search.py

import os
import streamlit as st
from langchain_groq import ChatGroq
from langchain_community.tools import ArxivQueryRun,WikipediaQueryRun,DuckDuckGoSearchRun
from langchain_community.utilities import WikipediaAPIWrapper,ArxivAPIWrapper
from langchain.agents import initialize_agent, AgentType
from langchain.callbacks import StreamlitCallbackHandler
from dotenv import load_dotenv

##CODE
## Arxiv and Wikipedia Tools
arxiv_wrapper=ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
arxiv=ArxivQueryRun(api_wrapper=arxiv_wrapper)

api_wrapper=WikipediaAPIWrapper(top_k_results=1,doc_content_chars_max=200)
wiki=WikipediaQueryRun(api_wrapper=api_wrapper)

search=DuckDuckGoSearchRun(name="Search")


st.title("🤖 NileAI - Your AI Search Companion")
"""

Welcome to NileSearch, your AI-powered chat agent for real-time web search and insights.

This app uses `StreamlitCallbackHandler` to display the agent's thoughts and actions transparently.

Explore more LangChain 🧠 Streamlit Agent examples at [github.com/langchain-ai/streamlit-agent](https://github.com/langchain-ai/streamlit-agent).

"""


## Sidebar for Settings
# st.sidebar.title("Settings")
# api_key = st.sidebar.text_input("Enter your Groq API key:", type="password")

api_key="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"

if "messages" not in st.session_state:
    st.session_state["messages"] = [
        {"role": "assistant", "content": "Hi! How can I assist you today?"}
    ]

    
for msg in st.session_state.messages:
    st.chat_message(msg["role"]).write(msg["content"])
    
if prompt:=st.chat_input(placeholder="What is machine learning?"):
    st.session_state.messages.append({"role":"user", "content":prompt})
    st.chat_message("user").write(prompt)
    
    llm = ChatGroq(groq_api_key=api_key, model_name="Llama3-8b-8192", streaming=True)
    tools = [search, arxiv, wiki]
    
    search_agent = initialize_agent(tools, llm, agent = AgentType.ZERO_SHOT_REACT_DESCRIPTION, handle_parsing_errors=True)
    
    with st.chat_message("assistant"):
        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
        response = search_agent.run(st.session_state.messages, callbacks=[st_cb])
        st.session_state.messages.append({'role':'assistant', 'content':response})
        st.write(response)