refinements of usecases amd readme file updated

README.md

@@ -51,16 +51,27 @@ Requirements
 ### LLM_OPTIONS
     Groq
 ### USECASE_OPTIONS
-#### MultiAgent Code Execution
-![alt text](image.png) 
-![alt text](image-3.png)
+
+#### Basic Example
+![alt text](basic_example.png)
 
 #### MultiAgent Chat
+prompt : As a user , create a asp.net form with razor view page for health insaurance feedback page
+![alt text](multiagent_chat.png)
+
+#### MultiAgent Code Execution
+
+![alt text](multiagent_code_execution.png) 
+![alt text](image-3.png)
 
 #### RAG Chat
+prompt : Explain
+docs or filename path : https://github.com/microsoft/autogen/blob/main/python/samples/agentchat_chainlit/README.md
+![alt text](rag_chat.png)
 
 #### With LLamaIndex Tool
 #### AgentChat Sql Spider
+
 ### GROQ_MODEL_OPTIONS
     mixtral-8x7b-32768
     llama3-8b-8192

app.py

@@ -8,6 +8,7 @@ from src.usecases.withllamaIndex import WithLlamaIndexMultiAgentChat
 from src.usecases.agentchatsqlspider import AgentChatSqlSpider
 from src.LLMS.groqllm import GroqLLM
 from src.usecases.multiagentragchat import MultiAgentRAGChat
+from src.usecases.basicexample import BasicExample
 
 
 # MAIN Function START
@@ -58,5 +59,12 @@ if __name__ == "__main__":
                                                     problem=problem)
 
             obj_sql_spider.run()
+            
+        elif user_input['selected_usecase'] == "Basic Example":
+            obj_basic_example = BasicExample(assistant_name="Assistant", user_proxy_name='Userproxy',
+                                                    llm_config=llm_config,
+                                                    problem=problem)
+            obj_basic_example.run()
+            
 
 

configfile.ini

@@ -1,6 +1,6 @@
 [DEFAULT]
 PAGE_TITLE = AUTOGEN IN ACTION
 LLM_OPTIONS = Groq, Huggingface
-USECASE_OPTIONS = MultiAgent Code Execution, MultiAgent Chat, RAG Chat, With LLamaIndex Tool, AgentChat Sql Spider
+USECASE_OPTIONS = Basic Example, MultiAgent Chat, MultiAgent Code Execution, RAG Chat, With LLamaIndex Tool, AgentChat Sql Spider
 GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
 

src/agents/assistantagent.py

@@ -5,8 +5,11 @@ from autogen.agentchat.contrib.llamaindex_conversable_agent import LLamaIndexCon
 
 class TrackableAssistantAgent(AssistantAgent):
     def _process_received_message(self, message, sender, silent):
-        with st.chat_message(sender.name):
-            st.write(message)
+        if message and type(message)== str and sender.name =="Userproxy":
+            with st.chat_message("user"):
+                st.write(message)
+        
+                
         return super()._process_received_message(message, sender, silent)
 
 

src/agents/qdrantretrieveuserproxyagent.py

@@ -5,6 +5,6 @@ from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import QdrantRet
 
 class TrackableQdrantRetrieveUserProxyAgent(QdrantRetrieveUserProxyAgent):
     def _process_received_message(self, message, sender, silent):
-        with st.chat_message("user"):
-            st.write(message)
+        with st.chat_message("ai"):
+            st.write(message["content"])
         return super()._process_received_message(message, sender, silent)

src/agents/retrieveassistantagent.py

@@ -7,6 +7,7 @@ from autogen.agentchat.contrib.qdrant_retrieve_user_proxy_agent import QdrantRet
 
 class TrackableRetrieveAssistantAgent(RetrieveAssistantAgent):
     def _process_received_message(self, message, sender, silent):
-        with st.chat_message(sender.name):
-            st.write(message)
+        if type(message)== str and sender.name =="Userproxy":
+            with st.chat_message("user"):
+                st.write(message)
         return super()._process_received_message(message, sender, silent)

src/agents/userproxyagent.py

@@ -4,6 +4,6 @@ import streamlit as st
 
 class TrackableUserProxyAgent(UserProxyAgent):
     def _process_received_message(self, message, sender, silent):
-        with st.chat_message("user"):
-            st.write(message)
+        with st.chat_message(sender.name.lower()):
+            st.write(message['content'])
         return super()._process_received_message(message, sender, silent)

src/usecases/basicexample.py

@@ -0,0 +1,30 @@
+import asyncio
+from src.agents.assistantagent import TrackableAssistantAgent
+from src.agents.userproxyagent import TrackableUserProxyAgent
+import streamlit as st
+
+
+class BasicExample:
+    def __init__(self, assistant_name, user_proxy_name, llm_config, problem):
+        self.assistant = TrackableAssistantAgent(name=assistant_name,
+                                                 system_message="""you are helpful assistant. Reply "TERMINATE" in 
+                                                 the end when everything is done """,
+                                                 human_input_mode="NEVER",
+                                                 llm_config=llm_config,
+                                                 )
+        self.user_proxy = TrackableUserProxyAgent(name=user_proxy_name,
+                                                  system_message="You are Admin",
+                                                  human_input_mode="NEVER",
+                                                  llm_config=llm_config,
+                                                  code_execution_config=False,
+                                                  is_termination_msg=lambda x: x.get("content", "").strip().endswith(
+                                                      "TERMINATE"))
+        self.problem = problem
+        self.loop = asyncio.new_event_loop()
+        asyncio.set_event_loop(self.loop)
+
+    async def initiate_chat(self):
+        await self.user_proxy.a_initiate_chat(self.assistant, max_turns=2,  message=self.problem, clear_history=st.session_state["chat_with_history"])
+
+    def run(self):
+        self.loop.run_until_complete(self.initiate_chat())

src/usecases/multiagentragchat.py

@@ -20,7 +20,8 @@ class MultiAgentRAGChat:
                                                     max_consecutive_auto_reply=4,
                                                     retrieve_config={
                                                         "task": "code",
-                                                        "docs_path": self.list_files(st.session_state["docs_path"]),
+                                                        #"docs_path": self.list_files(st.session_state["docs_path"]), This is for loading custion files from directory - logic implemented
+                                                        "docs_path": st.session_state["docs_path"],
                                                         "chunk_token_size": 500,
                                                         "model": llm_config["config_list"][0]["model"],
                                                         "client": QdrantClient(":memory:"),
@@ -44,10 +45,18 @@ class MultiAgentRAGChat:
         # Ensure the directory path ends with a slash
         if not directory.endswith('/'):
             directory += '/'
-        
-        # Use glob to get the list of files
-        files = glob.glob(os.path.join(directory, '*'))
-        file_list = [path.replace('\\', '/') for path in files]
+        try :
+            
+            # Use glob to get the list of files
+            files = glob.glob(os.path.join(directory, '*'))
+            file_list = [path.replace('\\', '/') for path in files]
+            
+            if file_list.count == 0:
+                raise ValueError('list of files is zero')
+            
+        except Exception as ex:
+            raise ValueError('issue with file path')
+            
         
         return file_list
 

src/usecases/multiagentschat.py

@@ -24,7 +24,7 @@ class MultiAgentChat:
         asyncio.set_event_loop(self.loop)
 
     async def initiate_chat(self):
-        await self.user_proxy.a_initiate_chat(self.assistant, message=self.problem, clear_history=st.session_state["chat_with_history"])
+        await self.user_proxy.a_initiate_chat(self.assistant, max_turns=2, message=self.problem, clear_history=st.session_state["chat_with_history"])
 
     def run(self):
         self.loop.run_until_complete(self.initiate_chat())