Teachability for any agent (#1091)

* Partial implementation

* Partial implementation

* Fixes

* update tests

* cleanup

* update tests

* comments

* logging

* wording

* underscore

* Extend notebook for teachable GPTAssistantAgent

* Notebook for teachable GPTAssistantAgents

* Update notebook

* Update notebook

* Update notebook

* Update notebook

* revert file

* Update blog post and other documentation.

* pre-commit

* Address reviewer feedback.

* Add new nb link to examples page.

---------

Co-authored-by: Chi Wang <wang.chi@microsoft.com>

Author: rickyloynd-microsoft

.github/workflows/contrib-tests.yml

@@ -155,11 +155,11 @@ jobs:
         run: |
           python -m pip install --upgrade pip wheel
           pip install pytest
-      - name: Install packages and dependencies for TeachableAgent
+      - name: Install packages and dependencies for Teachability
         run: |
           pip install -e .[teachable]
           pip uninstall -y openai
-      - name: Test TeachableAgent
+      - name: Test Teachability
         if: matrix.python-version != '3.9'  # diversify the python versions
         run: |
           pytest test/agentchat/contrib/test_teachable_agent.py

.gitignore

@@ -165,7 +165,7 @@ key_aoai.txt
 base_aoai.txt
 wolfram.txt
 
-# DB on disk for TeachableAgent
+# DB on disk for Teachability
 tmp/
 test/my_tmp/*
 

autogen/agentchat/contrib/capabilities/agent_capability.py

@@ -0,0 +1,15 @@
+from autogen.agentchat.assistant_agent import ConversableAgent
+
+
+class AgentCapability:
+    """Base class for composable capabilities that can be added to an agent."""
+
+    def __init__(self):
+        pass
+
+    def add_to_agent(self, agent: ConversableAgent):
+        """
+        Adds a particular capability to the given agent. Must be implemented by the capability subclass.
+        An implementation will typically call agent.register_hook() one or more times. See teachability.py as an example.
+        """
+        raise NotImplementedError

autogen/agentchat/contrib/teachable_agent.py renamed to autogen/agentchat/contrib/capabilities/teachability.py

@@ -29,14 +29,6 @@ def __init__(
                 Please refer to [OpenAIWrapper.create](/docs/reference/oai/client#create)
                 for available options.
                 To disable llm-based auto reply, set to False.
-            teach_config (dict or None): Additional parameters used by TeachableAgent.
-                To use default config, set to None. Otherwise, set to a dictionary with any of the following keys:
-                - verbosity (Optional, int): # 0 (default) for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists.
-                - reset_db (Optional, bool): True to clear the DB before starting. Default False.
-                - path_to_db_dir (Optional, str): path to the directory where the DB is stored. Default "./tmp/teachable_agent_db"
-                - prepopulate (Optional, int): True (default) to prepopulate the DB with a set of input-output pairs.
-                - recall_threshold (Optional, float): The maximum distance for retrieved memos, where 0.0 is exact match. Default 1.5. Larger values allow more (but less relevant) memos to be recalled.
-                - max_num_retrievals (Optional, int): The maximum number of memos to retrieve from the DB. Default 10.
             **kwargs (dict): other kwargs in [ConversableAgent](../conversable_agent#__init__).
         """
         super().__init__(
@@ -56,7 +48,7 @@ def _analyze_in_reply(
     ) -> Tuple[bool, Union[str, Dict, None]]:
         """Analyzes the given text as instructed, and returns the analysis as a message.
         Assumes exactly two messages containing the text to analyze and the analysis instructions.
-        See TeachableAgent.analyze for an example of how to use this method."""
+        See Teachability.analyze for an example of how to use this method."""
         if self.llm_config is False:
             raise ValueError("TextAnalyzerAgent requires self.llm_config to be set in its base class.")
         if messages is None:

autogen/agentchat/contrib/text_analyzer_agent.py

@@ -152,6 +152,10 @@ def __init__(
         self.register_reply([Agent, None], ConversableAgent.check_termination_and_human_reply)
         self.register_reply([Agent, None], ConversableAgent.a_check_termination_and_human_reply)
 
+        # Registered hooks are kept in lists, indexed by hookable method, to be called in their order of registration.
+        # New hookable methods should be added to this list as required to support new agent capabilities.
+        self.hook_lists = {self.process_last_message: []}  # This is currently the only hookable method.
+
     def register_reply(
         self,
         trigger: Union[Type[Agent], str, Agent, Callable[[Agent], bool], List],
@@ -757,7 +761,7 @@ def generate_code_execution_reply(
                 else:
                     messages_to_scan += 1
 
-        # iterate through the last n messages reversely
+        # iterate through the last n messages in reverse
         # if code blocks are found, execute the code blocks and return the output
         # if no code blocks are found, continue
         for i in range(min(len(messages), messages_to_scan)):
@@ -1173,6 +1177,10 @@ def generate_reply(
         if messages is None:
             messages = self._oai_messages[sender]
 
+        # Call the hookable method that gives registered hooks a chance to process the last message.
+        # Message modifications do not affect the incoming messages or self._oai_messages.
+        messages = self.process_last_message(messages)
+
         for reply_func_tuple in self._reply_func_list:
             reply_func = reply_func_tuple["reply_func"]
             if exclude and reply_func in exclude:
@@ -1225,6 +1233,10 @@ async def a_generate_reply(
         if messages is None:
             messages = self._oai_messages[sender]
 
+        # Call the hookable method that gives registered hooks a chance to process the last message.
+        # Message modifications do not affect the incoming messages or self._oai_messages.
+        messages = self.process_last_message(messages)
+
         for reply_func_tuple in self._reply_func_list:
             reply_func = reply_func_tuple["reply_func"]
             if exclude and reply_func in exclude:
@@ -1757,3 +1769,56 @@ def _decorator(func: F) -> F:
             return func
 
         return _decorator
+
+    def register_hook(self, hookable_method: Callable, hook: Callable):
+        """
+        Registers a hook to be called by a hookable method, in order to add a capability to the agent.
+        Registered hooks are kept in lists (one per hookable method), and are called in their order of registration.
+
+        Args:
+            hookable_method: A hookable method implemented by ConversableAgent.
+            hook: A method implemented by a subclass of AgentCapability.
+        """
+        assert hookable_method in self.hook_lists, f"{hookable_method} is not a hookable method."
+        hook_list = self.hook_lists[hookable_method]
+        assert hook not in hook_list, f"{hook} is already registered as a hook."
+        hook_list.append(hook)
+
+    def process_last_message(self, messages):
+        """
+        Calls any registered capability hooks to use and potentially modify the text of the last message,
+        as long as the last message is not a function call or exit command.
+        """
+
+        # If any required condition is not met, return the original message list.
+        hook_list = self.hook_lists[self.process_last_message]
+        if len(hook_list) == 0:
+            return messages  # No hooks registered.
+        if messages is None:
+            return None  # No message to process.
+        if len(messages) == 0:
+            return messages  # No message to process.
+        last_message = messages[-1]
+        if "function_call" in last_message:
+            return messages  # Last message is a function call.
+        if "context" in last_message:
+            return messages  # Last message contains a context key.
+        if "content" not in last_message:
+            return messages  # Last message has no content.
+        user_text = last_message["content"]
+        if not isinstance(user_text, str):
+            return messages  # Last message content is not a string. TODO: Multimodal agents will use a dict here.
+        if user_text == "exit":
+            return messages  # Last message is an exit command.
+
+        # Call each hook (in order of registration) to process the user's message.
+        processed_user_text = user_text
+        for hook in hook_list:
+            processed_user_text = hook(processed_user_text)
+        if processed_user_text == user_text:
+            return messages  # No hooks actually modified the user's message.
+
+        # Replace the last user message with the expanded one.
+        messages = messages.copy()
+        messages[-1]["content"] = processed_user_text
+        return messages