Spaces:
Sleeping
Sleeping
File size: 2,250 Bytes
809caea |
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 |
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("------------------------------------") |