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groq.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.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 the Groq API key
+groq_api_key='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'
+
+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_name="mixtral-8x7b-32768")
+
+prompt=ChatPromptTemplate.from_template(
+"""
+Answer the questions based on the provided context only.
+Please provide the most accurate response based on the question
+<context>
+{context}
+<context>
+Questions:{input}
+
+"""
+)
+document_chain = create_stuff_documents_chain(llm, prompt)
+retriever = st.session_state.vectors.as_retriever()
+retrieval_chain = create_retrieval_chain(retriever, document_chain)
+
+prompt=st.text_input("Input your prompt here")
+
+if prompt:
+    start=time.process_time()
+    response=retrieval_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("--------------------------------")

requirements.txt

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+langchain_openai 
+langchain_core
+python-dotenv
+streamlit
+langchain_community
+langserve
+fastapi
+uvicorn
+sse_starlette
+bs4
+pypdf
+chromadb
+faiss-cpu
+groq
+cassio
+beautifulsoup4
+langchain-groq
+wikipedia
+arxiv
+langchainhub
+sentence_transformers
+PyPDF2
+langchain-objectbox