TeachableAgent

.gitignore

@@ -164,3 +164,6 @@ key_openai.txt
 key_aoai.txt
 base_aoai.txt
 wolfram.txt
+
+# DB on disk for TeachableAgent
+tmp/

autogen/agentchat/contrib/text_analyzer_agent.py

@@ -0,0 +1,72 @@
+from autogen import oai
+from autogen.agentchat.agent import Agent
+from autogen.agentchat.assistant_agent import ConversableAgent
+from typing import Callable, Dict, Optional, Union, List, Tuple, Any
+
+system_message = """You are an expert in text analysis.
+The user will give you TEXT to analyze.
+The user will give you analysis INSTRUCTIONS copied twice, at both the beginning and the end.
+You will follow these INSTRUCTIONS in analyzing the TEXT, then give the results of your expert analysis in the format requested."""
+
+
+class TextAnalyzerAgent(ConversableAgent):
+    """Text Analysis agent, a subclass of ConversableAgent designed to answer specific questions about text."""
+
+    def __init__(
+        self,
+        name: str,
+        system_message: Optional[str] = system_message,
+        llm_config: Optional[Union[Dict, bool]] = None,
+        is_termination_msg: Optional[Callable[[Dict], bool]] = None,
+        max_consecutive_auto_reply: Optional[int] = None,
+        human_input_mode: Optional[str] = "NEVER",
+        code_execution_config: Optional[Union[Dict, bool]] = False,
+        **kwargs,
+    ):
+        super().__init__(
+            name,
+            system_message,
+            is_termination_msg,
+            max_consecutive_auto_reply,
+            human_input_mode,
+            code_execution_config=code_execution_config,
+            llm_config=llm_config,
+            **kwargs,
+        )
+        self.register_reply(Agent, TextAnalyzerAgent._analyze_in_reply)
+        self.use_cache = False  # 1 to skip LLM calls made previously by relying on cached responses.
+
+    def _analyze_in_reply(
+        self,
+        messages: Optional[List[Dict]] = None,
+        sender: Optional[Agent] = None,
+        config: Optional[Any] = None,
+    ) -> Tuple[bool, Union[str, Dict, None]]:
+        """Analyzes the given text as instructed, and returns the analysis.
+        Assumes exactly two messages containing the text to analyze and the analysis instructions respectively.
+        See TeachableAgent.analyze for an example of how to use this method."""
+        if self.llm_config is False:
+            return False, None  # Return if no LLM was provided.
+        if messages is None:
+            messages = self._oai_messages[sender]  # In case of a direct call.
+        assert len(messages) == 2
+
+        # Delegate to the analysis method.
+        return True, self.analyze_text(messages[0]["content"], messages[1]["content"])
+
+    def analyze_text(self, text_to_analyze, analysis_instructions):
+        """Analyzes the given text as instructed, and returns the analysis."""
+        # Assemble the message.
+        text_to_analyze = "# TEXT\n" + text_to_analyze + "\n"
+        analysis_instructions = "# INSTRUCTIONS\n" + analysis_instructions + "\n"
+        msg_text = "\n".join(
+            [analysis_instructions, text_to_analyze, analysis_instructions]
+        )  # Repeat the instructions.
+        messages = self._oai_system_message + [{"role": "user", "content": msg_text}]
+
+        # Generate and return the analysis string.
+        response = oai.ChatCompletion.create(
+            context=None, messages=messages, use_cache=self.use_cache, **self.llm_config
+        )
+        output_text = oai.ChatCompletion.extract_text_or_function_call(response)[0]
+        return output_text