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import dotenv
import streamlit as st
import web_rag as wr
import web_crawler as wc
from langchain_core.tracers.langchain import LangChainTracer
from langsmith.client import Client
dotenv.load_dotenv()
ls_tracer = LangChainTracer(
project_name="Search Agent UI",
client=Client()
)
chat = wr.get_chat_llm(provider="cohere")
st.title("🔍 Simple Search Agent 💬")
if "messages" not in st.session_state:
st.session_state["messages"] = [{"role": "assistant", "content": "How can I help you?"}]
for message in st.session_state.messages:
st.chat_message(message["role"]).write(message["content"])
if prompt := st.chat_input():
st.chat_message("user").write(prompt)
st.session_state.messages.append({"role": "user", "content": prompt})
message = "I first need to do some research"
st.chat_message("assistant").write(message)
st.session_state.messages.append({"role": "assistant", "content": message})
with st.spinner("Optimizing search query"):
optimize_search_query = wr.optimize_search_query(chat, query=prompt, callbacks=[ls_tracer])
message = f"I'll search the web for: {optimize_search_query}"
st.chat_message("assistant").write(message)
st.session_state.messages.append({"role": "assistant", "content": message})
with st.spinner(f"Searching the web for: {optimize_search_query}"):
sources = wc.get_sources(optimize_search_query, max_pages=20)
with st.spinner(f"I'm now retrieveing the {len(sources)} webpages and documents I found (be patient)"):
contents = wc.get_links_contents(sources)
with st.spinner( f"Reading through the {len(contents)} sources I managed to retrieve"):
vector_store = wc.vectorize(contents)
with st.spinner( "Ok I have now enough information to answer"):
response = wr.query_rag(chat, prompt, optimize_search_query, vector_store, callbacks=[ls_tracer])
st.chat_message("assistant").write(response)
st.session_state.messages.append({"role": "assistant", "content": response})
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