Update README.md

.github/ISSUE_TEMPLATE/bug_report.md

@@ -12,6 +12,7 @@ A clear and concise description of what the bug is.
 
 **To Reproduce**
 Steps to reproduce the behavior:
+
 1. You code
 2. How to execute
 3. See error
@@ -23,9 +24,10 @@ A clear and concise description of what you expected to happen.
 Detailed error messages.
 
 **Environment (please complete the following information):**
- - AgentScope Version: [e.g. 0.0.1 via `print(agentscope.__version__)`]
- - Python Version: [e.g. 3.9]
- - OS: [e.g. macos, windows]
+
+- AgentScope Version: [e.g. 0.0.2 via `print(agentscope.__version__)`]
+- Python Version: [e.g. 3.9]
+- OS: [e.g. macos, windows]
 
 **Additional context**
-Add any other context about the problem here.
+Add any other context about the problem here.

docs/README.md

@@ -12,7 +12,7 @@ pip install sphinx sphinx-autobuild sphinx_rtd_theme myst-parser sphinxcontrib-m
 cd sphinx_doc
 
 # step 3: build the sphinx doc
-make clean html
+make clean all
 
 # step 4: view sphinx_doc/build/html/index.html using your browser
 ```
@@ -32,14 +32,14 @@ src
 
 If a new package (`agentscope/new_package`) is added , please add the corresponding documents as follows:
 
-1. use the following script to generate template script (`sphinx_doc/source/agentscope.new_package.rst`) of new packages.
+1. use the following script to generate template script (`sphinx_doc/{language}/source/agentscope.new_package.rst`) of new packages.
 
 ```shell
 cd sphinx_doc
-sphinx-apidoc -o source ../../src/agentscope
+sphinx-apidoc -o {language}/source ../../src/agentscope
 ```
 
-2. edit `sphinx_doc/source/agentscope.new_package.rst`, modify the content of the generated template script. For example, modify
+2. edit `sphinx_doc/{language}/source/agentscope.new_package.rst`, modify the content of the generated template script. For example, modify
 
 ```
 agentscope.new\_package package
@@ -68,7 +68,7 @@ new\_module module
 ...
 ```
 
-1. modify the `sphinx_doc/source/index.rst`, add the new package into the table of contents.
+3. modify the `sphinx_doc/{language}/source/index.rst`, add the new package into the table of contents.
 
 ```
 .. toctree::
@@ -86,8 +86,7 @@ new\_module module
 4. rebuild the sphinx doc of AgentScope
 
 ```
-make clean
-make html
+make clean all
 ```
 
 ### Add doc for new modules
@@ -105,7 +104,7 @@ src
 
 If a new module (agentscope/existing_package/new_module.py) is added , please add the corresponding documents as follows:
 
-1. edit `sphinx_doc/source/agentscope.existing_package.rst` and add the following content.
+1. edit `sphinx_doc/{language}/source/agentscope.existing_package.rst` and add the following content.
 
 ```
 new\_module module
@@ -120,6 +119,5 @@ new\_module module
 2. rebuild the sphinx doc of AgentScope
 
 ```
-make clean
-make html
+make clean all
 ```

docs/sphinx_doc/en/source/_templates/language_selector.html

@@ -1,5 +1,5 @@
 <!-- language_selector.html -->
 <div class="language-selector">
-    <a href="{{ pathto('../en/index') }}">English</a></li> |
-    <a href="{{ pathto('../zh_CN/index') }}">中文</a></li>
+    <a href="{{ pathto('../en/' + pagename) }}">English</a></li> |
+    <a href="{{ pathto('../zh_CN/' + pagename) }}">中文</a></li>
 </div>

docs/sphinx_doc/en/source/tutorial/103-example.md

@@ -12,46 +12,37 @@ agents).
 AgentScope decouples the deployment and invocation of models to better build multi-agent applications.
 
 In terms of model deployment, users can use third-party model services such
-as OpenAI API, HuggingFace/ModelScope Inference API, and can also quickly
-deploy local open-source model services through the [scripts]
-(<https://github.com/modelscope/agentscope/blob/main/scripts/README.md>) in
-the repository. Currently, we support building basic model services quickly
-using Flask with Transformers (or ModelScope), and also support deploying
-local model services through FastChat and vllm inference engines.
-
-While in terms of model invocation, AgentScope provides a `ModelWrapper` class to encapsulate OpenAI API and RESTful Post Request calls.
-Currently, the supported OpenAI APIs include Chat, Image generation, and Embedding.
-Users can specify the model service by setting different model configs.
-
-|   Model Usage               | Supported APIs                                                              |
-| --------------------------- |-----------------------------------------------------------------------------|
-| Text generation             | Standard *OpenAI* chat API, FastChat and vllm                               |
-| Image generation            | *DALL-E* API for generating images                                          |
-| Embedding                   | API for text embeddings                                                     |
-| General usages in POST      | *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:
+as OpenAI API, Google Gemini API, HuggingFace/ModelScope Inference API, or
+quickly deploy local open-source model services through the [scripts](https://github.com/modelscope/agentscope/blob/main/scripts/README.md) in
+the repository.
+
+While for model invocation, users should prepare a model configuration to specify the model service. Taking OpenAI Chat API as an example, the model configuration is like this:
 
 ```python
 model_config = {
     "config_name": "{config_name}", # A unique name for the model config.
     "model_type": "openai",         # Choose from "openai", "openai_dall_e", or "openai_embedding".
+
     "model_name": "{model_name}",   # The model identifier used in the OpenAI API, such as "gpt-3.5-turbo", "gpt-4", or "text-embedding-ada-002".
     "api_key": "xxx",               # Your OpenAI API key. If unset, the environment variable OPENAI_API_KEY is used.
     "organization": "xxx",          # Your OpenAI organization ID. If unset, the environment variable OPENAI_ORGANIZATION is used.
 }
 ```
 
-For open-source models, we support integration with various model interfaces such as HuggingFace, ModelScope, FastChat, and vllm. You can find scripts on deploying these services in the `scripts` directory, and we defer the detailed instructions to [[Using Different Model Sources with Model API]](#203-model).
+More details about model invocation, deployment and open-source models please refer to [Model](203-model-en) section.
 
-You can register your configuration by calling AgentScope's initialization method as follow. Besides, you can also load more than one config by calling init multiple times.
+After preparing the model configuration, you can register your configuration by calling the `init` method of AgentScope. Additionally, you can load multiple model configurations at once.
 
 ```python
 import agentscope
 
 # init once by passing a list of config dict
-openai_cfg_dict = {...dict_filling...}
-modelscope_cfg_dict = {...dict_filling...}
+openai_cfg_dict = {
+    # ...
+}
+modelscope_cfg_dict = {
+    # ...
+}
 agentscope.init(model_configs=[openai_cfg_dict, modelscope_cfg_dict])
 ```
 
@@ -71,7 +62,7 @@ dialogAgent = DialogAgent(name="assistant", model_config_name="gpt-4", sys_promp
 userAgent = UserAgent()
 ```
 
-**NOTE**: Please refer to [[Customizing Your Custom Agent with Agent Pool]](201-agent) for all available agents.
+**NOTE**: Please refer to [Customizing Your Own Agent](201-agent-en) for all available agents.
 
 ## Step3: Agent Conversation
 
@@ -112,6 +103,6 @@ while x is None or x.content != "exit":
   x = sequentialpipeline([dialog_agent, user_agent])
 ```
 
-For more details about how to utilize pipelines for complex agent interactions, please refer to [[Agent Interactions: Dive deeper into Pipelines and Message Hub]](202-pipeline).
+For more details about how to utilize pipelines for complex agent interactions, please refer to [Pipeline and MsgHub](202-pipeline-en).
 
 [[Return to the top]](#103-start-en)

docs/sphinx_doc/en/source/tutorial/104-usecase.md

@@ -308,11 +308,5 @@ Moderator: The day is coming, all the players open your eyes. Last night is peac
 
 Now you've grasped how to conveniently set up a multi-agent application with AgentScope. Feel free to tailor the game to include additional roles and introduce more sophisticated strategies. For more advanced tutorials that delve deeper into more capabilities of AgentScope, such as *memory management* and *service functions* utilized by agents, please refer to the tutorials in the **Advanced Exploration** section and look up the API references.
 
-## Other Example Applications
-
-- Example of Simple Group Conversation: [examples/Simple Conversation](https://github.com/modelscope/agentscope/tree/main/examples/simple_chat/README.md)
-- Example of Werewolves: [examples/Werewolves](https://github.com/modelscope/agentscope/tree/main/examples/werewolves/README.md)
-- Example of Distributed Agents: [examples/Distributed Agents](https://github.com/modelscope/agentscope/tree/main/examples/distributed_agents/README.md)
-- ...
 
 [[Return to the top]](#104-usecase-en)

docs/sphinx_doc/en/source/tutorial/202-pipeline.md

@@ -1,6 +1,6 @@
 (202-pipeline-en)=
 
-# Agent Interactions: Dive deeper into Pipelines and Message Hub
+# Pipeline and MsgHub
 
 **Pipeline & MsgHub** (message hub) are one or a sequence of steps describing how the structured `Msg` passes between multi-agents, which streamlines the process of collaboration across agents.