teachable agent

.gitignore

@@ -7,3 +7,8 @@ codegen/tmp_code_3e1806a0bf22b99c6c5d2b77650fe9a8.py
 /tmp/chromadb
 /tmp/db
 /.cache
+teachability_db/2cff7ca5-81fd-4f89-a76a-2d5bfc940455/data_level0.bin
+teachability_db/2cff7ca5-81fd-4f89-a76a-2d5bfc940455/header.bin
+*.bin
+*.sqlite3
+*.pkl

README.md

@@ -55,6 +55,12 @@ Requirements
 #### Basic Example
 ![alt text](basic_example.png)
 
+#### Teachabke Agent
+Prompt1: who is Sachin Tiwari
+Prompt2: Sachin is from jharkhand working in uk
+prompt3 : who is sachin
+![alt text](teachable_agent.png)
+
 ### Chat with CAG
 prompt1: what is dotnet
 prompt2: what is python

app.py

@@ -11,6 +11,7 @@ from src.LLMS.groqllm import GroqLLM
 from src.usecases.multiagentragchat import MultiAgentRAGChat
 from src.usecases.basicexample import BasicExample
 from src.usecases.cag_chat import CAGLLMChat
+from src.usecases.teachableagent import TeachableAgent
 
 
 # MAIN Function START
@@ -79,5 +80,19 @@ if __name__ == "__main__":
             obj_cag_llm.process_cag_llm()
             
             
+        elif user_input['selected_usecase'] == "Teachable Agent":
+            
+            obj_chat = TeachableAgent(llm_config=llm_config,problem=problem)
+            response = obj_chat.start_chat()
+            with st.chat_message("user"):
+                st.write(problem)
+            with st.chat_message("ai"):
+                st.markdown(response.summary)
+            
+            
+               
+            
+            
+            
 
 

configfile.ini

@@ -1,6 +1,6 @@
 [DEFAULT]
 PAGE_TITLE = AUTOGEN IN ACTION
 LLM_OPTIONS = Groq, Huggingface
-USECASE_OPTIONS = Basic Example, Chat with CAG, MultiAgent Chat, MultiAgent Code Execution, RAG Chat, With LLamaIndex Tool
+USECASE_OPTIONS = Basic Example, Chat with CAG, Teachable Agent, MultiAgent Chat, MultiAgent Code Execution, RAG Chat, With LLamaIndex Tool
 GROQ_MODEL_OPTIONS = llama-3.3-70b-versatile, mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma2-9b-it
 

src/streamlitui/loadui.py

@@ -32,17 +32,24 @@ class LoadStreamlitUI:
                 st.session_state["docs_path"]  = st.text_input("Enter Docs path or filename")
                 
         
+            if self.user_controls['selected_usecase'] == "Teachable Agent":  
+                selected_chat_purpose = st.selectbox("Do you want me to ask or teach?",
+                              ("Ask", "Teach"))  
+                if selected_chat_purpose == "Ask":
+                     st.session_state["Chat_Purpose"]=True
+                     
+                else :
+                    st.session_state["Chat_Purpose"]=False
+            else:
+                if st.sidebar.toggle("Chat With History"):
+                    st.session_state["chat_with_history"]=False
+                else :
+                    st.session_state["chat_with_history"]=True
                 
-            
-            if st.sidebar.toggle("Chat With History"):
-                st.session_state["chat_with_history"]=False
-            else :
-                st.session_state["chat_with_history"]=True
-            
-            if st.sidebar.toggle("LLM Caching"):
-                st.session_state["Cache_Seed"]=True
-            else :
-                st.session_state["Cache_Seed"]=False
+                if st.sidebar.toggle("LLM Caching"):
+                    st.session_state["Cache_Seed"]=True
+                else :
+                    st.session_state["Cache_Seed"]=False
             
         if self.user_controls['selected_usecase'] == "With LLamaIndex Tool":
             st.subheader("🏝️ Trip Advisor Specialist using wikipedia")

src/usecases/cag_chat.py

@@ -10,7 +10,7 @@ class CAGLLMChat:
 
     
     def start_chat(self):
-        llm_config= self.llm_config
+        llm_config= st.session_state['llm_config']
         problem = self.problem
         assistant = AssistantAgent("assistant", llm_config=llm_config,code_execution_config=False,human_input_mode='NEVER')
         user_proxy = UserProxyAgent("user_proxy", code_execution_config=False,human_input_mode='NEVER')
@@ -19,7 +19,7 @@ class CAGLLMChat:
         response = user_proxy.initiate_chat(
             assistant,
             message=problem,
-            max_turns=1,
+            max_turns=2,
             clear_history=st.session_state["chat_with_history"]
         )
         return response

src/usecases/teachableagent.py

@@ -0,0 +1,46 @@
+import os
+from autogen import AssistantAgent, UserProxyAgent
+import streamlit as st
+from autogen import ConversableAgent, UserProxyAgent
+from autogen.agentchat.contrib.capabilities.teachability import Teachability
+
+
+class TeachableAgent:
+    def __init__(self,llm_config,problem):
+        self.llm_config = llm_config
+        self.problem = problem
+
+    
+    def start_chat(self):
+        llm_config= st.session_state['llm_config']
+        problem = self.problem
+        # Start by instantiating any agent that inherits from ConversableAgent.
+        teachable_agent = ConversableAgent(
+            name="teachable_agent",  # The name is flexible, but should not contain spaces to work in group chat.
+            llm_config=llm_config
+        )
+
+        # Instantiate the Teachability capability. Its parameters are all optional.
+        teachability = Teachability(
+            verbosity=0,  # 0 for basic info, 1 to add memory operations, 2 for analyzer messages, 3 for memo lists.
+            reset_db=False,
+            path_to_db_dir="./teachability_db",
+            recall_threshold=1.5,  # Higher numbers allow more (but less relevant) memos to be recalled.
+        )
+
+        # Now add the Teachability capability to the agent.
+        teachability.add_to_agent(teachable_agent)
+
+        # Instantiate a UserProxyAgent to represent the user. But in this notebook, all user input will be simulated.
+        user = UserProxyAgent(
+            name="user",
+            human_input_mode="NEVER",
+            is_termination_msg=lambda x: True if "TERMINATE" in x.get("content") else False,
+            max_consecutive_auto_reply=0,
+            code_execution_config={
+                "use_docker": False
+            },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.
+        )
+        #clear_history = False - Teach
+        response = user.initiate_chat(teachable_agent, message=problem, clear_history=st.session_state["Chat_Purpose"])
+        return response