Clean the repo by removing the deprecated modules and merging the files with the same functionalities.

docs/sphinx_doc/en/source/tutorial/206-prompt.md

@@ -551,67 +551,4 @@ print(prompt)
 ]
 ```
 
-## Prompt Engine (Will be deprecated in the future)
-
-AgentScope provides the `PromptEngine` class to simplify the process of crafting
-prompts for large language models (LLMs).
-
-## About `PromptEngine` Class
-
-The `PromptEngine` class provides a structured way to combine different components of a prompt, such as instructions, hints, conversation history, and user inputs, into a format that is suitable for the underlying language model.
-
-### Key Features of PromptEngine
-
-- **Model Compatibility**: It works with any `ModelWrapperBase` subclass.
-- **Prompt Type**: It supports both string and list-style prompts, aligning with the model's preferred input format.
-
-### Initialization
-
-When creating an instance of `PromptEngine`, you can specify the target model and, optionally, the shrinking policy, the maximum length of the prompt, the prompt type, and a summarization model (could be the same as the target model).
-
-```python
-model = OpenAIChatWrapper(...)
-engine = PromptEngine(model)
-```
-
-### Joining Prompt Components
-
-The `join` method of `PromptEngine` provides a unified interface to handle an arbitrary number of components for constructing the final prompt.
-
-#### Output String Type Prompt
-
-If the model expects a string-type prompt, components are joined with a newline character:
-
-```python
-system_prompt = "You're a helpful assistant."
-memory = ... # can be dict, list, or string
-hint_prompt = "Please respond in JSON format."
-
-prompt = engine.join(system_prompt, memory, hint_prompt)
-# the result will be [ "You're a helpful assistant.", {"name": "user", "content": "What's the weather like today?"}]
-```
-
-#### Output List Type Prompt
-
-For models that work with list-type prompts,e.g., OpenAI and Huggingface chat models, the components can be converted to Message objects, whose type is list of dict:
-
-```python
-system_prompt = "You're a helpful assistant."
-user_messages = [{"name": "user", "content": "What's the weather like today?"}]
-
-prompt = engine.join(system_prompt, user_messages)
-# the result should be: [{"role": "assistant", "content": "You're a helpful assistant."}, {"name": "user", "content": "What's the weather like today?"}]
-```
-
-#### Formatting Prompts in Dynamic Way
-
-The `PromptEngine` supports dynamic prompts using the `format_map` parameter, allowing you to flexibly inject various variables into the prompt components for different scenarios:
-
-```python
-variables = {"location": "London"}
-hint_prompt = "Find the weather in {location}."
-
-prompt = engine.join(system_prompt, user_input, hint_prompt, format_map=variables)
-```
-
 [[Return to the top]](#206-prompt-en)

docs/sphinx_doc/zh_CN/source/tutorial/206-prompt.md

@@ -485,62 +485,4 @@ print(prompt)
 ]
 ```
 
-## 关于`PromptEngine`类 （将会在未来版本弃用）
-
-`PromptEngine`类提供了一种结构化的方式来合并不同的提示组件，比如指令、提示、对话历史和用户输入，以适合底层语言模型的格式。
-
-### 提示工程的关键特性
-
-- **模型兼容性**：可以与任何 `ModelWrapperBase` 的子类一起工作。
-- **提示类型**：支持字符串和列表风格的提示，与模型首选的输入格式保持一致。
-
-### 初始化
-
-当创建 `PromptEngine` 的实例时，您可以指定目标模型，以及（可选的）缩减原则、提示的最大长度、提示类型和总结模型（可以与目标模型相同）。
-
-```python
-model = OpenAIChatWrapper(...)
-engine = PromptEngine(model)
-```
-
-### 合并提示组件
-
-`PromptEngine` 的 `join` 方法提供了一个统一的接口来处理任意数量的组件，以构建最终的提示。
-
-#### 输出字符串类型提示
-
-如果模型期望的是字符串类型的提示，组件会通过换行符连接：
-
-```python
-system_prompt = "You're a helpful assistant."
-memory = ... # 可以是字典、列表或字符串
-hint_prompt = "Please respond in JSON format."
-
-prompt = engine.join(system_prompt, memory, hint_prompt)
-# 结果将会是 ["You're a helpful assistant.", {"name": "user", "content": "What's the weather like today?"}]
-```
-
-#### 输出列表类型提示
-
-对于使用列表类型提示的模型，比如 OpenAI 和 Huggingface 聊天模型，组件可以转换为 `Message` 对象，其类型是字典列表：
-
-```python
-system_prompt = "You're a helpful assistant."
-user_messages = [{"name": "user", "content": "What's the weather like today?"}]
-
-prompt = engine.join(system_prompt, user_messages)
-# 结果将会是: [{"role": "assistant", "content": "You're a helpful assistant."}, {"name": "user", "content": "What's the weather like today?"}]
-```
-
-#### 动态格式化提示
-
-`PromptEngine` 支持使用 `format_map` 参数动态提示，允许您灵活地将各种变量注入到不同场景的提示组件中：
-
-```python
-variables = {"location": "London"}
-hint_prompt = "Find the weather in {location}."
-
-prompt = engine.join(system_prompt, user_input, hint_prompt, format_map=variables)
-```
-
 [[返回顶端]](#206-prompt-zh)

src/agentscope/agents/__init__.py

@@ -5,7 +5,6 @@
 from .dialog_agent import DialogAgent
 from .dict_dialog_agent import DictDialogAgent
 from .user_agent import UserAgent
-from .text_to_image_agent import TextToImageAgent
 from .rpc_agent import RpcAgent
 from .react_agent import ReActAgent
 from .rag_agent import LlamaIndexAgent
@@ -16,7 +15,6 @@
     "Operator",
     "DialogAgent",
     "DictDialogAgent",
-    "TextToImageAgent",
     "UserAgent",
     "ReActAgent",
     "DistConf",

src/agentscope/logging.py

@@ -6,10 +6,11 @@
 
 from loguru import logger
 
-from .utils.tools import _guess_type_by_extension
+
 from .message import Msg
 from .serialize import serialize
 from .studio._client import _studio_client
+from .utils.common import _guess_type_by_extension
 from .web.gradio.utils import (
     generate_image_from_name,
     send_msg,

src/agentscope/manager/_file.py

@@ -8,11 +8,11 @@
 import numpy as np
 from PIL import Image
 
-from agentscope.utils.tools import _download_file
-from agentscope.utils.tools import _hash_string
-from agentscope.utils.tools import _get_timestamp
-from agentscope.utils.tools import _generate_random_code
-from agentscope.constants import (
+from ..utils.common import _download_file
+from ..utils.common import _hash_string
+from ..utils.common import _get_timestamp
+from ..utils.common import _generate_random_code
+from ..constants import (
     _DEFAULT_SUBDIR_CODE,
     _DEFAULT_SUBDIR_FILE,
     _DEFAULT_SUBDIR_INVOKE,
@@ -32,7 +32,13 @@ def _get_text_embedding_record_hash(
     if isinstance(embedding_model, dict):
         # Format the dict to avoid duplicate keys
         embedding_model = json.dumps(embedding_model, sort_keys=True)
-    embedding_model_hash = _hash_string(embedding_model, hash_method)
+    elif isinstance(embedding_model, str):
+        embedding_model_hash = _hash_string(embedding_model, hash_method)
+    else:
+        raise RuntimeError(
+            f"The embedding model must be a string or a dict, got "
+            f"{type(embedding_model)}.",
+        )
 
     # Calculate the embedding id by hashing the hash codes of the
     # original data and the embedding model

src/agentscope/manager/_manager.py

@@ -9,7 +9,7 @@
 from ._file import FileManager
 from ._model import ModelManager
 from ..logging import LOG_LEVEL, setup_logger
-from ..utils.tools import (
+from ..utils.common import (
     _generate_random_code,
     _get_process_creation_time,
     _get_timestamp,

src/agentscope/manager/_monitor.py

@@ -10,7 +10,7 @@
 from sqlalchemy.orm import sessionmaker
 
 from ._file import FileManager
-from ..utils.tools import _is_windows
+from ..utils.common import _is_windows
 from ..constants import (
     _DEFAULT_SQLITE_DB_NAME,
     _DEFAULT_TABLE_NAME_FOR_CHAT_AND_EMBEDDING,

src/agentscope/message/msg.py

@@ -13,7 +13,7 @@
 from loguru import logger
 
 from ..serialize import is_serializable
-from ..utils.tools import (
+from ..utils.common import (
     _map_string_to_color_mark,
     _get_timestamp,
 )

src/agentscope/message/placeholder.py

@@ -9,7 +9,7 @@
 from .msg import Msg
 from ..rpc import RpcAgentClient, ResponseStub, call_in_thread
 from ..serialize import deserialize, is_serializable, serialize
-from ..utils.tools import _is_web_url
+from ..utils.common import _is_web_url
 
 
 class PlaceholderMessage(Msg):

src/agentscope/models/dashscope_model.py

@@ -10,7 +10,7 @@
 
 from ..manager import FileManager
 from ..message import Msg
-from ..utils.tools import _convert_to_str, _guess_type_by_extension
+from ..utils.common import _convert_to_str, _guess_type_by_extension
 
 try:
     import dashscope

src/agentscope/models/gemini_model.py

@@ -7,9 +7,9 @@
 
 from loguru import logger
 
-from agentscope.message import Msg
-from agentscope.models import ModelWrapperBase, ModelResponse
-from agentscope.utils.tools import _convert_to_str
+from ..message import Msg
+from ..models import ModelWrapperBase, ModelResponse
+from ..utils.common import _convert_to_str
 
 try:
     import google.generativeai as genai

src/agentscope/models/model.py

@@ -68,7 +68,7 @@
 from ..manager import FileManager
 from ..manager import MonitorManager
 from ..message import Msg
-from ..utils.tools import _get_timestamp, _convert_to_str
+from ..utils.common import _get_timestamp, _convert_to_str
 from ..constants import _DEFAULT_MAX_RETRIES
 from ..constants import _DEFAULT_RETRY_INTERVAL
 

src/agentscope/models/ollama_model.py

@@ -3,9 +3,9 @@
 from abc import ABC
 from typing import Sequence, Any, Optional, List, Union, Generator
 
-from agentscope.message import Msg
-from agentscope.models import ModelWrapperBase, ModelResponse
-from agentscope.utils.tools import _convert_to_str
+from ..message import Msg
+from ..models import ModelWrapperBase, ModelResponse
+from ..utils.common import _convert_to_str
 
 try:
     import ollama

src/agentscope/models/openai_model.py

@@ -21,7 +21,7 @@
 from .model import ModelWrapperBase, ModelResponse
 from ..manager import FileManager
 from ..message import Msg
-from ..utils.tools import _convert_to_str, _to_openai_image_url
+from ..utils.common import _convert_to_str, _to_openai_image_url
 
 from ..utils.token_utils import get_openai_max_length
 

src/agentscope/models/response.py

@@ -3,7 +3,7 @@
 import json
 from typing import Optional, Sequence, Any, Generator, Union, Tuple
 
-from agentscope.utils.tools import _is_json_serializable
+from ..utils.common import _is_json_serializable
 
 
 class ModelResponse:
@@ -56,6 +56,11 @@ def text(self) -> str:
                     self._text += chunk
         return self._text
 
+    @text.setter
+    def text(self, value: str) -> None:
+        """Set the text field."""
+        self._text = value
+
     @property
     def stream(self) -> Union[None, Generator[Tuple[bool, str], None, None]]:
         """Return the stream generator if it exists."""