create app.py

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import PyPDF2
+import faiss
+import numpy as np
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import OpenAI
+from langchain.chains import RAGChain
+
+def extract_text_from_pdf(pdf_file):
+    reader = PyPDF2.PdfReader(pdf_file)
+    text = ''
+    for page in reader.pages:
+        text += page.extract_text()
+    return text
+
+def create_embeddings(text):
+    embedding_model = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+    embeddings = embedding_model.embed_documents([text])
+    return embeddings
+
+def create_faiss_index(embeddings):
+    dim = len(embeddings[0])
+    index = faiss.IndexFlatL2(dim)
+    embeddings_np = np.array(embeddings).astype('float32')
+    index.add(embeddings_np)
+    return index
+
+def create_rag_chain(index):
+    llm = OpenAI(model="gpt-3.5-turbo")
+    rag_chain = RAGChain(llm=llm, vector_store=index)
+    return rag_chain
+
+def retrieve_and_generate(query, rag_chain):
+    response = rag_chain.run(query)
+    return response
+
+def main():
+    st.title("RAG Application with FAISS & PDF")
+    
+    pdf_file = st.file_uploader("Upload your PDF document", type="pdf")
+    
+    if pdf_file is not None:
+        text = extract_text_from_pdf(pdf_file)
+        st.subheader("Extracted Text from PDF")
+        st.write(text[:1000])
+
+        embeddings = create_embeddings(text)
+        index = create_faiss_index(embeddings)
+        rag_chain = create_rag_chain(index)
+        
+        query = st.text_input("Enter your query:")
+        
+        if query:
+            response = retrieve_and_generate(query, rag_chain)
+            st.subheader("Answer from RAG Model:")
+            st.write(response)
+
+if __name__ == "__main__":
+    main()