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Browse files- app.py +72 -0
- requirements.txt +11 -0
app.py
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import streamlit as st
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from langchain_groq import ChatGroq
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from langchain_huggingface import HuggingFaceEmbeddings
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from langchain.chains.combine_documents import create_stuff_documents_chain
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from langchain_core.prompts import ChatPromptTemplate
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from langchain.chains import create_retrieval_chain
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from langchain_community.vectorstores import FAISS
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from langchain_community.document_loaders import PyPDFLoader
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import os
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from dotenv import load_dotenv
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import tempfile
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import time
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load_dotenv()
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## Langsmith Tracking
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os.environ['LANGCHAIN_API_KEY'] = os.getenv('LANGCHAIN_API_KEY')
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os.environ['LANGCHAIN_TRACING_V2'] = 'true'
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os.environ['LANGCHAIN_PROJECT'] = "Simple Q&A Chatbot With OpenAI"
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os.environ['GROQ_API_KEY'] = os.getenv('GROQ_API_KEY')
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os.environ["HF_TOKEN"] = os.getenv('HF_TOKEN')
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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llm = ChatGroq(model="llama-3.1-70b-Versatile")
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prompt = ChatPromptTemplate.from_template(
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"""
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Answer the question based on provided context only.
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Please provide the most accurate response based on the question
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<context>
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{context}
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</context>
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Question: {input}
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"""
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)
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def create_vector_embeddings(pdf_file_path):
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st.session_state.embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
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st.session_state.loader = PyPDFLoader(pdf_file_path)
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st.session_state.docs = st.session_state.loader.load()
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st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
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st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
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st.session_state.vectors = FAISS.from_documents(st.session_state.final_documents, st.session_state.embeddings)
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uploaded_file = st.file_uploader("Upload a PDF", type="pdf", key="pdf_uploader")
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user_prompt = st.text_input("Enter your Query about PDF here:")
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if st.button("Document Embedding") and uploaded_file is not None:
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with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
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tmp_file.write(uploaded_file.getvalue())
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tmp_file_path = tmp_file.name
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create_vector_embeddings(tmp_file_path)
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st.write("Vector Database is ready")
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if user_prompt and "vectors" in st.session_state:
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document_chain = create_stuff_documents_chain(llm, prompt)
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retriever = st.session_state.vectors.as_retriever()
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retrieval_chain = create_retrieval_chain(retriever, document_chain)
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start = time.process_time()
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response = retrieval_chain.invoke({"input": user_prompt})
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st.write(f"Response Time: {time.process_time() - start}")
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st.write(response["answer"])
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with st.expander("Document Similarity Search"):
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for i, doc in enumerate(response["context"]):
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st.write(doc.page_content)
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st.write("---------------------------------------")
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requirements.txt
ADDED
@@ -0,0 +1,11 @@
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langchain_groq
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langchain
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python-dotenv
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langchain_community
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langchain_core
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streamlit
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langchain_huggingface
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langchain-text-splitters
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pypdf
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faiss-cpu
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langchain-openai
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