Imagine having a personal chatbot that can answer questions directly from your documentsbe it PDFs, research papers, or books. With Retrieval-Augmented Generation (RAG), this is not only possible but also straightforward to implement. In this tutorial, well learn how to build a chatbot that interacts with your documents, like PDFs, using Retrieval-Augmented Generation (RAG). Well use Groq for language model inference, Chroma as the vector store, and Gradio for the user interface.By the end, youll have a chatbot capable of answering questions directly from your documents, keeping context of your conversation, and providing concise, accurate answers.What is Retrieval-Augmented Generation (RAG)?Retrieval-Augmented Generation (RAG) is an AI architecture that enhances the capabilities of Large Language Models (LLMs) by integrating an information retrieval system. This system fetches relevant data from external sources, providing the LLM with grounded information to generate more accurate and contextually appropriate responses. By combining the generative abilities of LLMs with real-time data retrieval, RAG reduces inaccuracies and ensures up-to-date information in AI-generated content.PrerequisitesPython Installation: Ensure Python 3.9+ is installed on your system.Groq API Key: Sign up for a Groq account and generate an API key:Visit Groq Console.Navigate to API Keys and create a new key.Copy your API key for use in the project.Dependencies: Install the required libraries:pip install langchain langchain-community langchain-groq gradio sentence-transformers PyPDF2 chromadbThese libraries will help with language processing, building the user interface, model integration, PDF handling, and vector database management.Downloading the PDF ResourceFor this tutorial, well use a publicly available PDF containing information about diseases, their symptoms, and cures. Download the PDF and save it in your project directory (you are free to use any pdf).Step 1: Extracting Text from the PDFWell use PyPDF2 to extract text from the PDF:from PyPDF2 import PdfReaderdef extract_text_from_pdf(pdf_path): reader = PdfReader(pdf_path) text = "" for page in reader.pages: text += page.extract_text() return textpdf_path = 'diseases.pdf' # Replace with your PDF pathpdf_text = extract_text_from_pdf(pdf_path)Step 2: Split the Text into ChunksLong documents are divided into smaller, manageable chunks for processing.from langchain.text_splitter import RecursiveCharacterTextSplitterdef split_text_into_chunks(text, chunk_size=2000, chunk_overlap=200): text_splitter = RecursiveCharacterTextSplitter( chunk_size=chunk_size, chunk_overlap=chunk_overlap ) return text_splitter.split_text(text)text_chunks = split_text_into_chunks(pdf_text)Step 3: Create a Vector Store with ChromaWell embed the text chunks using a pre-trained model and store them in a Chroma vector database.from langchain.embeddings import SentenceTransformerEmbeddingsfrom langchain.vectorstores import Chromaembedding_model = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")vector_store = Chroma( collection_name="disease_info", embedding_function=embedding_model, persist_directory="./chroma_db")vector_store.add_texts(texts=text_chunks)Step 4: Initialize the Groq Language ModelTo use Groqs language model, set your API key and initialize the ChatGroq instance.import osfrom langchain_groq import ChatGroqos.environ["GROQ_API_KEY"] = 'your_groq_api_key_here' # Replace with your API keyllm = ChatGroq(model="mixtral-8x7b-32768", temperature=0.1)Step 5: Create the Conversational Retrieval ChainWith LangChains ConversationalRetrievalChain, we can link the language model and the vector database.from langchain.chains import ConversationalRetrievalChainretrieval_chain = ConversationalRetrievalChain.from_llm( llm=llm, retriever=vector_store.as_retriever(topk=3), return_source_documents=True)Step 6: Implement the Chatbot LogicWe define the logic for maintaining conversation history and generating responses.conversation_history = []def get_response(user_query): response = retrieval_chain({ "question": user_query, "chat_history": conversation_history }) conversation_history.append((user_query, response['answer'])) return response['answer']Step 7: Build the User Interface with GradioFinally, create a Gradio interface to interact with the chatbot.import gradio as grdef chat_interface(user_input, history): response = get_response(user_input) history.append((user_input, response)) return history, historywith gr.Blocks() as demo: chatbot = gr.Chatbot() state = gr.State([]) with gr.Row(): user_input = gr.Textbox(show_label=False, placeholder="Enter your question...") submit_btn = gr.Button("Send") submit_btn.click(chat_interface, inputs=[user_input, state], outputs=[chatbot, state])Running the CodeSave the script as app.py and runpython app.pyHurray! You are done. The Gradio interface will launch, allowing you to chat with your document.But why stop here? You can go further by trying to build any of the following functionalities in the chatbot.Enhanced Vector Store: Use other vector databases like Milvus or Pinecone for scalability.Fine-tuned Models: Experiment with fine-tuned Groq models for domain-specific accuracy.Multi-Document Support: Extend the system to handle multiple documents.Better Context Handling: Refine conversational logic to better manage longer chat histories.Custom UI: Design a more polished user interface with advanced styling and features.Congratulations! Youve successfully built a document-based chatbot using Groq and LangChain. Experiment with improvements and build something amazing! Resources:https://nios.ac.in/media/documents/SrSec314NewE/Lesson-29.pdfLangChain (https://www.langchain.com/)Groq (https://groq.com/)Also,dont forget to follow us onTwitter and join ourTelegram Channel andLinkedIn Group. Dont Forget to join our65k+ ML SubReddit.(Promoted) Vineet Kumar+ postsVineet Kumar is a consulting intern at MarktechPost. He is currently pursuing his BS from the Indian Institute of Technology(IIT), Kanpur. He is a Machine Learning enthusiast. He is passionate about research and the latest advancements in Deep Learning, Computer Vision, and related fields. Meet 'Height':The only autonomous project management tool (Sponsored)