Create app.py

app.py

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+import streamlit as st
+from langchain_community.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import HuggingFacePipeline
+from langchain.chains import RetrievalQA
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+import json
+import os
+
+# Define JSON file path
+dataset_path = "/mnt/data/constitution_qa.json"
+
+# Load JSON dataset
+with open(dataset_path, "r", encoding="utf-8") as f:
+    data = json.load(f)
+
+# Extract questions and answers
+texts = [f"Q: {item['question']}\nA: {item['answer']}" for item in data]
+
+# Load the embedding model
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+
+# Create FAISS vector database
+vector_db = FAISS.from_texts(texts, embeddings)
+
+# Load Open-Source LLM (LLaMA-2 7B Open Chat Model)
+tokenizer = AutoTokenizer.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+model = AutoModelForCausalLM.from_pretrained("NousResearch/Llama-2-7b-chat-hf")
+text_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer)
+llm = HuggingFacePipeline(pipeline=text_pipeline)
+
+# Create RAG pipeline
+qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=vector_db.as_retriever())
+
+# Streamlit UI
+st.title("Indian Constitution Q&A RAG App")
+query = st.text_input("Enter your legal query:")
+
+if query:
+    response = qa_chain.run(query)
+    st.write("### AI-Generated Answer:")
+    st.write(response)