minor fix according to zhijianma's comments

docs/tutorial/101-installation.md

@@ -33,6 +33,18 @@ source agentscope/bin/activate  # On Windows use `agentscope\Scripts\activate`
 
 ### Installing AgentScope
 
+#### Install with Pip
+
+If you prefer to install AgentScope from Pypi, you can do so easily using `pip`:
+
+```bash
+# For centralized multi-agent applications
+pip install agentscope
+# For distributed multi-agent applications
+pip install agentscope[distribute]  # On Mac use `pip install agentscope\[distribute\]`
+```
+
+
 #### Install from Source
 
 For users who prefer to install AgentScope directly from the source code, follow these steps to clone the repository and install the platform in editable mode:
@@ -50,18 +62,6 @@ pip install -e .
 pip install -e .[distribute]  # On Mac use `pip install -e .\[distribute\]`
 ```
 
-#### Install with Pip
-
-If you prefer to install AgentScope from Pypi, you can do so easily using `pip`:
-
-```bash
-# For centralized multi-agent applications
-pip install agentscope
-# For distributed multi-agent applications
-pip install agentscope[distribute]  # On Mac use `pip install agentscope\[distribute\]`
-```
-
-
 
 **Note**: The `[distribute]` option installs additional dependencies required for distributed applications. Remember to activate your virtual environment before running these commands.
 

docs/tutorial/102-concepts.md

@@ -7,7 +7,7 @@ In this tutorial, you'll have an initial understanding of the **fundamental conc
 ## Essential Terms and Concepts
 
 * **Agent** refers to an autonomous entity capable of performing actions to achieve specific objectives (probably powered by LLMs). In AgentScope, an agent takes the message as input and generates a corresponding response message. Agents can interact with each other to simulate human-like behaviors (e.g., discussion or debate) and cooperate to finish complicated tasks (e.g., generate runnable and reliable code).
-* **Message** is a carrier of communication information between agents. It encapsulates information that needs to be conveyed, such as instructions, multi-modal data, or status updates.  In AgentScope, a message is a subclass of Python's dict with additional features for inter-agent communication, including fields such as `name` and `content` for identification and payload delivery.
+* **Message** is a carrier of communication information among agents. It encapsulates information that needs to be conveyed, such as instructions, multi-modal data, or status updates.  In AgentScope, a message is a subclass of Python's dict with additional features for inter-agent communication, including fields such as `name` and `content` for identification and payload delivery.
 * **Memory** refers to the structures (e.g., list-like memory, database-based memory) used to store and manage `Message` that agents need to remember and store. This can include chat history, knowledge, or other data that informs the agent's future actions.
 * **Service** is a collection of functionality tools (e.g., web search, code interpreter, file processing) that provide specific capabilities or processes that are independent of an agent's memory state. Services can be invoked by agents or other components and designed to be reusable across different scenarios.
 * **Pipeline** refers to the interaction order or pattern of agents in a task. AgentScope provides built-in `pipelines` to streamline the process of collaboration across multiple agents, such as `SequentialPipeline` and `ForLoopPipeline`. When a `Pipeline` is executed, the `message` passes from predecessors to successors with intermediate results for the task.

docs/tutorial/103-example.md

@@ -6,12 +6,12 @@ AgentScope is a versatile platform for building and running multi-agent applicat
 
 Agent is the basic composition and communication unit in AgentScope. To initialize an model-based agent, you need to prepare your configs for avaliable models. AgentScope supports a variety of APIs for pre-trained models. Here is a table outlining the supported APIs and the type of arguments required for each:
 
-|   Model Usage               | Type Argument in AgentScope     | Supported APIs                                                         |
-| -------------------- | ------------------ | ------------------------------------------------------------ |
-| Text generation     | `openai`           | Standard OpenAI chat API, FastChat and vllm                     |
-| Image generation   | `openai_dall_e`    | DALL-E API for generating images                             |
-| Embedding | `openai_embedding` | API for text embeddings                                      |
-| General usages in POST       | `post_api`         | Huggingface/ModelScope Inference API, and customized post API |
+|   Model Usage               | Type Argument in AgentScope     | Supported APIs                                                              |
+| -------------------- | ------------------ |-----------------------------------------------------------------------------|
+| Text generation     | `openai`           | Standard *OpenAI* chat API, FastChat and vllm                               |
+| Image generation   | `openai_dall_e`    | *DALL-E* API for generating images                                          |
+| Embedding | `openai_embedding` | API for text embeddings                                                     |
+| General usages in POST       | `post_api`         | *Huggingface* and *ModelScope* Inference API, and other customized post API |
 
 
 Each API has its specific configuration requirements. For example, to configure an OpenAI API, you would need to fill out the following fields in the model config in a dict, a yaml file or a json file:
@@ -33,7 +33,7 @@ You can register your configuration by calling AgentScope's initilization method
 import agentscope
 
 agentscope.init(model_configs="./openai_model_configs.json")
-agentscope.init(model_configs="./modelscope_model_configs.json") # use openai and modelscope at the same time
+agentscope.init(model_configs="./modelscope_model_configs.json") # init again to use openai and modelscope at the same time
 
 # or you can init once by passing a list of config dict
 # openai_cfg_dict = {...dict_filling...}