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dd4e278 5256aa7 dd4e278 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 | import streamlit as st
from langchain_groq import ChatGroq
from langchain_community.utilities import ArxivAPIWrapper, WikipediaAPIWrapper
from langchain_community.tools import ArxivQueryRun, WikipediaQueryRun, DuckDuckGoSearchRun
from langchain.agents import initialize_agent, AgentType
from langchain_community.callbacks import StreamlitCallbackHandler
import os
from dotenv import load_dotenv
# 1. Create Tools
arxiv_wrapper = ArxivAPIWrapper(top_k_results=1, doc_content_chars_max=200)
arxiv = ArxivQueryRun(api_wrapper=arxiv_wrapper)
wiki_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=200)
wiki = WikipediaQueryRun(api_wrapper=wiki_wrapper)
search = DuckDuckGoSearchRun(
name="Search",
region="us-en",
safesearch="Off",
time="y",
max_results=3
)
# 2. Streamlit UI
st.title("🔎 LangChain - Chat with Improved Search Engine Agent")
st.sidebar.title("Settings")
api_key = st.sidebar.text_input("Enter your Groq API Key:", type="password")
if "messages" not in st.session_state:
st.session_state["messages"] = [
{
"role": "assistant",
"content": "Hi, I'm a chatbot with access to search tools. Ask me anything!"
}
]
for msg in st.session_state.messages:
st.chat_message(msg["role"]).write(msg["content"])
user_input = st.chat_input(placeholder="Ask me something, e.g. 'What is machine learning?'")
if user_input:
st.session_state.messages.append({"role": "user", "content": user_input})
st.chat_message("user").write(user_input)
# 3. Supply System-Like Instructions via the Agent Prefix
system_instructions = (
"You are a concise, knowledgeable assistant. "
"Use the provided tools ONLY IF absolutely necessary to answer the user's query. "
"Once you have enough information, STOP searching and compile a final, concise answer. "
"Do not keep repeating the same queries."
)
# 4. Create ChatGroq LLM
llm = ChatGroq(
groq_api_key=api_key,
model_name="Llama3-8b-8192",
streaming=True
# No `system_message` here; ChatGroq doesn't support that param
)
# 5. Initialize Agent with a prompt prefix
# max_iterations=3 ensures the agent doesn’t get stuck searching forever
agent = initialize_agent(
tools=[search, arxiv, wiki],
llm=llm,
agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
handle_parsing_errors=True,
max_iterations=3,
early_stopping_method="force",
verbose=False, # set True if you want more logs in your terminal
agent_kwargs={
"prefix": system_instructions,
}
)
# 6. Run the Agent
with st.chat_message("assistant"):
st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=False)
response = agent.invoke(
{"input": user_input},
callbacks=[st_cb]
)["output"]
# Save to session and display
st.session_state.messages.append({"role": "assistant", "content": response})
st.write(response)
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