
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.
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.
Lack of document-awareness in standard LLMs
Slow or inaccurate retrieval of contextually relevant information
Difficulty in maintaining source traceability
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.
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.
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.
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?") )
| Component | Technology Used | Purpose |
|---|---|---|
| LLM | GPT-3.5-turbo | Natural language generation with reasoning ability |
| Embedding Model | text-embedding-ada-002 | Semantic vector representation of text |
| Vector Store | FAISS | Fast and scalable document retrieval |
| Framework | LangChain | Orchestration of RAG pipeline |
| Interface | Streamlit / CLI | User-friendly query interface |
| Environment | .env key management | Secure configuration handling |
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))
Text splitting and cleaning via LangChainβs CharacterTextSplitter.
Uses OpenAI embeddings for each chunk.
Documents stored in FAISS vector database.
LangChainβs RetrievalQA integrates FAISS retriever with GPT-based response generation.
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())
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)
Every response is backed by real document evidence.
FAISS enables millisecond-level retrieval across large datasets.
Users can view which document sections informed the answer.
No external data exposure; all documents remain local or private.
Modular components enable quick integration of new models.
##6. Visual Workflow Diagram
User Query β Document Retrieval (FAISS) β Context Assembly β LLM Response Generation β Final Answer
Academic research assistants
Legal or compliance document query systems
Knowledge management bots for enterprises
Healthcare documentation analysis tools
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)
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.