diff --git a/Pippy/Pippy_AI_assistant.py b/Pippy/Pippy_AI_assistant.py
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+# Pippy's AI-Coding Assistant
+# Uses Llama3.1 model from Ollama
+
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings, OpenAIEmbeddings
+from langchain_community.document_loaders import PyMuPDFLoader, TextLoader
+from langchain.chains import RetrievalQA
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_ollama.llms import OllamaLLM
+import warnings
+import os
+
+# Suppress future warnings related to deprecations
+warnings.filterwarnings("ignore", category=FutureWarning)
+warnings.filterwarnings("ignore", category=DeprecationWarning)
+
+
+# Paths to your PDF/text documents to be retrieved in the RAG model (you can add more paths to this list)
+document_paths = [
+    '/home/kshitij/Downloads/AI-model/Pygame Documentation.pdf',
+    '/home/kshitij/Downloads/AI-model/AI-model(Streamlitfree)/Python GTK+3 Documentation.pdf',
+]
+
+
+def setup_vectorstore(file_paths):
+    """
+    Set up a vector store from the provided document files.
+
+    Args:
+        file_paths (list): List of paths to document files.
+
+    Returns:
+        retriever: A retriever object for document retrieval.
+    """
+    try:
+        all_documents = []
+        for file_path in file_paths:
+            if os.path.exists(file_path):
+                print(f"Loading document from: {file_path}")
+                # Load PDF or text documents based on file extension
+                if file_path.endswith(".pdf"):
+                    loader = PyMuPDFLoader(file_path)
+                else:
+                    loader = TextLoader(file_path)
+
+                documents = loader.load()
+                print(f"Loaded {len(documents)} documents from {file_path}.")
+                all_documents.extend(documents)  # Combine documents from all files
+            else:
+                print(f"File not found: {file_path}")
+
+        # Create embeddings using HuggingFace or OpenAI embeddings
+        embeddings = HuggingFaceEmbeddings()  # You can switch to OpenAIEmbeddings if needed
+
+        # Create a vector store with all combined documents
+        vector_store = FAISS.from_documents(all_documents, embeddings)
+
+        # Create a retriever
+        retriever = vector_store.as_retriever()
+        return retriever
+
+    except Exception as e:
+        print(f"Failed to set up the retriever: {e}")
+        return None
+
+
+# Defining a system prompt to prioritize coding-specific responses and guide the model
+system_prompt = """
+You are a highly intelligent Python coding assistant with access to both general knowledge and specific Pygame documentation.
+1. You only have to answer Python and GTK based coding queries.
+2. Prioritize answers based on the documentation when the query is related to it. However make sure you are not biased towards documentation provided to you.
+3. Make sure that you don't mention words like context or documentation stating what has been provided to you.
+4. Provide step-by-step explanations wherever applicable.
+5. If the documentation does not contain relevant information, use your general knowledge.
+6. Always be clear, concise, and provide examples where necessary.
+"""
+
+
+# Set up the RAG model with system prompt
+template = f"""{system_prompt}
+Question: {{question}}
+Answer: Let's think step by step.
+"""
+prompt = ChatPromptTemplate.from_template(template)
+model = OllamaLLM(model="llama3.1")
+
+# Initialize the vector store retriever using multiple documents
+retriever = setup_vectorstore(document_paths)
+
+# Used to calculate relevance of a query
+def get_relevant_document(query, retriever, threshold=0.5):
+    """
+    Check if the query is related to the document by using the retriever.
+
+    Args:
+        query (str): The user query.
+        retriever: The document retriever.
+        threshold (float): The confidence threshold to decide if a query is related to the document.
+
+    Returns:
+        result (dict): Retrieved document information and similarity score.
+    """
+    results = retriever.get_relevant_documents(query)
+    if results and len(results) > 0:
+        # Check the confidence score of the first result (assuming retriever returns sorted results)
+        top_result = results[0]
+        score = top_result.metadata.get("score", 0.0)  # Default to 0.0 if no score is found
+
+        # Return the result and its score if it meets the threshold
+        if score >= threshold:
+            return top_result, score
+    return None, 0.0
+
+if retriever:
+    # Combine the retriever and model into a Retrieval-Augmented Generation (RAG) chain
+    rag_chain = RetrievalQA.from_chain_type(llm=model, chain_type="stuff", retriever=retriever)
+
+    print("RAG model setup completed successfully!")
+
+    while True:
+        # Capture user input from the terminal
+        question = input("Enter your question (or type 'exit' to quit): ").strip()
+        
+        if question.lower() == 'exit':
+            print("Exiting the application. Goodbye!")
+            break
+
+        # Check if the query is relevant to the document content
+        doc_result, relevance_score = get_relevant_document(question, retriever)
+
+        if doc_result:
+            print(f"Document is relevant (Score: {relevance_score:.2f}). Using document-specific response...")
+            response = rag_chain({"query": question})
+            if 'result' in response:
+                print(f"Document Response: {response['result']}")
+            else:
+                print("No result found in documents, trying general knowledge...")
+                response = model.invoke(question)
+                print(f"Response: {response}")
+        else:
+            print(f"Document is not relevant (Score: {relevance_score:.2f}). Using general knowledge response...")
+            response = model.invoke(question)
+            print(f"Response: {response}")
+
+else:
+    print("Unable to set up the retriever. Please check the documents and try again.")
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