chore: 添加虚拟环境到仓库

- 添加 backend_service/venv 虚拟环境
- 包含所有Python依赖包
- 注意：虚拟环境约393MB，包含12655个文件

backend_service/venv/lib/python3.13/site-packages/agentscope/_utils/_common.py

@@ -0,0 +1,285 @@
+# -*- coding: utf-8 -*-
+"""The common utilities for agentscope library."""
+import asyncio
+import base64
+import functools
+import inspect
+import json
+import os
+import tempfile
+import types
+import typing
+import uuid
+from datetime import datetime
+from typing import Union, Any, Callable, Type, Dict
+
+import requests
+from json_repair import repair_json
+from pydantic import BaseModel
+
+from .._logging import logger
+
+if typing.TYPE_CHECKING:
+    from mcp.types import Tool
+else:
+    Tool = "mcp.types.Tool"
+
+
+def _json_loads_with_repair(
+    json_str: str,
+) -> Union[dict, list, str, float, int, bool, None]:
+    """The given json_str maybe incomplete, e.g. '{"key', so we need to
+    repair and load it into a Python object.
+    """
+    repaired = json_str
+    try:
+        repaired = repair_json(json_str)
+    except Exception:
+        pass
+
+    try:
+        return json.loads(repaired)
+    except json.JSONDecodeError as e:
+        raise ValueError(
+            f"Failed to decode JSON string `{json_str}` after repairing it "
+            f"into `{repaired}`. Error: {e}",
+        ) from e
+
+
+def _is_accessible_local_file(url: str) -> bool:
+    """Check if the given URL is a local URL."""
+    return os.path.isfile(url)
+
+
+def _get_timestamp(add_random_suffix: bool = False) -> str:
+    """Get the current timestamp in the format YYYY-MM-DD HH:MM:SS.sss."""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S.%f")[:-3]
+
+    if add_random_suffix:
+        # Add a random suffix to the timestamp
+        timestamp += f"_{os.urandom(3).hex()}"
+
+    return timestamp
+
+
+async def _is_async_func(func: Callable) -> bool:
+    """Check if the given function is an async function, including
+    coroutine functions, async generators, and coroutine objects.
+    """
+
+    return (
+        inspect.iscoroutinefunction(func)
+        or inspect.isasyncgenfunction(func)
+        or isinstance(func, types.CoroutineType)
+        or isinstance(func, types.GeneratorType)
+        and asyncio.iscoroutine(func)
+        or isinstance(func, functools.partial)
+        and await _is_async_func(func.func)
+    )
+
+
+async def _execute_async_or_sync_func(
+    func: Callable,
+    *args: Any,
+    **kwargs: Any,
+) -> Any:
+    """Execute an async or sync function based on its type.
+
+    Args:
+        func (`Callable`):
+            The function to be executed, which can be either async or sync.
+        *args (`Any`):
+            Positional arguments to be passed to the function.
+        **kwargs (`Any`):
+            Keyword arguments to be passed to the function.
+
+    Returns:
+        `Any`:
+            The result of the function execution.
+    """
+
+    if await _is_async_func(func):
+        return await func(*args, **kwargs)
+
+    return func(*args, **kwargs)
+
+
+def _get_bytes_from_web_url(
+    url: str,
+    max_retries: int = 3,
+) -> str:
+    """Get the bytes from a given URL.
+
+    Args:
+        url (`str`):
+            The URL to fetch the bytes from.
+        max_retries (`int`, defaults to `3`):
+            The maximum number of retries.
+    """
+    for _ in range(max_retries):
+        try:
+            response = requests.get(url)
+            response.raise_for_status()
+            return response.content.decode("utf-8")
+
+        except UnicodeDecodeError:
+            return base64.b64encode(response.content).decode("ascii")
+
+        except Exception as e:
+            logger.info(
+                "Failed to fetch bytes from URL %s. Error %s. Retrying...",
+                url,
+                str(e),
+            )
+
+    raise RuntimeError(
+        f"Failed to fetch bytes from URL `{url}` after {max_retries} retries.",
+    )
+
+
+def _save_base64_data(
+    media_type: str,
+    base64_data: str,
+) -> str:
+    """Save the base64 data to a temp file and return the file path. The
+    extension is guessed from the MIME type.
+
+    Args:
+        media_type (`str`):
+            The MIME type of the data, e.g. "image/png", "audio/mpeg".
+        base64_data (`str):
+            The base64 data to be saved.
+    """
+    extension = "." + media_type.split("/")[-1]
+
+    with tempfile.NamedTemporaryFile(
+        suffix=f".{extension}",
+        delete=False,
+    ) as temp_file:
+        decoded_data = base64.b64decode(base64_data)
+        temp_file.write(decoded_data)
+        temp_file.close()
+        return temp_file.name
+
+
+def _extract_json_schema_from_mcp_tool(tool: Tool) -> dict[str, Any]:
+    """Extract JSON schema from MCP tool."""
+
+    return {
+        "type": "function",
+        "function": {
+            "name": tool.name,
+            "description": tool.description,
+            "parameters": {
+                "type": "object",
+                "properties": tool.inputSchema.get(
+                    "properties",
+                    {},
+                ),
+                "required": tool.inputSchema.get(
+                    "required",
+                    [],
+                ),
+            },
+        },
+    }
+
+
+def _remove_title_field(schema: dict) -> None:
+    """Remove the title field from the JSON schema to avoid
+    misleading the LLM."""
+    # The top level title field
+    if "title" in schema:
+        schema.pop("title")
+
+    # properties
+    if "properties" in schema:
+        for prop in schema["properties"].values():
+            if isinstance(prop, dict):
+                _remove_title_field(prop)
+
+    # items
+    if "items" in schema and isinstance(schema["items"], dict):
+        _remove_title_field(schema["items"])
+
+    # additionalProperties
+    if "additionalProperties" in schema and isinstance(
+        schema["additionalProperties"],
+        dict,
+    ):
+        _remove_title_field(
+            schema["additionalProperties"],
+        )
+
+
+def _create_tool_from_base_model(
+    structured_model: Type[BaseModel],
+    tool_name: str = "generate_structured_output",
+) -> Dict[str, Any]:
+    """Create a function tool definition from a Pydantic BaseModel.
+    This function converts a Pydantic BaseModel class into a tool definition
+    that can be used with function calling API. The resulting tool
+    definition includes the model's JSON schema as parameters, enabling
+    structured output generation by forcing the model to call this function
+    with properly formatted data.
+
+    Args:
+        structured_model (`Type[BaseModel]`):
+            A Pydantic BaseModel class that defines the expected structure
+            for the tool's output.
+        tool_name (`str`, default `"generate_structured_output"`):
+            The tool name that used to force the LLM to generate structured
+            output by calling this function.
+
+    Returns:
+        `Dict[str, Any]`: A tool definition dictionary compatible with
+            function calling API, containing type ("function") and
+            function dictionary with name, description, and parameters
+            (JSON schema).
+
+    .. code-block:: python
+        :caption: Example usage
+
+        from pydantic import BaseModel
+
+        class PersonInfo(BaseModel):
+            name: str
+            age: int
+            email: str
+
+        tool = _create_tool_from_base_model(PersonInfo, "extract_person")
+        print(tool["function"]["name"])  # extract_person
+        print(tool["type"])              # function
+
+    .. note:: The function automatically removes the 'title' field from
+        the JSON schema to ensure compatibility with function calling
+        format. This is handled by the internal ``_remove_title_field()``
+        function.
+    """
+    schema = structured_model.model_json_schema()
+
+    _remove_title_field(schema)
+    tool_definition = {
+        "type": "function",
+        "function": {
+            "name": tool_name,
+            "description": "Generate the required structured output with "
+            "this function",
+            "parameters": schema,
+        },
+    }
+    return tool_definition
+
+
+def _map_text_to_uuid(text: str) -> str:
+    """Map the given text to a deterministic UUID string.
+
+    Args:
+        text (`str`):
+            The input text to be mapped to a UUID.
+
+    Returns:
+        `str`:
+            A deterministic UUID string derived from the input text.
+    """
+    return str(uuid.uuid3(uuid.NAMESPACE_DNS, text))

backend_service/venv/lib/python3.13/site-packages/agentscope/_utils/_mixin.py

@@ -0,0 +1,9 @@
+# -*- coding: utf-8 -*-
+"""The mixin for agentscope."""
+
+
+class DictMixin(dict):
+    """The dictionary mixin that allows attribute-style access."""
+
+    __setattr__ = dict.__setitem__
+    __getattr__ = dict.__getitem__