refinement of llamaindex tool

README.md

@@ -70,6 +70,11 @@ docs or filename path : https://github.com/microsoft/autogen/blob/main/python/sa
 ![alt text](rag_chat.png)
 
 #### With LLamaIndex Tool
+prompt: What can i find in Tokyo related to Hayao Miyazaki and its moveis like Spirited Away?.
+![alt text](with_llamatool.png)
+
+
+
 #### AgentChat Sql Spider
 
 ### GROQ_MODEL_OPTIONS

app.py

@@ -47,18 +47,19 @@ if __name__ == "__main__":
             obj_usecases_rag_multichat.run()
 
         elif user_input['selected_usecase'] == "With LLamaIndex Tool":
+           
             obj_usecases_with_llamaIndex_multichat = WithLlamaIndexMultiAgentChat(assistant_name='Assistant', user_proxy_name='Userproxy',
                                                     llm_config=llm_config,
                                                     problem=problem,user_input=user_input)
             obj_usecases_with_llamaIndex_multichat.run()
 
 
-        elif user_input['selected_usecase'] == "AgentChat Sql Spider":
-            obj_sql_spider = AgentChatSqlSpider(assistant_name="Assistant", user_proxy_name='Userproxy',
-                                                    llm_config=llm_config,
-                                                    problem=problem)
+        # elif user_input['selected_usecase'] == "AgentChat Sql Spider":
+        #     obj_sql_spider = AgentChatSqlSpider(assistant_name="Assistant", user_proxy_name='Userproxy',
+        #                                             llm_config=llm_config,
+        #                                             problem=problem)
 
-            obj_sql_spider.run()
+        #     obj_sql_spider.run()
             
         elif user_input['selected_usecase'] == "Basic Example":
             obj_basic_example = BasicExample(assistant_name="Assistant", user_proxy_name='Userproxy',

configfile.ini

@@ -1,6 +1,6 @@
 [DEFAULT]
 PAGE_TITLE = AUTOGEN IN ACTION
 LLM_OPTIONS = Groq, Huggingface
-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
+USECASE_OPTIONS = Basic Example, 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/agents/assistantagent.py

@@ -15,8 +15,14 @@ class TrackableAssistantAgent(AssistantAgent):
 
 class TrackableLLamaIndexConversableAgent(LLamaIndexConversableAgent):
     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)
+                
+        else: 
+            with st.chat_message("ai"):
+                st.write(message['content'])
+            
         return super()._process_received_message(message, sender, silent)
 
 

src/agents/userproxyagent.py

@@ -5,5 +5,8 @@ import streamlit as st
 class TrackableUserProxyAgent(UserProxyAgent):
     def _process_received_message(self, message, sender, silent):
         with st.chat_message(sender.name.lower()):
-            st.write(message['content'])
+            if type(message)==str:
+                st.write(message)
+            else :
+                st.write(message['content'])
         return super()._process_received_message(message, sender, silent)

src/agents/wikipediaagent.py

@@ -17,16 +17,16 @@ class WikipediaAgent:
         self.wikipedia_tool = None
         self.agent = None
 
-    def create_agent(self, max_iterations=10, verbose=True):
+    def create_agent(self, max_iterations=8, verbose=True):
         # Create the Wikipedia tool specification
         wiki_spec = WikipediaToolSpec()
 
         # Get the search Wikipedia tool
-        self.wikipedia_tool = wiki_spec.to_tool_list()[1]
+        self.wikipedia_tool = wiki_spec.to_tool_list()
 
         # Create the ReActAgent with the Wikipedia tool
         self.agent = ReActAgent.from_tools(
-            tools=[self.wikipedia_tool],
+            tools=self.wikipedia_tool,
             llm=self.llm,
             max_iterations=max_iterations,
             verbose=verbose

src/streamlitui/loadui.py

@@ -31,7 +31,12 @@ class LoadStreamlitUI:
             if self.user_controls['selected_usecase'] == "RAG Chat":
                 st.session_state["docs_path"]  = st.text_input("Enter Docs path or filename")
                 
+        
+                
             
             st.session_state["chat_with_history"] = st.sidebar.toggle("Chat With History")
+            
+        if self.user_controls['selected_usecase'] == "With LLamaIndex Tool":
+            st.subheader("🏝️ Trip Advisor Specialist using wikipedia")
 
         return self.user_controls

src/usecases/withllamaIndex.py

@@ -29,7 +29,7 @@ class WithLlamaIndexMultiAgentChat:
         self.user_input = user_input
 
         self.trip_assistant = TrackableLLamaIndexConversableAgent(
-            "trip_specialist",
+            assistant_name,
             llama_index_agent=self.get_location_specialist(),
             system_message='''You help customers finding more about places they would like to 
             visit. You can use external resources to provide more details as you engage with 
@@ -49,7 +49,7 @@ class WithLlamaIndexMultiAgentChat:
         asyncio.set_event_loop(self.loop)
 
     async def initiate_chat(self):
-        await self.user_proxy.a_initiate_chat(self.trip_assistant, message=self.problem,
+        await self.user_proxy.a_initiate_chat(self.trip_assistant, message=self.problem,max_turns=2,
                                               clear_history=st.session_state["chat_with_history"])
 
     def run(self):
@@ -59,12 +59,11 @@ class WithLlamaIndexMultiAgentChat:
         # create a react agent to use wikipedia tool
         wiki_spec = WikipediaToolSpec()
         # Get the search wikipedia tool
-        wikipedia_tool = wiki_spec.to_tool_list()[1]
+        wikipedia_tool = wiki_spec.to_tool_list()
 
         llm = Groq(model=self.user_input['selected_groq_model'], api_key=st.session_state["GROQ_API_KEY"])
-        llm_70b = Groq(model="llama3-70b-8192")
 
-        location_specialist = ReActAgent.from_tools(tools=[wikipedia_tool], llm=llm, max_iterations=1,
+        location_specialist = ReActAgent.from_tools(tools=wikipedia_tool, llm=llm,
                                                     verbose=True)
 
         return location_specialist