SmartDoc RAG Chatbot: Retrieval-Augmented Document Intelligence System

Abstract:

The SmartDoc RAG Chatbot is a document-aware conversational system that integrates Retrieval-Augmented Generation (RAG) to deliver contextually accurate, source-grounded responses.
By combining large language model (LLM) reasoning with vector-based semantic retrieval, the chatbot enables users to query private or domain-specific documents safely and efficiently.
This project demonstrates how information retrieval pipelines can enhance enterprise and research workflows through intelligent automation.

1. Introduction

Problem Statement

Traditional chatbots rely solely on pre-trained LLMs, which often generate responses that are unverifiable or hallucinated.
Users in academic, legal, and enterprise contexts need factually grounded answers derived directly from trusted document sources.

The Challenge

Lack of document-awareness in standard LLMs
Slow or inaccurate retrieval of contextually relevant information
Difficulty in maintaining source traceability

Our Enhanced Solution

SmartDoc RAG Chatbot addresses these issues by introducing a Retrieval-Augmented Generation pipeline that merges semantic document retrieval with context-aware response synthesis.
This hybrid design ensures both accuracy and explainability in generated answers.

2. System Architecture & Design

High-Level Architecture

The system follows a modular three-stage RAG pipeline:
Document Loading & Chunking.
Documents (TXT, DOCX, PDF) are loaded from local or cloud sources.
The text is divided into overlapping chunks to enhance contextual mapping.

Embedding & Vector Storage

The OpenAI text-embedding-ada-002 model converts text into dense vector representations.
These embeddings are indexed in a FAISS vector database for high-speed similarity search.

Retrieval-Augmented Generation

On receiving a query, the system retrieves the top-k most similar chunks from FAISS.
These are passed into GPT-3.5-turbo to generate context-grounded answers.

# RAG pipeline overview
documents = loader.load()
chunks = splitter.split_documents(documents)
vectorstore = FAISS.from_documents(chunks, embeddings)
response = qa.run("What is the summary of this document?")
)

Enhanced System Components

3. Implementation Details

Frontend Integration

Simple user interface built using Streamlit for document upload and question input.
Displays real-time responses with retrieval context.
Configurable parameters such as chunk size and number of retrieved documents.

import streamlit as st

st.title("SmartDoc RAG Chatbot")
uploaded_file = st.file_uploader("Upload your document", type=["pdf", "docx", "txt"])
query = st.text_input("Ask a question:")
if st.button("Get Answer"):
    st.write("Answer:", response))

This can also be run in CLI mode

Backend Processing

Document Preprocessing:

                                                        Text splitting and cleaning via LangChain’s CharacterTextSplitter.

Embedding Generation:

                                                    Uses OpenAI embeddings for each chunk.

Vector Indexing:

                                         Documents stored in FAISS vector database.

Retrieval Chain:

                                      LangChain’s RetrievalQA integrates FAISS retriever with GPT-based response generation.

API Security:

                                  Environment variables managed via .env for API key safety.

import os 
from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.chains import RetrievalQA
from langchain_groq import ChatGroq

load_dotenv()

loader = TextLoader("docs.txt")
documents = loader.load()

splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
docs = splitter.split_documents(documents)

embeddings = HuggingFaceEmbeddings()
vectorstore = FAISS.from_documents(docs, embeddings)

llm = ChatGroq(model="llama-3.1-8b-instant")
qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectorstore.as_retriever())

“This pipeline integrates document retrieval, semantic embedding, and Groq LLM response generation for context-grounded QA.”

4. Evaluation & Optimization

The chatbot was tested on multi-document datasets to evaluate:
Retrieval accuracy (semantic similarity of retrieved chunks)
Response relevance (context adherence)
Latency (response generation time)
Optimization Plans
Introduce retrieval metrics (Precision, Recall, MRR)
Improve prompt templates for contextual consistency
Add caching for repeated queries
Extend support for multimodal documents (PDFs, images with OCR)

from sklearn.metrics.pairwise import cosine_similarity
similarity = cosine_similarity(vec1, vec2)
print("Retrieval similarity:", similarity)

5. Benefits

Source-grounded Answers:

                                                          Every response is backed by real document evidence.

Fast Search:

                                 FAISS enables millisecond-level retrieval across large datasets.

Explainability:

                                  Users can view which document sections informed the answer.

Secure Access:

                                      No external data exposure; all documents remain local or private.

Flexible Design:

                                       Modular components enable quick integration of new models.

##6. Visual Workflow Diagram

User Query → Document Retrieval (FAISS) → Context Assembly → LLM Response Generation → Final Answer

7. Use Cases

Academic research assistants
Legal or compliance document query systems
Knowledge management bots for enterprises
Healthcare documentation analysis tools

8. Future Enhancements

Integration with ChromaDB or Milvus for distributed retrieval
Multi-language embedding support
Hybrid RAG pipelines combining structured + unstructured data
Voice-based query interface using speech-to-text

# Future: integrate ChromaDB retriever
from langchain.vectorstores import Chroma
vectorstore = Chroma.from_documents(docs, embeddings)

9. Conclusion

The SmartDoc RAG Chatbot demonstrates how modern AI systems can move beyond pure generation toward evidence-based intelligence.
By leveraging the RAG framework, this solution ensures that every response is traceable, accurate, and context-aware, making it ideal for real-world enterprise, academic, and data-driven applications.
It provides a scalable foundation for future knowledge-grounded conversational AI systems.