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app.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain.embeddings import HuggingFaceEmbeddings
+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
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from dotenv import load_dotenv
+import time
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Retrieve the API keys from environment variables
+huggingfacehub_api_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# Check if the keys are retrieved correctly
+if huggingfacehub_api_token is None:
+    raise ValueError("HUGGINGFACEHUB_API_TOKEN environment variable is not set")
+if groq_api_key is None:
+    raise ValueError("GROQ_API_KEY environment variable is not set")
+
+# Set environment variables for Hugging Face
+os.environ['HUGGINGFACEHUB_API_TOKEN'] = huggingfacehub_api_token
+
+# Initialize the ChatGroq LLM with the retrieved API key
+llm = ChatGroq(api_key=groq_api_key, model_name="Llama3-8b-8192")
+
+st.title("DataScience Chatgroq With Llama3")
+
+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}
+"""
+)
+
+def vector_embedding():
+    if "vectors" not in st.session_state:
+        st.session_state.embeddings = HuggingFaceEmbeddings()
+        st.session_state.loader = PyPDFDirectoryLoader("./Data_Science")  # Data Ingestion
+        st.session_state.docs = st.session_state.loader.load()  # Document Loading
+        st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)  # Chunk Creation
+        st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs[:20])  # Splitting
+        st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)  # Vector HuggingFace embeddings
+
+prompt1 = st.text_input("Enter Your Question From Documents")
+
+if st.button("Documents Embedding"):
+    vector_embedding()
+    st.write("Vector Store DB Is Ready")
+
+if prompt1:
+    document_chain = create_stuff_documents_chain(llm, prompt)
+    retriever = st.session_state.vectors.as_retriever()
+    retrieval_chain = create_retrieval_chain(retriever, document_chain)
+    start = time.process_time()
+    response = retrieval_chain.invoke({'input': prompt1})
+    st.write("Response time: ", time.process_time() - start)
+    st.write(response['answer'])
+
+    with st.expander("Document Similarity Search"):
+        for i, doc in enumerate(response["context"]):
+            st.write(doc.page_content)
+            st.write("--------------------------------")

requirements.txt

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+groq==0.5.0
+langchain==0.1.19
+langchain-community==0.0.38
+langchain-core==0.1.52
+langchain-groq==0.1.3
+langchain-text-splitters==0.0.1
+langsmith==0.1.56
+pandas==2.2.2
+pillow==10.3.0
+streamlit==1.34.0
+langchain_openai 
+langchain_core
+python-dotenv
+langchain_community
+langserve
+fastapi
+uvicorn
+sse_starlette
+pypdf
+faiss-cpu
+cassio
+langchain-groq
+langchainhub
+sentence_transformers
+PyPDF2
+langchain-objectbox