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import streamlit as st | |
import os | |
from langchain_groq import ChatGroq | |
from langchain_community.document_loaders import WebBaseLoader | |
from langchain_community.embeddings import OllamaEmbeddings | |
from langchain.text_splitter import RecursiveCharacterTextSplitter | |
from langchain.chains.combine_documents import create_stuff_documents_chain | |
from langchain_core.prompts import ChatPromptTemplate | |
from langchain.chains import create_retrieval_chain | |
from langchain_community.vectorstores import FAISS | |
import time | |
from dotenv import load_dotenv | |
load_dotenv() | |
## Load Groq API Key | |
groq_api_key = os.environ['GROQ_API_KEY'] | |
if "vector" not in st.session_state: | |
st.session_state.embeddings=OllamaEmbeddings() | |
st.session_state.loader=WebBaseLoader("https://docs.smith.langchain.com/") | |
st.session_state.docs=st.session_state.loader.load() | |
st.session_state.text_splitter=RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200) | |
st.session_state.final_documents=st.session_state.text_splitter.split_documents(st.session_state.docs[:50]) | |
st.session_state.vectors=FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings) | |
st.title("Chatgroq Demo") | |
llm=ChatGroq(groq_api_key=groq_api_key, | |
model="gemma-7b-it") | |
prompt = ChatPromptTemplate.from_template( | |
""" | |
Answer the question based on the provided context only. | |
Please provide the most accurate response based on the question. | |
<context> | |
{context} | |
<context> | |
Question: {input} | |
""" | |
) | |
document_chain = create_stuff_documents_chain(llm, prompt) | |
retriver = st.session_state.vectors.as_retriever() | |
retriver_chain = create_retrieval_chain(retriver, document_chain) | |
prompt=st.text_input("Input your prompt here") | |
if prompt: | |
start=time.process_time() | |
response = retriver_chain.invoke({"input": prompt}) | |
print("Response time :",time.process_time() - start) | |
st.write(response['answer']) | |
# With a Streamlit expander | |
with st.expander("Document Similarity Search"): | |
# Find the relevant chunks | |
for i, doc in enumerate(response["context"]): | |
st.write(doc.page_content) | |
st.write("------------------------------------") |