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.env

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+groq_api_key="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"

app.py

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+import os
+import time
+import streamlit as st
+from langchain_groq import ChatGroq
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_community.vectorstores import FAISS
+from dotenv import load_dotenv
+from sentence_transformers import SentenceTransformer
+
+# Load environment variables
+load_dotenv()
+groq_api_key = os.getenv("GROQ_API_KEY")
+
+# Streamlit Title
+st.title("ChatGroq RAG with PDF")
+
+# Initialize LLM
+llm = ChatGroq(groq_api_key=groq_api_key, model="llama3-8b-8192")
+
+# Define Prompt Template
+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>
+
+Question: {input}
+"""
+)
+
+# Initialize Embedding Model
+
+# Embedding Function
+def vector_embedding():
+    if "vectors" not in st.session_state:
+       
+        st.session_state.loader = PyPDFDirectoryLoader("./pdf")
+        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_document = st.session_state.text_splitter.split_documents(
+            st.session_state.docs
+        )
+        model_name = "sentence-transformers/all-mpnet-base-v2"
+        st.session_state.embeddings = HuggingFaceEmbeddings(model_name=model_name)
+        #model = SentenceTransformer("jxm/cde-small-v1", trust_remote_code=True)
+        #st.session_state.embeddings = HuggingFaceEmbeddings(model=model)
+        st.session_state.vectors = FAISS.from_documents(
+            st.session_state.final_document, st.session_state.embeddings
+        )
+
+# UI for User Input
+prompt1 = st.text_input("Enter Your Question from Documents")
+
+# Embed Documents Button
+if st.button("Document Embedding"):
+    with st.spinner("Embedding documents..."):
+        vector_embedding()
+        st.success("Vector Store created.")
+
+# Handle Queries
+if prompt1.strip():
+    if "vectors" not in st.session_state or st.session_state.vectors is None:
+        st.error("Please embed the documents first by clicking the 'Document Embedding' button.")
+    else:
+        with st.spinner("Fetching response..."):
+            start = time.time()
+            document_chain = create_stuff_documents_chain(llm, prompt)
+            retriever = st.session_state.vectors.as_retriever()
+            retrieval_chain = create_retrieval_chain(retriever, document_chain)
+            response = retrieval_chain.invoke({"input": prompt1})
+            end = time.time()
+
+        st.write(response['answer'])
+        st.write(f"Response generated in {end - start:.2f} seconds.")
+
+        with st.expander("Document Similarity Search"):
+            context = response.get('context', [])
+            if not context:
+                st.write("No similar documents found.")
+            else:
+                for i, doc in enumerate(context):
+                    st.write(doc.page_content)
+                    st.write("-----------------------------------------------")

requirements.txt

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+streamlit
+langchain
+langchain-community
+langchain-core
+langchain-text-splitters
+langchain-huggingface
+langchain-groq
+sentence-transformers
+faiss-cpu
+python-dotenv
+pypdf