Create app.py

app.py

@@ -0,0 +1,44 @@
+# Continue with the rest of the code
+from langchain.chains import RetrievalQA
+from langchain.document_loaders import TextLoader
+from langchain.embeddings import SentenceTransformerEmbeddings
+from langchain.vectorstores import FAISS
+from transformers import pipeline
+
+
+
+# Paste your data here
+data = """
+Enter your text data here. For example:
+"""
+
+# Split data into chunks for embedding
+def chunk_text(text, chunk_size=500):
+    words = text.split()
+    chunks = [" ".join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
+    return chunks
+
+# Prepare the text chunks
+text_chunks = chunk_text(data)
+
+# Generate embeddings and index the data
+embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+vectorstore = FAISS.from_texts(text_chunks, embeddings)
+
+# Load a simple LLM (Hugging Face model)
+from transformers import pipeline
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+
+# Define a function to perform QA
+def answer_question(question):
+    retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
+    relevant_docs = retriever.get_relevant_documents(question)
+    context = " ".join([doc.page_content for doc in relevant_docs])
+    answer = qa_pipeline(question=question, context=context)
+    return answer["answer"]
+
+# Ask a question
+print("Paste the text and ask your question.")
+question = input("Your question: ")
+answer = answer_question(question)
+print("Answer:", answer)