A Coding Implementation to Build a Conversational Research Assistant with FAISS, Langchain, Pypdf, and TinyLlama-1.1B-Chat-v1.0

!pip install langchain-community langchain pypdf sentence-transformers faiss-cpu transformers accelerate einops

import os
import torch
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain_community.llms import HuggingFacePipeline
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import pandas as pd 
from IPython.display import display, Markdown

from google.colab import drive
drive.mount('/content/drive')
print("Google Drive mounted")

def load_documents(pdf_folder_path):
    documents = []


    if not pdf_folder_path:
        print("Downloading a sample paper...")
        !wget -q  -O attention.pdf
        pdf_docs = ["attention.pdf"]
    else:
        pdf_docs = [os.path.join(pdf_folder_path, f) for f in os.listdir(pdf_folder_path)
                   if f.endswith('.pdf')]


    print(f"Found {len(pdf_docs)} PDF documents")


    for pdf_path in pdf_docs:
        try:
            loader = PyPDFLoader(pdf_path)
            documents.extend(loader.load())
            print(f"Loaded: {pdf_path}")
        except Exception as e:
            print(f"Error loading {pdf_path}: {e}")


    return documents




documents = load_documents("")

def split_documents(documents):
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=1000,
        chunk_overlap=200,
        length_function=len,
    )
    chunks = text_splitter.split_documents(documents)
    print(f"Split {len(documents)} documents into {len(chunks)} chunks")
    return chunks


chunks = split_documents(documents)

def create_vector_store(chunks):
    print("Loading embedding model...")
    embedding_model = HuggingFaceEmbeddings(
        model_name="sentence-transformers/all-MiniLM-L6-v2",
        model_kwargs={'device': 'cuda' if torch.cuda.is_available() else 'cpu'}
    )


    print("Creating vector store...")
    vector_store = FAISS.from_documents(chunks, embedding_model)
    print("Vector store created successfully!")
    return vector_store


vector_store = create_vector_store(chunks)

def load_language_model():
    print("Loading language model...")
    model_id = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"


    try:
        import subprocess
        print("Installing/updating bitsandbytes...")
        subprocess.check_call(["pip", "install", "-U", "bitsandbytes"])
        print("Successfully installed/updated bitsandbytes")
    except:
        print("Could not update bitsandbytes, will proceed without 8-bit quantization")


    from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig, pipeline
    import torch


    tokenizer = AutoTokenizer.from_pretrained(model_id)


    if torch.cuda.is_available():
        try:
            quantization_config = BitsAndBytesConfig(
                load_in_8bit=True,
                llm_int8_threshold=6.0,
                llm_int8_has_fp16_weight=False
            )


            model = AutoModelForCausalLM.from_pretrained(
                model_id,
                torch_dtype=torch.bfloat16,
                device_map="auto",
                quantization_config=quantization_config
            )
            print("Model loaded with 8-bit quantization")
        except Exception as e:
            print(f"Error with quantization: {e}")
            print("Falling back to standard model loading without quantization")
            model = AutoModelForCausalLM.from_pretrained(
                model_id,
                torch_dtype=torch.bfloat16,
                device_map="auto"
            )
    else:
        model = AutoModelForCausalLM.from_pretrained(
            model_id,
            torch_dtype=torch.float32,
            device_map="auto"
        )


    pipe = pipeline(
        "text-generation",
        model=model,
        tokenizer=tokenizer,
        max_length=2048,
        temperature=0.2,
        top_p=0.95,
        repetition_penalty=1.2,
        return_full_text=False
    )


    from langchain_community.llms import HuggingFacePipeline
    llm = HuggingFacePipeline(pipeline=pipe)
    print("Language model loaded successfully!")
    return llm


llm = load_language_model()

def format_research_assistant_output(query, response, sources):
    output = f"n{'=' * 50}n"
    output += f"USER QUERY: {query}n"
    output += f"{'-' * 50}nn"
    output += f"ASSISTANT RESPONSE:n{response}nn"
    output += f"{'-' * 50}n"
    output += f"SOURCES REFERENCED:nn"


    for i, doc in enumerate(sources):
        output += f"Source #{i+1}:n"
        content_preview = doc.page_content[:200] + "..." if len(doc.page_content) > 200 else doc.page_content
        wrapped_content = textwrap.fill(content_preview, width=80)
        output += f"{wrapped_content}nn"


    output += f"{'=' * 50}n"
    return output


import textwrap


research_assistant = create_research_assistant(vector_store, llm)


test_queries = [
    "What is the key idea behind the Transformer model?",
    "Explain self-attention mechanism in simple terms.",
    "Who are the authors of the paper?",
    "What are the main advantages of using attention mechanisms?"
]


for query in test_queries:
    response, sources = research_assistant(query, return_sources=True)
    formatted_output = format_research_assistant_output(query, response, sources)
    print(formatted_output)