[Feature] support building tool descriptions automatically (#96)

* redundancy reduction

* add `tool_api` to annotate a tool method

* improve json parsing

* enhance parsers

* update README.md

---------

Co-authored-by: wangzy <[email protected]>

README.md

@@ -83,7 +83,7 @@ Below is an example of running ReWOO with GPT-3.5
 ```python
 # Import necessary modules and classes from the "lagent" library.
 from lagent.agents import ReWOO
-from lagent.actions import ActionExecutor, GoogleSearch, LLMQA
+from lagent.actions import ActionExecutor, GoogleSearch
 from lagent.llms import GPTAPI
 
 # Initialize the Language Model (llm) and provide your API key.
@@ -92,14 +92,11 @@ llm = GPTAPI(model_type='gpt-3.5-turbo', key=['Your OPENAI_API_KEY'])
 # Initialize the Google Search tool and provide your API key.
 search_tool = GoogleSearch(api_key='Your SERPER_API_KEY')
 
-# Initialize the LLMQA tool using the Language Model (llm).
-llmqa_tool = LLMQA(llm)
-
 # Create a chatbot by configuring the ReWOO agent.
 chatbot = ReWOO(
     llm=llm,  # Provide the Language Model instance.
     action_executor=ActionExecutor(
-        actions=[search_tool, llmqa_tool]  # Specify the actions the chatbot can perform.
+        actions=[search_tool]  # Specify the actions the chatbot can perform.
     ),
 )
 
@@ -144,6 +141,7 @@ response = chatbot.chat('若$z=-1+\sqrt{3}i$,则$\frac{z}{{z\overline{z}-1}}=\le
 print(response.response)  # Output the response generated by the chatbot.
 >>> $-\\frac{1}{3}+\\frac{{\\sqrt{3}}}{3}i$
 ```
+
 ### All Thanks To Our Contributors:
   <a href="https://github.com/InternLM/lagent/graphs/contributors">
   <img src="https://contrib.rocks/image?repo=InternLM/lagent" />

lagent/actions/__init__.py

@@ -1,6 +1,8 @@
+from typing import Type
+
 from .action_executor import ActionExecutor
 from .arxiv_search import ArxivSearch
-from .base_action import BaseAction
+from .base_action import TOOL_REGISTRY, BaseAction, tool_api
 from .bing_map import BINGMap
 from .builtin_actions import FinishAction, InvalidAction, NoAction
 from .google_scholar_search import GoogleScholar
@@ -13,5 +15,45 @@
     'BaseAction', 'ActionExecutor', 'InvalidAction', 'FinishAction',
     'NoAction', 'BINGMap', 'ArxivSearch', 'FinishAction', 'GoogleSearch',
     'GoogleScholar', 'PythonInterpreter', 'PPT', 'BaseParser', 'JsonParser',
-    'TupleParser'
+    'TupleParser', 'tool_api', 'list_tools', 'get_tool_cls', 'get_tool'
 ]
+
+
+def list_tools(with_class: bool = False):
+    """List available tools
+
+    Args:
+        with_class (bool): whether to return the action class along 
+            with its name. Defaults to ``False``.
+
+    Returns:
+        list: all action names
+    """
+    return list(TOOL_REGISTRY.items()) if with_class else list(
+        TOOL_REGISTRY.keys())
+
+
+def get_tool_cls(specifier: str) -> Type[BaseAction]:
+    """Get the action class
+
+    Args:
+        specifier (:class:`str`): tool name
+
+    Returns:
+        Type[BaseAction]: action class
+    """
+    return TOOL_REGISTRY.get_class(specifier)
+
+
+def get_tool(specifier: str, *args, **kwargs) -> BaseAction:
+    """Instantiate an action
+
+    Args:
+        specifier (str): tool name
+        args: positional arguments passed to the action's ``__init__`` method
+        kwargs: keyword arguments passed to the action's ``__init__`` method
+
+    Returns:
+        :class:`BaseAction`: action object
+    """
+    return TOOL_REGISTRY.get(specifier, *args, **kwargs)

lagent/actions/arxiv_search.py

@@ -2,55 +2,40 @@
 
 import arxiv
 
-from lagent.actions.base_action import BaseAction
+from lagent.actions.base_action import BaseAction, tool_api
 from lagent.actions.parser import BaseParser, JsonParser
 from lagent.schema import ActionReturn, ActionStatusCode
 
-DEFAULT_DESCRIPTION = dict(
-    name='ArxivSearch',
-    description='Search information from Arxiv.org '
-    'Useful for when you need to answer questions about Physics, Mathematics, '
-    'Computer Science, Quantitative Biology, Quantitative Finance, Statistics, '
-    'Electrical Engineering, and Economics '
-    'from scientific articles on arxiv.org',
-    api_list=[
-        dict(
-            name='get_arxiv_article_information',
-            description=
-            'Run Arxiv search and get the article meta information.',
-            parameters=[
-                dict(
-                    name='query',
-                    type='STRING',
-                    description='the content of search query')
-            ],
-            required=['query'],
-            return_data=[
-                dict(
-                    name='content',
-                    description='a list of 3 arxiv search papers'),
-            ],
-        )
-    ],
-)
-
 
 class ArxivSearch(BaseAction):
-    """ArxivSearch action"""
+    """Search information from Arxiv.org. \
+Useful for when you need to answer questions about Physics, Mathematics, \
+Computer Science, Quantitative Biology, Quantitative Finance, Statistics, \
+Electrical Engineering, and Economics from scientific articles on arxiv.org.
+    """
 
     def __init__(self,
                  top_k_results: int = 3,
                  max_query_len: int = 300,
                  doc_content_chars_max: int = 1500,
                  description: Optional[dict] = None,
                  parser: Type[BaseParser] = JsonParser,
-                 enable: bool = True) -> None:
-        super().__init__(description or DEFAULT_DESCRIPTION, parser, enable)
+                 enable: bool = True):
+        super().__init__(description, parser, enable)
         self.top_k_results = top_k_results
         self.max_query_len = max_query_len
         self.doc_content_chars_max = doc_content_chars_max
 
-    def get_arxiv_article_information(self, query: str):
+    @tool_api(return_dict=True)
+    def get_arxiv_article_information(self, query: str) -> dict:
+        """Run Arxiv search and get the article meta information.
+
+        Args:
+            query (:class:`str`): the content of search query
+
+        Returns:
+            content (:class:`str`): a list of 3 arxiv search papers
+        """
         try:
             results = arxiv.Search(  # type: ignore
                 query[:self.max_query_len],