with llamaIndex tool wikipedia

app.py

@@ -3,6 +3,7 @@ import streamlit as st
 from configfile import Config
 from src.streamlitui.loadui import LoadStreamlitUI
 from src.usecases.multiagentschat import MultiAgentChat
+from src.usecases.withllamaIndex import WithLlamaIndexMultiAgentChat
 from src.LLMS.groqllm import GroqLLM
 
 
@@ -26,6 +27,14 @@ if __name__ == "__main__":
 
     if problem:
         # start multichat
-        obj_usecases = MultiAgentChat(assistant_name='Assistant', user_proxy_name='Userproxy', llm_config=llm_config,
-                                      problem=problem)
-        obj_usecases.run()
+        if user_input['selected_usecase'] == "MultiAgent Chat":
+            obj_usecases_multichat = MultiAgentChat(assistant_name='Assistant', user_proxy_name='Userproxy', llm_config=llm_config,
+                                          problem=problem)
+            obj_usecases_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()
+

configfile.ini

@@ -2,5 +2,5 @@
 PAGE_TITLE = AUTOGEN IN ACTION
 LLM_OPTIONS = Groq, Huggingface
 USECASE_OPTIONS = MultiAgent Chat, RAG Chat, With LLamaIndex Tool, Teachable Agent, With Langchain
-GROQ_MODEL_OPTIONS = Mixtral 8x7b, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
+GROQ_MODEL_OPTIONS = mixtral-8x7b-32768, llama3-8b-8192, llama3-70b-8192, gemma-7b-i
 

requirements.txt

@@ -1,3 +1,8 @@
 streamlit
 pyautogen
-groq
+groq
+llama-index
+llama-index-tools-wikipedia
+llama-index-readers-wikipedia
+wikipedia
+llama-index-llms-groq

src/LLMS/groqllm.py

@@ -15,6 +15,6 @@ class GroqLLM:
              "cache_seed": None}
         ]
 
-        llm_config = {"config_list": config_list}
+        llm_config = {"config_list": config_list, "request_timeout": 60}
         st.session_state['llm_config'] = llm_config
         return llm_config

src/agents/assistantagent.py

@@ -1,5 +1,6 @@
 from autogen import AssistantAgent
 import streamlit as st
+from autogen.agentchat.contrib.llamaindex_conversable_agent import LLamaIndexConversableAgent
 
 
 class TrackableAssistantAgent(AssistantAgent):
@@ -7,3 +8,13 @@ class TrackableAssistantAgent(AssistantAgent):
         with st.chat_message(sender.name):
             st.write(message)
         return super()._process_received_message(message, sender, silent)
+
+
+class TrackableLLamaIndexConversableAgent(LLamaIndexConversableAgent):
+    def _process_received_message(self, message, sender, silent):
+        with st.chat_message(sender.name):
+            st.write(message)
+        return super()._process_received_message(message, sender, silent)
+
+
+

src/agents/wikipediaagent.py

@@ -0,0 +1,45 @@
+# from some_module import WikipediaToolSpec, ReActAgent  # Ensure you have the correct imports
+# from some_llm_module import llm  # Import the LLM you're using
+
+from llama_index.core import Settings
+from llama_index.core.agent import ReActAgent
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.llms.openai import OpenAI
+from llama_index.tools.wikipedia import WikipediaToolSpec
+
+
+
+
+
+class WikipediaAgent:
+    def __init__(self, llm):
+        self.llm = llm
+        self.wikipedia_tool = None
+        self.agent = None
+
+    def create_agent(self, max_iterations=10, verbose=True):
+        # Create the Wikipedia tool specification
+        wiki_spec = WikipediaToolSpec()
+
+        # Get the search Wikipedia tool
+        self.wikipedia_tool = wiki_spec.to_tool_list()[1]
+
+        # Create the ReActAgent with the Wikipedia tool
+        self.agent = ReActAgent.from_tools(
+            tools=[self.wikipedia_tool],
+            llm=self.llm,
+            max_iterations=max_iterations,
+            verbose=verbose
+        )
+
+        return self.agent
+
+
+
+
+# Usage
+# wiki_agent = WikipediaAgent(llm)
+# agent = wiki_agent.create_agent()
+
+
+

src/usecases/withllamaIndex.py

@@ -0,0 +1,70 @@
+import asyncio
+
+from autogen.agentchat.contrib.llamaindex_conversable_agent import LLamaIndexConversableAgent
+from llama_index.tools.wikipedia import WikipediaToolSpec
+
+from src.agents.assistantagent import TrackableAssistantAgent, TrackableLLamaIndexConversableAgent
+from src.agents.userproxyagent import TrackableUserProxyAgent
+from src.agents.wikipediaagent import WikipediaAgent
+from llama_index.core import Settings
+from llama_index.core.agent import ReActAgent
+from llama_index.embeddings.openai import OpenAIEmbedding
+from llama_index.llms.openai import OpenAI
+from llama_index.tools.wikipedia import WikipediaToolSpec
+from llama_index.llms.groq import Groq
+
+import streamlit as st
+
+
+class WithLlamaIndexMultiAgentChat:
+    def __init__(self, assistant_name, user_proxy_name, llm_config, problem, user_input):
+        # 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.llm_config = llm_config
+        self.user_input = user_input
+
+        self.trip_assistant = TrackableLLamaIndexConversableAgent(
+            "trip_specialist",
+            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 
+            the customer. Reply 'TERMINATE' if all task is done ''',
+            description="This agents helps customers discover locations to visit, things to do, and other details about a location. It can use external resources to provide more details. This agent helps in finding attractions, history and all that there si to know about a place",
+        )
+
+        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.trip_assistant, message=self.problem,
+                                              clear_history=st.session_state["chat_with_history"])
+
+    def run(self):
+        self.loop.run_until_complete(self.initiate_chat())
+
+    def get_location_specialist(self):
+        # create a react agent to use wikipedia tool
+        wiki_spec = WikipediaToolSpec()
+        # Get the search wikipedia tool
+        wikipedia_tool = wiki_spec.to_tool_list()[1]
+
+        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=3,
+                                                    verbose=True)
+
+        return location_specialist