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The Bharatiya Nyaya Sanhita (BNS), replacing the Indian Penal Code on July 1, 2024, offers a modern legal framework. This chatbot provides reliable information on BNS for reference, catering to legal professionals, students, and the public. Note: It is for informational use only, not a substitute for legal advice

app.py

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+import streamlit as st
+from config import setup_environment
+from rag_initializer import initialize_rag_chain
+from chatbot import chatbot_response
+
+setup_environment()
+
+st.set_page_config(page_title="Bharatiya Nyaya Sanhita Chatbot", page_icon="⚖️", layout="wide")
+
+def main():
+    st.title("Bharatiya Nyaya Sanhita Chatbot")
+    st.sidebar.info("Bharatiya Nyaya Sanhita came into effect on 1 July, 2024 after being passed by the parliament in December 2023 to replace the Indian Penal Code (IPC), which dated back to the period of British India")
+    st.sidebar.warning("Disclaimer: This chatbot provides information based on the Bharatiya Nyaya Sanhita for reference purposes only. It is not a substitute for professional legal advice.")
+
+    rag_chain = initialize_rag_chain()
+    
+    if rag_chain is None:
+        st.error("Failed to initialize the chatbot. Please try again later.")
+        return
+    
+    st.write("Welcome! I'm here to help you with information about the Bharatiya Nyaya Sanhita. What would you like to know?")
+    
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+
+    if prompt := st.chat_input("Your question:"):
+        st.session_state.messages.append({"role": "user", "content": prompt})
+        with st.chat_message("user"):
+            st.markdown(prompt)
+
+        with st.chat_message("assistant"):
+            with st.spinner("AI is thinking..."):
+                response = chatbot_response(prompt, rag_chain)
+            st.markdown(response)
+        
+        st.session_state.messages.append({"role": "assistant", "content": response})
+    
+    
+if __name__ == "__main__":
+    main()

chatbot.py

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+def chatbot_response(query, rag_chain):
+    try:
+        return rag_chain.invoke({"input": query})["answer"]
+    except Exception as e:
+        return f"Error processing query: {str(e)}"

config.py

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+import os
+from dotenv import load_dotenv
+
+GOOGLE_API_KEY = "AIzaSyBKzMLEIN6IoNFzpxJtrNH-c53Sx1DQzNU"
+def setup_environment():
+    load_dotenv()
+    return GOOGLE_API_KEY

document_processor.py

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+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+
+def read_documents(directory):
+    return PyPDFLoader(directory).load()
+
+def chunk_data(docs, chunk_size=800, chunk_overlap=40):
+    return RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=chunk_overlap).split_documents(docs)

embeddings_store.py

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+from langchain_google_genai import GoogleGenerativeAIEmbeddings, ChatGoogleGenerativeAI
+from langchain_chroma import Chroma
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+
+def create_embeddings_and_store(doc_chunks):
+    vectorstore = Chroma.from_documents(
+        documents=doc_chunks, 
+        embedding=GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+    )
+    
+    retriever = vectorstore.as_retriever(search_type="similarity", search_kwargs={"k": 2})
+    
+    system_prompt = """
+You are an AI assistant for question-answering tasks about the Bharatiya Nyaya Sanhita.Bharatiya Nyaya Sanhita came into effect on 1 July, 2024 after being passed by the parliament in December 2023 to replace the Indian Penal Code (IPC), which dated back to the period of British India
+ Analyze the provided context and answer the user's question concisely. Follow these guidelines:
+
+1. Use only the given context to formulate your response.
+2. If the answer cannot be derived from the context, state "I don't have enough information to answer this question."
+3. Provide a clear, direct answer in seven sentences or fewer.
+
+Context:
+{context}
+"""
+    
+    prompt = ChatPromptTemplate.from_messages([
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ])
+
+    llm_model = ChatGoogleGenerativeAI(model="gemini-1.5-pro", temperature=0.3, max_tokens=250)
+    
+    question_answer_chain = create_stuff_documents_chain(llm_model, prompt)
+    return create_retrieval_chain(retriever, question_answer_chain)

rag_initializer.py

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+import streamlit as st
+from document_processor import read_documents, chunk_data
+from embeddings_store import create_embeddings_and_store
+
+@st.cache_resource
+def initialize_rag_chain():
+    try:
+        docs = read_documents("bns3.pdf")
+        doc_chunks = chunk_data(docs)
+        return create_embeddings_and_store(doc_chunks)
+    except Exception as e:
+        st.error(f"Error initializing RAG chain: {str(e)}")
+        return None

requirements.txt

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+streamlit
+python-dotenv
+langchain
+langchain-google-genai
+langchain-chroma
+google-generativeai
+langchain_community
+langchain_experimental
+pypdf