default: :str:`round_robin`)
-    """
-
-    def __init__(
-        self,
-        system_message: Optional[Union[BaseMessage, str]] = None,
-        model: Optional[
-            Union[BaseModelBackend, List[BaseModelBackend]]
-        ] = None,
-        memory: Optional[AgentMemory] = None,
-        message_window_size: Optional[int] = None,
-        token_limit: Optional[int] = None,
-        output_language: Optional[str] = None,
-        tools: Optional[List[FunctionTool]] = None,
-        external_tools: Optional[List[FunctionTool]] = None,
-        response_terminators: Optional[List[ResponseTerminator]] = None,
-        scheduling_strategy: str = "round_robin",
-    ) -> None:
-        from copy import deepcopy
-        if isinstance(system_message, str):
-            system_message = BaseMessage.make_assistant_message(
-                role_name='Assistant', content=system_message
-            )
-
-        self.orig_sys_message: Optional[BaseMessage] = system_message
-        self._system_message: Optional[BaseMessage] = system_message
-        self.role_name: str = (
-            getattr(system_message, 'role_name', None) or "assistant"
-        )
-        self.role_type: RoleType = (
-            getattr(system_message, 'role_type', None) or RoleType.ASSISTANT
-        )
-        self.model_backend = ModelManager(
-            model
-            if model is not None
-            else ModelFactory.create(
-                model_platform=ModelPlatformType.DEFAULT,
-                model_type=ModelType.DEFAULT,
-            ),
-            scheduling_strategy=scheduling_strategy,
-        )
-
-        self.model_type = self.model_backend.model_type
-
-        # Tool registration
-        external_tools = external_tools or []
-        tools = tools or []
-        all_tools = tools + external_tools
-        self.external_tool_names = [
-            tool.get_function_name() for tool in external_tools
-        ]
-        self.func_dict = {
-            tool.get_function_name(): tool.func for tool in all_tools
-        }
-        self.tool_dict = {tool.get_function_name(): tool for tool in all_tools}
-        self._all_tools = all_tools
-
-        # If the user set tools from `ChatAgent`, it will override the
-        # configured tools in `BaseModelBackend`.
-        if all_tools:
-            # logger.warning(
-            #     "Overriding the configured tools in `BaseModelBackend` with the tools from `ChatAgent`."
-            # )
-            tool_schema_list = [
-                tool.get_openai_tool_schema() for tool in all_tools
-            ]
-            self.model_backend.model_config_dict['tools'] = tool_schema_list
-            self.tool_schema_list = tool_schema_list
-            
-        from copy import deepcopy
-        self.model_config_dict = deepcopy(self.model_backend.model_config_dict)
-
-        self.model_token_limit = token_limit or self.model_backend.token_limit
-        context_creator = ScoreBasedContextCreator(
-            self.model_backend.token_counter,
-            self.model_token_limit,
-        )
-        self.memory: AgentMemory = memory or ChatHistoryMemory(
-            context_creator, window_size=message_window_size
-        )
-
-        self.output_language: Optional[str] = output_language
-        if self.output_language is not None:
-            self.set_output_language(self.output_language)
-
-        self.terminated: bool = False
-        self.response_terminators = response_terminators or []
-        self.init_messages()
-
-        self.tool_prompt_added = False
-
-    # ruff: noqa: E501
-    def _generate_tool_prompt(self, tool_schema_list: List[Dict]) -> str:
-        r"""Generates a tool prompt based on the provided tool schema list.
-
-        Args:
-            tool_schema_list (List[Dict]): A list of dictionaries, each
-                containing a tool schema.
-
-        Returns:
-            str: A string representing the tool prompt.
-        """
-        tool_prompts = []
-
-        for tool in tool_schema_list:
-            tool_info = tool['function']
-            tool_name = tool_info['name']
-            tool_description = tool_info['description']
-            tool_json = json.dumps(tool_info, indent=4)
-
-            prompt = f"Use the function '{tool_name}' to '{tool_description}':\n{tool_json}\n"
-            tool_prompts.append(prompt)
-
-        tool_prompt_str = "\n".join(tool_prompts)
-
-        final_prompt = f'''
-    # Tool prompt
-    TOOL_PROMPT = f"""
-    You have access to the following functions:
-
-    {tool_prompt_str}
-
-    If you choose to call a function ONLY reply in the following format with no
-    prefix or suffix:
-
-    <function=example_function_name>{{"example_name": "example_value"}}
-    </function>
-
-    Reminder:
-    - Function calls MUST follow the specified format, start with <function=
-      and end with </function>
-    - Required parameters MUST be specified
-    - Only call one function at a time
-    - Put the entire function call reply on one line
-    - If there is no function call available, answer the question like normal 
-      with your current knowledge and do not tell the user about function calls
-    """
-    '''
-        return final_prompt
-
-    def _parse_tool_response(self, response: str):
-        r"""Parses the tool response to extract the function name and
-        arguments.
-
-        Args:
-            response (str): The response from the model containing the
-                function call.
-
-        Returns:
-            Optional[Dict[str, Any]]: The parsed function name and arguments
-                if found, otherwise :obj:`None`.
-        """
-        function_regex = r"<function=(\w+)>(.*?)</function>"
-        match = re.search(function_regex, response)
-
-        if match:
-            function_name, args_string = match.groups()
-            try:
-                args = json.loads(args_string)
-                return {"function": function_name, "arguments": args}
-            except json.JSONDecodeError as error:
-                print(f"Error parsing function arguments: {error}")
-                return None
-        return None
-
-    def reset(self):
-        r"""Resets the :obj:`ChatAgent` to its initial state."""
-        self.terminated = False
-        self.init_messages()
-        for terminator in self.response_terminators:
-            terminator.reset()
-
-    @property
-    def system_message(self) -> Optional[BaseMessage]:
-        r"""The getter method for the property :obj:`system_message`.
-
-        Returns:
-            Optional[BaseMessage]: The system message of this agent if set,
-                else :obj:`None`.
-        """
-        return self._system_message
-
-    @system_message.setter
-    def system_message(self, message: BaseMessage) -> None:
-        r"""The setter method for the property :obj:`system_message`.
-
-        Args:
-            message (BaseMessage): The message to be set as the
-                new system message of this agent.
-        """
-        self._system_message = message
-
-    def is_tools_added(self) -> bool:
-        r"""Whether OpenAI function calling is enabled for this agent.
-
-        Returns:
-            bool: Whether OpenAI function calling is enabled for this
-                agent, determined by whether the dictionary of tools
-                is empty.
-        """
-        return len(self.func_dict) > 0
-
-    def update_memory(
-        self, message: BaseMessage, role: OpenAIBackendRole
-    ) -> None:
-        r"""Updates the agent memory with a new message.
-
-        Args:
-            message (BaseMessage): The new message to add to the stored
-                messages.
-            role (OpenAIBackendRole): The backend role type.
-        """
-        self.memory.write_record(
-            MemoryRecord(message=message, role_at_backend=role)
-        )
-
-    def set_output_language(self, output_language: str) -> BaseMessage:
-        r"""Sets the output language for the system message. This method
-        updates the output language for the system message. The output
-        language determines the language in which the output text should be
-        generated.
-
-        Args:
-            output_language (str): The desired output language.
-
-        Returns:
-            BaseMessage: The updated system message object.
-        """
-        self.output_language = output_language
-        language_prompt = (
-            "\nRegardless of the input language, "
-            f"you must output text in {output_language}."
-        )
-        if self.orig_sys_message is not None:
-            content = self.orig_sys_message.content + language_prompt
-            self._system_message = self.orig_sys_message.create_new_instance(
-                content
-            )
-        else:
-            self._system_message = BaseMessage.make_assistant_message(
-                role_name="Assistant",
-                content=language_prompt,
-            )
-
-        system_record = MemoryRecord(
-            message=self._system_message,
-            role_at_backend=OpenAIBackendRole.SYSTEM,
-        )
-        self.memory.clear()
-        self.memory.write_record(system_record)
-        return self._system_message
-
-    def get_info(
-        self,
-        session_id: Optional[str],
-        usage: Optional[Dict[str, int]],
-        termination_reasons: List[str],
-        num_tokens: int,
-        tool_calls: List[FunctionCallingRecord],
-        external_tool_request: Optional[ChatCompletionMessageToolCall] = None,
-    ) -> Dict[str, Any]:
-        r"""Returns a dictionary containing information about the chat session.
-
-        Args:
-            session_id (str, optional): The ID of the chat session.
-            usage (Dict[str, int], optional): Information about the usage of
-                the LLM model.
-            termination_reasons (List[str]): The reasons for the termination
-                of the chat session.
-            num_tokens (int): The number of tokens used in the chat session.
-            tool_calls (List[FunctionCallingRecord]): The list of function
-                calling records, containing the information of called tools.
-            external_tool_request
-                (Optional[ChatCompletionMessageToolCall], optional):
-                The tool calling request of external tools from the model.
-                These requests are directly returned to the user instead of
-                being processed by the agent automatically.
-                (default: :obj:`None`)
-
-        Returns:
-            Dict[str, Any]: The chat session information.
-        """
-        return {
-            "id": session_id,
-            "usage": usage,
-            "termination_reasons": termination_reasons,
-            "num_tokens": num_tokens,
-            "tool_calls": tool_calls,
-            "external_tool_request": external_tool_request,
-        }
-
-    def init_messages(self) -> None:
-        r"""Initializes the stored messages list with the current system
-        message.
-        """
-        if self._system_message is not None:
-            system_record = MemoryRecord(
-                message=self._system_message,
-                role_at_backend=OpenAIBackendRole.SYSTEM,
-            )
-            self.memory.clear()
-            self.memory.write_record(system_record)
-        else:
-            self.memory.clear()
-    
-    def _transform_function_calling_format(self, openai_messages: List[dict]):
-        r"""Used in deepseek-chat backend. It can modify function calling records' format to match the deepseek-chat backend's format."""
-        from copy import deepcopy
-        _messages = deepcopy(openai_messages)
-        modified_messages = []
-        for message in _messages:
-            if message['role'] == 'function':
-                new_message = {
-                    'role': 'tool',
-                    'tool_call_id': message['name'],
-                    'content': message['content']
-                }
-                modified_messages.append(new_message)
-            else:
-                modified_messages.append(message)
-
-        return modified_messages
-
-
-    def record_message(self, message: BaseMessage) -> None:
-        r"""Records the externally provided message into the agent memory as if
-        it were an answer of the :obj:`ChatAgent` from the backend. Currently,
-        the choice of the critic is submitted with this method.
-
-        Args:
-            message (BaseMessage): An external message to be recorded in the
-                memory.
-        """
-        self.update_memory(message, OpenAIBackendRole.ASSISTANT)
-
-    def step(
-        self,
-        input_message: Union[BaseMessage, str],
-        response_format: Optional[Type[BaseModel]] = None,
-    ) -> ChatAgentResponse:
-        r"""Performs a single step in the chat session by generating a response
-        to the input message.
-
-        Args:
-            input_message (Union[BaseMessage, str]): The input message to the
-                agent. For BaseMessage input, its `role` field that specifies
-                the role at backend may be either `user` or `assistant` but it
-                will be set to `user` anyway since for the self agent any
-                incoming message is external. For str input, the `role_name` would be `User`.
-            response_format (Optional[Type[BaseModel]], optional): A pydantic
-                model class that includes value types and field descriptions
-                used to generate a structured response by LLM. This schema
-                helps in defining the expected output format. (default:
-                :obj:`None`)
-
-        Returns:
-            ChatAgentResponse: A struct containing the output messages,
-                a boolean indicating whether the chat session has terminated,
-                and information about the chat session.
-        """
-        from copy import deepcopy
-        self.model_backend.model_config_dict = deepcopy(self.model_config_dict)
-        self.tool_dict = {tool.get_function_name(): tool for tool in self._all_tools}
-        if (
-            self.model_backend.model_config_dict.get("response_format")
-            and response_format
-        ):
-            raise ValueError(
-                "The `response_format` parameter cannot be set both in "
-                "the model configuration and in the ChatAgent step."
-            )
-
-        if isinstance(input_message, str):
-            input_message = BaseMessage.make_user_message(
-                role_name='User', content=input_message
-            )
-
-        if "llama" in self.model_type.lower():
-            if (
-                self.model_backend.model_config_dict.get("tools", None)
-                and not self.tool_prompt_added
-            ):
-                tool_prompt = self._generate_tool_prompt(self.tool_schema_list)
-
-                tool_sys_msg = BaseMessage.make_assistant_message(
-                    role_name="Assistant",
-                    content=tool_prompt,
-                )
-
-                self.update_memory(tool_sys_msg, OpenAIBackendRole.SYSTEM)
-                self.tool_prompt_added = True
-
-            self.update_memory(input_message, OpenAIBackendRole.USER)
-
-            tool_call_records: List[FunctionCallingRecord] = []
-            while True:
-                # Check if token has exceeded
-                try:
-                    openai_messages, num_tokens = self.memory.get_context()
-                except RuntimeError as e:
-                    return self._step_token_exceed(
-                        e.args[1], tool_call_records, "max_tokens_exceeded"
-                    )
-                (
-                    response,
-                    output_messages,
-                    finish_reasons,
-                    usage_dict,
-                    response_id,
-                ) = self._step_model_response(openai_messages, num_tokens)
-                # If the model response is not a function call, meaning the
-                # model has generated a message response, break the loop
-                if (
-                    not self.is_tools_added()
-                    or not isinstance(response, ChatCompletion)
-                    or "</function>" not in response.choices[0].message.content  # type: ignore[operator]
-                ):
-                    break
-
-                parsed_content = self._parse_tool_response(
-                    response.choices[0].message.content  # type: ignore[arg-type]
-                )
-
-                response.choices[0].message.tool_calls = [
-                    ChatCompletionMessageToolCall(
-                        id=str(uuid.uuid4()),
-                        function=Function(
-                            arguments=str(parsed_content["arguments"]).replace(
-                                "'", '"'
-                            ),
-                            name=str(parsed_content["function"]),
-                        ),
-                        type="function",
-                    )
-                ]
-
-                # Check for external tool call
-                tool_call_request = response.choices[0].message.tool_calls[0]
-                if tool_call_request.function.name in self.external_tool_names:
-                    # if model calls an external tool, directly return the
-                    # request
-                    info = self._step_get_info(
-                        output_messages,
-                        finish_reasons,
-                        usage_dict,
-                        response_id,
-                        tool_call_records,
-                        num_tokens,
-                        tool_call_request,
-                    )
-                    return ChatAgentResponse(
-                        msgs=output_messages,
-                        terminated=self.terminated,
-                        info=info,
-                    )
-
-                # Normal function calling
-                tool_call_records.append(
-                    self._step_tool_call_and_update(response)
-                )
-
-            if response_format is not None:
-                (
-                    output_messages,
-                    finish_reasons,
-                    usage_dict,
-                    response_id,
-                    tool_call,
-                    num_tokens,
-                ) = self._structure_output_with_function(response_format)
-                tool_call_records.append(tool_call)
-
-            info = self._step_get_info(
-                output_messages,
-                finish_reasons,
-                usage_dict,
-                response_id,
-                tool_call_records,
-                num_tokens,
-            )
-
-            if len(output_messages) == 1:
-                # Auto record if the output result is a single message
-                self.record_message(output_messages[0])
-            else:
-                logger.warning(
-                    "Multiple messages returned in `step()`, message won't be "
-                    "recorded automatically. Please call `record_message()` "
-                    "to record the selected message manually."
-                )
-
-            return ChatAgentResponse(
-                msgs=output_messages, terminated=self.terminated, info=info
-            )
-
-        else:
-            self.update_memory(input_message, OpenAIBackendRole.USER)
-            # try:
-
-            tool_call_records: List[FunctionCallingRecord] = []  # type: ignore[no-redef]
-            while True:
-                # Check if token has exceeded
-                try:
-                    openai_messages, num_tokens = self.memory.get_context()
-                except RuntimeError as e:
-                    return self._step_token_exceed(
-                        e.args[1], tool_call_records, "max_tokens_exceeded"
-                    )
-
-                (
-                    response,
-                    output_messages,
-                    finish_reasons,
-                    usage_dict,
-                    response_id,
-                ) = self._step_model_response(openai_messages, num_tokens)
-                # If the model response is not a function call, meaning the
-                # model has generated a message response, break the loop
-                if (
-                    not self.is_tools_added()
-                    or not isinstance(response, ChatCompletion)
-                    or not response.choices[0].message.tool_calls
-                ):
-                    break
-
-                # Check for external tool call
-                tool_call_request = response.choices[0].message.tool_calls[0]
-
-                if tool_call_request.function.name in self.external_tool_names:
-                    # if model calls an external tool, directly return the
-                    # request
-                    info = self._step_get_info(
-                        output_messages,
-                        finish_reasons,
-                        usage_dict,
-                        response_id,
-                        tool_call_records,
-                        num_tokens,
-                        tool_call_request,
-                    )
-                    return ChatAgentResponse(
-                        msgs=output_messages,
-                        terminated=self.terminated,
-                        info=info,
-                    )
-
-                # Normal function calling
-                tool_call_records.append(
-                    self._step_tool_call_and_update(response)
-                )
-
-            if (
-                response_format is not None
-                and self.model_type.support_native_tool_calling
-            ):
-                (
-                    output_messages,
-                    finish_reasons,
-                    usage_dict,
-                    response_id,
-                    tool_call,
-                    num_tokens,
-                ) = self._structure_output_with_function(response_format)
-                tool_call_records.append(tool_call)
-
-            info = self._step_get_info(
-                output_messages,
-                finish_reasons,
-                usage_dict,
-                response_id,
-                tool_call_records,
-                num_tokens,
-            )
-
-            if len(output_messages) == 1:
-                # Auto record if the output result is a single message
-                self.record_message(output_messages[0])
-            else:
-                logger.warning(
-                    "Multiple messages returned in `step()`, message won't be "
-                    "recorded automatically. Please call `record_message()` "
-                    "to record the selected message manually."
-                )
-
-            return ChatAgentResponse(
-                msgs=output_messages, terminated=self.terminated, info=info
-            )
-
-            # except Exception as e:
-            #     logger.error(e)
-            #     breakpoint()
-            #     raise e
-
-    async def step_async(
-        self,
-        input_message: Union[BaseMessage, str],
-        response_format: Optional[Type[BaseModel]] = None,
-    ) -> ChatAgentResponse:
-        r"""Performs a single step in the chat session by generating a response
-        to the input message. This agent step can call async function calls.
-
-        Args:
-            input_message (Union[BaseMessage, str]): The input message to the
-                agent. For BaseMessage input, its `role` field that specifies
-                the role at backend may be either `user` or `assistant` but it
-                will be set to `user` anyway since for the self agent any
-                incoming message is external. For str input, the `role_name` would be `User`.
-            response_format (Optional[Type[BaseModel]], optional): A pydantic
-                model class that includes value types and field descriptions
-                used to generate a structured response by LLM. This schema
-                helps in defining the expected output format. (default:
-                :obj:`None`)
-
-        Returns:
-            ChatAgentResponse: A struct containing the output messages,
-                a boolean indicating whether the chat session has terminated,
-                and information about the chat session.
-        """
-        if isinstance(input_message, str):
-            input_message = BaseMessage.make_user_message(
-                role_name='User', content=input_message
-            )
-
-        self.update_memory(input_message, OpenAIBackendRole.USER)
-
-        tool_call_records: List[FunctionCallingRecord] = []
-        while True:
-            try:
-                openai_messages, num_tokens = self.memory.get_context()
-            except RuntimeError as e:
-                return self._step_token_exceed(
-                    e.args[1], tool_call_records, "max_tokens_exceeded"
-                )
-
-            (
-                response,
-                output_messages,
-                finish_reasons,
-                usage_dict,
-                response_id,
-            ) = self._step_model_response(openai_messages, num_tokens)
-
-            if (
-                not self.is_tools_added()
-                or not isinstance(response, ChatCompletion)
-                or response.choices[0].message.tool_calls is None
-            ):
-                break
-
-            # Check for external tool call
-            tool_call_request = response.choices[0].message.tool_calls[0]
-            if tool_call_request.function.name in self.external_tool_names:
-                # if model calls an external tool, directly return the request
-                info = self._step_get_info(
-                    output_messages,
-                    finish_reasons,
-                    usage_dict,
-                    response_id,
-                    tool_call_records,
-                    num_tokens,
-                    tool_call_request,
-                )
-                return ChatAgentResponse(
-                    msgs=output_messages, terminated=self.terminated, info=info
-                )
-
-            # Normal function calling
-            tool_call_records.append(
-                await self._step_tool_call_and_update_async(response)
-            )
-
-        if (
-            response_format is not None
-            and self.model_type.support_native_tool_calling
-        ):
-            (
-                output_messages,
-                finish_reasons,
-                usage_dict,
-                response_id,
-                tool_call_record,
-                num_tokens,
-            ) = self._structure_output_with_function(response_format)
-            tool_call_records.append(tool_call_record)
-
-        info = self._step_get_info(
-            output_messages,
-            finish_reasons,
-            usage_dict,
-            response_id,
-            tool_call_records,
-            num_tokens,
-        )
-
-        if len(output_messages) == 1:
-            # Auto record if the output result is a single message
-            self.record_message(output_messages[0])
-        else:
-            logger.warning(
-                "Multiple messages returned in `step()`, message won't be "
-                "recorded automatically. Please call `record_message()` to "
-                "record the selected message manually."
-            )
-
-        return ChatAgentResponse(
-            msgs=output_messages, terminated=self.terminated, info=info
-        )
-
-    def _step_tool_call_and_update(
-        self, response: ChatCompletion
-    ) -> FunctionCallingRecord:
-        r"""Processes a function call within the chat completion response,
-        records the function call in the provided list of tool calls and
-        updates the memory of the current agent.
-
-        Args:
-            response (ChatCompletion): The response object from the chat
-                completion.
-
-        Returns:
-            FunctionCallingRecord: The record of calling the function.
-        """
-
-        # Perform function calling
-        func_assistant_msg, func_result_msg, tool_call_record = (
-            self.step_tool_call(response)
-        )
-
-        # Update the messages
-        self.update_memory(func_assistant_msg, OpenAIBackendRole.ASSISTANT)
-        self.update_memory(func_result_msg, OpenAIBackendRole.FUNCTION)
-
-        return tool_call_record
-
-    async def _step_tool_call_and_update_async(
-        self, response: ChatCompletion
-    ) -> FunctionCallingRecord:
-        (
-            func_assistant_msg,
-            func_result_msg,
-            func_record,
-        ) = await self.step_tool_call_async(response)
-
-        self.update_memory(func_assistant_msg, OpenAIBackendRole.ASSISTANT)
-        self.update_memory(func_result_msg, OpenAIBackendRole.FUNCTION)
-
-        return func_record
-
-    def _structure_output_with_function(
-        self, response_format: Type[BaseModel]
-    ) -> Tuple[
-        List[BaseMessage],
-        List[str],
-        Dict[str, int],
-        str,
-        FunctionCallingRecord,
-        int,
-    ]:
-        r"""Internal function of structuring the output of the agent based on
-        the given output schema.
-
-        Args:
-            response_format (Type[BaseModel]): The output schema to use for
-                structuring the output.
-
-        Returns:
-            Tuple[List[BaseMessage], List[str], Dict[str, int], str,
-                FunctionCallingRecord, int]:
-                A tuple containing the output messages, finish reasons, usage
-                dictionary, response ID, function calling record, and number of
-                tokens.
-        """
-        from camel.toolkits import FunctionTool
-
-        schema_json = get_pydantic_object_schema(response_format)
-        func_str = json_to_function_code(schema_json)
-        func_callable = func_string_to_callable(func_str)
-        func = FunctionTool(func_callable)
-
-        original_func_dict = self.func_dict
-        original_model_dict = self.model_backend.model_config_dict
-
-        # Replace the original tools with the structuring function
-        self.func_dict = {func.get_function_name(): func.func}
-        self.tool_dict = {func.get_function_name(): func}
-        self.model_backend.model_config_dict = original_model_dict.copy()
-        self.model_backend.model_config_dict["tools"] = [
-            func.get_openai_tool_schema()
-        ]
-        self.model_backend.model_config_dict["tool_choice"] = "required"
-
-        openai_messages, num_tokens = self.memory.get_context()
-        (
-            response,
-            output_messages,
-            finish_reasons,
-            usage_dict,
-            response_id,
-        ) = self._step_model_response(openai_messages, num_tokens)
-
-        if isinstance(response, ChatCompletion):
-            tool_call_record = self._step_tool_call_and_update(response)
-        else:
-            raise ValueError(
-                "Structured output is not supported for stream responses."
-            )
-
-        for base_message_item in output_messages:
-            base_message_item.content = str(tool_call_record.result)
-
-        # Recover the original tools
-        self.func_dict = original_func_dict
-        self.model_backend.model_config_dict = original_model_dict
-
-        return (
-            output_messages,
-            finish_reasons,
-            usage_dict,
-            response_id,
-            tool_call_record,
-            num_tokens,
-        )
-
-    def _step_model_response(
-        self,
-        openai_messages: List[OpenAIMessage],
-        num_tokens: int,
-    ) -> tuple[
-        Union[ChatCompletion, Stream],
-        List[BaseMessage],
-        List[str],
-        Dict[str, int],
-        str,
-    ]:
-        r"""Internal function for agent step model response."""
-
-        response = None
-        # Obtain the model's response
-        for _ in range(len(self.model_backend.models)):
-            try:
-                response = self.model_backend.run(openai_messages)
-                break
-            except Exception as exc:
-                logger.error(
-                    f"An error occurred while running model "
-                    f"{self.model_backend.model_type}, "
-                    f"index: {self.model_backend.current_model_index}",
-                    exc_info=exc,
-                )
-                continue
-        if not response:
-            raise ModelProcessingError(
-                "Unable to process messages: none of the provided models "
-                "run succesfully."
-            )
-
-        # logger.debug(
-        #     f"Model {self.model_backend.model_type}, "
-        #     f"index {self.model_backend.current_model_index}, "
-        #     f"processed these messages: {openai_messages}"
-        # )
-
-        if isinstance(response, ChatCompletion):
-            output_messages, finish_reasons, usage_dict, response_id = (
-                self.handle_batch_response(response)
-            )
-        else:
-            output_messages, finish_reasons, usage_dict, response_id = (
-                self.handle_stream_response(response, num_tokens)
-            )
-        return (
-            response,
-            output_messages,
-            finish_reasons,
-            usage_dict,
-            response_id,
-        )
-
-    def _step_get_info(
-        self,
-        output_messages: List[BaseMessage],
-        finish_reasons: List[str],
-        usage_dict: Dict[str, int],
-        response_id: str,
-        tool_calls: List[FunctionCallingRecord],
-        num_tokens: int,
-        external_tool_request: Optional[ChatCompletionMessageToolCall] = None,
-    ) -> Dict[str, Any]:
-        r"""Process the output of a chat step and gather information about the
-        step.
-
-        This method checks for termination conditions, updates the agent's
-        state, and collects information about the chat step, including tool
-        calls and termination reasons.
-
-        Args:
-            output_messages (List[BaseMessage]): The messages generated in
-                this step.
-            finish_reasons (List[str]): The reasons for finishing the
-                generation for each message.
-            usage_dict (Dict[str, int]): Dictionary containing token usage
-                information.
-            response_id (str): The ID of the response from the model.
-            tool_calls (List[FunctionCallingRecord]): Records of function calls
-                made during this step.
-            num_tokens (int): The number of tokens used in this step.
-            external_tool_request (Optional[ChatCompletionMessageToolCall]):
-                Any external tool request made during this step.
-                (default::obj:`None`)
-
-        Returns:
-            Dict[str, Any]: A dictionary containing information about the chat
-                step, including termination status, reasons, and tool call
-                information.
-
-        Note:
-            This method iterates over all response terminators and checks if
-            any of them signal termination. If a terminator signals
-            termination, the agent's state is updated accordingly, and the
-            termination reason is recorded.
-        """
-        termination = [
-            terminator.is_terminated(output_messages)
-            for terminator in self.response_terminators
-        ]
-        # Terminate the agent if any of the terminator terminates
-        self.terminated, termination_reason = next(
-            (
-                (terminated, termination_reason)
-                for terminated, termination_reason in termination
-                if terminated
-            ),
-            (False, None),
-        )
-        # For now only retain the first termination reason
-        if self.terminated and termination_reason is not None:
-            finish_reasons = [termination_reason] * len(finish_reasons)
-
-        info = self.get_info(
-            response_id,
-            usage_dict,
-            finish_reasons,
-            num_tokens,
-            tool_calls,
-            external_tool_request,
-        )
-        return info
-
-    def handle_batch_response(
-        self, response: ChatCompletion
-    ) -> Tuple[List[BaseMessage], List[str], Dict[str, int], str]:
-        r"""Process a batch response from the model and extract the necessary
-        information.
-
-        Args:
-            response (dict): Model response.
-
-        Returns:
-            tuple: A tuple of list of output `ChatMessage`, list of
-                finish reasons, usage dictionary, and response id.
-        """
-        output_messages: List[BaseMessage] = []
-        for choice in response.choices:
-            chat_message = BaseMessage(
-                role_name=self.role_name,
-                role_type=self.role_type,
-                meta_dict=dict(),
-                content=choice.message.content or "",
-                parsed=getattr(choice.message, 'parsed', None),
-            )
-            # Process log probabilities and append to the message meta information
-            if choice.logprobs is not None:
-                tokens_logprobs = choice.logprobs.content
-
-                if tokens_logprobs is not None:
-                    # Extract and structure logprob information
-                    logprobs_info = [
-                        {
-                            "token": token_logprob.token,
-                            "logprob": token_logprob.logprob,
-                            "top_logprobs": [
-                                (top_logprob.token, top_logprob.logprob)
-                                for top_logprob in token_logprob.top_logprobs
-                            ],
-                        }
-                        for token_logprob in tokens_logprobs
-                    ]
-                # Ensure meta_dict exists before adding logprobs info
-                if chat_message.meta_dict is None:
-                    chat_message.meta_dict = {}
-                chat_message.meta_dict["logprobs_info"] = logprobs_info
-            # Append the processed chat message to output
-            output_messages.append(chat_message)
-
-        finish_reasons = [
-            str(choice.finish_reason) for choice in response.choices
-        ]
-        usage = (
-            self._safe_model_dump(response.usage)
-            if response.usage is not None
-            else {}
-        )
-        return (
-            output_messages,
-            finish_reasons,
-            usage,
-            response.id,
-        )
-
-    def _safe_model_dump(self, obj) -> dict:
-        r"""Safely dump a Pydantic model to a dictionary.
-
-        This method attempts to use the `model_dump` method if available,
-        otherwise it falls back to the `dict` method.
-
-        Args:
-            obj: The Pydantic model instance to be dumped.
-
-        Returns:
-            dict: A dictionary representation of the Pydantic model.
-        """
-        # Check if the `model_dump` method exists (Pydantic v2)
-        if hasattr(obj, 'model_dump'):
-            return obj.model_dump()
-        # Fallback to `dict()` method (Pydantic v1)
-        elif hasattr(obj, 'dict'):
-            return obj.dict()
-        else:
-            raise TypeError("The object is not a Pydantic model")
-
-    def handle_stream_response(
-        self,
-        response: Stream[ChatCompletionChunk],
-        prompt_tokens: int,
-    ) -> Tuple[List[BaseMessage], List[str], Dict[str, int], str]:
-        r"""Process a stream response from the model and extract the necessary
-        information.
-
-        Args:
-            response (dict): Model response.
-            prompt_tokens (int): Number of input prompt tokens.
-
-        Returns:
-            tuple: A tuple of list of output `ChatMessage`, list of
-                finish reasons, usage dictionary, and response id.
-        """
-        content_dict: defaultdict = defaultdict(lambda: "")
-        finish_reasons_dict: defaultdict = defaultdict(lambda: "")
-        output_messages: List[BaseMessage] = []
-        response_id: str = ""
-        # All choices in one response share one role
-        for chunk in response:
-            response_id = chunk.id
-            for choice in chunk.choices:
-                index = choice.index
-                delta = choice.delta
-                if delta.content is not None:
-                    # When response has not been stopped
-                    # Notice that only the first chunk_dict has the "role"
-                    content_dict[index] += delta.content
-                if choice.finish_reason:
-                    finish_reasons_dict[index] = choice.finish_reason
-                    chat_message = BaseMessage(
-                        role_name=self.role_name,
-                        role_type=self.role_type,
-                        meta_dict=dict(),
-                        content=content_dict[index],
-                    )
-                    output_messages.append(chat_message)
-        finish_reasons = [
-            finish_reasons_dict[i] for i in range(len(finish_reasons_dict))
-        ]
-        usage_dict = self.get_usage_dict(output_messages, prompt_tokens)
-        return output_messages, finish_reasons, usage_dict, response_id
-
-    def _step_token_exceed(
-        self,
-        num_tokens: int,
-        tool_calls: List[FunctionCallingRecord],
-        termination_reason: str,
-    ) -> ChatAgentResponse:
-        r"""Return trivial response containing number of tokens and information
-        of called functions when the number of tokens exceeds.
-
-        Args:
-            num_tokens (int): Number of tokens in the messages.
-            tool_calls (List[FunctionCallingRecord]): List of information
-                objects of functions called in the current step.
-            termination_reason (str): String of termination reason.
-
-        Returns:
-            ChatAgentResponse: The struct containing trivial outputs and
-                information about token number and called functions.
-        """
-        self.terminated = True
-        output_messages: List[BaseMessage] = []
-
-        info = self.get_info(
-            None,
-            None,
-            [termination_reason],
-            num_tokens,
-            tool_calls,
-        )
-
-        return ChatAgentResponse(
-            msgs=output_messages,
-            terminated=self.terminated,
-            info=info,
-        )
-
-    def step_tool_call(
-        self,
-        response: ChatCompletion,
-    ) -> Tuple[
-        FunctionCallingMessage, FunctionCallingMessage, FunctionCallingRecord
-    ]:
-        r"""Execute the function with arguments following the model's response.
-
-        Args:
-            response (Dict[str, Any]): The response obtained by calling the
-                model.
-
-        Returns:
-            tuple: A tuple consisting of two obj:`FunctionCallingMessage`,
-                one about the arguments and the other about the execution
-                result, and a struct for logging information about this
-                function call.
-        """
-        choice = response.choices[0]
-        if choice.message.tool_calls is None:
-            raise RuntimeError("Tool call is None")
-        func_name = choice.message.tool_calls[0].function.name
-
-        args = json.loads(choice.message.tool_calls[0].function.arguments)
-        tool = self.tool_dict[func_name]
-
-        result = tool(**args)
-
-        assist_msg = FunctionCallingMessage(
-            role_name=self.role_name,
-            role_type=self.role_type,
-            meta_dict=None,
-            content="",
-            func_name=func_name,
-            args=args,
-        )
-        func_msg = FunctionCallingMessage(
-            role_name=self.role_name,
-            role_type=self.role_type,
-            meta_dict=None,
-            content="",
-            func_name=func_name,
-            result=result,
-        )
-
-        # Record information about this function call
-        func_record = FunctionCallingRecord(
-            func_name=func_name, args=args, result=result
-        )
-        return assist_msg, func_msg, func_record
-
-    async def step_tool_call_async(
-        self,
-        response: ChatCompletion,
-    ) -> Tuple[
-        FunctionCallingMessage, FunctionCallingMessage, FunctionCallingRecord
-    ]:
-        r"""Execute the async function with arguments following the model's
-        response.
-
-        Args:
-            response (Dict[str, Any]): The response obtained by calling the
-                model.
-
-        Returns:
-            tuple: A tuple consisting of two obj:`FunctionCallingMessage`,
-                one about the arguments and the other about the execution
-                result, and a struct for logging information about this
-                function call.
-        """
-        # Note that when function calling is enabled, `n` is set to 1.
-        choice = response.choices[0]
-        if choice.message.tool_calls is None:
-            raise RuntimeError("Tool call is None")
-        func_name = choice.message.tool_calls[0].function.name
-
-        args = json.loads(choice.message.tool_calls[0].function.arguments)
-        tool = self.tool_dict[func_name]
-        result = await tool(**args)
-
-        assist_msg = FunctionCallingMessage(
-            role_name=self.role_name,
-            role_type=self.role_type,
-            meta_dict=None,
-            content="",
-            func_name=func_name,
-            args=args,
-        )
-        func_msg = FunctionCallingMessage(
-            role_name=self.role_name,
-            role_type=self.role_type,
-            meta_dict=None,
-            content="",
-            func_name=func_name,
-            result=result,
-        )
-
-        # Record information about this function call
-        func_record = FunctionCallingRecord(
-            func_name=func_name, args=args, result=result
-        )
-        return assist_msg, func_msg, func_record
-
-    def get_usage_dict(
-        self, output_messages: List[BaseMessage], prompt_tokens: int
-    ) -> Dict[str, int]:
-        r"""Get usage dictionary when using the stream mode.
-
-        Args:
-            output_messages (list): List of output messages.
-            prompt_tokens (int): Number of input prompt tokens.
-
-        Returns:
-            dict: Usage dictionary.
-        """
-        encoding = get_model_encoding(self.model_type.value_for_tiktoken)
-        completion_tokens = 0
-        for message in output_messages:
-            completion_tokens += len(encoding.encode(message.content))
-        usage_dict = dict(
-            completion_tokens=completion_tokens,
-            prompt_tokens=prompt_tokens,
-            total_tokens=completion_tokens + prompt_tokens,
-        )
-        return usage_dict
-
-    def add_model_scheduling_strategy(self, name: str, strategy_fn: Callable):
-        r"""Add a scheduling strategy method provided by user to ModelManger.
-
-        Args:
-            name (str): The name of the strategy.
-            strategy_fn (Callable): The scheduling strategy function.
-        """
-        self.model_backend.add_strategy(name, strategy_fn)
-
-    def __repr__(self) -> str:
-        r"""Returns a string representation of the :obj:`ChatAgent`.
-
-        Returns:
-            str: The string representation of the :obj:`ChatAgent`.
-        """
-        return (
-            f"ChatAgent({self.role_name}, {self.role_type}, {self.model_type})"
-        )

owl/camel/agents/critic_agent.py

@@ -1,202 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import random
-import warnings
-from typing import Any, Dict, Optional, Sequence
-
-from colorama import Fore
-
-from camel.agents.chat_agent import ChatAgent
-from camel.memories import AgentMemory
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.responses import ChatAgentResponse
-from camel.utils import get_first_int, print_text_animated
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-@track_agent(name="CriticAgent")
-class CriticAgent(ChatAgent):
-    r"""A class for the critic agent that assists in selecting an option.
-
-    Args:
-        system_message (BaseMessage): The system message for the critic
-            agent.
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        message_window_size (int, optional): The maximum number of previous
-            messages to include in the context window. If `None`, no windowing
-            is performed. (default: :obj:`6`)
-        retry_attempts (int, optional): The number of retry attempts if the
-            critic fails to return a valid option. (default: :obj:`2`)
-        verbose (bool, optional): Whether to print the critic's messages.
-        logger_color (Any): The color of the menu options displayed to the
-            user. (default: :obj:`Fore.MAGENTA`)
-    """
-
-    def __init__(
-        self,
-        system_message: BaseMessage,
-        model: Optional[BaseModelBackend] = None,
-        memory: Optional[AgentMemory] = None,
-        message_window_size: int = 6,
-        retry_attempts: int = 2,
-        verbose: bool = False,
-        logger_color: Any = Fore.MAGENTA,
-    ) -> None:
-        super().__init__(
-            system_message,
-            model=model,
-            memory=memory,
-            message_window_size=message_window_size,
-        )
-        self.options_dict: Dict[str, str] = dict()
-        self.retry_attempts = retry_attempts
-        self.verbose = verbose
-        self.logger_color = logger_color
-
-    def flatten_options(self, messages: Sequence[BaseMessage]) -> str:
-        r"""Flattens the options to the critic.
-
-        Args:
-            messages (Sequence[BaseMessage]): A list of `BaseMessage` objects.
-
-        Returns:
-            str: A string containing the flattened options to the critic.
-        """
-        options = [message.content for message in messages]
-        flatten_options = (
-            f"> Proposals from "
-            f"{messages[0].role_name} ({messages[0].role_type}). "
-            "Please choose an option:\n"
-        )
-        for index, option in enumerate(options):
-            flatten_options += f"Option {index + 1}:\n{option}\n\n"
-            self.options_dict[str(index + 1)] = option
-        format = (
-            f"Please first enter your choice ([1-{len(self.options_dict)}]) "
-            "and then your explanation and comparison: "
-        )
-        return flatten_options + format
-
-    def get_option(self, input_message: BaseMessage) -> str:
-        r"""Gets the option selected by the critic.
-
-        Args:
-            input_message (BaseMessage): A `BaseMessage` object representing
-                the input message.
-
-        Returns:
-            str: The option selected by the critic.
-        """
-        # TODO: Add support for editing options by the critic.
-        msg_content = input_message.content
-        i = 0
-        while i < self.retry_attempts:
-            critic_response = self.step(input_message)
-
-            if critic_response.msgs is None or len(critic_response.msgs) == 0:
-                raise RuntimeError("Got None critic messages.")
-            if critic_response.terminated:
-                raise RuntimeError("Critic step failed.")
-
-            critic_msg = critic_response.msg
-            if self.verbose:
-                print_text_animated(
-                    self.logger_color + "\n> Critic response: "
-                    f"\x1b[3m{critic_msg.content}\x1b[0m\n"
-                )
-            choice = self.parse_critic(critic_msg)
-
-            if choice in self.options_dict:
-                return self.options_dict[choice]
-            else:
-                input_message = BaseMessage(
-                    role_name=input_message.role_name,
-                    role_type=input_message.role_type,
-                    meta_dict=input_message.meta_dict,
-                    content="> Invalid choice. Please choose again.\n"
-                    + msg_content,
-                )
-                i += 1
-        warnings.warn(
-            "Critic failed to get a valid option. "
-            f"After {self.retry_attempts} attempts. "
-            "Returning a random option."
-        )
-        return random.choice(list(self.options_dict.values()))
-
-    def parse_critic(self, critic_msg: BaseMessage) -> Optional[str]:
-        r"""Parses the critic's message and extracts the choice.
-
-        Args:
-            critic_msg (BaseMessage): A `BaseMessage` object representing the
-                critic's response.
-
-        Returns:
-            Optional[str]: The critic's choice as a string, or None if the
-                message could not be parsed.
-        """
-        choice = str(get_first_int(critic_msg.content))
-        return choice
-
-    def reduce_step(
-        self,
-        input_messages: Sequence[BaseMessage],
-    ) -> ChatAgentResponse:
-        r"""Performs one step of the conversation by flattening options to the
-        critic, getting the option, and parsing the choice.
-
-        Args:
-            input_messages (Sequence[BaseMessage]): A list of BaseMessage
-                objects.
-
-        Returns:
-            ChatAgentResponse: A `ChatAgentResponse` object includes the
-                critic's choice.
-        """
-        meta_chat_message = BaseMessage(
-            role_name=input_messages[0].role_name,
-            role_type=input_messages[0].role_type,
-            meta_dict=input_messages[0].meta_dict,
-            content="",
-        )
-
-        flatten_options = self.flatten_options(input_messages)
-        if self.verbose:
-            print_text_animated(
-                self.logger_color + f"\x1b[3m{flatten_options}\x1b[0m\n"
-            )
-        input_msg = meta_chat_message.create_new_instance(flatten_options)
-
-        option = self.get_option(input_msg)
-        output_msg = meta_chat_message.create_new_instance(option)
-
-        # TODO: The return `info` can be improved.
-        return ChatAgentResponse(
-            msgs=[output_msg],
-            terminated=False,
-            info={},
-        )

owl/camel/agents/deductive_reasoner_agent.py

@@ -1,303 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import re
-from typing import Dict, List, Optional, Union
-
-from camel.agents.chat_agent import ChatAgent
-from camel.logger import get_logger
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.prompts import TextPrompt
-from camel.types import RoleType
-
-logger = get_logger(__name__)
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-@track_agent(name="DeductiveReasonerAgent")
-class DeductiveReasonerAgent(ChatAgent):
-    r"""An agent responsible for deductive reasoning. Model of deductive
-    reasoning:
-        - L: A ⊕ C -> q * B
-        - A represents the known starting state.
-        - B represents the known target state.
-        - C represents the conditions required to transition from A to B.
-        - Q represents the quality or effectiveness of the transition from
-        A to B.
-        - L represents the path or process from A to B.
-
-    Args:
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-    """
-
-    def __init__(
-        self,
-        model: Optional[BaseModelBackend] = None,
-    ) -> None:
-        system_message = BaseMessage(
-            role_name="Insight Agent",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You assign roles based on tasks.",
-        )
-        super().__init__(system_message, model=model)
-
-    def deduce_conditions_and_quality(
-        self,
-        starting_state: str,
-        target_state: str,
-        role_descriptions_dict: Optional[Dict[str, str]] = None,
-    ) -> Dict[str, Union[List[str], Dict[str, str]]]:
-        r"""Derives the conditions and quality from the starting state and the
-        target state based on the model of the deductive reasoning and the
-        knowledge base. It can optionally consider the roles involved in the
-        scenario, which allows tailoring the output more closely to the AI
-        agent's environment.
-
-        Args:
-            starting_state (str): The initial or starting state from which
-                conditions are deduced.
-            target_state (str): The target state of the task.
-            role_descriptions_dict (Optional[Dict[str, str]], optional): The
-                descriptions of the roles. (default: :obj:`None`)
-            role_descriptions_dict (Optional[Dict[str, str]], optional): A
-                dictionary describing the roles involved in the scenario. This
-                is optional and can be used to provide a context for the
-                CAMEL's role-playing, enabling the generation of more relevant
-                and tailored conditions and quality assessments. This could be
-                generated using a `RoleAssignmentAgent()` or defined manually
-                by the user.
-
-        Returns:
-            Dict[str, Union[List[str], Dict[str, str]]]: A dictionary with the
-                extracted data from the message. The dictionary contains three
-                keys:
-                - 'conditions': A list where each key is a condition ID and
-                    each value is the corresponding condition text.
-                - 'labels': A list of label strings extracted from the message.
-                - 'quality': A string of quality assessment strings extracted
-                    from the message.
-        """
-        self.reset()
-
-        deduce_prompt = """You are a deductive reasoner. You are tasked to 
-        complete the TASK based on the THOUGHT OF DEDUCTIVE REASONING, the 
-        STARTING STATE A and the TARGET STATE B. You are given the CONTEXT 
-        CONTENT to help you complete the TASK.
-Your answer MUST strictly adhere to the structure of ANSWER TEMPLATE, ONLY 
-fill in the BLANKs, and DO NOT alter or modify any other part of the template
-
-===== MODELING OF DEDUCTIVE REASONING =====
-You are tasked with understanding a mathematical model based on the components 
-${A, B, C, Q, L}$. In this model: ``L: A ⊕ C -> q * B``.
-- $A$ represents the known starting state.
-- $B$ represents the known target state.
-- $C$ represents the conditions required to transition from $A$ to $B$.
-- $Q$ represents the quality or effectiveness of the transition from $A$ to 
-$B$.
-- $L$ represents the path or process from $A$ to $B$.
-
-===== THOUGHT OF DEDUCTIVE REASONING =====
-1. Define the Parameters of A and B:
-    - Characterization: Before delving into transitions, thoroughly understand 
-    the nature and boundaries of both $A$ and $B$. This includes the type, 
-    properties, constraints, and possible interactions between the two.
-    - Contrast and Compare: Highlight the similarities and differences between 
-    $A$ and $B$. This comparative analysis will give an insight into what 
-    needs changing and what remains constant.
-2. Historical & Empirical Analysis:
-    - Previous Transitions according to the Knowledge Base of GPT: (if 
-    applicable) Extract conditions and patterns from the historical instances 
-    where a similar transition from a state comparable to $A$ moved towards 
-    $B$.
-    - Scientific Principles: (if applicable) Consider the underlying 
-    scientific principles governing or related to the states and their 
-    transition. For example, if $A$ and $B$ are physical states, laws of 
-    physics might apply.
-3. Logical Deduction of Conditions ($C$):
-    - Direct Path Analysis: What are the immediate and direct conditions 
-    required to move from $A$ to $B$?
-    - Intermediate States: Are there states between $A$ and $B$ that must be 
-    traversed or can be used to make the transition smoother or more 
-    efficient? If yes, what is the content?
-    - Constraints & Limitations: Identify potential barriers or restrictions 
-    in moving from $A$ to $B$. These can be external (e.g., environmental 
-    factors) or internal (properties of $A$ or $B$).
-    - Resource and Information Analysis: What resources and information are 
-    required for the transition? This could be time, entity, factor, code 
-    language, software platform, unknowns, etc.
-    - External Influences: Consider socio-economic, political, or 
-    environmental factors (if applicable) that could influence the transition 
-    conditions.
-    - Creative/Heuristic Reasoning: Open your mind to multiple possible $C$'s, 
-    no matter how unconventional they might seem. Utilize analogies, 
-    metaphors, or brainstorming techniques to envision possible conditions or 
-    paths from $A$ to $B$.
-    - The conditions $C$ should be multiple but in one sentence. And each 
-    condition should be concerned with one aspect/entity.
-4. Entity/Label Recognition of Conditions ($C$):
-    - Identify and categorize entities of Conditions ($C$) such as the names, 
-    locations, dates, specific technical terms or contextual parameters that 
-    might be associated with events, innovations post-2022.
-    - The output of the entities/labels will be used as tags or labels for 
-    semantic similarity searches. The entities/labels may be the words, or 
-    phrases, each of them should contain valuable, high information entropy 
-    information, and should be independent.
-    - Ensure that the identified entities are formatted in a manner suitable 
-    for database indexing and retrieval. Organize the entities into 
-    categories, and combine the category with its instance into a continuous 
-    phrase, without using colons or other separators.
-    - Format these entities for database indexing: output the category rather 
-    than its instance/content into a continuous phrase. For example, instead 
-    of "Jan. 02", identify it as "Event time".
-5. Quality Assessment ($Q$):
-    - Efficiency: How efficient is the transition from $A$ to $B$, which 
-    measures the resources used versus the desired outcome?
-    - Effectiveness: Did the transition achieve the desired outcome or was the 
-    target state achieved as intended?
-    - Safety & Risks: Assess any risks associated with the transition and the 
-    measures to mitigate them.
-    - Feedback Mechanisms: Incorporate feedback loops to continuously monitor 
-    and adjust the quality of transition, making it more adaptive.
-6. Iterative Evaluation:
-    - Test & Refine: Based on the initially deduced conditions and assessed 
-    quality, iterate the process to refine and optimize the transition. This 
-    might involve tweaking conditions, employing different paths, or changing 
-    resources.
-    - Feedback Integration: Use feedback to make improvements and increase the 
-    quality of the transition.
-7. Real-world scenarios often present challenges that may not be captured by 
-models and frameworks. While using the model, maintain an adaptive mindset:
-    - Scenario Exploration: Continuously imagine various possible scenarios, 
-    both positive and negative, to prepare for unexpected events.
-    - Flexibility: Be prepared to modify conditions ($C$) or alter the path/
-    process ($L$) if unforeseen challenges arise.
-    - Feedback Integration: Rapidly integrate feedback from actual 
-    implementations to adjust the model's application, ensuring relevancy and 
-    effectiveness.
-
-===== TASK =====
-Given the starting state $A$ and the target state $B$, assuming that a path 
-$L$ always exists between $A$ and $B$, how can one deduce or identify the 
-necessary conditions $C$ and the quality $Q$ of the transition?
-
-===== STARTING STATE $A$ =====
-{starting_state}
-
-===== TARGET STATE $B$ =====
-{target_state}
-
-{role_with_description_prompt}
-===== ANSWER TEMPLATE =====
-- Characterization and comparison of $A$ and $B$:\n<BLANK>
-- Historical & Empirical Analysis:\n<BLANK>/None
-- Logical Deduction of Conditions ($C$) (multiple conditions can be deduced):
-    condition <NUM>:
-        <BLANK>.
-- Entity/Label Recognition of Conditions:\n[<BLANK>, <BLANK>, ...] (include 
-square brackets)
-- Quality Assessment ($Q$) (do not use symbols):
-    <BLANK>.
-- Iterative Evaluation:\n<BLANK>/None"""
-
-        if role_descriptions_dict is not None:
-            role_names = role_descriptions_dict.keys()
-            role_with_description_prompt = (
-                "===== ROLES WITH DESCRIPTIONS =====\n"
-                + "\n".join(
-                    f"{role_name}:\n{role_descriptions_dict[role_name]}\n"
-                    for role_name in role_names
-                )
-                + "\n\n"
-            )
-        else:
-            role_with_description_prompt = ""
-        deduce_prompt = TextPrompt(deduce_prompt)
-
-        deduce = deduce_prompt.format(
-            starting_state=starting_state,
-            target_state=target_state,
-            role_with_description_prompt=role_with_description_prompt,
-        )
-
-        conditions_and_quality_generation_msg = BaseMessage.make_user_message(
-            role_name="Deductive Reasoner", content=deduce
-        )
-
-        response = self.step(
-            input_message=conditions_and_quality_generation_msg
-        )
-
-        if response.terminated:
-            raise RuntimeError(
-                "Deduction failed. Error:\n" + f"{response.info}"
-            )
-        msg: BaseMessage = response.msg
-        logger.info(f"Message content:\n{msg.content}")
-
-        # Extract the conditions from the message
-        conditions_dict = {
-            f"condition {i}": cdt.replace("<", "")
-            .replace(">", "")
-            .strip()
-            .strip('\n')
-            for i, cdt in re.findall(
-                r"condition (\d+):\s*(.+?)(?=condition \d+|- Entity)",
-                msg.content,
-                re.DOTALL,
-            )
-        }
-
-        # Extract the labels from the message
-        labels = [
-            label.strip().strip('\n').strip("\"'")
-            for label in re.findall(
-                r"Entity/Label Recognition of Conditions:\n\[(.+?)\]",
-                msg.content,
-                re.DOTALL,
-            )[0].split(",")
-        ]
-
-        # Extract the quality from the message
-        quality = next(
-            q.strip().strip('\n')
-            for q in re.findall(
-                r"Quality Assessment \(\$Q\$\) \(do not use symbols\):"
-                r"\n(.+?)- Iterative",
-                msg.content,
-                re.DOTALL,
-            )
-        )
-
-        # Convert them into JSON format
-        conditions_and_quality_json: Dict[
-            str, Union[List[str], Dict[str, str]]
-        ] = {}
-        conditions_and_quality_json["conditions"] = conditions_dict
-        conditions_and_quality_json["labels"] = labels
-        conditions_and_quality_json["evaluate_quality"] = quality
-
-        return conditions_and_quality_json

owl/camel/agents/embodied_agent.py

@@ -1,201 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import Any, List, Optional
-
-from colorama import Fore
-
-from camel.agents.chat_agent import ChatAgent
-from camel.agents.tool_agents.base import BaseToolAgent
-from camel.interpreters import (
-    BaseInterpreter,
-    InternalPythonInterpreter,
-    SubprocessInterpreter,
-)
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.responses import ChatAgentResponse
-from camel.utils import print_text_animated
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-@track_agent(name="EmbodiedAgent")
-class EmbodiedAgent(ChatAgent):
-    r"""Class for managing conversations of CAMEL Embodied Agents.
-
-    Args:
-        system_message (BaseMessage): The system message for the chat agent.
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        message_window_size (int, optional): The maximum number of previous
-            messages to include in the context window. If `None`, no windowing
-            is performed. (default: :obj:`None`)
-        tool_agents (List[BaseToolAgent], optional): The tools agents to use in
-            the embodied agent. (default: :obj:`None`)
-        code_interpreter (BaseInterpreter, optional): The code interpreter to
-            execute codes. If `code_interpreter` and `tool_agent` are both
-            `None`, default to `SubProcessInterpreter`. If `code_interpreter`
-            is `None` and `tool_agents` is not `None`, default to
-            `InternalPythonInterpreter`.  (default: :obj:`None`)
-        verbose (bool, optional): Whether to print the critic's messages.
-        logger_color (Any): The color of the logger displayed to the user.
-            (default: :obj:`Fore.MAGENTA`)
-    """
-
-    def __init__(
-        self,
-        system_message: BaseMessage,
-        model: Optional[BaseModelBackend] = None,
-        message_window_size: Optional[int] = None,
-        tool_agents: Optional[List[BaseToolAgent]] = None,
-        code_interpreter: Optional[BaseInterpreter] = None,
-        verbose: bool = False,
-        logger_color: Any = Fore.MAGENTA,
-    ) -> None:
-        self.tool_agents = tool_agents
-        self.code_interpreter: BaseInterpreter
-        if code_interpreter is not None:
-            self.code_interpreter = code_interpreter
-        elif self.tool_agents:
-            self.code_interpreter = InternalPythonInterpreter()
-        else:
-            self.code_interpreter = SubprocessInterpreter()
-
-        if self.tool_agents:
-            system_message = self._set_tool_agents(system_message)
-        self.verbose = verbose
-        self.logger_color = logger_color
-        super().__init__(
-            system_message=system_message,
-            model=model,
-            message_window_size=message_window_size,
-        )
-
-    def _set_tool_agents(self, system_message: BaseMessage) -> BaseMessage:
-        action_space_prompt = self._get_tool_agents_prompt()
-        result_message = system_message.create_new_instance(
-            content=system_message.content.format(
-                action_space=action_space_prompt
-            )
-        )
-        if self.tool_agents is not None:
-            self.code_interpreter.update_action_space(
-                {tool.name: tool for tool in self.tool_agents}
-            )
-        return result_message
-
-    def _get_tool_agents_prompt(self) -> str:
-        r"""Returns the action space prompt.
-
-        Returns:
-            str: The action space prompt.
-        """
-        if self.tool_agents is not None:
-            return "\n".join(
-                [
-                    f"*** {tool.name} ***:\n {tool.description}"
-                    for tool in self.tool_agents
-                ]
-            )
-        else:
-            return ""
-
-    def get_tool_agent_names(self) -> List[str]:
-        r"""Returns the names of tool agents.
-
-        Returns:
-            List[str]: The names of tool agents.
-        """
-        if self.tool_agents is not None:
-            return [tool.name for tool in self.tool_agents]
-        else:
-            return []
-
-    # ruff: noqa: E501
-    def step(self, input_message: BaseMessage) -> ChatAgentResponse:  # type: ignore[override]
-        r"""Performs a step in the conversation.
-
-        Args:
-            input_message (BaseMessage): The input message.
-
-        Returns:
-            ChatAgentResponse: A struct containing the output messages,
-                a boolean indicating whether the chat session has terminated,
-                and information about the chat session.
-        """
-        response = super().step(input_message)
-
-        if response.msgs is None or len(response.msgs) == 0:
-            raise RuntimeError("Got None output messages.")
-        if response.terminated:
-            raise RuntimeError(f"{self.__class__.__name__} step failed.")
-
-        # NOTE: Only single output messages are supported
-        explanations, codes = response.msg.extract_text_and_code_prompts()
-
-        if self.verbose:
-            for explanation, code in zip(explanations, codes):
-                print_text_animated(
-                    self.logger_color + f"> Explanation:\n{explanation}"
-                )
-                print_text_animated(self.logger_color + f"> Code:\n{code}")
-
-            if len(explanations) > len(codes):
-                print_text_animated(
-                    self.logger_color + f"> Explanation:\n{explanations[-1]}"
-                )
-
-        content = response.msg.content
-
-        if codes is not None:
-            try:
-                content = "\n> Executed Results:\n"
-                for block_idx, code in enumerate(codes):
-                    executed_output = self.code_interpreter.run(
-                        code, code.code_type
-                    )
-                    content += (
-                        f"Executing code block {block_idx}: {{\n"
-                        + executed_output
-                        + "}\n"
-                    )
-            except InterruptedError as e:
-                content = (
-                    f"\n> Running code fail: {e}\n"
-                    "Please regenerate the code."
-                )
-
-        # TODO: Handle errors
-        content = input_message.content + f"\n> Embodied Actions:\n{content}"
-        message = BaseMessage(
-            input_message.role_name,
-            input_message.role_type,
-            input_message.meta_dict,
-            content,
-        )
-        return ChatAgentResponse(
-            msgs=[message],
-            terminated=response.terminated,
-            info=response.info,
-        )

owl/camel/agents/knowledge_graph_agent.py

@@ -1,259 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import TYPE_CHECKING, Optional, Union
-
-if TYPE_CHECKING:
-    from unstructured.documents.elements import Element
-
-from camel.agents import ChatAgent
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.prompts import TextPrompt
-from camel.storages.graph_storages.graph_element import (
-    GraphElement,
-    Node,
-    Relationship,
-)
-from camel.types import RoleType
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-text_prompt = """
-You are tasked with extracting nodes and relationships from given content and 
-structures them into Node and Relationship objects. Here's the outline of what 
-you needs to do:
-
-Content Extraction:
-You should be able to process input content and identify entities mentioned 
-within it.
-Entities can be any noun phrases or concepts that represent distinct entities 
-in the context of the given content.
-
-Node Extraction:
-For each identified entity, you should create a Node object.
-Each Node object should have a unique identifier (id) and a type (type).
-Additional properties associated with the node can also be extracted and 
-stored.
-
-Relationship Extraction:
-You should identify relationships between entities mentioned in the content.
-For each relationship, create a Relationship object.
-A Relationship object should have a subject (subj) and an object (obj) which 
-are Node objects representing the entities involved in the relationship.
-Each relationship should also have a type (type), and additional properties if 
-applicable.
-
-Output Formatting:
-The extracted nodes and relationships should be formatted as instances of the 
-provided Node and Relationship classes.
-Ensure that the extracted data adheres to the structure defined by the classes.
-Output the structured data in a format that can be easily validated against 
-the provided code.
-
-Instructions for you:
-Read the provided content thoroughly.
-Identify distinct entities mentioned in the content and categorize them as 
-nodes.
-Determine relationships between these entities and represent them as directed 
-relationships.
-Provide the extracted nodes and relationships in the specified format below.
-Example for you:
-
-Example Content:
-"John works at XYZ Corporation. He is a software engineer. The company is 
-located in New York City."
-
-Expected Output:
-
-Nodes:
-
-Node(id='John', type='Person')
-Node(id='XYZ Corporation', type='Organization')
-Node(id='New York City', type='Location')
-
-Relationships:
-
-Relationship(subj=Node(id='John', type='Person'), obj=Node(id='XYZ 
-Corporation', type='Organization'), type='WorksAt')
-Relationship(subj=Node(id='John', type='Person'), obj=Node(id='New York City', 
-type='Location'), type='ResidesIn')
-
-===== TASK =====
-Please extracts nodes and relationships from given content and structures them 
-into Node and Relationship objects. 
-
-{task}
-"""
-
-
-@track_agent(name="KnowledgeGraphAgent")
-class KnowledgeGraphAgent(ChatAgent):
-    r"""An agent that can extract node and relationship information for
-    different entities from given `Element` content.
-
-    Attributes:
-        task_prompt (TextPrompt): A prompt for the agent to extract node and
-            relationship information for different entities.
-    """
-
-    def __init__(
-        self,
-        model: Optional[BaseModelBackend] = None,
-    ) -> None:
-        r"""Initialize the `KnowledgeGraphAgent`.
-
-        Args:
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        """
-        system_message = BaseMessage(
-            role_name="Graphify",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="Your mission is to transform unstructured content "
-            "into structured graph data. Extract nodes and relationships with "
-            "precision, and let the connections unfold. Your graphs will "
-            "illuminate the hidden connections within the chaos of "
-            "information.",
-        )
-        super().__init__(system_message, model=model)
-
-    def run(
-        self,
-        element: "Element",
-        parse_graph_elements: bool = False,
-    ) -> Union[str, GraphElement]:
-        r"""Run the agent to extract node and relationship information.
-
-        Args:
-            element (Element): The input element.
-            parse_graph_elements (bool, optional): Whether to parse into
-                `GraphElement`. Defaults to `False`.
-
-        Returns:
-            Union[str, GraphElement]: The extracted node and relationship
-                information. If `parse_graph_elements` is `True` then return
-                `GraphElement`, else return `str`.
-        """
-        self.reset()
-        self.element = element
-
-        knowledge_graph_prompt = TextPrompt(text_prompt)
-        knowledge_graph_generation = knowledge_graph_prompt.format(
-            task=str(element)
-        )
-
-        knowledge_graph_generation_msg = BaseMessage.make_user_message(
-            role_name="Graphify", content=knowledge_graph_generation
-        )
-
-        response = self.step(input_message=knowledge_graph_generation_msg)
-
-        content = response.msg.content
-
-        if parse_graph_elements:
-            content = self._parse_graph_elements(content)
-
-        return content
-
-    def _validate_node(self, node: Node) -> bool:
-        r"""Validate if the object is a valid Node.
-
-        Args:
-            node (Node): Object to be validated.
-
-        Returns:
-            bool: True if the object is a valid Node, False otherwise.
-        """
-        return (
-            isinstance(node, Node)
-            and isinstance(node.id, (str, int))
-            and isinstance(node.type, str)
-        )
-
-    def _validate_relationship(self, relationship: Relationship) -> bool:
-        r"""Validate if the object is a valid Relationship.
-
-        Args:
-            relationship (Relationship): Object to be validated.
-
-        Returns:
-            bool: True if the object is a valid Relationship, False otherwise.
-        """
-        return (
-            isinstance(relationship, Relationship)
-            and self._validate_node(relationship.subj)
-            and self._validate_node(relationship.obj)
-            and isinstance(relationship.type, str)
-        )
-
-    def _parse_graph_elements(self, input_string: str) -> GraphElement:
-        r"""Parses graph elements from given content.
-
-        Args:
-            input_string (str): The input content.
-
-        Returns:
-            GraphElement: The parsed graph elements.
-        """
-        import re
-
-        # Regular expressions to extract nodes and relationships
-        node_pattern = r"Node\(id='(.*?)', type='(.*?)'\)"
-        rel_pattern = (
-            r"Relationship\(subj=Node\(id='(.*?)', type='(.*?)'\), "
-            r"obj=Node\(id='(.*?)', type='(.*?)'\), type='(.*?)'\)"
-        )
-
-        nodes = {}
-        relationships = []
-
-        # Extract nodes
-        for match in re.finditer(node_pattern, input_string):
-            id, type = match.groups()
-            properties = {'source': 'agent_created'}
-            if id not in nodes:
-                node = Node(id=id, type=type, properties=properties)
-                if self._validate_node(node):
-                    nodes[id] = node
-
-        # Extract relationships
-        for match in re.finditer(rel_pattern, input_string):
-            subj_id, subj_type, obj_id, obj_type, rel_type = match.groups()
-            properties = {'source': 'agent_created'}
-            if subj_id in nodes and obj_id in nodes:
-                subj = nodes[subj_id]
-                obj = nodes[obj_id]
-                relationship = Relationship(
-                    subj=subj, obj=obj, type=rel_type, properties=properties
-                )
-                if self._validate_relationship(relationship):
-                    relationships.append(relationship)
-
-        return GraphElement(
-            nodes=list(nodes.values()),
-            relationships=relationships,
-            source=self.element,
-        )

owl/camel/agents/role_assignment_agent.py

@@ -1,141 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import re
-from typing import Dict, Optional, Union
-
-from camel.agents.chat_agent import ChatAgent
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.prompts import TextPrompt
-from camel.types import RoleType
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-@track_agent(name="RoleAssignmentAgent")
-class RoleAssignmentAgent(ChatAgent):
-    r"""An agent that generates role names based on the task prompt.
-
-    Args:
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-
-    Attributes:
-        role_assignment_prompt (TextPrompt): A prompt for the agent to generate
-        role names.
-    """
-
-    def __init__(
-        self,
-        model: Optional[BaseModelBackend] = None,
-    ) -> None:
-        system_message = BaseMessage(
-            role_name="Role Assigner",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You assign roles based on tasks.",
-        )
-        super().__init__(system_message, model=model)
-
-    def run(
-        self,
-        task_prompt: Union[str, TextPrompt],
-        num_roles: int = 2,
-    ) -> Dict[str, str]:
-        r"""Generate role names based on the input task prompt.
-
-        Args:
-            task_prompt (Union[str, TextPrompt]): The prompt
-                for the task based on which the roles are to be generated.
-            num_roles (int, optional): The number of roles to generate.
-                (default: :obj:`2`)
-
-        Returns:
-            Dict[str, str]: A dictionary mapping role names to their
-                descriptions.
-        """
-        self.reset()
-
-        expert_prompt = "===== ANSWER PROMPT =====\n" + "\n".join(
-            f"Domain expert {i + 1}: <BLANK>\n"
-            f"Associated competencies, characteristics, duties "
-            f"and workflows: <BLANK>. End."
-            for i in range(num_roles or 0)
-        )
-        role_assignment_generation_prompt = TextPrompt(
-            "You are a role assignment agent, and you're in charge of "
-            + "recruiting {num_roles} experts for the following task."
-            + "\n==== TASK =====\n {task}\n\n"
-            + "Identify the domain experts you'd recruit and detail their "
-            + "associated competencies, characteristics, duties and workflows "
-            + "to complete the task.\n "
-            + "Your answer MUST adhere to the format of ANSWER PROMPT, and "
-            + "ONLY answer the BLANKs.\n"
-            + expert_prompt
-        )
-        role_assignment_generation = role_assignment_generation_prompt.format(
-            num_roles=num_roles, task=task_prompt
-        )
-
-        role_assignment_generation_msg = BaseMessage.make_user_message(
-            role_name="Role Assigner", content=role_assignment_generation
-        )
-
-        response = self.step(input_message=role_assignment_generation_msg)
-
-        msg = response.msg  # type: BaseMessage
-        terminated = response.terminated
-
-        # Distribute the output completions into role names and descriptions
-        role_names = [
-            desc.replace("<|", "").replace("|>", "")
-            for desc in re.findall(
-                r"Domain expert \d: (.+?)\nAssociated competencies,",
-                msg.content,
-                re.DOTALL,
-            )
-        ]
-        role_descriptions = [
-            desc.replace("<|", "").replace("|>", "")
-            for desc in re.findall(
-                r"Associated competencies, characteristics, "
-                r"duties and workflows: (.+?) End.",
-                msg.content,
-                re.DOTALL,
-            )
-        ]
-
-        if len(role_names) != num_roles or len(role_descriptions) != num_roles:
-            raise RuntimeError(
-                "Got None or insufficient information of roles."
-            )
-        if terminated:
-            raise RuntimeError("Role assignment failed.")
-
-        role_descriptions_dict = {
-            role_name: description
-            for role_name, description in zip(role_names, role_descriptions)
-        }
-
-        return role_descriptions_dict

owl/camel/agents/search_agent.py

@@ -1,133 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import Optional
-
-from camel.agents.chat_agent import ChatAgent
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.prompts import TextPrompt
-from camel.types import RoleType
-from camel.utils import create_chunks
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-@track_agent(name="SearchAgent")
-class SearchAgent(ChatAgent):
-    r"""An agent that summarizes text based on a query and evaluates the
-    relevance of an answer.
-
-    Args:
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-    """
-
-    def __init__(
-        self,
-        model: Optional[BaseModelBackend] = None,
-    ) -> None:
-        system_message = BaseMessage(
-            role_name="Assistant",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You are a helpful assistant.",
-        )
-        super().__init__(system_message, model=model)
-
-    def summarize_text(self, text: str, query: str) -> str:
-        r"""Summarize the information from the text, base on the query.
-
-        Args:
-            text (str): Text to summarize.
-            query (str): What information you want.
-
-        Returns:
-            str: Strings with information.
-        """
-        self.reset()
-
-        summary_prompt = TextPrompt(
-            '''Gather information from this text that relative to the
-            question, but do not directly answer the question.\nquestion:
-            {query}\ntext '''
-        )
-        summary_prompt = summary_prompt.format(query=query)
-        # Max length of each chunk
-        max_len = 3000
-        results = ""
-        chunks = create_chunks(text, max_len)
-        # Summarize
-        for i, chunk in enumerate(chunks, start=1):
-            prompt = summary_prompt + str(i) + ": " + chunk
-            user_msg = BaseMessage.make_user_message(
-                role_name="User",
-                content=prompt,
-            )
-            result = self.step(user_msg).msg.content
-            results += result + "\n"
-
-        # Final summarization
-        final_prompt = TextPrompt(
-            '''Here are some summarized texts which split from one text. Using
-            the information to answer the question. If can't find the answer,
-            you must answer "I can not find the answer to the query" and
-            explain why.\n Query:\n{query}.\n\nText:\n'''
-        )
-        final_prompt = final_prompt.format(query=query)
-        prompt = final_prompt + results
-
-        user_msg = BaseMessage.make_user_message(
-            role_name="User",
-            content=prompt,
-        )
-        response = self.step(user_msg).msg.content
-
-        return response
-
-    def continue_search(self, query: str, answer: str) -> bool:
-        r"""Ask whether to continue search or not based on the provided answer.
-
-        Args:
-            query (str): The question.
-            answer (str): The answer to the question.
-
-        Returns:
-            bool: `True` if the user want to continue search, `False`
-            otherwise.
-        """
-        prompt = TextPrompt(
-            "Do you think the ANSWER can answer the QUERY? "
-            "Use only 'yes' or 'no' to answer.\n"
-            "===== QUERY =====\n{query}\n\n"
-            "===== ANSWER =====\n{answer}"
-        )
-        prompt = prompt.format(query=query, answer=answer)
-        user_msg = BaseMessage.make_user_message(
-            role_name="User",
-            content=prompt,
-        )
-        response = self.step(user_msg).msg.content
-        if "yes" in str(response).lower():
-            return False
-        return True

owl/camel/agents/task_agent.py

@@ -1,410 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import Any, Dict, List, Optional, Union
-
-from camel.agents.chat_agent import ChatAgent
-from camel.messages import BaseMessage
-from camel.models import BaseModelBackend
-from camel.prompts import PromptTemplateGenerator, TextPrompt
-from camel.types import RoleType, TaskType
-from camel.utils import get_task_list
-
-# AgentOps decorator setting
-try:
-    import os
-
-    if os.getenv("AGENTOPS_API_KEY") is not None:
-        from agentops import track_agent
-    else:
-        raise ImportError
-except (ImportError, AttributeError):
-    from camel.utils import track_agent
-
-
-@track_agent(name="TaskSpecifyAgent")
-class TaskSpecifyAgent(ChatAgent):
-    r"""An agent that specifies a given task prompt by prompting the user to
-    provide more details.
-
-    Attributes:
-        DEFAULT_WORD_LIMIT (int): The default word limit for the task prompt.
-        task_specify_prompt (TextPrompt): The prompt for specifying the task.
-
-    Args:
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        task_type (TaskType, optional): The type of task for which to generate
-            a prompt. (default: :obj:`TaskType.AI_SOCIETY`)
-        task_specify_prompt (Union[str, TextPrompt], optional): The prompt for
-            specifying the task. (default: :obj:`None`)
-        word_limit (int, optional): The word limit for the task prompt.
-            (default: :obj:`50`)
-        output_language (str, optional): The language to be output by the
-            agent. (default: :obj:`None`)
-    """
-
-    DEFAULT_WORD_LIMIT = 50
-
-    def __init__(
-        self,
-        model: Optional[BaseModelBackend] = None,
-        task_type: TaskType = TaskType.AI_SOCIETY,
-        task_specify_prompt: Optional[Union[str, TextPrompt]] = None,
-        word_limit: int = DEFAULT_WORD_LIMIT,
-        output_language: Optional[str] = None,
-    ) -> None:
-        self.task_specify_prompt: Union[str, TextPrompt]
-        if task_specify_prompt is None:
-            task_specify_prompt_template = (
-                PromptTemplateGenerator().get_task_specify_prompt(task_type)
-            )
-
-            self.task_specify_prompt = task_specify_prompt_template.format(
-                word_limit=word_limit
-            )
-        else:
-            self.task_specify_prompt = TextPrompt(task_specify_prompt)
-
-        system_message = BaseMessage(
-            role_name="Task Specifier",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You can make a task more specific.",
-        )
-
-        super().__init__(
-            system_message,
-            model=model,
-            output_language=output_language,
-        )
-
-    def run(
-        self,
-        task_prompt: Union[str, TextPrompt],
-        meta_dict: Optional[Dict[str, Any]] = None,
-    ) -> TextPrompt:
-        r"""Specify the given task prompt by providing more details.
-
-        Args:
-            task_prompt (Union[str, TextPrompt]): The original task
-                prompt.
-            meta_dict (Dict[str, Any], optional): A dictionary containing
-                additional information to include in the prompt.
-                (default: :obj:`None`)
-
-        Returns:
-            TextPrompt: The specified task prompt.
-        """
-        self.reset()
-        task_specify_prompt = self.task_specify_prompt.format(task=task_prompt)
-
-        if meta_dict is not None:
-            task_specify_prompt = task_specify_prompt.format(**meta_dict)
-        task_msg = BaseMessage.make_user_message(
-            role_name="Task Specifier", content=task_specify_prompt
-        )
-        specifier_response = self.step(task_msg)
-
-        if specifier_response.terminated:
-            raise RuntimeError("Task specification failed.")
-        if len(specifier_response.msgs) == 0:
-            raise RuntimeError("Got no specification message.")
-
-        specified_task_msg = specifier_response.msgs[0]
-
-        return TextPrompt(specified_task_msg.content)
-
-
-@track_agent(name="TaskPlannerAgent")
-class TaskPlannerAgent(ChatAgent):
-    r"""An agent that helps divide a task into subtasks based on the input
-    task prompt.
-
-    Attributes:
-        task_planner_prompt (TextPrompt): A prompt for the agent to divide
-            the task into subtasks.
-
-    Args:
-        model (BaseModelBackend, optional): The model backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        output_language (str, optional): The language to be output by the
-            agent. (default: :obj:`None`)
-    """
-
-    def __init__(
-        self,
-        model: Optional[BaseModelBackend] = None,
-        output_language: Optional[str] = None,
-    ) -> None:
-        self.task_planner_prompt = TextPrompt(
-            "Divide this task into subtasks: {task}. Be concise."
-        )
-        system_message = BaseMessage(
-            role_name="Task Planner",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You are a helpful task planner.",
-        )
-
-        super().__init__(
-            system_message,
-            model=model,
-            output_language=output_language,
-        )
-
-    def run(
-        self,
-        task_prompt: Union[str, TextPrompt],
-    ) -> TextPrompt:
-        r"""Generate subtasks based on the input task prompt.
-
-        Args:
-            task_prompt (Union[str, TextPrompt]): The prompt for the task to
-                be divided into subtasks.
-
-        Returns:
-            TextPrompt: A prompt for the subtasks generated by the agent.
-        """
-        # TODO: Maybe include roles information.
-        self.reset()
-        task_planner_prompt = self.task_planner_prompt.format(task=task_prompt)
-
-        task_msg = BaseMessage.make_user_message(
-            role_name="Task Planner", content=task_planner_prompt
-        )
-
-        task_response = self.step(task_msg)
-
-        if task_response.terminated:
-            raise RuntimeError("Task planning failed.")
-        if len(task_response.msgs) == 0:
-            raise RuntimeError("Got no task planning message.")
-
-        sub_tasks_msg = task_response.msgs[0]
-        return TextPrompt(sub_tasks_msg.content)
-
-
-@track_agent(name="TaskCreationAgent")
-class TaskCreationAgent(ChatAgent):
-    r"""An agent that helps create new tasks based on the objective
-    and last completed task. Compared to :obj:`TaskPlannerAgent`,
-    it's still a task planner, but it has more context information
-    like last task and incomplete task list. Modified from
-    `BabyAGI <https://github.com/yoheinakajima/babyagi>`_.
-
-    Attributes:
-        task_creation_prompt (TextPrompt): A prompt for the agent to
-            create new tasks.
-
-    Args:
-        role_name (str): The role name of the Agent to create the task.
-        objective (Union[str, TextPrompt]): The objective of the Agent to
-            perform the task.
-        model (BaseModelBackend, optional): The LLM backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        output_language (str, optional): The language to be output by the
-            agent. (default: :obj:`None`)
-        message_window_size (int, optional): The maximum number of previous
-            messages to include in the context window. If `None`, no windowing
-            is performed. (default: :obj:`None`)
-        max_task_num (int, optional): The maximum number of planned
-            tasks in one round. (default: :obj:3)
-    """
-
-    def __init__(
-        self,
-        role_name: str,
-        objective: Union[str, TextPrompt],
-        model: Optional[BaseModelBackend] = None,
-        output_language: Optional[str] = None,
-        message_window_size: Optional[int] = None,
-        max_task_num: Optional[int] = 3,
-    ) -> None:
-        task_creation_prompt = TextPrompt(
-            """Create new a task with the following objective: {objective}.
-Never forget you are a Task Creator of {role_name}.
-You must instruct me based on my expertise and your needs to solve the task.
-You should consider past solved tasks and in-progress tasks: {task_list}.
-The new created tasks must not overlap with these past tasks.
-The result must be a numbered list in the format:
-
-    #. First Task
-    #. Second Task
-    #. Third Task
-
-You can only give me up to {max_task_num} tasks at a time. \
-Each task should be concise, concrete and doable for a {role_name}.
-You should make task plan and not ask me questions.
-If you think no new tasks are needed right now, write "No tasks to add."
-Now start to give me new tasks one by one. No more than three tasks.
-Be concrete.
-"""
-        )
-
-        self.task_creation_prompt = task_creation_prompt.format(
-            objective=objective, role_name=role_name, max_task_num=max_task_num
-        )
-        self.objective = objective
-
-        system_message = BaseMessage(
-            role_name="Task Creator",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You are a helpful task creator.",
-        )
-
-        super().__init__(
-            system_message,
-            model=model,
-            output_language=output_language,
-            message_window_size=message_window_size,
-        )
-
-    def run(
-        self,
-        task_list: List[str],
-    ) -> List[str]:
-        r"""Generate subtasks based on the previous task results and
-        incomplete task list.
-
-        Args:
-            task_list (List[str]): The completed or in-progress
-                tasks which should not overlap with new created tasks.
-
-        Returns:
-            List[str]: The new task list generated by the Agent.
-        """
-
-        if len(task_list) > 0:
-            task_creation_prompt = self.task_creation_prompt.format(
-                task_list=task_list
-            )
-        else:
-            task_creation_prompt = self.task_creation_prompt.format(
-                task_list=""
-            )
-
-        task_msg = BaseMessage.make_user_message(
-            role_name="Task Creator", content=task_creation_prompt
-        )
-        task_response = self.step(task_msg)
-
-        if task_response.terminated:
-            raise RuntimeError("Task creation failed.")
-        if len(task_response.msgs) == 0:
-            raise RuntimeError("Got no task creation message.")
-
-        sub_tasks_msg = task_response.msgs[0]
-        return get_task_list(sub_tasks_msg.content)
-
-
-@track_agent(name="TaskPrioritizationAgent")
-class TaskPrioritizationAgent(ChatAgent):
-    r"""An agent that helps re-prioritize the task list and
-    returns numbered prioritized list. Modified from
-    `BabyAGI <https://github.com/yoheinakajima/babyagi>`_.
-
-    Attributes:
-        task_prioritization_prompt (TextPrompt): A prompt for the agent to
-            prioritize tasks.
-
-    Args:
-        objective (Union[str, TextPrompt]): The objective of the Agent to
-            perform the task.
-        model (BaseModelBackend, optional): The LLM backend to use for
-            generating responses. (default: :obj:`OpenAIModel` with
-            `GPT_4O_MINI`)
-        output_language (str, optional): The language to be output by the
-            agent. (default: :obj:`None`)
-        message_window_size (int, optional): The maximum number of previous
-            messages to include in the context window. If `None`, no windowing
-            is performed. (default: :obj:`None`)
-    """
-
-    def __init__(
-        self,
-        objective: Union[str, TextPrompt],
-        model: Optional[BaseModelBackend] = None,
-        output_language: Optional[str] = None,
-        message_window_size: Optional[int] = None,
-    ) -> None:
-        task_prioritization_prompt = TextPrompt(
-            """Prioritize the following tasks : {task_list}.
-Consider the ultimate objective of you: {objective}.
-Tasks should be sorted from highest to lowest priority, where higher-priority \
-tasks are those that act as pre-requisites or are more essential for meeting \
-the objective. Return one task per line in your response.
-Do not remove or modify any tasks.
-The result must be a numbered list in the format:
-
-    #. First task
-    #. Second task
-
-The entries must be consecutively numbered, starting with 1.
-The number of each entry must be followed by a period.
-Do not include any headers before your ranked list or follow your list \
-with any other output."""
-        )
-
-        self.task_prioritization_prompt = task_prioritization_prompt.format(
-            objective=objective
-        )
-        self.objective = objective
-
-        system_message = BaseMessage(
-            role_name="Task Prioritizer",
-            role_type=RoleType.ASSISTANT,
-            meta_dict=None,
-            content="You are a helpful task prioritizer.",
-        )
-
-        super().__init__(
-            system_message,
-            model=model,
-            output_language=output_language,
-            message_window_size=message_window_size,
-        )
-
-    def run(
-        self,
-        task_list: List[str],
-    ) -> List[str]:
-        r"""Prioritize the task list given the agent objective.
-
-        Args:
-            task_list (List[str]): The unprioritized tasks of agent.
-
-        Returns:
-            List[str]: The new prioritized task list generated by the Agent.
-        """
-        task_prioritization_prompt = self.task_prioritization_prompt.format(
-            task_list=task_list
-        )
-
-        task_msg = BaseMessage.make_user_message(
-            role_name="Task Prioritizer", content=task_prioritization_prompt
-        )
-
-        task_response = self.step(task_msg)
-
-        if task_response.terminated:
-            raise RuntimeError("Task prioritization failed.")
-        if len(task_response.msgs) == 0:
-            raise RuntimeError("Got no task prioritization message.")
-
-        sub_tasks_msg = task_response.msgs[0]
-        return get_task_list(sub_tasks_msg.content)

owl/camel/agents/tool_agents/__init__.py

@@ -1,20 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from .base import BaseToolAgent
-from .hugging_face_tool_agent import HuggingFaceToolAgent
-
-__all__ = [
-    'BaseToolAgent',
-    'HuggingFaceToolAgent',
-]

owl/camel/agents/tool_agents/base.py

@@ -1,39 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from camel.agents import BaseAgent
-
-
-class BaseToolAgent(BaseAgent):
-    r"""Creates a :obj:`BaseToolAgent` object with the specified name and
-        description.
-
-    Args:
-        name (str): The name of the tool agent.
-        description (str): The description of the tool agent.
-    """
-
-    def __init__(self, name: str, description: str) -> None:
-        self.name = name
-        self.description = description
-
-    def reset(self) -> None:
-        r"""Resets the agent to its initial state."""
-        pass
-
-    def step(self) -> None:
-        r"""Performs a single step of the agent."""
-        pass
-
-    def __str__(self) -> str:
-        return f"{self.name}: {self.description}"

owl/camel/agents/tool_agents/hugging_face_tool_agent.py

@@ -1,206 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import Any, Optional
-
-from camel.agents.tool_agents.base import BaseToolAgent
-
-
-# flake8: noqa :E501
-class HuggingFaceToolAgent(BaseToolAgent):
-    r"""Tool agent for calling HuggingFace models. This agent is a wrapper
-        around agents from the `transformers` library. For more information
-        about the available models, please see the `transformers` documentation
-        at https://huggingface.co/docs/transformers/transformers_agents.
-
-    Args:
-        name (str): The name of the agent.
-        *args (Any): Additional positional arguments to pass to the underlying
-            Agent class.
-        remote (bool, optional): Flag indicating whether to run the agent
-            remotely. (default: :obj:`True`)
-        **kwargs (Any): Additional keyword arguments to pass to the underlying
-            Agent class.
-    """
-
-    def __init__(
-        self,
-        name: str,
-        *args: Any,
-        remote: bool = True,
-        **kwargs: Any,
-    ) -> None:
-        try:
-            # TODO: Support other tool agents
-            import transformers
-            from packaging import version
-
-            if version.parse(transformers.__version__) < version.parse(
-                "4.31.0"
-            ):
-                raise ValueError(
-                    "The version of \"transformers\" package should >= 4.31.0"
-                )
-
-            from transformers.tools import OpenAiAgent
-            from transformers.tools.agent_types import AgentImage
-        except (ImportError, ValueError):
-            raise ValueError(
-                "Could not import transformers tool agents. "
-                "Please setup the environment with "
-                "pip install huggingface_hub==0.14.1 transformers==4.31.0 diffusers accelerate==0.20.3 datasets torch soundfile sentencepiece opencv-python"
-            )
-        self.agent_image_type = AgentImage
-        self.agent = OpenAiAgent(*args, **kwargs)
-        description = f"""The `{name}` is a tool agent that can perform a variety of tasks including:
-- Document question answering: given a document (such as a PDF) in image format, answer a question on this document
-- Text question answering: given a long text and a question, answer the question in the text
-- Unconditional image captioning: Caption the image!
-- Image question answering: given an image, answer a question on this image
-- Image segmentation: given an image and a prompt, output the segmentation mask of that prompt
-- Speech to text: given an audio recording of a person talking, transcribe the speech into text
-- Text to speech: convert text to speech
-- Zero-shot text classification: given a text and a list of labels, identify to which label the text corresponds the most
-- Text summarization: summarize a long text in one or a few sentences
-- Translation: translate the text into a given language
-- Text downloading: to download a text from a web URL
-- Text to image: generate an image according to a prompt, leveraging stable diffusion
-- Image transformation: modify an image given an initial image and a prompt, leveraging instruct pix2pix stable diffusion
-- Text to video: generate a small video according to a prompt
-
-Here are some python code examples of what you can do with this agent:
-
-Single execution (step) mode, the single execution method is when using the step() method of the agent:
-```
-# Text to image
-rivers_and_lakes_image = {name}.step("Draw me a picture of rivers and lakes.")
-rivers_and_lakes_image.save("./rivers_and_lakes_image.png")
-
-# Text to image -> Image transformation
-sea_add_island_image = {name}.step("Draw me a picture of the sea then transform the picture to add an island")
-sea_add_island_image.save("./sea_add_island_image.png")
-
-# If you'd like to keep a state across executions or to pass non-text objects to the agent, 
-# you can do so by specifying variables that you would like the agent to use. For example,
-# you could generate the first image of rivers and lakes, and ask the model to update that picture to add an island by doing the following:
-picture = {name}.step("Generate a picture of rivers and lakes.")
-picture.save("./picture.png")
-updated_picture = {name}.step("Transform the image in `picture` to add an island to it.", picture=picture)
-updated_picture.save("./updated_picture.png")
-
-capybara_sea_image = {name}.step("Draw me a picture of the `prompt`", prompt="a capybara swimming in the sea")
-capybara_sea_image.save("./capybara_sea_image.png")
-
-# Document question answering
-answer = {name}.step(
-    "In the following `document`, where will the TRRF Scientific Advisory Council Meeting take place?",
-    document=document,
-)
-print(answer)
-
-
-# Text to image
-boat_image = {name}.step("Generate an image of a boat in the water")
-boat_image.save("./boat_image.png")
-
-# Unconditional image captioning
-boat_image_caption = {name}.step("Can you caption the `boat_image`?", boat_image=boat_image)
-print(boat_image_caption)
-
-# Text to image -> Unconditional image captioning -> Text to speech
-boat_audio = {name}.step("Can you generate an image of a boat? Please read out loud the contents of the image afterwards")
-
-# Text downloading
-document = {name}.step("Download the text from http://hf.co")
-print(document)
-
-# Text summarization
-summary = {name}.step("Summarize the following text: `document`", document=document)
-print(summary)
-
-# Text downloading -> Text summarization -> Text to speech
-audio = {name}.step("Read out loud the summary of http://hf.co")
-```
-
-Chat-based execution (chat), the agent also has a chat-based approach, using the chat() method:
-```
-# Clean the chat history
-{name}.reset()
-
-# Text to image
-capybara_image = {name}.chat("Show me an an image of a capybara")
-capybara_image.save("./capybara_image.png")
-
-# Image transformation
-transformed_capybara_image = {name}.chat("Transform the image so that it snows")
-transformed_capybara_image.save("./transformed_capybara_image.png")
-
-# Image segmentation
-segmented_transformed_capybara_image = {name}.chat("Show me a mask of the snowy capybaras")
-segmented_transformed_capybara_image.save("./segmented_transformed_capybara_image.png")
-```
-"""
-        super(HuggingFaceToolAgent, self).__init__(name, description)
-        self.remote = remote
-
-    def reset(self) -> None:
-        r"""Resets the chat history of the agent."""
-        self.agent.prepare_for_new_chat()
-
-    def step(
-        self,
-        *args: Any,
-        remote: Optional[bool] = None,
-        **kwargs: Any,
-    ) -> Any:
-        r"""Runs the agent in single execution mode.
-
-        Args:
-            *args (Any): Positional arguments to pass to the agent.
-            remote (bool, optional): Flag indicating whether to run the agent
-                remotely. Overrides the default setting. (default: :obj:`None`)
-            **kwargs (Any): Keyword arguments to pass to the agent.
-
-        Returns:
-            str: The response from the agent.
-        """
-        if remote is None:
-            remote = self.remote
-        agent_output = self.agent.run(*args, remote=remote, **kwargs)
-        if isinstance(agent_output, self.agent_image_type):
-            agent_output = agent_output.to_raw()
-        return agent_output
-
-    def chat(
-        self,
-        *args: Any,
-        remote: Optional[bool] = None,
-        **kwargs: Any,
-    ) -> Any:
-        r"""Runs the agent in a chat conversation mode.
-
-        Args:
-            *args (Any): Positional arguments to pass to the agent.
-            remote (bool, optional): Flag indicating whether to run the agent
-                remotely. Overrides the default setting. (default: :obj:`None`)
-            **kwargs (Any): Keyword arguments to pass to the agent.
-
-        Returns:
-            str: The response from the agent.
-        """
-        if remote is None:
-            remote = self.remote
-        agent_output = self.agent.chat(*args, remote=remote, **kwargs)
-        if isinstance(agent_output, self.agent_image_type):
-            agent_output = agent_output.to_raw()
-        return agent_output

owl/camel/benchmarks/__init__.py

@@ -1,17 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-
-from .base import BaseBenchmark
-
-__all__ = ["BaseBenchmark"]

owl/camel/benchmarks/base.py

@@ -1,152 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-
-import logging
-from abc import ABC, abstractmethod
-from pathlib import Path
-from typing import Any, Dict, List, Literal, Optional
-
-from camel.agents import ChatAgent
-
-logger = logging.getLogger(__name__)
-
-
-class BaseBenchmark(ABC):
-    r"""Base class for benchmarks.
-
-    Attributes:
-        name (str): Name of the benchmark.
-        data_dir (str): Path to the data directory.
-        save_to (str): Path to save the results.
-        processes (int): Number of processes to use for parallel
-            processing. :(default: :obj:`1`)
-    """
-
-    def __init__(
-        self, name: str, data_dir: str, save_to: str, processes: int = 1
-    ):
-        r"""Initialize the benchmark.
-
-        Args:
-            name (str): Name of the benchmark.
-            data_dir (str): Path to the data directory.
-            save_to (str): Path to save the results.
-            processes (int): Number of processes to use for parallel
-                processing. :(default: :obj:`1`)
-
-        """
-        self.name = name
-        self.data_dir = Path(data_dir)
-        self.processes = processes
-        self.save_to = save_to
-        if not self.data_dir.exists():
-            logger.info(
-                f"Data directory {data_dir} does not exist. Creating it."
-            )
-            self.data_dir.mkdir(parents=True, exist_ok=True)
-        if not self.data_dir.is_dir():
-            raise NotADirectoryError(
-                f"Data directory {data_dir} is not a directory"
-            )
-        self._data: Dict[str, List[Dict[str, Any]]] = dict()
-        self._results: List[Dict[str, Any]] = []
-
-    @abstractmethod
-    def download(self) -> "BaseBenchmark":
-        r"""Download the benchmark data.
-
-        Returns:
-            BaseBenchmark: The benchmark instance.
-        """
-        pass
-
-    @abstractmethod
-    def load(self, force_download: bool = False) -> "BaseBenchmark":
-        r"""Load the benchmark data.
-
-        Args:
-            force_download (bool): Whether to force download the data.
-
-        Returns:
-            BaseBenchmark: The benchmark instance.
-        """
-        pass
-
-    @property
-    def train(self) -> List[Dict[str, Any]]:
-        r"""Get the training data.
-
-        Returns:
-            List[Dict[str, Any]]: The training data.
-        """
-        if not self._data:
-            logger.info("Data not loaded. Loading data.")
-            self.load()
-        return self._data["train"]
-
-    @property
-    def valid(self) -> List[Dict[str, Any]]:
-        r"""Get the validation data.
-
-        Returns:
-            List[Dict[str, Any]]: The validation data.
-        """
-        if not self._data:
-            logger.info("Data not loaded. Loading data.")
-            self.load()
-        return self._data["valid"]
-
-    @property
-    def test(self) -> List[Dict[str, Any]]:
-        r"""Get the test data.
-
-        Returns:
-            List[Dict[str, Any]]: The test data.
-        """
-        if not self._data:
-            logger.info("Data not loaded. Loading data.")
-            self.load()
-        return self._data["test"]
-
-    @abstractmethod
-    def run(
-        self,
-        agent: ChatAgent,
-        on: Literal["train", "valid", "test"],
-        randomize: bool = False,
-        subset: Optional[int] = None,
-        *args,
-        **kwargs,
-    ) -> "BaseBenchmark":
-        r"""Run the benchmark.
-
-        Args:
-            agent (ChatAgent): The chat agent.
-            on (str): The data split to run the benchmark on.
-            randomize (bool): Whether to randomize the data.
-            subset (int): The subset of the data to run the benchmark on.
-
-        Returns:
-            BaseBenchmark: The benchmark instance.
-        """
-        pass
-
-    @property
-    def results(self) -> List[Dict[str, Any]]:
-        r"""Get the results.
-
-        Returns:
-            List[Dict[str, Any]]: The results.
-        """
-        return self._results

owl/camel/bots/__init__.py

@@ -1,34 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from .discord_app import DiscordApp
-from .slack.models import (
-    SlackAppMentionEventBody,
-    SlackAppMentionEventProfile,
-    SlackAuthProfile,
-    SlackEventBody,
-    SlackEventProfile,
-)
-from .slack.slack_app import SlackApp
-from .telegram_bot import TelegramBot
-
-__all__ = [
-    'DiscordApp',
-    'SlackApp',
-    'SlackAppMentionEventBody',
-    'SlackAppMentionEventProfile',
-    'SlackAuthProfile',
-    'SlackEventBody',
-    'SlackEventProfile',
-    'TelegramBot',
-]

owl/camel/bots/discord_app.py

@@ -1,138 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import logging
-import os
-from typing import TYPE_CHECKING, List, Optional
-
-from camel.utils import dependencies_required
-
-if TYPE_CHECKING:
-    from discord import Message
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class DiscordApp:
-    r"""A class representing a Discord app that uses the `discord.py` library
-    to interact with Discord servers.
-
-    This bot can respond to messages in specific channels and only reacts to
-    messages that mention the bot.
-
-    Attributes:
-        channel_ids (Optional[List[int]]): A list of allowed channel IDs. If
-            provided, the bot will only respond to messages in these channels.
-        token (Optional[str]): The Discord bot token used for authentication.
-    """
-
-    @dependencies_required('discord')
-    def __init__(
-        self,
-        channel_ids: Optional[List[int]] = None,
-        token: Optional[str] = None,
-    ) -> None:
-        r"""Initialize the DiscordApp instance by setting up the Discord client
-        and event handlers.
-
-        Args:
-            channel_ids (Optional[List[int]]): A list of allowed channel IDs.
-                The bot will only respond to messages in these channels if
-                provided.
-            token (Optional[str]): The Discord bot token for authentication.
-                If not provided, the token will be retrieved from the
-                environment variable `DISCORD_TOKEN`.
-
-        Raises:
-            ValueError: If the `DISCORD_TOKEN` is not found in environment
-                variables.
-        """
-        self.token = token or os.getenv('DISCORD_TOKEN')
-        self.channel_ids = channel_ids
-
-        if not self.token:
-            raise ValueError(
-                "`DISCORD_TOKEN` not found in environment variables. Get it"
-                " here: `https://discord.com/developers/applications`."
-            )
-
-        import discord
-
-        intents = discord.Intents.default()
-        intents.message_content = True
-        self._client = discord.Client(intents=intents)
-
-        # Register event handlers
-        self._client.event(self.on_ready)
-        self._client.event(self.on_message)
-
-    async def start(self):
-        r"""Asynchronously start the Discord bot using its token.
-
-        This method starts the bot and logs into Discord asynchronously using
-        the provided token. It should be awaited when used in an async
-        environment.
-        """
-        await self._client.start(self.token)
-
-    def run(self) -> None:
-        r"""Start the Discord bot using its token.
-
-        This method starts the bot and logs into Discord synchronously using
-        the provided token. It blocks execution and keeps the bot running.
-        """
-        self._client.run(self.token)  # type: ignore[arg-type]
-
-    async def on_ready(self) -> None:
-        r"""Event handler that is called when the bot has successfully
-        connected to the Discord server.
-
-        When the bot is ready and logged into Discord, it prints a message
-        displaying the bot's username.
-        """
-        logger.info(f'We have logged in as {self._client.user}')
-
-    async def on_message(self, message: 'Message') -> None:
-        r"""Event handler for processing incoming messages.
-
-        This method is called whenever a new message is received by the bot. It
-        will ignore messages sent by the bot itself, only respond to messages
-        in allowed channels (if specified), and only to messages that mention
-        the bot.
-
-        Args:
-            message (discord.Message): The message object received from
-                Discord.
-        """
-        # If the message author is the bot itself,
-        # do not respond to this message
-        if message.author == self._client.user:
-            return
-
-        # If allowed channel IDs are provided,
-        # only respond to messages in those channels
-        if self.channel_ids and message.channel.id not in self.channel_ids:
-            return
-
-        # Only respond to messages that mention the bot
-        if not self._client.user or not self._client.user.mentioned_in(
-            message
-        ):
-            return
-
-        logger.info(f"Received message: {message.content}")
-
-    @property
-    def client(self):
-        return self._client

owl/camel/bots/slack/__init__.py

@@ -1,30 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from .models import (
-    SlackAppMentionEventBody,
-    SlackAppMentionEventProfile,
-    SlackAuthProfile,
-    SlackEventBody,
-    SlackEventProfile,
-)
-from .slack_app import SlackApp
-
-__all__ = [
-    'SlackApp',
-    'SlackAppMentionEventBody',
-    'SlackAppMentionEventProfile',
-    'SlackAuthProfile',
-    'SlackEventBody',
-    'SlackEventProfile',
-]

owl/camel/bots/slack/models.py

@@ -1,158 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import Optional
-
-from pydantic import BaseModel
-
-
-class SlackAuthProfile(BaseModel):
-    r"""Represents the authorization profile within a Slack event.
-
-    Events will contain a single, compact authorizations field that shows one
-    installation of your app that the event is visible to.
-    In other words, lists of authorizations will be truncated to one element.
-
-    If there's more than one installing party that your app is keeping track
-    of, it's best not to rely on the single party listed in authorizations to
-    be any particular one.
-
-    To get a full list of who can see events, call the apps.event.
-    authorizations.list method after obtaining an app-level token. Read more on
-    the changes here; they have taken effect for existing apps as of
-    February 24, 2021.
-
-    References:
-
-    - https://api.slack.com/apis/events-api#authorizations
-    - https://api.slack.com/changelog/2020-09-15-events-api-truncate-authed-users#no_context
-    """
-
-    enterprise_id: Optional[str] = None
-    """The ID of the enterprise associated with the authorization."""
-
-    team_id: str
-    """The ID of the team associated with the authorization."""
-
-    user_id: str
-    """The ID of the user associated with the authorization."""
-
-    is_bot: bool
-    """Whether the authorized user is a bot."""
-
-    is_enterprise_install: bool
-    """Whether the authorization is for an enterprise installation."""
-
-
-class SlackEventProfile(BaseModel):
-    r"""Represents the detailed profile of a Slack event, including user,
-    message, and context data.
-    """
-
-    user: str
-    """The ID of the user associated with the event."""
-
-    type: str
-    """The type of the event (e.g., 'message')."""
-
-    ts: str
-    """A timestamp representing when the event was triggered."""
-
-    thread_ts: Optional[str] = None
-    """The timestamp of the parent message in a thread."""
-
-    client_msg_id: str
-    """A unique ID generated by the client for the message (if available)."""
-
-    text: str
-    """The message content text."""
-
-    team: str
-    """The ID of the team that the event is associated with."""
-
-    blocks: list
-    """The list of message blocks, providing structured information."""
-
-    channel: str
-    """The ID of the Slack channel where the event happened."""
-
-    event_ts: str
-    """The event-specific timestamp when it occurred."""
-
-    channel_type: Optional[str]
-    """The type of Slack channel (e.g., 'channel', 'im')."""
-
-
-class SlackEventBody(BaseModel):
-    r"""Represents the entire body of a Slack event, including the event
-    profile, authorization, and context.
-    """
-
-    token: str
-    """The token to verify the source of the event."""
-
-    team_id: str
-    """The ID of the team where the event is happening."""
-
-    context_team_id: Optional[str]
-    """The team ID for the shared channel context, if applicable."""
-
-    context_enterprise_id: Optional[str] = None
-    """The enterprise ID for the shared channel context, if applicable."""
-
-    api_app_id: str
-    """The unique identifier for the Slack app that received the event."""
-
-    event: SlackEventProfile
-    """A detailed profile of the event"""
-
-    type: str
-    """The overall type of event received (e.g., 'event_callback')."""
-
-    event_id: str
-    """A unique identifier assigned to this event by Slack."""
-
-    event_time: int
-    """The timestamp (in seconds) representing when the event was triggered."""
-
-    authorizations: Optional[list[SlackAuthProfile]] = None
-    """An optional list of authorizations that describe which installation can 
-    see the event."""
-
-    is_ext_shared_channel: bool
-    """Indicates if the event is part of a shared channel between different 
-    organizations."""
-
-    event_context: str
-    """A unique string representing the context of the event."""
-
-
-class SlackAppMentionEventProfile(SlackEventProfile):
-    r"""Represents the detailed profile of a Slack event where the app was
-    mentioned in a message.
-    """
-
-    channel_type: Optional[str] = None
-    """The type of Slack channel. it's None for app mentions."""
-
-
-class SlackAppMentionEventBody(SlackEventBody):
-    r"""Represents the entire body of a Slack event where the app was mentioned
-    in a message.
-    """
-
-    context_team_id: Optional[str] = None
-    """A detailed profile of the event. it's None for app mentions."""
-
-    event: SlackAppMentionEventProfile
-    """A detailed profile of the event"""

owl/camel/bots/slack/slack_app.py

@@ -1,255 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import logging
-import os
-from typing import TYPE_CHECKING, Any, Dict, Optional
-
-from slack_sdk.oauth.installation_store.async_installation_store import (
-    AsyncInstallationStore,
-)
-from starlette import requests, responses
-
-from camel.bots.slack.models import (
-    SlackAppMentionEventBody,
-    SlackAppMentionEventProfile,
-    SlackEventBody,
-    SlackEventProfile,
-)
-from camel.utils import dependencies_required
-
-if TYPE_CHECKING:
-    from slack_bolt.context.async_context import AsyncBoltContext
-    from slack_bolt.context.say.async_say import AsyncSay
-    from slack_sdk.web.async_client import AsyncWebClient
-
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-
-class SlackApp:
-    r"""Represents a Slack app that is powered by a Slack Bolt `AsyncApp`.
-
-    This class is responsible for initializing and managing the Slack
-    application by setting up event handlers, running the app server, and
-    handling events such as messages and mentions from Slack.
-
-    Args:
-        token (Optional[str]): Slack API token for authentication.
-        scopes (Optional[str]): Slack app scopes for permissions.
-        signing_secret (Optional[str]): Signing secret for verifying Slack
-            requests.
-        client_id (Optional[str]): Slack app client ID.
-        client_secret (Optional[str]): Slack app client secret.
-        redirect_uri_path (str): The URI path for OAuth redirect, defaults to
-            "/slack/oauth_redirect".
-        installation_store (Optional[AsyncInstallationStore]): The installation
-            store for handling OAuth installations.
-    """
-
-    @dependencies_required('slack_bolt')
-    def __init__(
-        self,
-        token: Optional[str] = None,
-        scopes: Optional[str] = None,
-        signing_secret: Optional[str] = None,
-        client_id: Optional[str] = None,
-        client_secret: Optional[str] = None,
-        redirect_uri_path: str = "/slack/oauth_redirect",
-        installation_store: Optional[AsyncInstallationStore] = None,
-    ) -> None:
-        r"""Initializes the SlackApp instance by setting up the Slack Bolt app
-        and configuring event handlers and OAuth settings.
-
-        Args:
-            token (Optional[str]): The Slack API token.
-            scopes (Optional[str]): The scopes for Slack app permissions.
-            signing_secret (Optional[str]): The signing secret for verifying
-                requests.
-            client_id (Optional[str]): The Slack app client ID.
-            client_secret (Optional[str]): The Slack app client secret.
-            redirect_uri_path (str): The URI path for handling OAuth redirects
-                (default is "/slack/oauth_redirect").
-            installation_store (Optional[AsyncInstallationStore]): An optional
-                installation store for OAuth installations.
-        """
-        from slack_bolt.adapter.starlette.async_handler import (
-            AsyncSlackRequestHandler,
-        )
-        from slack_bolt.app.async_app import AsyncApp
-        from slack_bolt.oauth.async_oauth_settings import AsyncOAuthSettings
-
-        self.token: Optional[str] = token or os.getenv("SLACK_TOKEN")
-        self.scopes: Optional[str] = scopes or os.getenv("SLACK_SCOPES")
-        self.signing_secret: Optional[str] = signing_secret or os.getenv(
-            "SLACK_SIGNING_SECRET"
-        )
-        self.client_id: Optional[str] = client_id or os.getenv(
-            "SLACK_CLIENT_ID"
-        )
-        self.client_secret: Optional[str] = client_secret or os.getenv(
-            "SLACK_CLIENT_SECRET"
-        )
-
-        if not all([self.token, self.scopes, self.signing_secret]):
-            raise ValueError(
-                "`SLACK_TOKEN`, `SLACK_SCOPES`, and `SLACK_SIGNING_SECRET` "
-                "environment variables must be set. Get it here: "
-                "`https://api.slack.com/apps`."
-            )
-
-        # Setup OAuth settings if client ID and secret are provided
-        if self.client_id and self.client_secret:
-            self._app = AsyncApp(
-                oauth_settings=AsyncOAuthSettings(
-                    client_id=self.client_id,
-                    client_secret=self.client_secret,
-                    scopes=self.scopes,
-                    redirect_uri_path=redirect_uri_path,
-                ),
-                logger=logger,
-                signing_secret=self.signing_secret,
-                installation_store=installation_store,
-                token=self.token,
-            )
-        else:
-            # Initialize Slack Bolt AsyncApp with settings
-            self._app = AsyncApp(
-                logger=logger,
-                signing_secret=self.signing_secret,
-                installation_store=installation_store,
-                token=self.token,
-            )
-
-        self._handler = AsyncSlackRequestHandler(self._app)
-        self.setup_handlers()
-
-    def setup_handlers(self) -> None:
-        r"""Sets up the event handlers for Slack events, such as `app_mention`
-        and `message`.
-
-        This method registers the `app_mention` and `on_message` event handlers
-        with the Slack Bolt app to respond to Slack events.
-        """
-        self._app.event("app_mention")(self.app_mention)
-        self._app.event("message")(self.on_message)
-
-    def run(
-        self,
-        port: int = 3000,
-        path: str = "/slack/events",
-        host: Optional[str] = None,
-    ) -> None:
-        r"""Starts the Slack Bolt app server to listen for incoming Slack
-        events.
-
-        Args:
-            port (int): The port on which the server should run (default is
-                3000).
-            path (str): The endpoint path for receiving Slack events (default
-                is "/slack/events").
-            host (Optional[str]): The hostname to bind the server (default is
-                None).
-        """
-        self._app.start(port=port, path=path, host=host)
-
-    async def handle_request(
-        self, request: requests.Request
-    ) -> responses.Response:
-        r"""Handles incoming requests from Slack through the request handler.
-
-        Args:
-            request (Request): A Starlette request object representing the
-                incoming request.
-
-        Returns:
-            The response generated by the Slack Bolt handler.
-        """
-        return await self._handler.handle(request)
-
-    async def app_mention(
-        self,
-        context: "AsyncBoltContext",
-        client: "AsyncWebClient",
-        event: Dict[str, Any],
-        body: Dict[str, Any],
-        say: "AsyncSay",
-    ) -> None:
-        r"""Event handler for `app_mention` events.
-
-        This method is triggered when someone mentions the app in Slack.
-
-        Args:
-            context (AsyncBoltContext): The Slack Bolt context for the event.
-            client (AsyncWebClient): The Slack Web API client.
-            event (Dict[str, Any]): The event data for the app mention.
-            body (Dict[str, Any]): The full request body from Slack.
-            say (AsyncSay): A function to send a response back to the channel.
-        """
-        event_profile = SlackAppMentionEventProfile(**event)
-        event_body = SlackAppMentionEventBody(**body)
-
-        logger.info(f"app_mention, context: {context}")
-        logger.info(f"app_mention, client: {client}")
-        logger.info(f"app_mention, event_profile: {event_profile}")
-        logger.info(f"app_mention, event_body: {event_body}")
-        logger.info(f"app_mention, say: {say}")
-
-    async def on_message(
-        self,
-        context: "AsyncBoltContext",
-        client: "AsyncWebClient",
-        event: Dict[str, Any],
-        body: Dict[str, Any],
-        say: "AsyncSay",
-    ) -> None:
-        r"""Event handler for `message` events.
-
-        This method is triggered when the app receives a message in Slack.
-
-        Args:
-            context (AsyncBoltContext): The Slack Bolt context for the event.
-            client (AsyncWebClient): The Slack Web API client.
-            event (Dict[str, Any]): The event data for the message.
-            body (Dict[str, Any]): The full request body from Slack.
-            say (AsyncSay): A function to send a response back to the channel.
-        """
-        await context.ack()
-
-        event_profile = SlackEventProfile(**event)
-        event_body = SlackEventBody(**body)
-
-        logger.info(f"on_message, context: {context}")
-        logger.info(f"on_message, client: {client}")
-        logger.info(f"on_message, event_profile: {event_profile}")
-        logger.info(f"on_message, event_body: {event_body}")
-        logger.info(f"on_message, say: {say}")
-
-        logger.info(f"Received message: {event_profile.text}")
-
-    def mention_me(
-        self, context: "AsyncBoltContext", body: SlackEventBody
-    ) -> bool:
-        r"""Check if the bot is mentioned in the message.
-
-        Args:
-            context (AsyncBoltContext): The Slack Bolt context for the event.
-            body (SlackEventBody): The body of the Slack event.
-
-        Returns:
-            bool: True if the bot is mentioned in the message, False otherwise.
-        """
-        message = body.event.text
-        bot_user_id = context.bot_user_id
-        mention = f"<@{bot_user_id}>"
-        return mention in message

owl/camel/bots/telegram_bot.py

@@ -1,82 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import os
-from typing import TYPE_CHECKING, Optional
-
-from camel.agents import ChatAgent
-from camel.messages import BaseMessage
-from camel.utils import dependencies_required
-
-# Conditionally import telebot types only for type checking
-if TYPE_CHECKING:
-    from telebot.types import (  # type: ignore[import-untyped]
-        Message,
-    )
-
-
-class TelegramBot:
-    r"""Represents a Telegram bot that is powered by an agent.
-
-    Attributes:
-        chat_agent (ChatAgent): Chat agent that will power the bot.
-        telegram_token (str, optional): The bot token.
-    """
-
-    @dependencies_required('telebot')
-    def __init__(
-        self,
-        chat_agent: ChatAgent,
-        telegram_token: Optional[str] = None,
-    ) -> None:
-        self.chat_agent = chat_agent
-
-        if not telegram_token:
-            self.token = os.getenv('TELEGRAM_TOKEN')
-            if not self.token:
-                raise ValueError(
-                    "`TELEGRAM_TOKEN` not found in environment variables. "
-                    "Get it from t.me/BotFather."
-                )
-        else:
-            self.token = telegram_token
-
-        import telebot  # type: ignore[import-untyped]
-
-        self.bot = telebot.TeleBot(token=self.token)
-
-        # Register the message handler within the constructor
-        self.bot.message_handler(func=lambda message: True)(self.on_message)
-
-    def run(self) -> None:
-        r"""Start the Telegram bot."""
-        print("Telegram bot is running...")
-        self.bot.infinity_polling()
-
-    def on_message(self, message: 'Message') -> None:
-        r"""Handles incoming messages from the user.
-
-        Args:
-            message (types.Message): The incoming message object.
-        """
-        self.chat_agent.reset()
-
-        if not message.text:
-            return
-
-        user_msg = BaseMessage.make_user_message(
-            role_name="User", content=message.text
-        )
-        assistant_response = self.chat_agent.step(user_msg)
-
-        self.bot.reply_to(message, assistant_response.msg.content)

owl/camel/configs/__init__.py

@@ -1,76 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from .anthropic_config import ANTHROPIC_API_PARAMS, AnthropicConfig
-from .base_config import BaseConfig
-from .cohere_config import COHERE_API_PARAMS, CohereConfig
-from .deepseek_config import DEEPSEEK_API_PARAMS, DeepSeekConfig
-from .gemini_config import Gemini_API_PARAMS, GeminiConfig
-from .groq_config import GROQ_API_PARAMS, GroqConfig
-from .litellm_config import LITELLM_API_PARAMS, LiteLLMConfig
-from .mistral_config import MISTRAL_API_PARAMS, MistralConfig
-from .nvidia_config import NVIDIA_API_PARAMS, NvidiaConfig
-from .ollama_config import OLLAMA_API_PARAMS, OllamaConfig
-from .openai_config import OPENAI_API_PARAMS, ChatGPTConfig
-from .qwen_config import QWEN_API_PARAMS, QwenConfig
-from .reka_config import REKA_API_PARAMS, RekaConfig
-from .samba_config import (
-    SAMBA_CLOUD_API_PARAMS,
-    SAMBA_VERSE_API_PARAMS,
-    SambaCloudAPIConfig,
-    SambaVerseAPIConfig,
-)
-from .togetherai_config import TOGETHERAI_API_PARAMS, TogetherAIConfig
-from .vllm_config import VLLM_API_PARAMS, VLLMConfig
-from .yi_config import YI_API_PARAMS, YiConfig
-from .zhipuai_config import ZHIPUAI_API_PARAMS, ZhipuAIConfig
-
-__all__ = [
-    'BaseConfig',
-    'ChatGPTConfig',
-    'OPENAI_API_PARAMS',
-    'AnthropicConfig',
-    'ANTHROPIC_API_PARAMS',
-    'GROQ_API_PARAMS',
-    'GroqConfig',
-    'LiteLLMConfig',
-    'LITELLM_API_PARAMS',
-    'NvidiaConfig',
-    'NVIDIA_API_PARAMS',
-    'OllamaConfig',
-    'OLLAMA_API_PARAMS',
-    'ZhipuAIConfig',
-    'ZHIPUAI_API_PARAMS',
-    'GeminiConfig',
-    'Gemini_API_PARAMS',
-    'VLLMConfig',
-    'VLLM_API_PARAMS',
-    'MistralConfig',
-    'MISTRAL_API_PARAMS',
-    'RekaConfig',
-    'REKA_API_PARAMS',
-    'SambaVerseAPIConfig',
-    'SAMBA_VERSE_API_PARAMS',
-    'SambaCloudAPIConfig',
-    'SAMBA_CLOUD_API_PARAMS',
-    'TogetherAIConfig',
-    'TOGETHERAI_API_PARAMS',
-    'CohereConfig',
-    'COHERE_API_PARAMS',
-    'YiConfig',
-    'YI_API_PARAMS',
-    'QwenConfig',
-    'QWEN_API_PARAMS',
-    'DeepSeekConfig',
-    'DEEPSEEK_API_PARAMS',
-]

owl/camel/configs/anthropic_config.py

@@ -1,69 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import List, Union
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class AnthropicConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Anthropic API.
-
-    See: https://docs.anthropic.com/claude/reference/complete_post
-    Args:
-        max_tokens (int, optional): The maximum number of tokens to
-            generate before stopping. Note that Anthropic models may stop
-            before reaching this maximum. This parameter only specifies the
-            absolute maximum number of tokens to generate.
-            (default: :obj:`256`)
-        stop_sequences (List[str], optional): Sequences that will cause the
-            model to stop generating completion text. Anthropic models stop
-            on "\n\nHuman:", and may include additional built-in stop sequences
-            in the future. By providing the stop_sequences parameter, you may
-            include additional strings that will cause the model to stop
-            generating.
-        temperature (float, optional): Amount of randomness injected into the
-            response. Defaults to 1. Ranges from 0 to 1. Use temp closer to 0
-            for analytical / multiple choice, and closer to 1 for creative
-            and generative tasks.
-            (default: :obj:`1`)
-        top_p (float, optional): Use nucleus sampling. In nucleus sampling, we
-            compute the cumulative distribution over all the options for each
-            subsequent token in decreasing probability order and cut it off
-            once it reaches a particular probability specified by `top_p`.
-            You should either alter `temperature` or `top_p`,
-            but not both.
-            (default: :obj:`0.7`)
-        top_k (int, optional): Only sample from the top K options for each
-            subsequent token. Used to remove "long tail" low probability
-            responses.
-            (default: :obj:`5`)
-        metadata: An object describing metadata about the request.
-        stream (bool, optional): Whether to incrementally stream the response
-            using server-sent events. (default: :obj:`False`)
-    """
-
-    max_tokens: int = 256
-    stop_sequences: Union[List[str], NotGiven] = NOT_GIVEN
-    temperature: float = 1
-    top_p: Union[float, NotGiven] = NOT_GIVEN
-    top_k: Union[int, NotGiven] = NOT_GIVEN
-    metadata: NotGiven = NOT_GIVEN
-    stream: bool = False
-
-
-ANTHROPIC_API_PARAMS = {param for param in AnthropicConfig.model_fields.keys()}

owl/camel/configs/base_config.py

@@ -1,89 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from abc import ABC
-from typing import Any, List, Optional
-
-from pydantic import BaseModel, ConfigDict, field_validator
-
-
-class BaseConfig(ABC, BaseModel):
-    r"""Base configuration class for all models.
-
-    This class provides a common interface for all models, ensuring that all
-    models have a consistent set of attributes and methods.
-    """
-
-    model_config = ConfigDict(
-        arbitrary_types_allowed=True,
-        extra="forbid",
-        frozen=True,
-        # UserWarning: conflict with protected namespace "model_"
-        protected_namespaces=(),
-    )
-
-    tools: Optional[List[Any]] = None
-    """A list of tools the model may
-    call. Currently, only functions are supported as a tool. Use this
-    to provide a list of functions the model may generate JSON inputs
-    for. A max of 128 functions are supported.
-    """
-
-    @field_validator("tools", mode="before")
-    @classmethod
-    def fields_type_checking(cls, tools):
-        r"""Validate the type of tools in the configuration.
-
-        This method ensures that the tools provided in the configuration are
-        instances of `FunctionTool`. If any tool is not an instance of
-        `FunctionTool`, it raises a ValueError.
-        """
-        if tools is not None:
-            from camel.toolkits import FunctionTool
-
-            for tool in tools:
-                if not isinstance(tool, FunctionTool):
-                    raise ValueError(
-                        f"The tool {tool} should "
-                        "be an instance of `FunctionTool`."
-                    )
-        return tools
-
-    def as_dict(self) -> dict[str, Any]:
-        r"""Convert the current configuration to a dictionary.
-
-        This method converts the current configuration object to a dictionary
-        representation, which can be used for serialization or other purposes.
-
-        Returns:
-            dict[str, Any]: A dictionary representation of the current
-                configuration.
-        """
-        config_dict = self.model_dump()
-
-        tools_schema = None
-        if self.tools:
-            from camel.toolkits import FunctionTool
-
-            tools_schema = []
-            for tool in self.tools:
-                if not isinstance(tool, FunctionTool):
-                    raise ValueError(
-                        f"The tool {tool} should "
-                        "be an instance of `FunctionTool`."
-                    )
-                tools_schema.append(tool.get_openai_tool_schema())
-        config_dict["tools"] = tools_schema
-        return config_dict

owl/camel/configs/cohere_config.py

@@ -1,76 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import List, Optional
-
-from camel.configs.base_config import BaseConfig
-
-
-class CohereConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Cohere API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.3`)
-        documents (list, optional): A list of relevant documents that the
-            model can cite to generate a more accurate reply. Each document is
-            either a string or document object with content and metadata.
-            (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens the model
-            will generate as part of the response. (default: :obj:`None`)
-        stop_sequences (List(str), optional): A list of up to 5 strings that
-            the model will use to stop generation. If the model generates a
-            string that matches any of the strings in the list, it will stop
-            generating tokens and return the generated text up to that point
-            not including the stop sequence. (default: :obj:`None`)
-        seed (int, optional): If specified, the backend will make a best
-            effort to sample tokens deterministically, such that repeated
-            requests with the same seed and parameters should return the same
-            result. However, determinism cannot be totally guaranteed.
-            (default: :obj:`None`)
-        frequency_penalty (float, optional): Min value of `0.0`, max value of
-            `1.0`. Used to reduce repetitiveness of generated tokens. The
-            higher the value, the stronger a penalty is applied to previously
-            present tokens, proportional to how many times they have already
-            appeared in the prompt or prior generation. (default: :obj:`0.0`)
-        presence_penalty (float, optional): Min value of `0.0`, max value of
-            `1.0`. Used to reduce repetitiveness of generated tokens. Similar
-            to `frequency_penalty`, except that this penalty is applied
-            equally to all tokens that have already appeared, regardless of
-            their exact frequencies. (default: :obj:`0.0`)
-        k (int, optional): Ensures only the top k most likely tokens are
-            considered for generation at each step. Min value of `0`, max
-            value of `500`. (default: :obj:`0`)
-        p (float, optional): Ensures that only the most likely tokens, with
-            total probability mass of `p`, are considered for generation at
-            each step. If both k and p are enabled, `p` acts after `k`. Min
-            value of `0.01`, max value of `0.99`. (default: :obj:`0.75`)
-    """
-
-    temperature: Optional[float] = 0.2
-    documents: Optional[list] = None
-    max_tokens: Optional[int] = None
-    stop_sequences: Optional[List[str]] = None
-    seed: Optional[int] = None
-    frequency_penalty: Optional[float] = 0.0
-    presence_penalty: Optional[float] = 0.0
-    k: Optional[int] = 0
-    p: Optional[float] = 0.75
-
-
-COHERE_API_PARAMS = {param for param in CohereConfig().model_fields.keys()}

owl/camel/configs/deepseek_config.py

@@ -1,134 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-
-from __future__ import annotations
-
-from typing import Any, Optional, Sequence, Type, Union
-
-from pydantic import BaseModel
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class DeepSeekConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    DeepSeek API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): Controls the diversity and focus of the
-            generated results. Higher values make the output more diverse,
-            while lower values make it more focused. (default: :obj:`1.0`)
-        response_format (object, optional): Specifies the format of the
-            returned content. The available values are `{"type": "text"}` or
-            `{"type": "json_object"}`. Setting it to `{"type": "json_object"}`
-            will output a standard JSON string.
-            (default: :obj:`{"type": "text"}`)
-        stream (bool, optional): If set, partial message deltas will be sent.
-            Tokens will be sent as data-only server-sent events (SSE) as
-            they become available, with the stream terminated by a
-            data: [DONE] message. (default: :obj:`False`)
-        stop (Union[str, list[str]], optional): Up to 16 sequences where
-            the API will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens that can
-            be generated in the chat completion. The total length of input
-            tokens and generated tokens is limited by the model's context
-            length. (default: :obj:`None`)
-        presence_penalty (float, optional): Number between -2.0 and 2.0.
-            Positive values penalize new tokens based on whether they
-            appear in the text so far, increasing the model's likelihood
-            to talk about new topics. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between -2.0 and 2.0.
-            Positive values penalize new tokens based on their existing
-            frequency in the text so far, decreasing the model's likelihood
-            to repeat the same line verbatim. (default: :obj:`0`)
-        tools (list[FunctionTool], optional): A list of tools the model may
-            call. Currently, only functions are supported as a tool. Use
-            this to provide a list of functions the model may generate JSON
-            inputs for. A max of 128 functions are supported.
-            (default: :obj:`None`)
-        tool_choice (Union[dict[str, str], str], optional): Controls which
-            (if any) tool is called by the model. "none" means the model
-            will not call any tool and instead generates a message. "auto"
-            means the model can pick between generating a message or calling
-            one or more tools. "required" means the model must call one or
-            more tools. Specifying a particular tool via
-            {"type": "function", "function": {"name": "my_function"}} forces
-            the model to call that tool. "none" is the default when no tools
-            are present. "auto" is the default if tools are present.
-            (default: :obj:`"auto"`)
-        logprobs (bool, optional): Whether to return log probabilities of
-            the output tokens or not. If true, returns the log probabilities
-            of each output token returned in the content of message.
-            (default: :obj:`False`)
-        top_logprobs (int, optional): An integer between 0 and 20 specifying
-            the number of most likely tokens to return at each token
-            position, each with an associated log probability. logprobs
-            must be set to true if this parameter is used.
-            (default: :obj:`None`)
-        include_usage (bool, optional): When streaming, specifies whether to
-            include usage information in `stream_options`. (default:
-            :obj:`True`)
-    """
-
-    temperature: float = 0.2  # deepseek default: 1.0
-    top_p: float = 1.0
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[Type[BaseModel], dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-    tool_choice: Optional[Union[dict[str, str], str]] = None
-    logprobs: bool = False
-    top_logprobs: Optional[int] = None
-
-    def __init__(self, include_usage: bool = True, **kwargs):
-        super().__init__(**kwargs)
-        # Only set stream_options when stream is True
-        # Otherwise, it will raise error when calling the API
-        if self.stream:
-            self.stream_options = {"include_usage": include_usage}
-
-    def as_dict(self) -> dict[str, Any]:
-        r"""Convert the current configuration to a dictionary.
-
-        This method converts the current configuration object to a dictionary
-        representation, which can be used for serialization or other purposes.
-
-        Returns:
-            dict[str, Any]: A dictionary representation of the current
-                configuration.
-        """
-        config_dict = self.model_dump()
-        if self.tools:
-            from camel.toolkits import FunctionTool
-
-            tools_schema = []
-            for tool in self.tools:
-                if not isinstance(tool, FunctionTool):
-                    raise ValueError(
-                        f"The tool {tool} should "
-                        "be an instance of `FunctionTool`."
-                    )
-                tools_schema.append(tool.get_openai_tool_schema())
-        config_dict["tools"] = NOT_GIVEN
-        return config_dict
-
-
-DEEPSEEK_API_PARAMS = {param for param in DeepSeekConfig.model_fields.keys()}

owl/camel/configs/gemini_config.py

@@ -1,114 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-
-from __future__ import annotations
-
-from typing import Any, Optional, Sequence, Type, Union
-
-from pydantic import BaseModel
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class GeminiConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Gemini API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        n (int, optional): How many chat completion choices to generate for
-            each input message. (default: :obj:`1`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        tools (list[FunctionTool], optional): A list of tools the model may
-            call. Currently, only functions are supported as a tool. Use this
-            to provide a list of functions the model may generate JSON inputs
-            for. A max of 128 functions are supported.
-        tool_choice (Union[dict[str, str], str], optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools.  :obj:`"required"` means the
-            model must call one or more tools. Specifying a particular tool
-            via {"type": "function", "function": {"name": "my_function"}}
-            forces the model to call that tool. :obj:`"none"` is the default
-            when no tools are present. :obj:`"auto"` is the default if tools
-            are present.
-    """
-
-    temperature: float = 0.2  # openai default: 1.0
-    top_p: float = 1.0
-    n: int = 1
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    response_format: Union[Type[BaseModel], dict, NotGiven] = NOT_GIVEN
-    tool_choice: Optional[Union[dict[str, str], str]] = None
-
-    def as_dict(self) -> dict[str, Any]:
-        r"""Convert the current configuration to a dictionary.
-
-        This method converts the current configuration object to a dictionary
-        representation, which can be used for serialization or other purposes.
-
-        Returns:
-            dict[str, Any]: A dictionary representation of the current
-                configuration.
-        """
-        config_dict = self.model_dump()
-        if self.tools:
-            from camel.toolkits import FunctionTool
-
-            tools_schema = []
-            for tool in self.tools:
-                if not isinstance(tool, FunctionTool):
-                    raise ValueError(
-                        f"The tool {tool} should "
-                        "be an instance of `FunctionTool`."
-                    )
-                tools_schema.append(tool.get_openai_tool_schema())
-        config_dict["tools"] = NOT_GIVEN
-        return config_dict
-
-
-Gemini_API_PARAMS = {param for param in GeminiConfig.model_fields.keys()}

owl/camel/configs/groq_config.py

@@ -1,104 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Optional, Sequence, Union
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class GroqConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using OpenAI
-    compatibility.
-
-    Reference: https://console.groq.com/docs/openai
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        n (int, optional): How many chat completion choices to generate for
-            each input message. (default: :obj:`1`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-        user (str, optional): A unique identifier representing your end-user,
-            which can help OpenAI to monitor and detect abuse.
-            (default: :obj:`""`)
-        tools (list[FunctionTool], optional): A list of tools the model may
-            call. Currently, only functions are supported as a tool. Use this
-            to provide a list of functions the model may generate JSON inputs
-            for. A max of 128 functions are supported.
-        tool_choice (Union[dict[str, str], str], optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools.  :obj:`"required"` means the
-            model must call one or more tools. Specifying a particular tool
-            via {"type": "function", "function": {"name": "my_function"}}
-            forces the model to call that tool. :obj:`"none"` is the default
-            when no tools are present. :obj:`"auto"` is the default if tools
-            are present.
-    """
-
-    temperature: float = 0.2  # openai default: 1.0
-    top_p: float = 1.0
-    n: int = 1
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-    user: str = ""
-    tool_choice: Optional[Union[dict[str, str], str]] = "auto"
-
-
-GROQ_API_PARAMS = {param for param in GroqConfig.model_fields.keys()}

owl/camel/configs/litellm_config.py

@@ -1,97 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import List, Optional, Union
-
-from camel.configs.base_config import BaseConfig
-
-
-class LiteLLMConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    LiteLLM API.
-
-    Args:
-        timeout (Optional[Union[float, str]], optional): Request timeout.
-            (default: None)
-        temperature (Optional[float], optional): Temperature parameter for
-            controlling randomness. (default: None)
-        top_p (Optional[float], optional): Top-p parameter for nucleus
-            sampling. (default: None)
-        n (Optional[int], optional): Number of completions to generate.
-            (default: None)
-        stream (Optional[bool], optional): Whether to return a streaming
-            response. (default: None)
-        stream_options (Optional[dict], optional): Options for the streaming
-            response. (default: None)
-        stop (Optional[Union[str, List[str]]], optional): Sequences where the
-            API will stop generating further tokens. (default: None)
-        max_tokens (Optional[int], optional): Maximum number of tokens to
-            generate. (default: None)
-        presence_penalty (Optional[float], optional): Penalize new tokens
-            based on their existence in the text so far. (default: None)
-        frequency_penalty (Optional[float], optional): Penalize new tokens
-            based on their frequency in the text so far. (default: None)
-        logit_bias (Optional[dict], optional): Modify the probability of
-            specific tokens appearing in the completion. (default: None)
-        user (Optional[str], optional): A unique identifier representing the
-            end-user. (default: None)
-        response_format (Optional[dict], optional): Response format
-            parameters. (default: None)
-        seed (Optional[int], optional): Random seed. (default: None)
-        tools (Optional[List], optional): List of tools. (default: None)
-        tool_choice (Optional[Union[str, dict]], optional): Tool choice
-            parameters. (default: None)
-        logprobs (Optional[bool], optional): Whether to return log
-            probabilities of the output tokens. (default: None)
-        top_logprobs (Optional[int], optional): Number of most likely tokens
-            to return at each token position. (default: None)
-        deployment_id (Optional[str], optional): Deployment ID. (default: None)
-        extra_headers (Optional[dict], optional): Additional headers for the
-            request. (default: None)
-        api_version (Optional[str], optional): API version. (default: None)
-        mock_response (Optional[str], optional): Mock completion response for
-            testing or debugging. (default: None)
-        custom_llm_provider (Optional[str], optional): Non-OpenAI LLM
-            provider. (default: None)
-        max_retries (Optional[int], optional): Maximum number of retries.
-            (default: None)
-    """
-
-    timeout: Optional[Union[float, str]] = None
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
-    n: Optional[int] = None
-    stream: Optional[bool] = None
-    stream_options: Optional[dict] = None
-    stop: Optional[Union[str, List[str]]] = None
-    max_tokens: Optional[int] = None
-    presence_penalty: Optional[float] = None
-    frequency_penalty: Optional[float] = None
-    logit_bias: Optional[dict] = None
-    user: Optional[str] = None
-    response_format: Optional[dict] = None
-    seed: Optional[int] = None
-    tool_choice: Optional[Union[str, dict]] = None
-    logprobs: Optional[bool] = None
-    top_logprobs: Optional[int] = None
-    deployment_id: Optional[str] = None
-    extra_headers: Optional[dict] = None
-    api_version: Optional[str] = None
-    mock_response: Optional[str] = None
-    custom_llm_provider: Optional[str] = None
-    max_retries: Optional[int] = None
-
-
-LITELLM_API_PARAMS = {param for param in LiteLLMConfig.model_fields.keys()}

owl/camel/configs/mistral_config.py

@@ -1,79 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Any, Dict, Optional, Union
-
-from pydantic import field_validator
-
-from camel.configs.base_config import BaseConfig
-
-
-class MistralConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Mistral API.
-
-    reference: https://github.com/mistralai/client-python/blob/9d238f88c41689821d7b08570f13b43426f97fd6/src/mistralai/client.py#L195
-
-    #TODO: Support stream mode
-
-    Args:
-        temperature (Optional[float], optional): temperature the temperature
-            to use for sampling, e.g. 0.5.
-        top_p (Optional[float], optional): the cumulative probability of
-            tokens to generate, e.g. 0.9. Defaults to None.
-        max_tokens (Optional[int], optional): the maximum number of tokens to
-            generate, e.g. 100. Defaults to None.
-        stop (Optional[Union[str,list[str]]]): Stop generation if this token
-            is detected. Or if one of these tokens is detected when providing
-            a string list.
-        random_seed (Optional[int], optional): the random seed to use for
-            sampling, e.g. 42. Defaults to None.
-        safe_prompt (bool, optional): whether to use safe prompt, e.g. true.
-            Defaults to False.
-        response_format (Union[Dict[str, str], ResponseFormat): format of the
-            response.
-        tool_choice (str, optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools.  :obj:`"any"` means the
-            model must call one or more tools. :obj:`"auto"` is the default
-            value.
-    """
-
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
-    max_tokens: Optional[int] = None
-    stop: Optional[Union[str, list[str]]] = None
-    random_seed: Optional[int] = None
-    safe_prompt: bool = False
-    response_format: Optional[Union[Dict[str, str], Any]] = None
-    tool_choice: Optional[str] = "auto"
-
-    @field_validator("response_format", mode="before")
-    @classmethod
-    def fields_type_checking(cls, response_format):
-        if response_format and not isinstance(response_format, dict):
-            from mistralai.models import ResponseFormat
-
-            if not isinstance(response_format, ResponseFormat):
-                raise ValueError(
-                    f"The tool {response_format} should be an instance "
-                    "of `mistralai.models.ResponseFormat`."
-                )
-        return response_format
-
-
-MISTRAL_API_PARAMS = {param for param in MistralConfig().model_fields.keys()}

owl/camel/configs/nvidia_config.py

@@ -1,70 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import List, Optional, Union
-
-from pydantic import Field
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class NvidiaConfig(BaseConfig):
-    r"""Configuration class for NVIDIA API models.
-
-    This class defines the configuration parameters for NVIDIA's language
-    models, including temperature, sampling parameters, and response format
-    settings.
-
-    Args:
-        stream (bool, optional): Whether to stream the response.
-            (default: :obj:`False`)
-        temperature (float, optional): Controls randomness in the response.
-            Higher values make output more random, lower values make it more
-            deterministic. Range: [0.0, 2.0]. (default: :obj:`0.7`)
-        top_p (float, optional): Controls diversity via nucleus sampling.
-            Range: [0.0, 1.0]. (default: :obj:`0.95`)
-        presence_penalty (float, optional): Penalizes new tokens based on
-            whether they appear in the text so far. Range: [-2.0, 2.0].
-            (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Penalizes new tokens based on
-            their frequency in the text so far. Range: [-2.0, 2.0].
-            (default: :obj:`0.0`)
-        max_tokens (Union[int, NotGiven], optional): Maximum number of tokens
-            to generate. If not provided, model will use its default maximum.
-            (default: :obj:`NOT_GIVEN`)
-        seed (Optional[int], optional): Random seed for deterministic sampling.
-            (default: :obj:`None`)
-        tools (Optional[List[Dict]], optional): List of tools available to the
-            model. This includes tools such as a text editor, a calculator, or
-            a search engine. (default: :obj:`None`)
-        tool_choice (Optional[str], optional): Tool choice configuration.
-            (default: :obj:`None`)
-        stop (Optional[List[str]], optional): List of stop sequences.
-            (default: :obj:`None`)
-    """
-
-    stream: bool = Field(default=False)
-    temperature: float = Field(default=0.7)
-    top_p: float = Field(default=0.95)
-    presence_penalty: float = Field(default=0.0)
-    frequency_penalty: float = Field(default=0.0)
-    max_tokens: Union[int, NotGiven] = Field(default=NOT_GIVEN)
-    seed: Optional[int] = Field(default=None)
-    tool_choice: Optional[str] = Field(default=None)
-    stop: Optional[List[str]] = Field(default=None)
-
-
-NVIDIA_API_PARAMS = {param for param in NvidiaConfig.model_fields.keys()}

owl/camel/configs/ollama_config.py

@@ -1,82 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Sequence, Union
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class OllamaConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using OpenAI
-    compatibility
-
-    Reference: https://github.com/ollama/ollama/blob/main/docs/openai.md
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-    """
-
-    temperature: float = 0.2
-    top_p: float = 1.0
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-
-
-OLLAMA_API_PARAMS = {param for param in OllamaConfig.model_fields.keys()}

owl/camel/configs/openai_config.py

@@ -1,139 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Any, Optional, Sequence, Type, Union
-
-from pydantic import BaseModel, Field
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class ChatGPTConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    OpenAI API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        n (int, optional): How many chat completion choices to generate for
-            each input message. (default: :obj:`1`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-        logit_bias (dict, optional): Modify the likelihood of specified tokens
-            appearing in the completion. Accepts a json object that maps tokens
-            (specified by their token ID in the tokenizer) to an associated
-            bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias
-            is added to the logits generated by the model prior to sampling.
-            The exact effect will vary per model, but values between:obj:` -1`
-            and :obj:`1` should decrease or increase likelihood of selection;
-            values like :obj:`-100` or :obj:`100` should result in a ban or
-            exclusive selection of the relevant token. (default: :obj:`{}`)
-        user (str, optional): A unique identifier representing your end-user,
-            which can help OpenAI to monitor and detect abuse.
-            (default: :obj:`""`)
-        tools (list[FunctionTool], optional): A list of tools the model may
-            call. Currently, only functions are supported as a tool. Use this
-            to provide a list of functions the model may generate JSON inputs
-            for. A max of 128 functions are supported.
-        tool_choice (Union[dict[str, str], str], optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools.  :obj:`"required"` means the
-            model must call one or more tools. Specifying a particular tool
-            via {"type": "function", "function": {"name": "my_function"}}
-            forces the model to call that tool. :obj:`"none"` is the default
-            when no tools are present. :obj:`"auto"` is the default if tools
-            are present.
-    """
-
-    temperature: float = 0.2  # openai default: 1.0
-    top_p: float = 1.0
-    n: int = 1
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[Type[BaseModel], dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-    logit_bias: dict = Field(default_factory=dict)
-    user: str = ""
-    tool_choice: Optional[Union[dict[str, str], str]] = None
-
-    def as_dict(self) -> dict[str, Any]:
-        r"""Convert the current configuration to a dictionary.
-
-        This method converts the current configuration object to a dictionary
-        representation, which can be used for serialization or other purposes.
-
-        Returns:
-            dict[str, Any]: A dictionary representation of the current
-                configuration.
-        """
-        config_dict = self.model_dump()
-        if self.tools:
-            from camel.toolkits import FunctionTool
-
-            tools_schema = []
-            for tool in self.tools:
-                if not isinstance(tool, FunctionTool):
-                    raise ValueError(
-                        f"The tool {tool} should "
-                        "be an instance of `FunctionTool`."
-                    )
-                tools_schema.append(tool.get_openai_tool_schema())
-        config_dict["tools"] = NOT_GIVEN
-        return config_dict
-
-
-OPENAI_API_PARAMS = {param for param in ChatGPTConfig.model_fields.keys()}

owl/camel/configs/qwen_config.py

@@ -1,91 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import ClassVar, Optional, Union
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class QwenConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Qwen API. You can refer to the following link for more details:
-    https://help.aliyun.com/zh/model-studio/developer-reference/use-qwen-by-calling-api
-
-    Args:
-        stream (bool, optional): Whether to stream the response.
-            (default: :obj:`False`)
-        temperature (float, optional): Controls the diversity and focus of
-            the generated results. Lower values make the output more focused,
-            while higher values make it more diverse. (default: :obj:`0.3`)
-        top_p (float, optional): Controls the diversity and focus of the
-            generated results. Higher values make the output more diverse,
-            while lower values make it more focused. (default: :obj:`0.9`)
-        presence_penalty (float, optional): Controls the repetition of
-            content in the generated results. Positive values reduce the
-            repetition of content, while negative values increase it.
-            (default: :obj:`0.0`)
-        response_format (object, optional): Specifies the format of the
-            returned content. The available values are `{"type": "text"}` or
-            `{"type": "json_object"}`. Setting it to `{"type": "json_object"}`
-            will output a standard JSON string.
-            (default: :obj:`{"type": "text"}`)
-        max_tokens (Union[int, NotGiven], optional): Allows the model to
-            generate the maximum number of tokens.
-            (default: :obj:`NOT_GIVEN`)
-        seed (int, optional): Sets the seed parameter to make the text
-            generation process more deterministic, typically used to ensure
-            that the results are consistent across model runs. By passing the
-            same seed value (specified by you) in each model call while
-            keeping other parameters unchanged, the model is likely to return
-            the same result.
-            (default: :obj:`None`)
-        stop (str or list, optional): Using the stop parameter, the model will
-            automatically stop generating text when it is about to include the
-            specified string or token_id. You can use the stop parameter to
-            control the output of the model by passing sensitive words.
-            (default: :obj:`None`)
-        tools (list, optional): Specifies an array of tools that the model can
-            call. It can contain one or more tool objects. During a function
-            call process, the model will select one tool from the array.
-            (default: :obj:`None`)
-        extra_body (dict, optional): Additional parameters to be sent to the
-            Qwen API. If you want to enable internet search, you can set this
-            parameter to `{"enable_search": True}`.
-            (default: :obj:`{"enable_search": False}`)
-        include_usage (bool, optional): When streaming, specifies whether to
-            include usage information in `stream_options`. (default:
-            :obj:`True`)
-    """
-
-    stream: bool = False
-    temperature: float = 0.3
-    top_p: float = 0.9
-    presence_penalty: float = 0.0
-    response_format: ClassVar[dict] = {"type": "text"}
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    seed: Optional[int] = None
-    stop: Optional[Union[str, list]] = None
-    extra_body: ClassVar[dict] = {"enable_search": False}
-
-    def __init__(self, include_usage: bool = True, **kwargs):
-        super().__init__(**kwargs)
-        # Only set stream_options when stream is True
-        # Otherwise, it will raise error when calling the API
-        if self.stream:
-            self.stream_options = {"include_usage": include_usage}
-
-
-QWEN_API_PARAMS = {param for param in QwenConfig.model_fields.keys()}

owl/camel/configs/reka_config.py

@@ -1,74 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Any, Optional, Union
-
-from camel.configs.base_config import BaseConfig
-
-
-class RekaConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Reka API.
-
-    Reference: https://docs.reka.ai/api-reference/chat/create
-
-    Args:
-        temperature (Optional[float], optional): temperature the temperature
-            to use for sampling, e.g. 0.5.
-        top_p (Optional[float], optional): the cumulative probability of
-            tokens to generate, e.g. 0.9. Defaults to None.
-        top_k (Optional[int], optional): Parameter which forces the model to
-            only consider the tokens with the `top_k` highest probabilities at
-            the next step. Defaults to 1024.
-        max_tokens (Optional[int], optional): the maximum number of tokens to
-            generate, e.g. 100. Defaults to None.
-        stop (Optional[Union[str,list[str]]]): Stop generation if this token
-            is detected. Or if one of these tokens is detected when providing
-            a string list.
-        seed (Optional[int], optional): the random seed to use for sampling, e.
-            g. 42. Defaults to None.
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-        use_search_engine (Optional[bool]): Whether to consider using search
-            engine to complete the request. Note that even if this is set to
-            `True`, the model might decide to not use search.
-    """
-
-    temperature: Optional[float] = None
-    top_p: Optional[float] = None
-    top_k: Optional[int] = None
-    max_tokens: Optional[int] = None
-    stop: Optional[Union[str, list[str]]] = None
-    seed: Optional[int] = None
-    frequency_penalty: float = 0.0
-    presence_penalty: float = 0.0
-    use_search_engine: Optional[bool] = False
-
-    def as_dict(self) -> dict[str, Any]:
-        config_dict = super().as_dict()
-        if "tools" in config_dict:
-            del config_dict["tools"]  # Reka does not support tool calling
-        return config_dict
-
-
-REKA_API_PARAMS = {param for param in RekaConfig().model_fields.keys()}

owl/camel/configs/samba_config.py

@@ -1,170 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Any, Optional, Sequence, Union
-
-from pydantic import Field
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class SambaVerseAPIConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    SambaVerse API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.7`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`0.95`)
-        top_k (int, optional): Only sample from the top K options for each
-            subsequent token. Used to remove "long tail" low probability
-            responses.
-            (default: :obj:`50`)
-        max_tokens (Optional[int], optional): The maximum number of tokens to
-            generate, e.g. 100.
-            (default: :obj:`2048`)
-        repetition_penalty (Optional[float], optional): The parameter for
-            repetition penalty. 1.0 means no penalty.
-            (default: :obj:`1.0`)
-        stop (Optional[Union[str,list[str]]]): Stop generation if this token
-            is detected. Or if one of these tokens is detected when providing
-            a string list.
-            (default: :obj:`""`)
-        stream (Optional[bool]): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            Currently SambaVerse API doesn't support stream mode.
-            (default: :obj:`False`)
-    """
-
-    temperature: Optional[float] = 0.7
-    top_p: Optional[float] = 0.95
-    top_k: Optional[int] = 50
-    max_tokens: Optional[int] = 2048
-    repetition_penalty: Optional[float] = 1.0
-    stop: Optional[Union[str, list[str]]] = ""
-    stream: Optional[bool] = False
-
-    def as_dict(self) -> dict[str, Any]:
-        config_dict = super().as_dict()
-        if "tools" in config_dict:
-            del config_dict["tools"]  # SambaNova does not support tool calling
-        return config_dict
-
-
-SAMBA_VERSE_API_PARAMS = {
-    param for param in SambaVerseAPIConfig().model_fields.keys()
-}
-
-
-class SambaCloudAPIConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    OpenAI API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        n (int, optional): How many chat completion choices to generate for
-            each input message. (default: :obj:`1`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-        logit_bias (dict, optional): Modify the likelihood of specified tokens
-            appearing in the completion. Accepts a json object that maps tokens
-            (specified by their token ID in the tokenizer) to an associated
-            bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias
-            is added to the logits generated by the model prior to sampling.
-            The exact effect will vary per model, but values between:obj:` -1`
-            and :obj:`1` should decrease or increase likelihood of selection;
-            values like :obj:`-100` or :obj:`100` should result in a ban or
-            exclusive selection of the relevant token. (default: :obj:`{}`)
-        user (str, optional): A unique identifier representing your end-user,
-            which can help OpenAI to monitor and detect abuse.
-            (default: :obj:`""`)
-        tools (list[FunctionTool], optional): A list of tools the model may
-            call. Currently, only functions are supported as a tool. Use this
-            to provide a list of functions the model may generate JSON inputs
-            for. A max of 128 functions are supported.
-        tool_choice (Union[dict[str, str], str], optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools.  :obj:`"required"` means the
-            model must call one or more tools. Specifying a particular tool
-            via {"type": "function", "function": {"name": "my_function"}}
-            forces the model to call that tool. :obj:`"none"` is the default
-            when no tools are present. :obj:`"auto"` is the default if tools
-            are present.
-    """
-
-    temperature: float = 0.2  # openai default: 1.0
-    top_p: float = 1.0
-    n: int = 1
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-    logit_bias: dict = Field(default_factory=dict)
-    user: str = ""
-    tool_choice: Optional[Union[dict[str, str], str]] = None
-
-
-SAMBA_CLOUD_API_PARAMS = {
-    param for param in SambaCloudAPIConfig().model_fields.keys()
-}

owl/camel/configs/togetherai_config.py

@@ -1,107 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Any, Sequence, Union
-
-from pydantic import Field
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class TogetherAIConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    OpenAI API.
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        n (int, optional): How many chat completion choices to generate for
-            each input message. (default: :obj:`1`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-        logit_bias (dict, optional): Modify the likelihood of specified tokens
-            appearing in the completion. Accepts a json object that maps tokens
-            (specified by their token ID in the tokenizer) to an associated
-            bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias
-            is added to the logits generated by the model prior to sampling.
-            The exact effect will vary per model, but values between:obj:` -1`
-            and :obj:`1` should decrease or increase likelihood of selection;
-            values like :obj:`-100` or :obj:`100` should result in a ban or
-            exclusive selection of the relevant token. (default: :obj:`{}`)
-        user (str, optional): A unique identifier representing your end-user,
-            which can help OpenAI to monitor and detect abuse.
-            (default: :obj:`""`)
-    """
-
-    temperature: float = 0.2  # openai default: 1.0
-    top_p: float = 1.0
-    n: int = 1
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-    logit_bias: dict = Field(default_factory=dict)
-    user: str = ""
-
-    def as_dict(self) -> dict[str, Any]:
-        config_dict = super().as_dict()
-        if "tools" in config_dict:
-            del config_dict["tools"]  # Currently does not support tool calling
-        return config_dict
-
-
-TOGETHERAI_API_PARAMS = {
-    param for param in TogetherAIConfig.model_fields.keys()
-}

owl/camel/configs/vllm_config.py

@@ -1,111 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Optional, Sequence, Union
-
-from pydantic import Field
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-# flake8: noqa: E501
-class VLLMConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    OpenAI API.
-
-    Reference: https://docs.vllm.ai/en/latest/serving/openai_compatible_server.html
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`1.0`)
-        n (int, optional): How many chat completion choices to generate for
-            each input message. (default: :obj:`1`)
-        response_format (object, optional): An object specifying the format
-            that the model must output. Compatible with GPT-4 Turbo and all
-            GPT-3.5 Turbo models newer than gpt-3.5-turbo-1106. Setting to
-            {"type": "json_object"} enables JSON mode, which guarantees the
-            message the model generates is valid JSON. Important: when using
-            JSON mode, you must also instruct the model to produce JSON
-            yourself via a system or user message. Without this, the model
-            may generate an unending stream of whitespace until the generation
-            reaches the token limit, resulting in a long-running and seemingly
-            "stuck" request. Also note that the message content may be
-            partially cut off if finish_reason="length", which indicates the
-            generation exceeded max_tokens or the conversation exceeded the
-            max context length.
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        presence_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on whether
-            they appear in the text so far, increasing the model's likelihood
-            to talk about new topics. See more information about frequency and
-            presence penalties. (default: :obj:`0.0`)
-        frequency_penalty (float, optional): Number between :obj:`-2.0` and
-            :obj:`2.0`. Positive values penalize new tokens based on their
-            existing frequency in the text so far, decreasing the model's
-            likelihood to repeat the same line verbatim. See more information
-            about frequency and presence penalties. (default: :obj:`0.0`)
-        logit_bias (dict, optional): Modify the likelihood of specified tokens
-            appearing in the completion. Accepts a json object that maps tokens
-            (specified by their token ID in the tokenizer) to an associated
-            bias value from :obj:`-100` to :obj:`100`. Mathematically, the bias
-            is added to the logits generated by the model prior to sampling.
-            The exact effect will vary per model, but values between:obj:` -1`
-            and :obj:`1` should decrease or increase likelihood of selection;
-            values like :obj:`-100` or :obj:`100` should result in a ban or
-            exclusive selection of the relevant token. (default: :obj:`{}`)
-        user (str, optional): A unique identifier representing your end-user,
-            which can help OpenAI to monitor and detect abuse.
-            (default: :obj:`""`)
-        logprobs: Whether to return log probabilities of the output tokens or
-            not. If true, returns the log probabilities of each output token
-            returned in the `logits` of `message`. (default: :obj:`None`)
-        top_logprobs: An integer between 0 and 20 specifying the number of
-            most likely tokens to return at each token position, each with an
-            associated log probability. `logprobs` must be set to `true` if
-            this parameter is used. (default: :obj:`None`)
-    """
-
-    temperature: float = 0.2  # openai default: 1.0
-    top_p: float = 1.0
-    n: int = 1
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    presence_penalty: float = 0.0
-    response_format: Union[dict, NotGiven] = NOT_GIVEN
-    frequency_penalty: float = 0.0
-    logit_bias: dict = Field(default_factory=dict)
-    user: str = ""
-    logprobs: Optional[bool] = None
-    top_logprobs: Optional[int] = None
-
-
-VLLM_API_PARAMS = {param for param in VLLMConfig.model_fields.keys()}

owl/camel/configs/yi_config.py

@@ -1,58 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Optional, Union
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class YiConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using the
-    Yi API. You can refer to the following link for more details:
-    https://platform.lingyiwanwu.com/docs/api-reference
-
-    Args:
-        tool_choice (Union[dict[str, str], str], optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools. :obj:`"required"` or
-            specifying a particular tool via
-            {"type": "function", "function": {"name": "some_function"}}
-            can be used to guide the model to use tools more strongly.
-            (default: :obj:`None`)
-        max_tokens (int, optional): Specifies the maximum number of tokens
-            the model can generate. This sets an upper limit, but does not
-            guarantee that this number will always be reached.
-            (default: :obj:`5000`)
-        top_p (float, optional): Controls the randomness of the generated
-            results. Lower values lead to less randomness, while higher
-            values increase randomness. (default: :obj:`0.9`)
-        temperature (float, optional): Controls the diversity and focus of
-            the generated results. Lower values make the output more focused,
-            while higher values make it more diverse. (default: :obj:`0.3`)
-        stream (bool, optional): If True, enables streaming output.
-            (default: :obj:`False`)
-    """
-
-    tool_choice: Optional[Union[dict[str, str], str]] = None
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    top_p: float = 0.9
-    temperature: float = 0.3
-    stream: bool = False
-
-
-YI_API_PARAMS = {param for param in YiConfig.model_fields.keys()}

owl/camel/configs/zhipuai_config.py

@@ -1,71 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from __future__ import annotations
-
-from typing import Optional, Sequence, Union
-
-from camel.configs.base_config import BaseConfig
-from camel.types import NOT_GIVEN, NotGiven
-
-
-class ZhipuAIConfig(BaseConfig):
-    r"""Defines the parameters for generating chat completions using OpenAI
-    compatibility
-
-    Reference: https://open.bigmodel.cn/dev/api#glm-4v
-
-    Args:
-        temperature (float, optional): Sampling temperature to use, between
-            :obj:`0` and :obj:`2`. Higher values make the output more random,
-            while lower values make it more focused and deterministic.
-            (default: :obj:`0.2`)
-        top_p (float, optional): An alternative to sampling with temperature,
-            called nucleus sampling, where the model considers the results of
-            the tokens with top_p probability mass. So :obj:`0.1` means only
-            the tokens comprising the top 10% probability mass are considered.
-            (default: :obj:`0.6`)
-        stream (bool, optional): If True, partial message deltas will be sent
-            as data-only server-sent events as they become available.
-            (default: :obj:`False`)
-        stop (str or list, optional): Up to :obj:`4` sequences where the API
-            will stop generating further tokens. (default: :obj:`None`)
-        max_tokens (int, optional): The maximum number of tokens to generate
-            in the chat completion. The total length of input tokens and
-            generated tokens is limited by the model's context length.
-            (default: :obj:`None`)
-        tools (list[FunctionTool], optional): A list of tools the model may
-            call. Currently, only functions are supported as a tool. Use this
-            to provide a list of functions the model may generate JSON inputs
-            for. A max of 128 functions are supported.
-        tool_choice (Union[dict[str, str], str], optional): Controls which (if
-            any) tool is called by the model. :obj:`"none"` means the model
-            will not call any tool and instead generates a message.
-            :obj:`"auto"` means the model can pick between generating a
-            message or calling one or more tools.  :obj:`"required"` means the
-            model must call one or more tools. Specifying a particular tool
-            via {"type": "function", "function": {"name": "my_function"}}
-            forces the model to call that tool. :obj:`"none"` is the default
-            when no tools are present. :obj:`"auto"` is the default if tools
-            are present.
-    """
-
-    temperature: float = 0.2
-    top_p: float = 0.6
-    stream: bool = False
-    stop: Union[str, Sequence[str], NotGiven] = NOT_GIVEN
-    max_tokens: Union[int, NotGiven] = NOT_GIVEN
-    tool_choice: Optional[Union[dict[str, str], str]] = None
-
-
-ZHIPUAI_API_PARAMS = {param for param in ZhipuAIConfig.model_fields.keys()}

owl/camel/datahubs/__init__.py

@@ -1,23 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-
-from .base import BaseDatasetManager
-from .huggingface import HuggingFaceDatasetManager
-from .models import Record
-
-__all__ = [
-    "BaseDatasetManager",
-    "Record",
-    "HuggingFaceDatasetManager",
-]

owl/camel/datahubs/base.py

@@ -1,136 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from abc import ABC, abstractmethod
-from typing import Any, List
-
-from camel.datahubs.models import Record
-
-
-class BaseDatasetManager(ABC):
-    r"""Abstract base class for dataset managers."""
-
-    @abstractmethod
-    def create_dataset(self, name: str, **kwargs: Any) -> str:
-        r"""Creates a new dataset.
-
-        Args:
-            name (str): The name of the dataset.
-            kwargs (Any): Additional keyword arguments.
-
-        Returns:
-            str: The URL of the created dataset.
-        """
-        pass
-
-    @abstractmethod
-    def list_datasets(
-        self, username: str, limit: int = 100, **kwargs: Any
-    ) -> List[str]:
-        r"""Lists all datasets for the current user.
-
-        Args:
-            username (str): The username of the user whose datasets to list.
-            limit (int): The maximum number of datasets to list.
-                (default::obj:`100`)
-            kwargs (Any): Additional keyword arguments.
-
-        Returns:
-            List[str]: A list of dataset ids.
-        """
-        pass
-
-    @abstractmethod
-    def delete_dataset(self, dataset_name: str, **kwargs: Any) -> None:
-        r"""Deletes a dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset to delete.
-            kwargs (Any): Additional keyword arguments.
-        """
-        pass
-
-    @abstractmethod
-    def add_records(
-        self,
-        dataset_name: str,
-        records: List[Record],
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> None:
-        r"""Adds records to a dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            records (List[Record]): A list of records to add to the dataset.
-            filepath (str): The path to the file containing the records.
-                (default::obj:`"records/records.json"`)
-            kwargs (Any): Additional keyword arguments.
-        """
-        pass
-
-    @abstractmethod
-    def update_records(
-        self,
-        dataset_name: str,
-        records: List[Record],
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> None:
-        r"""Updates records in a dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            records (List[Record]): A list of records to update in the dataset.
-            filepath (str): The path to the file containing the records.
-                (default::obj:`"records/records.json"`)
-            kwargs (Any): Additional keyword arguments.
-        """
-        pass
-
-    @abstractmethod
-    def list_records(
-        self,
-        dataset_name: str,
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> List[Record]:
-        r"""Lists records in a dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            filepath (str): The path to the file containing the records.
-                (default::obj:`"records/records.json"`)
-            kwargs (Any): Additional keyword arguments.
-        """
-        pass
-
-    # New method for record deletion
-    @abstractmethod
-    def delete_record(
-        self,
-        dataset_name: str,
-        record_id: str,
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> None:
-        r"""Deletes a record from the dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            record_id (str): The ID of the record to delete.
-            filepath (str): The path to the file containing the records.
-                (default::obj:`"records/records.json"`)
-            kwargs (Any): Additional keyword arguments.
-        """
-        pass

owl/camel/datahubs/huggingface.py

@@ -1,433 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-import json
-import os
-import tempfile
-from typing import Any, List, Optional
-
-from camel.datahubs.base import BaseDatasetManager
-from camel.datahubs.models import Record
-from camel.logger import get_logger
-from camel.types import HuggingFaceRepoType
-from camel.utils import api_keys_required, dependencies_required
-
-logger = get_logger(__name__)
-
-
-class HuggingFaceDatasetManager(BaseDatasetManager):
-    r"""A dataset manager for Hugging Face datasets. This class provides
-    methods to create, add, update, delete, and list records in a dataset on
-    the Hugging Face Hub.
-
-    Args:
-        token (str): The Hugging Face API token. If not provided, the token
-            will be read from the environment variable `HUGGING_FACE_TOKEN`.
-    """
-
-    @api_keys_required("HUGGING_FACE_TOKEN")
-    @dependencies_required('huggingface_hub')
-    def __init__(self, token: Optional[str] = None):
-        from huggingface_hub import HfApi
-
-        self._api_key = token or os.getenv("HUGGING_FACE_TOKEN")
-        self.api = HfApi(token=self._api_key)
-
-    def create_dataset_card(
-        self,
-        dataset_name: str,
-        description: str,
-        license: Optional[str] = None,
-        version: Optional[str] = None,
-        tags: Optional[List[str]] = None,
-        authors: Optional[List[str]] = None,
-        size_category: Optional[List[str]] = None,
-        language: Optional[List[str]] = None,
-        task_categories: Optional[List[str]] = None,
-        content: Optional[str] = None,
-    ) -> None:
-        r"""Creates and uploads a dataset card to the Hugging Face Hub in YAML
-            format.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            description (str): A description of the dataset.
-            license (str): The license of the dataset. (default: :obj:`None`)
-            version (str): The version of the dataset. (default: :obj:`None`)
-            tags (list): A list of tags for the dataset.(default: :obj:`None`)
-            authors (list): A list of authors of the dataset. (default:
-                :obj:`None`)
-            size_category (list): A size category for the dataset. (default:
-                :obj:`None`)
-            language (list): A list of languages the dataset is in. (default:
-                :obj:`None`)
-            task_categories (list): A list of task categories. (default:
-                :obj:`None`)
-            content (str): Custom markdown content that the user wants to add
-                to the dataset card. (default: :obj:`None`)
-        """
-        import yaml
-
-        metadata = {
-            "license": license,
-            "authors": authors,
-            "task_categories": task_categories,
-            "language": language,
-            "tags": tags,
-            "pretty_name": dataset_name,
-            "size_categories": size_category,
-            "version": version,
-            "description": description,
-        }
-
-        # Remove keys with None values
-        metadata = {k: v for k, v in metadata.items() if v}
-
-        card_content = (
-            "---\n"
-            + yaml.dump(metadata, default_flow_style=False, allow_unicode=True)
-            + "\n---"
-        )
-
-        if content:
-            card_content += f"\n\n# Additional Information\n{content}\n"
-
-        self._upload_file(
-            file_content=card_content,
-            dataset_name=dataset_name,
-            filepath="README.md",
-            file_type="md",
-        )
-
-    def create_dataset(
-        self, name: str, private: bool = False, **kwargs: Any
-    ) -> str:
-        r"""Creates a new dataset on the Hugging Face Hub.
-
-        Args:
-            name (str): The name of the dataset.
-            private (bool): Whether the dataset should be private. defaults to
-                False.
-            kwargs (Any): Additional keyword arguments.
-
-        Returns:
-            str: The URL of the created dataset.
-        """
-        from huggingface_hub.errors import RepositoryNotFoundError
-
-        try:
-            self.api.repo_info(
-                repo_id=name,
-                repo_type=HuggingFaceRepoType.DATASET.value,
-                **kwargs,
-            )
-        except RepositoryNotFoundError:
-            self.api.create_repo(
-                repo_id=name,
-                repo_type=HuggingFaceRepoType.DATASET.value,
-                private=private,
-            )
-
-        return f"https://huggingface.co/datasets/{name}"
-
-    def list_datasets(
-        self, username: str, limit: int = 100, **kwargs: Any
-    ) -> List[str]:
-        r"""Lists all datasets for the current user.
-
-        Args:
-            username (str): The username of the user whose datasets to list.
-            limit (int): The maximum number of datasets to list.
-                (default: :obj:`100`)
-            kwargs (Any): Additional keyword arguments.
-
-        Returns:
-            List[str]: A list of dataset ids.
-        """
-        try:
-            return [
-                dataset.id
-                for dataset in self.api.list_datasets(
-                    author=username, limit=limit, **kwargs
-                )
-            ]
-        except Exception as e:
-            logger.error(f"Error listing datasets: {e}")
-            return []
-
-    def delete_dataset(self, dataset_name: str, **kwargs: Any) -> None:
-        r"""Deletes a dataset from the Hugging Face Hub.
-
-        Args:
-            dataset_name (str): The name of the dataset to delete.
-            kwargs (Any): Additional keyword arguments.
-        """
-        try:
-            self.api.delete_repo(
-                repo_id=dataset_name,
-                repo_type=HuggingFaceRepoType.DATASET.value,
-                **kwargs,
-            )
-            logger.info(f"Dataset '{dataset_name}' deleted successfully.")
-        except Exception as e:
-            logger.error(f"Error deleting dataset '{dataset_name}': {e}")
-            raise
-
-    def add_records(
-        self,
-        dataset_name: str,
-        records: List[Record],
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> None:
-        r"""Adds records to a dataset on the Hugging Face Hub.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            records (List[Record]): A list of records to add to the dataset.
-            filepath (str): The path to the file containing the records.
-            kwargs (Any): Additional keyword arguments.
-
-        Raises:
-            ValueError: If the dataset already has a records file.
-        """
-        existing_records = self._download_records(
-            dataset_name=dataset_name, filepath=filepath, **kwargs
-        )
-
-        if existing_records:
-            raise ValueError(
-                f"Dataset '{filepath}' already exists. "
-                f"Use `update_records` to modify."
-            )
-
-        self._upload_records(
-            records=records,
-            dataset_name=dataset_name,
-            filepath=filepath,
-            **kwargs,
-        )
-
-    def update_records(
-        self,
-        dataset_name: str,
-        records: List[Record],
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> None:
-        r"""Updates records in a dataset on the Hugging Face Hub.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            records (List[Record]): A list of records to update in the dataset.
-            filepath (str): The path to the file containing the records.
-            kwargs (Any): Additional keyword arguments.
-
-        Raises:
-            ValueError: If the dataset does not have an existing file to update
-                records in.
-        """
-        existing_records = self._download_records(
-            dataset_name=dataset_name, filepath=filepath, **kwargs
-        )
-
-        if not existing_records:
-            logger.warning(
-                f"Dataset '{dataset_name}' does not have existing "
-                "records. Adding new records."
-            )
-            self._upload_records(
-                records=records,
-                dataset_name=dataset_name,
-                filepath=filepath,
-                **kwargs,
-            )
-            return
-
-        old_dict = {record.id: record for record in existing_records}
-        new_dict = {record.id: record for record in records}
-        merged_dict = old_dict.copy()
-        merged_dict.update(new_dict)
-
-        self._upload_records(
-            records=list(merged_dict.values()),
-            dataset_name=dataset_name,
-            filepath=filepath,
-            **kwargs,
-        )
-
-    def delete_record(
-        self,
-        dataset_name: str,
-        record_id: str,
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> None:
-        r"""Deletes a record from the dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            record_id (str): The ID of the record to delete.
-            filepath (str): The path to the file containing the records.
-            kwargs (Any): Additional keyword arguments.
-
-        Raises:
-            ValueError: If the dataset does not have an existing file to delete
-                records from.
-        """
-        existing_records = self._download_records(
-            dataset_name=dataset_name, filepath=filepath, **kwargs
-        )
-
-        if not existing_records:
-            raise ValueError(
-                f"Dataset '{dataset_name}' does not have an existing file to "
-                f"delete records from."
-            )
-
-        filtered_records = [
-            record for record in existing_records if record.id != record_id
-        ]
-
-        self._upload_records(
-            records=filtered_records,
-            dataset_name=dataset_name,
-            filepath=filepath,
-            **kwargs,
-        )
-
-    def list_records(
-        self,
-        dataset_name: str,
-        filepath: str = "records/records.json",
-        **kwargs: Any,
-    ) -> List[Record]:
-        r"""Lists all records in a dataset.
-
-        Args:
-            dataset_name (str): The name of the dataset.
-            filepath (str): The path to the file containing the records.
-            kwargs (Any): Additional keyword arguments.
-
-        Returns:
-            List[Record]: A list of records in the dataset.
-        """
-        return self._download_records(
-            dataset_name=dataset_name, filepath=filepath, **kwargs
-        )
-
-    def _download_records(
-        self, dataset_name: str, filepath: str, **kwargs: Any
-    ) -> List[Record]:
-        from huggingface_hub import hf_hub_download
-        from huggingface_hub.errors import EntryNotFoundError
-
-        try:
-            downloaded_file_path = hf_hub_download(
-                repo_id=dataset_name,
-                filename=filepath,
-                repo_type=HuggingFaceRepoType.DATASET.value,
-                token=self._api_key,
-                **kwargs,
-            )
-
-            with open(downloaded_file_path, "r") as f:
-                records_data = json.load(f)
-
-            return [Record(**record) for record in records_data]
-        except EntryNotFoundError:
-            logger.info(f"No records found for dataset '{dataset_name}'.")
-            return []
-        except Exception as e:
-            logger.error(f"Error downloading or processing records: {e}")
-            raise e
-
-    def _upload_records(
-        self,
-        records: List[Record],
-        dataset_name: str,
-        filepath: str,
-        **kwargs: Any,
-    ):
-        with tempfile.NamedTemporaryFile(
-            delete=False, mode="w", newline="", encoding="utf-8"
-        ) as f:
-            json.dump([record.model_dump() for record in records], f)
-            temp_file_path = f.name
-
-        try:
-            self.api.upload_file(
-                path_or_fileobj=temp_file_path,
-                path_in_repo=filepath,
-                repo_id=dataset_name,
-                repo_type=HuggingFaceRepoType.DATASET.value,
-                **kwargs,
-            )
-        except Exception as e:
-            logger.error(f"Error uploading records file: {e}")
-            raise
-        finally:
-            if os.path.exists(temp_file_path):
-                os.remove(temp_file_path)
-
-    def _upload_file(
-        self,
-        file_content: str,
-        dataset_name: str,
-        filepath: str,
-        file_type: str = "json",
-        **kwargs: Any,
-    ):
-        with tempfile.NamedTemporaryFile(
-            mode="w", delete=False, suffix=f".{file_type}"
-        ) as f:
-            if file_type == "json":
-                if isinstance(file_content, str):
-                    try:
-                        json_content = json.loads(file_content)
-                    except json.JSONDecodeError:
-                        raise ValueError(
-                            "Invalid JSON string provided for file_content."
-                        )
-                else:
-                    try:
-                        json.dumps(file_content)
-                        json_content = file_content
-                    except (TypeError, ValueError):
-                        raise ValueError(
-                            "file_content is not JSON serializable."
-                        )
-
-                json.dump(json_content, f)
-            elif file_type == "md" or file_type == "txt":
-                f.write(file_content)
-            else:
-                raise ValueError(f"Unsupported file type: {file_type}")
-
-            temp_file_path = f.name
-
-        try:
-            self.api.upload_file(
-                path_or_fileobj=temp_file_path,
-                path_in_repo=filepath,
-                repo_id=dataset_name,
-                repo_type=HuggingFaceRepoType.DATASET.value,
-                **kwargs,
-            )
-            logger.info(f"File uploaded successfully: {filepath}")
-        except Exception as e:
-            logger.error(f"Error uploading file: {e}")
-            raise
-
-        if os.path.exists(temp_file_path):
-            os.remove(temp_file_path)

owl/camel/datahubs/models.py

@@ -1,22 +0,0 @@
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. 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.
-# ========= Copyright 2023-2024 @ CAMEL-AI.org. All Rights Reserved. =========
-from typing import Any, Dict, Optional
-
-from pydantic import BaseModel
-
-
-class Record(BaseModel):
-    id: Optional[str] = None
-    metadata: Optional[Dict[str, Any]] = None
-    content: Dict[str, Any]