refactor: ChatAgent

.gitignore

@@ -431,3 +431,5 @@ benchmark/gaia/results.jsonl
 
 # Secret files for docker
 .container/.env
+
+examples/datagen/star/outputs/

camel/agents/_types.py

@@ -0,0 +1,41 @@
+# ========= 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 openai import AsyncStream, Stream
+from pydantic import BaseModel, ConfigDict
+
+from camel.messages import BaseMessage
+from camel.types import ChatCompletion
+
+
+class ToolCallRequest(BaseModel):
+    r"""The request for tool calling."""
+
+    func_name: str
+    args: Dict[str, Any]
+    tool_call_id: str
+
+
+class ModelResponse(BaseModel):
+    r"""The response from the model."""
+
+    model_config = ConfigDict(arbitrary_types_allowed=True)
+
+    response: Union[ChatCompletion, Stream, AsyncStream]
+    tool_call_request: Optional[ToolCallRequest]
+    output_messages: List[BaseMessage]
+    finish_reasons: List[str]
+    usage_dict: Dict[str, Any]
+    response_id: str

camel/agents/_utils.py

@@ -0,0 +1,230 @@
+# ========= 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 logging
+import re
+import textwrap
+import uuid
+from typing import Any, Callable, Dict, List, Optional, Union
+
+from openai.types.chat.chat_completion_message_tool_call import (
+    ChatCompletionMessageToolCall,
+    Function,
+)
+
+from camel.toolkits import FunctionTool
+from camel.types import Choice
+from camel.types.agents import ToolCallingRecord
+
+logger = logging.getLogger(__name__)
+
+
+def generate_tool_prompt(tool_schema_list: List[Dict[str, Any]]) -> str:
+    r"""Generates a tool prompt based on the provided tool schema list.
+
+    Returns:
+        str: A string representing the tool prompt.
+    """
+    tool_prompts = []
+
+    for tool in tool_schema_list:
+        tool_info = tool["function"]
+        tool_name = tool_info["name"]
+        tool_description = tool_info["description"]
+        tool_json = json.dumps(tool_info, indent=4)
+
+        prompt = (
+            f"Use the function '{tool_name}' to '{tool_description}':\n"
+            f"{tool_json}\n"
+        )
+        tool_prompts.append(prompt)
+
+    tool_prompt_str = "\n".join(tool_prompts)
+
+    final_prompt = textwrap.dedent(
+        f"""\
+        You have access to the following functions:
+
+        {tool_prompt_str}
+
+        If you choose to call a function ONLY reply in the following format with no prefix or suffix:
+
+        <function=example_function_name>{{"example_name": "example_value"}}</function>
+
+        Reminder:
+        - Function calls MUST follow the specified format, start with <function= and end with </function>
+        - Required parameters MUST be specified
+        - Only call one function at a time
+        - Put the entire function call reply on one line
+        - If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls.
+        """  # noqa: E501
+    )
+    return final_prompt
+
+
+def _parse_tool_response(response: str) -> Optional[Dict[str, Any]]:
+    r"""Parses the tool response to extract the function name and
+    arguments.
+
+    Args:
+        response (str): The response from the model containing the
+            function call.
+
+    Returns:
+        Optional[Dict[str, Any]]: The parsed function name and arguments
+            if found, otherwise :obj:`None`.
+    """
+    function_regex = r"<function=(\w+)>(.*?)</function>"
+    match = re.search(function_regex, response)
+
+    if match:
+        function_name, args_string = match.groups()
+        try:
+            args = json.loads(args_string)
+            return {"function": function_name, "arguments": args}
+        except json.JSONDecodeError as error:
+            logger.error(f"Error parsing function arguments: {error}")
+            return None
+    return None
+
+
+def extract_tool_call(
+    self, response: Any
+) -> Optional[ChatCompletionMessageToolCall]:
+    r"""Extract the tool call from the model response, if present.
+
+    Args:
+        response (Any): The model's response object.
+
+    Returns:
+        Optional[ChatCompletionMessageToolCall]: The parsed tool call if
+            present, otherwise None.
+    """
+    # Check if the response contains tool calls
+    if (
+        self.has_tools
+        and not self.model_type.support_native_tool_calling
+        and "</function>" in response.choices[0].message.content
+    ):
+        parsed_content = _parse_tool_response(
+            response.choices[0].message.content
+        )
+        if parsed_content:
+            return ChatCompletionMessageToolCall(
+                id=str(uuid.uuid4()),
+                function=Function(
+                    arguments=str(parsed_content["arguments"]).replace(
+                        "'", '"'
+                    ),
+                    name=str(parsed_content["function"]),
+                ),
+                type="function",
+            )
+    elif (
+        self.has_tools
+        and self.model_type.support_native_tool_calling
+        and response.choices[0].message.tool_calls
+    ):
+        return response.choices[0].message.tool_calls[0]
+
+    # No tool call found
+    return None
+
+
+def safe_model_dump(obj) -> Dict[str, Any]:
+    r"""Safely dump a Pydantic model to a dictionary.
+
+    This method attempts to use the `model_dump` method if available,
+    otherwise it falls back to the `dict` method.
+    """
+    # Check if the `model_dump` method exists (Pydantic v2)
+    if hasattr(obj, "model_dump"):
+        return obj.model_dump()
+    # Fallback to `dict()` method (Pydantic v1)
+    elif hasattr(obj, "dict"):
+        return obj.dict()
+    else:
+        raise TypeError("The object is not a Pydantic model")
+
+
+def convert_to_function_tool(
+    tool: Union[FunctionTool, Callable],
+) -> FunctionTool:
+    r"""Convert a tool to a FunctionTool from Callable."""
+    return tool if isinstance(tool, FunctionTool) else FunctionTool(tool)
+
+
+def convert_to_schema(
+    tool: Union[FunctionTool, Callable, Dict[str, Any]],
+) -> Dict[str, Any]:
+    r"""Convert a tool to a schema from Callable or FunctionTool."""
+    if isinstance(tool, FunctionTool):
+        return tool.get_openai_tool_schema()
+    elif callable(tool):
+        return FunctionTool(tool).get_openai_tool_schema()
+    else:
+        return tool
+
+
+def get_info_dict(
+    session_id: Optional[str],
+    usage: Optional[Dict[str, int]],
+    termination_reasons: List[str],
+    num_tokens: int,
+    tool_calls: List[ToolCallingRecord],
+) -> Dict[str, Any]:
+    r"""Returns a dictionary containing information about the chat session.
+
+    Args:
+        session_id (str, optional): The ID of the chat session.
+        usage (Dict[str, int], optional): Information about the usage of
+            the LLM.
+        termination_reasons (List[str]): The reasons for the termination
+            of the chat session.
+        num_tokens (int): The number of tokens used in the chat session.
+        tool_calls (List[ToolCallingRecord]): The list of function
+            calling records, containing the information of called tools.
+
+    Returns:
+        Dict[str, Any]: The chat session information.
+    """
+    return {
+        "id": session_id,
+        "usage": usage,
+        "termination_reasons": termination_reasons,
+        "num_tokens": num_tokens,
+        "tool_calls": tool_calls,
+    }
+
+
+def handle_logprobs(choice: Choice) -> Optional[List[Dict[str, Any]]]:
+    if choice.logprobs is None:
+        return None
+
+    tokens_logprobs = choice.logprobs.content
+
+    if tokens_logprobs is None:
+        return None
+
+    return [
+        {
+            "token": token_logprob.token,
+            "logprob": token_logprob.logprob,
+            "top_logprobs": [
+                (top_logprob.token, top_logprob.logprob)
+                for top_logprob in token_logprob.top_logprobs
+            ],
+        }
+        for token_logprob in tokens_logprobs
+    ]