initial update

.gitignore

@@ -0,0 +1,5 @@
+.dist
+deep-swarm/data
+deep-swarm/tmp
+deep-swarm/.env
+deep-swarm/utils/__pycache__/

README.md

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+# DeepSwarm
+
+## Overview
+
+DeepSwarm is a multi-agent framework based from [camel](https://github.com/camel-ai/camel/). It achieved open-source state-of-the-art performance on the [GAIA](https://huggingface.co/datasets/gaia-benchmark/GAIA) benchmark.
+
+## Quickstart
+
+It is recommended to run the code in linux environment.
+To get started, follow these steps:
+
+1. **Clone the Github repository:**
+
+```bash
+$ git clone xxx
+```
+
+2. **Set up Python Environment:**
+
+```bash
+$ conda create -n deepswarm python=3.11
+$ conda activate deepswarm
+```
+
+3. **Install Dependencies:**
+
+```bash
+$ pip install -r requirements.txt
+```
+
+4. **Set API Keys:** We use `dotenv` to manage API keys. Please copy and check the `.env.example` file to `.env` and fill in the necessary API keys.
+
+5. **Run the Demo Code:**
+   
+```bash
+$ python run.py
+```
+
+## Reproduce the Results in GAIA
+
+We have provided a script to reproduce the results in GAIA. You can check the `run_gaia_roleplaying.py` file and run the following command:
+
+```bash
+$ python run_gaia_roleplaying.py
+```

deep-swarm/.env_template

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+
+# OPENAI API
+OPENAI_API_KEY = ""
+
+# Hugging Face API (https://huggingface.co/join)
+HF_TOKEN=""
+
+# Qwen API (https://help.aliyun.com/document_detail/611472.html)
+QWEN_API_KEY=""
+
+
+#===========================================
+# Tools & Services API
+#===========================================
+
+# Google Search API (https://developers.google.com/custom-search/v1/overview)
+GOOGLE_API_KEY=""
+SEARCH_ENGINE_ID=""
+
+# Chunkr API (https://chunkr.ai/)
+CHUNKR_API_KEY=""
+
+# Firecrawl API (https://www.firecrawl.dev/)
+FIRECRAWL_API_KEY=""
+

deep-swarm/camel/__init__.py

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+# ========= 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',
+]

deep-swarm/camel/agents/__init__.py

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+# ========= 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',
+]

deep-swarm/camel/agents/base.py

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+# ========= 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

deep-swarm/camel/agents/critic_agent.py

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+# ========= 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={},
+        )

deep-swarm/camel/agents/deductive_reasoner_agent.py

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+# ========= 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

deep-swarm/camel/agents/embodied_agent.py

@@ -0,0 +1,201 @@
+# ========= 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,
+        )

deep-swarm/camel/agents/knowledge_graph_agent.py

@@ -0,0 +1,259 @@
+# ========= 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,
+        )

deep-swarm/camel/agents/role_assignment_agent.py

@@ -0,0 +1,141 @@
+# ========= 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

deep-swarm/camel/agents/search_agent.py

@@ -0,0 +1,133 @@
+# ========= 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

deep-swarm/camel/agents/task_agent.py

@@ -0,0 +1,410 @@
+# ========= 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)

deep-swarm/camel/agents/tool_agents/__init__.py

@@ -0,0 +1,20 @@
+# ========= 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',
+]

deep-swarm/camel/agents/tool_agents/base.py

@@ -0,0 +1,39 @@
+# ========= 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}"

deep-swarm/camel/agents/tool_agents/hugging_face_tool_agent.py

@@ -0,0 +1,206 @@
+# ========= 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

deep-swarm/camel/benchmarks/__init__.py

@@ -0,0 +1,17 @@
+# ========= 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"]

deep-swarm/camel/benchmarks/base.py

@@ -0,0 +1,152 @@
+# ========= 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

deep-swarm/camel/bots/__init__.py

@@ -0,0 +1,34 @@
+# ========= 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',
+]

deep-swarm/camel/bots/discord_app.py

@@ -0,0 +1,138 @@
+# ========= 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

deep-swarm/camel/bots/slack/__init__.py

@@ -0,0 +1,30 @@
+# ========= 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',
+]

deep-swarm/camel/bots/slack/models.py

@@ -0,0 +1,158 @@
+# ========= 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"""

deep-swarm/camel/bots/slack/slack_app.py

@@ -0,0 +1,255 @@
+# ========= 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

deep-swarm/camel/bots/telegram_bot.py

@@ -0,0 +1,82 @@
+# ========= 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)

deep-swarm/camel/configs/__init__.py

@@ -0,0 +1,76 @@
+# ========= 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',
+]

deep-swarm/camel/configs/anthropic_config.py

@@ -0,0 +1,69 @@
+# ========= 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()}

deep-swarm/camel/configs/base_config.py

@@ -0,0 +1,89 @@
+# ========= 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

deep-swarm/camel/configs/cohere_config.py

@@ -0,0 +1,76 @@
+# ========= 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()}

deep-swarm/camel/configs/deepseek_config.py

@@ -0,0 +1,134 @@
+# ========= 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()}

deep-swarm/camel/configs/gemini_config.py

@@ -0,0 +1,114 @@
+# ========= 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()}

deep-swarm/camel/configs/groq_config.py

@@ -0,0 +1,104 @@
+# ========= 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()}

deep-swarm/camel/configs/litellm_config.py

@@ -0,0 +1,97 @@
+# ========= 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()}

deep-swarm/camel/configs/mistral_config.py

@@ -0,0 +1,79 @@
+# ========= 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()}

deep-swarm/camel/configs/nvidia_config.py

@@ -0,0 +1,70 @@
+# ========= 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()}

deep-swarm/camel/configs/ollama_config.py

@@ -0,0 +1,82 @@
+# ========= 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()}

deep-swarm/camel/configs/openai_config.py

@@ -0,0 +1,139 @@
+# ========= 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()}

deep-swarm/camel/configs/qwen_config.py

@@ -0,0 +1,91 @@
+# ========= 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()}

deep-swarm/camel/configs/reka_config.py

@@ -0,0 +1,74 @@
+# ========= 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()}

deep-swarm/camel/configs/samba_config.py

@@ -0,0 +1,170 @@
+# ========= 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()
+}

deep-swarm/camel/configs/togetherai_config.py

@@ -0,0 +1,107 @@
+# ========= 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()
+}

deep-swarm/camel/configs/vllm_config.py

@@ -0,0 +1,111 @@
+# ========= 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()}

deep-swarm/camel/configs/yi_config.py

@@ -0,0 +1,58 @@
+# ========= 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()}

deep-swarm/camel/configs/zhipuai_config.py

@@ -0,0 +1,71 @@
+# ========= 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()}

deep-swarm/camel/datahubs/__init__.py

@@ -0,0 +1,23 @@
+# ========= 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",
+]

deep-swarm/camel/datahubs/base.py

@@ -0,0 +1,136 @@
+# ========= 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

deep-swarm/camel/datahubs/huggingface.py

@@ -0,0 +1,433 @@
+# ========= 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)

deep-swarm/camel/datahubs/models.py

@@ -0,0 +1,22 @@
+# ========= 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]

deep-swarm/camel/embeddings/__init__.py

@@ -0,0 +1,28 @@
+# ========= 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 BaseEmbedding
+from .mistral_embedding import MistralEmbedding
+from .openai_compatible_embedding import OpenAICompatibleEmbedding
+from .openai_embedding import OpenAIEmbedding
+from .sentence_transformers_embeddings import SentenceTransformerEncoder
+from .vlm_embedding import VisionLanguageEmbedding
+
+__all__ = [
+    "BaseEmbedding",
+    "OpenAIEmbedding",
+    "SentenceTransformerEncoder",
+    "VisionLanguageEmbedding",
+    "MistralEmbedding",
+    "OpenAICompatibleEmbedding",
+]

deep-swarm/camel/embeddings/base.py

@@ -0,0 +1,67 @@
+# ========= 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, abstractmethod
+from typing import Any, Generic, TypeVar
+
+T = TypeVar('T')
+
+
+class BaseEmbedding(ABC, Generic[T]):
+    r"""Abstract base class for text embedding functionalities."""
+
+    @abstractmethod
+    def embed_list(
+        self,
+        objs: list[T],
+        **kwargs: Any,
+    ) -> list[list[float]]:
+        r"""Generates embeddings for the given texts.
+
+        Args:
+            objs (list[T]): The objects for which to generate the embeddings.
+            **kwargs (Any): Extra kwargs passed to the embedding API.
+
+        Returns:
+            list[list[float]]: A list that represents the
+                generated embedding as a list of floating-point numbers.
+        """
+        pass
+
+    def embed(
+        self,
+        obj: T,
+        **kwargs: Any,
+    ) -> list[float]:
+        r"""Generates an embedding for the given text.
+
+        Args:
+            obj (T): The object for which to generate the embedding.
+            **kwargs (Any): Extra kwargs passed to the embedding API.
+
+        Returns:
+            list[float]: A list of floating-point numbers representing the
+                generated embedding.
+        """
+        return self.embed_list([obj], **kwargs)[0]
+
+    @abstractmethod
+    def get_output_dim(self) -> int:
+        r"""Returns the output dimension of the embeddings.
+
+        Returns:
+            int: The dimensionality of the embedding for the current model.
+        """
+        pass

deep-swarm/camel/embeddings/mistral_embedding.py

@@ -0,0 +1,89 @@
+# ========= 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 os
+from typing import Any
+
+from camel.embeddings.base import BaseEmbedding
+from camel.types import EmbeddingModelType
+from camel.utils import api_keys_required
+
+
+class MistralEmbedding(BaseEmbedding[str]):
+    r"""Provides text embedding functionalities using Mistral's models.
+
+    Args:
+        model_type (EmbeddingModelType, optional): The model type to be
+            used for text embeddings.
+            (default: :obj:`MISTRAL_EMBED`)
+        api_key (str, optional): The API key for authenticating with the
+            Mistral service. (default: :obj:`None`)
+        dimensions (int, optional): The text embedding output dimensions.
+            (default: :obj:`None`)
+
+    Raises:
+        RuntimeError: If an unsupported model type is specified.
+    """
+
+    def __init__(
+        self,
+        model_type: EmbeddingModelType = (EmbeddingModelType.MISTRAL_EMBED),
+        api_key: str | None = None,
+        dimensions: int | None = None,
+    ) -> None:
+        from mistralai import Mistral
+
+        if not model_type.is_mistral:
+            raise ValueError("Invalid Mistral embedding model type.")
+        self.model_type = model_type
+        if dimensions is None:
+            self.output_dim = model_type.output_dim
+        else:
+            assert isinstance(dimensions, int)
+            self.output_dim = dimensions
+        self._api_key = api_key or os.environ.get("MISTRAL_API_KEY")
+        self._client = Mistral(api_key=self._api_key)
+
+    @api_keys_required("MISTRAL_API_KEY")
+    def embed_list(
+        self,
+        objs: list[str],
+        **kwargs: Any,
+    ) -> list[list[float]]:
+        r"""Generates embeddings for the given texts.
+
+        Args:
+            objs (list[str]): The texts for which to generate the embeddings.
+            **kwargs (Any): Extra kwargs passed to the embedding API.
+
+        Returns:
+            list[list[float]]: A list that represents the generated embedding
+                as a list of floating-point numbers.
+        """
+        # TODO: count tokens
+        response = self._client.embeddings.create(
+            inputs=objs,
+            model=self.model_type.value,
+            **kwargs,
+        )
+        return [data.embedding for data in response.data]  # type: ignore[misc,union-attr]
+
+    def get_output_dim(self) -> int:
+        r"""Returns the output dimension of the embeddings.
+
+        Returns:
+            int: The dimensionality of the embedding for the current model.
+        """
+        return self.output_dim

deep-swarm/camel/embeddings/openai_compatible_embedding.py

@@ -0,0 +1,91 @@
+# ========= 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 os
+from typing import Any, Optional
+
+from openai import OpenAI
+
+from camel.embeddings.base import BaseEmbedding
+from camel.utils import api_keys_required
+
+
+class OpenAICompatibleEmbedding(BaseEmbedding[str]):
+    r"""Provides text embedding functionalities supporting OpenAI
+    compatibility.
+
+    Args:
+        model_type (str): The model type to be used for text embeddings.
+        api_key (str): The API key for authenticating with the model service.
+        url (str): The url to the model service.
+    """
+
+    def __init__(
+        self,
+        model_type: str,
+        api_key: Optional[str] = None,
+        url: Optional[str] = None,
+    ) -> None:
+        self.model_type = model_type
+        self.output_dim: Optional[int] = None
+
+        self._api_key = api_key or os.environ.get(
+            "OPENAI_COMPATIBILIY_API_KEY"
+        )
+        self._url = url or os.environ.get("OPENAI_COMPATIBILIY_API_BASE_URL")
+        self._client = OpenAI(
+            timeout=60,
+            max_retries=3,
+            api_key=self._api_key,
+            base_url=self._url,
+        )
+
+    @api_keys_required("OPENAI_COMPATIBILIY_API_KEY")
+    def embed_list(
+        self,
+        objs: list[str],
+        **kwargs: Any,
+    ) -> list[list[float]]:
+        r"""Generates embeddings for the given texts.
+
+        Args:
+            objs (list[str]): The texts for which to generate the embeddings.
+            **kwargs (Any): Extra kwargs passed to the embedding API.
+
+        Returns:
+            list[list[float]]: A list that represents the generated embedding
+                as a list of floating-point numbers.
+        """
+
+        response = self._client.embeddings.create(
+            input=objs,
+            model=self.model_type,
+            **kwargs,
+        )
+        self.output_dim = len(response.data[0].embedding)
+        return [data.embedding for data in response.data]
+
+    def get_output_dim(self) -> int:
+        r"""Returns the output dimension of the embeddings.
+
+        Returns:
+            int: The dimensionality of the embedding for the current model.
+        """
+        if self.output_dim is None:
+            raise ValueError(
+                "Output dimension is not yet determined. Call "
+                "'embed_list' first."
+            )
+        return self.output_dim

deep-swarm/camel/embeddings/openai_embedding.py

@@ -0,0 +1,99 @@
+# ========= 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 os
+from typing import Any
+
+from openai import OpenAI
+
+from camel.embeddings.base import BaseEmbedding
+from camel.types import NOT_GIVEN, EmbeddingModelType, NotGiven
+from camel.utils import api_keys_required
+
+
+class OpenAIEmbedding(BaseEmbedding[str]):
+    r"""Provides text embedding functionalities using OpenAI's models.
+
+    Args:
+        model_type (EmbeddingModelType, optional): The model type to be
+            used for text embeddings.
+            (default: :obj:`TEXT_EMBEDDING_3_SMALL`)
+        api_key (str, optional): The API key for authenticating with the
+            OpenAI service. (default: :obj:`None`)
+        dimensions (int, optional): The text embedding output dimensions.
+            (default: :obj:`NOT_GIVEN`)
+
+    Raises:
+        RuntimeError: If an unsupported model type is specified.
+    """
+
+    def __init__(
+        self,
+        model_type: EmbeddingModelType = (
+            EmbeddingModelType.TEXT_EMBEDDING_3_SMALL
+        ),
+        api_key: str | None = None,
+        dimensions: int | NotGiven = NOT_GIVEN,
+    ) -> None:
+        if not model_type.is_openai:
+            raise ValueError("Invalid OpenAI embedding model type.")
+        self.model_type = model_type
+        if dimensions == NOT_GIVEN:
+            self.output_dim = model_type.output_dim
+        else:
+            assert isinstance(dimensions, int)
+            self.output_dim = dimensions
+        self._api_key = api_key or os.environ.get("OPENAI_API_KEY")
+        self.client = OpenAI(timeout=60, max_retries=3, api_key=self._api_key)
+
+    @api_keys_required("OPENAI_API_KEY")
+    def embed_list(
+        self,
+        objs: list[str],
+        **kwargs: Any,
+    ) -> list[list[float]]:
+        r"""Generates embeddings for the given texts.
+
+        Args:
+            objs (list[str]): The texts for which to generate the embeddings.
+            **kwargs (Any): Extra kwargs passed to the embedding API.
+
+        Returns:
+            list[list[float]]: A list that represents the generated embedding
+                as a list of floating-point numbers.
+        """
+        # TODO: count tokens
+        if self.model_type == EmbeddingModelType.TEXT_EMBEDDING_ADA_2:
+            response = self.client.embeddings.create(
+                input=objs,
+                model=self.model_type.value,
+                **kwargs,
+            )
+        else:
+            response = self.client.embeddings.create(
+                input=objs,
+                model=self.model_type.value,
+                dimensions=self.output_dim,
+                **kwargs,
+            )
+        return [data.embedding for data in response.data]
+
+    def get_output_dim(self) -> int:
+        r"""Returns the output dimension of the embeddings.
+
+        Returns:
+            int: The dimensionality of the embedding for the current model.
+        """
+        return self.output_dim

deep-swarm/camel/embeddings/sentence_transformers_embeddings.py

@@ -0,0 +1,80 @@
+# ========= 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
+
+from numpy import ndarray
+
+from camel.embeddings.base import BaseEmbedding
+
+
+class SentenceTransformerEncoder(BaseEmbedding[str]):
+    r"""This class provides functionalities to generate text
+    embeddings using `Sentence Transformers`.
+
+    References:
+        https://www.sbert.net/
+    """
+
+    def __init__(
+        self,
+        model_name: str = "intfloat/e5-large-v2",
+        **kwargs,
+    ):
+        r"""Initializes the: obj: `SentenceTransformerEmbedding` class
+        with the specified transformer model.
+
+        Args:
+            model_name (str, optional): The name of the model to use.
+                (default: :obj:`intfloat/e5-large-v2`)
+            **kwargs (optional): Additional arguments of
+                :class:`SentenceTransformer`, such as :obj:`prompts` etc.
+        """
+        from sentence_transformers import SentenceTransformer
+
+        self.model = SentenceTransformer(model_name, **kwargs)
+
+    def embed_list(
+        self,
+        objs: list[str],
+        **kwargs: Any,
+    ) -> list[list[float]]:
+        r"""Generates embeddings for the given texts using the model.
+
+        Args:
+            objs (list[str]): The texts for which to generate the
+                embeddings.
+
+        Returns:
+            list[list[float]]: A list that represents the generated embedding
+                as a list of floating-point numbers.
+        """
+        if not objs:
+            raise ValueError("Input text list is empty")
+        embeddings = self.model.encode(
+            objs, normalize_embeddings=True, **kwargs
+        )
+        assert isinstance(embeddings, ndarray)
+        return embeddings.tolist()
+
+    def get_output_dim(self) -> int:
+        r"""Returns the output dimension of the embeddings.
+
+        Returns:
+            int: The dimensionality of the embeddings.
+        """
+        output_dim = self.model.get_sentence_embedding_dimension()
+        assert isinstance(output_dim, int)
+        return output_dim

deep-swarm/camel/embeddings/vlm_embedding.py

@@ -0,0 +1,149 @@
+# ========= 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, Union
+
+from PIL import Image
+
+from camel.embeddings import BaseEmbedding
+from camel.logger import get_logger
+
+logger = get_logger(__name__)
+
+
+class VisionLanguageEmbedding(BaseEmbedding[Union[str, Image.Image]]):
+    r"""Provides image embedding functionalities using multimodal model.
+
+    Args:
+        model_name : The model type to be used for generating embeddings.
+            And the default value is: obj:`openai/clip-vit-base-patch32`.
+
+    Raises:
+        RuntimeError: If an unsupported model type is specified.
+    """
+
+    def __init__(
+        self, model_name: str = "openai/clip-vit-base-patch32"
+    ) -> None:
+        r"""Initializes the: obj: `VisionLanguageEmbedding` class with a
+        specified model and return the dimension of embeddings.
+
+        Args:
+            model_name (str, optional): The version name of the model to use.
+                (default: :obj:`openai/clip-vit-base-patch32`)
+        """
+        from transformers import AutoModel, AutoProcessor
+
+        try:
+            self.model = AutoModel.from_pretrained(model_name)
+            self.processor = AutoProcessor.from_pretrained(model_name)
+        except Exception as e:
+            raise RuntimeError(f"Failed to load model '{model_name}': {e}")
+
+        self.valid_processor_kwargs = []
+        self.valid_model_kwargs = []
+
+        try:
+            self.valid_processor_kwargs = (
+                self.processor.image_processor._valid_processor_keys
+            )
+            self.valid_model_kwargs = [
+                "pixel_values",
+                "return_dict",
+                "interpolate_pos_encoding",
+            ]
+        except Exception:
+            logger.warning("not typically processor and model structure")
+            pass
+        self.dim: Optional[int] = None
+
+    def embed_list(
+        self, objs: List[Union[Image.Image, str]], **kwargs: Any
+    ) -> List[List[float]]:
+        """Generates embeddings for the given images or texts.
+
+        Args:
+            objs (List[Image.Image|str]): The list of images or texts for
+                which to generate the embeddings.
+            image_processor_kwargs: Extra kwargs passed to the image processor.
+            tokenizer_kwargs: Extra kwargs passed to the text tokenizer
+                (processor).
+            model_kwargs: Extra kwargs passed to the main model.
+
+        Returns:
+            List[List[float]]: A list that represents the generated embedding
+                as a list of floating-point numbers.
+
+        Raises:
+            ValueError: If the input type is not `Image.Image` or `str`.
+        """
+        if not objs:
+            raise ValueError("Input objs list is empty.")
+
+        image_processor_kwargs: Optional[dict] = kwargs.get(
+            'image_processor_kwargs', {}
+        )
+        tokenizer_kwargs: Optional[dict] = kwargs.get('tokenizer_kwargs', {})
+        model_kwargs: Optional[dict] = kwargs.get('model_kwargs', {})
+
+        result_list = []
+        for obj in objs:
+            if isinstance(obj, Image.Image):
+                image_input = self.processor(
+                    images=obj,
+                    return_tensors="pt",
+                    padding=True,
+                    **image_processor_kwargs,
+                )
+                image_feature = (
+                    self.model.get_image_features(
+                        **image_input, **model_kwargs
+                    )
+                    .squeeze(dim=0)
+                    .tolist()
+                )
+                result_list.append(image_feature)
+            elif isinstance(obj, str):
+                text_input = self.processor(
+                    text=obj,
+                    return_tensors="pt",
+                    padding=True,
+                    **tokenizer_kwargs,
+                )
+                text_feature = (
+                    self.model.get_text_features(**text_input, **model_kwargs)
+                    .squeeze(dim=0)
+                    .tolist()
+                )
+                result_list.append(text_feature)
+            else:
+                raise ValueError("Input type is not image nor text.")
+
+        self.dim = len(result_list[0])
+
+        if any(len(result) != self.dim for result in result_list):
+            raise ValueError("Dimensionality is not consistent.")
+
+        return result_list
+
+    def get_output_dim(self) -> int:
+        r"""Returns the output dimension of the embeddings.
+
+        Returns:
+            int: The dimensionality of the embedding for the current model.
+        """
+        if self.dim is None:
+            text = 'dimension'
+            inputs = self.processor(text=[text], return_tensors="pt")
+            self.dim = self.model.get_text_features(**inputs).shape[1]
+        return self.dim

deep-swarm/camel/generators.py

@@ -0,0 +1,375 @@
+# ========= 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 Dict, Generator, List, Optional, Set, Tuple
+
+from camel.messages import BaseMessage
+from camel.prompts import PromptTemplateGenerator, TextPrompt
+from camel.types import RoleType, TaskType
+
+
+class SystemMessageGenerator:
+    r"""System message generator for agents.
+
+    Args:
+        task_type (TaskType, optional): The task type.
+            (default: :obj:`TaskType.AI_SOCIETY`)
+        sys_prompts (Optional[Dict[RoleType, str]], optional): The prompts of
+            the system messages for each role type. (default: :obj:`None`)
+        sys_msg_meta_dict_keys (Optional[Set[str]], optional): The set of keys
+            of the meta dictionary used to fill the prompts.
+            (default: :obj:`None`)
+    """
+
+    def __init__(
+        self,
+        task_type: TaskType = TaskType.AI_SOCIETY,
+        sys_prompts: Optional[Dict[RoleType, str]] = None,
+        sys_msg_meta_dict_keys: Optional[Set[str]] = None,
+    ) -> None:
+        self.sys_prompts: Dict[RoleType, str]
+
+        if sys_prompts is not None:
+            self.sys_prompts = sys_prompts
+            self.sys_msg_meta_dict_keys = sys_msg_meta_dict_keys or set()
+        else:
+            assistant_prompt_template = (
+                PromptTemplateGenerator().get_system_prompt(
+                    task_type,
+                    RoleType.ASSISTANT,
+                )
+            )
+            user_prompt_template = PromptTemplateGenerator().get_system_prompt(
+                task_type,
+                RoleType.USER,
+            )
+            critic_prompt_template = (
+                PromptTemplateGenerator().get_system_prompt(
+                    task_type,
+                    RoleType.CRITIC,
+                )
+            )
+            embodiment_prompt_template = (
+                PromptTemplateGenerator().get_system_prompt(
+                    task_type,
+                    RoleType.EMBODIMENT,
+                )
+            )
+
+            self.sys_prompts = dict()
+            self.sys_prompts[RoleType.ASSISTANT] = assistant_prompt_template
+            self.sys_prompts[RoleType.USER] = user_prompt_template
+            self.sys_prompts[RoleType.CRITIC] = critic_prompt_template
+            self.sys_prompts[RoleType.EMBODIMENT] = embodiment_prompt_template
+
+            self.sys_msg_meta_dict_keys = (
+                assistant_prompt_template.key_words
+                | user_prompt_template.key_words
+                | critic_prompt_template.key_words
+                | embodiment_prompt_template.key_words
+            )
+
+        if RoleType.DEFAULT not in self.sys_prompts:
+            self.sys_prompts[RoleType.DEFAULT] = "You are a helpful assistant."
+
+    def validate_meta_dict_keys(self, meta_dict: Dict[str, str]) -> None:
+        r"""Validates the keys of the meta_dict.
+
+        Args:
+            meta_dict (Dict[str, str]): The dictionary to validate.
+        """
+        if not set(meta_dict.keys()).issubset(self.sys_msg_meta_dict_keys):
+            raise ValueError(
+                "The keys of the meta_dict should be in "
+                f"{self.sys_msg_meta_dict_keys}. "
+                f"Got {set(meta_dict.keys())} instead."
+            )
+
+    def from_dict(
+        self,
+        meta_dict: Dict[str, str],
+        role_tuple: Tuple[str, RoleType] = ("", RoleType.DEFAULT),
+    ) -> BaseMessage:
+        r"""Generates a system message from a dictionary.
+
+        Args:
+            meta_dict (Dict[str, str]): The dictionary containing the
+                information to generate the system message.
+            role_tuple (Tuple[str, RoleType], optional): The tuple containing
+                the role name and role type. (default: ("", RoleType.DEFAULT))
+
+        Returns:
+            BaseMessage: The generated system message.
+        """
+        self.validate_meta_dict_keys(meta_dict)
+        role_name, role_type = role_tuple
+        sys_prompt = self.sys_prompts[role_type]
+        sys_prompt = sys_prompt.format(**meta_dict)
+        return BaseMessage(
+            role_name=role_name,
+            role_type=role_type,
+            meta_dict=meta_dict,
+            content=sys_prompt,
+        )
+
+    def from_dicts(
+        self,
+        meta_dicts: List[Dict[str, str]],
+        role_tuples: List[Tuple[str, RoleType]],
+    ) -> List[BaseMessage]:
+        r"""Generates a list of system messages from a list of dictionaries.
+
+        Args:
+            meta_dicts (List[Dict[str, str]]): A list of dictionaries
+                containing the information to generate the system messages.
+            role_tuples (List[Tuple[str, RoleType]]): A list of tuples
+                containing the role name and role type for each system message.
+
+        Returns:
+            List[BaseMessage]: A list of generated system messages.
+
+        Raises:
+            ValueError: If the number of meta_dicts and role_tuples are
+                different.
+        """
+        if len(meta_dicts) != len(role_tuples):
+            raise ValueError(
+                "The number of meta_dicts and role_types should be the same."
+            )
+
+        return [
+            self.from_dict(meta_dict, role_tuple)
+            for meta_dict, role_tuple in zip(meta_dicts, role_tuples)
+        ]
+
+
+class RoleNameGenerator:
+    r"""Role name generator for role-playing workers.
+
+    Args:
+        assistant_role_names_path (str, optional): The path to the file
+            containing the assistant role names.
+            (default: :obj:`"data/ai_society/assistant_roles.txt"`)
+        user_role_names_path (str, optional): The path to the file
+            containing the user role names.
+            (default: :obj:`"data/ai_society/user_roles.txt"`)
+        assistant_role_names (Optional[List[str]], optional): The list of
+            assistant role names. (default: :obj:`None`)
+        user_role_names (Optional[List[str]], optional): The list of user role
+            names. (default: :obj:`None`)
+    """
+
+    def __init__(
+        self,
+        assistant_role_names_path: str = "data/ai_society/assistant_roles.txt",
+        user_role_names_path: str = "data/ai_society/user_roles.txt",
+        assistant_role_names: Optional[List[str]] = None,
+        user_role_names: Optional[List[str]] = None,
+    ) -> None:
+        if assistant_role_names is None:
+            with open(assistant_role_names_path, "r") as f:
+                assistant_role_names_: List[str] = f.read().splitlines()
+                self.assistant_role_names = [
+                    " ".join(name.split(" ")[1:])
+                    for name in assistant_role_names_
+                ]
+        else:
+            self.assistant_role_names = assistant_role_names
+
+        if user_role_names is None:
+            with open(user_role_names_path, "r") as f:
+                user_role_names_: List[str] = f.read().splitlines()
+                self.user_role_names = [
+                    " ".join(name.split(" ")[1:]) for name in user_role_names_
+                ]
+        else:
+            self.user_role_names = user_role_names
+
+    def from_role_files(self) -> Generator[Tuple, None, None]:
+        r"""Generate role names from the file.
+
+        Returns:
+            Generator[Tuple, None, None]: A generator that yields tuples of
+                assistant role names and user role names.
+        """
+        for assistant_role_name in self.assistant_role_names:
+            for user_role_name in self.user_role_names:
+                yield (assistant_role_name, user_role_name)
+
+
+class AISocietyTaskPromptGenerator:
+    r"""Task prompt generator for AI society tasks.
+
+    Args:
+        num_tasks (int, optional): The number of tasks to generate.
+            (default: :obj:`10`)
+    """
+
+    def __init__(
+        self,
+        num_tasks: int = 10,
+    ) -> None:
+        self.generate_tasks_prompt = (
+            PromptTemplateGenerator().get_generate_tasks_prompt(
+                TaskType.AI_SOCIETY
+            )
+        )
+
+        self.num_tasks = num_tasks
+
+    # TODO: Return role names for user and assistant with the generator.
+    def from_role_files(
+        self,
+        assistant_role_names_path: str = "data/ai_society/assistant_roles.txt",
+        user_role_names_path: str = "data/ai_society/user_roles.txt",
+    ) -> Generator[Tuple[str, Tuple[str, str]], None, None]:
+        r"""Generate tasks from role files.
+
+        Args:
+            assistant_role_names_path (str, optional): The path to the file
+                containing the assistant role names.
+                (default: :obj:`"data/ai_society/assistant_roles.txt"`)
+            user_role_names_path (str, optional): The path to the file
+                containing the user role names.
+                (default: :obj:`"data/ai_society/user_roles.txt"`)
+
+        Returns:
+            Generator[Tuple[str, Tuple[str, str]], None, None]: A generator
+                that yields tuples of task prompts and role names.
+        """
+        roles_generator = RoleNameGenerator(
+            assistant_role_names_path, user_role_names_path
+        ).from_role_files()
+        for role_1, role_2 in roles_generator:
+            generate_tasks_prompt = self.generate_tasks_prompt.format(
+                assistant_role=role_1,
+                user_role=role_2,
+                num_tasks=self.num_tasks,
+            )
+
+            yield (generate_tasks_prompt, (role_1, role_2))
+
+    def from_role_generator(
+        self, role_generator: Generator[Tuple, None, None]
+    ) -> Generator[Tuple[str, Tuple[str, str]], None, None]:
+        r"""Generate tasks from a role generator.
+
+        Args:
+            role_generator (Generator[Tuple, None, None]): A generator that
+                yields tuples of role names.
+
+        Returns:
+            Generator[Tuple[str, Tuple[str, str]], None, None]: A generator
+                that yields tuples of task prompts and role names.
+        """
+        for role_1, role_2 in role_generator:
+            generate_tasks_prompt = self.generate_tasks_prompt.format(
+                assistant_role=role_1,
+                user_role=role_2,
+                num_tasks=self.num_tasks,
+            )
+
+            yield (generate_tasks_prompt, (role_1, role_2))
+
+
+class SingleTxtGenerator:
+    r"""Single text generator for role-playing workers.
+
+    Args:
+        text_file_path (str): The path to the file containing the text data.
+    """
+
+    def __init__(
+        self,
+        text_file_path: str,
+    ) -> None:
+        with open(text_file_path, "r") as f:
+            data_list: List[str] = f.read().splitlines()
+            self.data_list = [
+                " ".join(name.split(" ")[1:]) for name in data_list
+            ]
+
+    def from_role_files(self) -> Generator[str, None, None]:
+        r"""Generate text from the file.
+
+        Returns:
+            Generator[str, None, None]: A generator that yields the text data.
+        """
+        for data in self.data_list:
+            yield data
+
+
+class CodeTaskPromptGenerator:
+    r"""Code task prompt generator for code tasks.
+
+    Args:
+        num_tasks (int, optional): The number of tasks to generate.
+            (default: :obj:`50`)
+    """
+
+    def __init__(
+        self,
+        num_tasks: int = 50,
+    ) -> None:
+        self.generate_tasks_prompt = (
+            PromptTemplateGenerator().get_generate_tasks_prompt(TaskType.CODE)
+        )
+
+        self.num_tasks = num_tasks
+
+    def from_role_files(
+        self,
+        languages_path: str = "data/code/languages.txt",
+        domains_path: str = "data/code/domains.txt",
+    ) -> Generator[Tuple[TextPrompt, str, str], None, None]:
+        r"""Generate tasks from role files.
+
+        Args:
+            languages_path (str, optional): The path to the file containing
+                the language names. (default: :obj:`"data/code/languages.txt"`)
+            domains_path (str, optional): The path to the file containing
+                the domain names. (default: :obj:`"data/code/domains.txt"`)
+
+        Returns:
+            Generator[Tuple[TextPrompt, str, str], None, None]: A generator
+                that yields tuples of task prompts, language names, and domain
+                names.
+        """
+        language_generator = SingleTxtGenerator(
+            languages_path
+        ).from_role_files()
+
+        for language in language_generator:
+            domains_generator = SingleTxtGenerator(
+                domains_path
+            ).from_role_files()
+            for domain in domains_generator:
+                generated_tasks_prompt = self.generate_tasks_prompt.format(
+                    language=language, domain=domain, num_tasks=self.num_tasks
+                )
+                yield generated_tasks_prompt, language, domain
+
+    def from_role_generator(
+        self, role_generator: Generator[Tuple, None, None]
+    ) -> Generator[str, None, None]:
+        r"""Generate tasks from a role generator.
+
+        Args:
+            role_generator (Generator[Tuple, None, None]): A generator that
+                yields tuples of role names.
+
+        Returns:
+            Generator[str, None, None]: A generator that yields the task
+                prompts.
+        """
+        raise NotImplementedError