microsoft · ShilinHe · Jan 30, 2024 · Jan 30, 2024 · Jan 30, 2024 · Jan 30, 2024
diff --git a/README.md b/README.md
@@ -22,14 +22,15 @@ of plugins in the form of functions to execute data analytics tasks in a statefu
 
 
 ## 🆕 News
+- 📅2024-01-30: TaskWeaver introduces a new plugin-only mode that securely generates calls to specified plugins without producing extraneous code.🪡 
 - 📅2024-01-23: TaskWeaver can now be personalized by transforming your chat histories into enduring [experiences](https://microsoft.github.io/TaskWeaver/docs/experience) 🎉
 - 📅2024-01-17: TaskWeaver now has a plugin [vision_web_explorer](https://github.com/microsoft/TaskWeaver/blob/main/project/plugins/README.md#vision_web_explorer) that can open a web browser and explore websites.🌐
 - 📅2024-01-15: TaskWeaver now supports Streaming♒ in both UI and command line.✌️
 - 📅2024-01-01: Welcome join TaskWeaver [Discord](https://discord.gg/Z56MXmZgMb).
-- 📅2023-12-21: TaskWeaver now supports a number of LLMs, such as LiteLLM, Ollama, Gemini, and QWen🎈.
-- 📅2023-12-21: TaskWeaver Website is now [available](https://microsoft.github.io/TaskWeaver/) with more documentations.
+<!-- - 📅2023-12-21: TaskWeaver now supports a number of LLMs, such as LiteLLM, Ollama, Gemini, and QWen🎈.) -->
+<!-- - 📅2023-12-21: TaskWeaver Website is now [available]&#40;https://microsoft.github.io/TaskWeaver/&#41; with more documentations.) -->
 <!-- - 📅2023-12-12: A simple UI demo is available in playground/UI folder, try it [here](https://microsoft.github.io/TaskWeaver/docs/usage/webui)! -->
-<!-- - [2023-11-30] TaskWeaver is released on GitHub🎈.  -->
+<!-- - 📅2023-11-30: TaskWeaver is released on GitHub🎈.  -->
 
 
 ## 💥 Highlights

diff --git a/taskweaver/code_interpreter/code_interpreter_plugin_only.py b/taskweaver/code_interpreter/code_interpreter_plugin_only.py
@@ -64,7 +64,7 @@ def reply(
             code: List[str] = []
             for i, f in enumerate(functions):
                 function_name = f["name"]
-                function_args = json.loads(f["arguments"])
+                function_args = f["arguments"]
                 function_call = (
                     f"r{self.return_index + i}={function_name}("
                     + ", ".join(

diff --git a/taskweaver/llm/google_genai.py b/taskweaver/llm/google_genai.py
@@ -100,28 +100,16 @@ def chat_completion(
         stop: Optional[List[str]] = None,
         **kwargs: Any,
     ) -> Generator[ChatMessageType, None, None]:
-        try:
-            return self._chat_completion(
-                messages=messages,
-                use_backup_engine=use_backup_engine,
-                stream=stream,
-                temperature=temperature,
-                max_tokens=max_tokens,
-                top_p=top_p,
-                stop=stop,
-                **kwargs,
-            )
-        except Exception:
-            return self._completion(
-                messages=messages,
-                use_backup_engine=use_backup_engine,
-                stream=stream,
-                temperature=temperature,
-                max_tokens=max_tokens,
-                top_p=top_p,
-                stop=stop,
-                **kwargs,
-            )
+        return self._chat_completion(
+            messages=messages,
+            use_backup_engine=use_backup_engine,
+            stream=stream,
+            temperature=temperature,
+            max_tokens=max_tokens,
+            top_p=top_p,
+            stop=stop,
+            **kwargs,
+        )
 
     def _chat_completion(
         self,

diff --git a/taskweaver/llm/openai.py b/taskweaver/llm/openai.py
@@ -197,13 +197,17 @@ def chat_completion(
                     message=oai_response.content if oai_response.content is not None else "",
                 )
                 if oai_response.tool_calls is not None:
+                    import json
+
                     response["role"] = "function"
-                    response["content"] = (
-                        "["
-                        + ",".join(
-                            [t.function.model_dump_json() for t in oai_response.tool_calls],
-                        )
-                        + "]"
+                    response["content"] = json.dumps(
+                        [
+                            {
+                                "name": t.function.name,
+                                "arguments": json.loads(t.function.arguments),
+                            }
+                            for t in oai_response.tool_calls
+                        ],
                     )
                 yield response
 

diff --git a/taskweaver/llm/zhipuai.py b/taskweaver/llm/zhipuai.py
@@ -1,6 +1,9 @@
 from typing import Any, Generator, List, Optional
+
 from injector import inject
+
 from taskweaver.llm.util import ChatMessageType, format_chat_message
+
 from .base import CompletionService, EmbeddingService, LLMServiceConfig
 
 DEFAULT_STOP_TOKEN: List[str] = ["</s>"]
@@ -56,34 +59,32 @@ class ZhipuAIService(CompletionService, EmbeddingService):
 
     @inject
     def __init__(self, config: ZhipuAIServiceConfig):
-
         if ZhipuAIService.zhipuai is None:
             try:
                 import zhipuai
+
                 ZhipuAIService.zhipuai = zhipuai
             except Exception:
                 raise Exception(
                     "Package zhipuai>=2.0.0 is required for using ZhipuAI API.",
                 )
 
         self.config = config
-        self.client = (
-            ZhipuAIService.zhipuai.ZhipuAI(
-                base_url=self.config.api_base,
-                api_key=self.config.api_key,
-            )
+        self.client = ZhipuAIService.zhipuai.ZhipuAI(
+            base_url=self.config.api_base,
+            api_key=self.config.api_key,
         )
 
     def chat_completion(
-            self,
-            messages: List[ChatMessageType],
-            use_backup_engine: bool = False,
-            stream: bool = False,
-            temperature: Optional[float] = None,
-            max_tokens: Optional[int] = None,
-            top_p: Optional[float] = None,
-            stop: Optional[List[str]] = None,
-            **kwargs: Any,
+        self,
+        messages: List[ChatMessageType],
+        use_backup_engine: bool = False,
+        stream: bool = False,
+        temperature: Optional[float] = None,
+        max_tokens: Optional[int] = None,
+        top_p: Optional[float] = None,
+        stop: Optional[List[str]] = None,
+        **kwargs: Any,
     ) -> Generator[ChatMessageType, None, None]:
         engine = self.config.model
         backup_engine = self.config.backup_model
@@ -136,13 +137,17 @@ def chat_completion(
                     message=zhipuai_response.content if zhipuai_response.content is not None else "",
                 )
                 if zhipuai_response.tool_calls is not None:
+                    import json
+
                     response["role"] = "function"
-                    response["content"] = (
-                            "["
-                            + ",".join(
-                        [t.function.model_dump_json() for t in zhipuai_response.tool_calls],
-                    )
-                            + "]"
+                    response["content"] = json.dumps(
+                        [
+                            {
+                                "name": t.function.name,
+                                "arguments": json.loads(t.function.arguments),
+                            }
+                            for t in zhipuai_response.tool_calls
+                        ],
                     )
                 yield response
         except Exception as e:

diff --git a/website/docs/customization/plugin/plugin_only.md b/website/docs/customization/plugin/plugin_only.md
@@ -0,0 +1,71 @@
+---
+id: plugin_only
+description: The Plugin Only Mode
+slug: /plugin/plugin_only
+---
+
+# The Plugin-Only Mode
+
+## What is the plugin-only mode?
+The plugin-only mode is a restricted mode of TaskWeaver that only allows you to use plugins. 
+Compared to the full mode, the plugin-only mode has the following restrictions:
+
+1. The generated code only contains the calls to the plugins. 
+   For example, the following code only calls the `ascii_render` plugin and does not contain any "free-form" code.
+    ```python
+    r1=ascii_render(text="Hello")
+    r1
+    ```
+
+2. Only the plugins with `plugin_only: true` in the yaml file will be loaded. 
+   For example, the following plugin will be loaded in the plugin-only mode:
+    ```yaml
+    name: ascii_render
+    code: ascii_render
+    plugin_only: true
+    ...
+    ```
+   If this field is not specified, the default value is `false`.
+   For plugins in the plugin-only mode, the argument type can only be `str`, `int`, `boolean`, or `float`.
+   Other types such as `DataFrame` are not allowed.
+   Essentially, we consider these plugins only produce "text-like" output that can be directly consumed by the LLM.
+
+To enable the plugin-only mode, you can add the configuration `"session.plugin_only_mode": true` 
+in the project configuration file `taskweaver_config.json`.
+
+## Why do we need the plugin-only mode?
+
+Although the plugin-only mode is restricted, it is still useful in some scenarios.
+For example, you may want to use TaskWeaver to only generate the code to call a certain plugin, 
+and you want to get the response from the plugin directly, without generating any other code 
+for safety reasons.
+
+## How is the plugin-only mode implemented?
+
+The plugin-only mode is implemented based on the [function calling](https://platform.openai.com/docs/guides/function-calling) mode of LLMs.
+In this mode, the LLM is trained to generate a JSON object that contains the function name and the arguments.
+For example, the following JSON object is generated by the LLM:
+```json
+{
+  "function": "ascii_render",
+  "arguments": {
+    "text": "Hello"
+  }
+}
+```
+With this JSON object, we assemble the code to call the plugin:
+```python
+r1=ascii_render(text="Hello")
+r1
+```
+Then, we execute the code and get the response from the plugin.
+Therefore, the code is not directly generated by the LLM in the plugin-only mode.
+
+## Which models support the plugin-only mode?
+
+Currently, the plugin-only mode is only supported by 
+
+- [OpenAI models](https://platform.openai.com/docs/guides/function-calling)
+- [ZhipuAI models](https://open.bigmodel.cn/dev/api)
+
+Likely other models that are compatible with the OpenAI models will also support the plugin-only mode.
diff --git a/website/sidebars.js b/website/sidebars.js
@@ -60,7 +60,7 @@ const sidebars = {
         label: 'Plugin',
         collapsible: true,
         collapsed: true,
-        items: ['customization/plugin/plugin_intro', 'customization/plugin/plugin_selection', 'customization/plugin/embedding', 'customization/plugin/develop_plugin', 'customization/plugin/multi_yaml_single_impl'],
+        items: ['customization/plugin/plugin_intro', 'customization/plugin/plugin_selection', 'customization/plugin/embedding', 'customization/plugin/develop_plugin', 'customization/plugin/multi_yaml_single_impl', 'customization/plugin/plugin_only'],
       },
       {
         type: 'category',