This content originally appeared on DEV Community and was authored by kaustubh yerkade
Introduction
In this guide, we will build a RAG-based chatbot that acts as an IBM AIX expert who will answer all the our AIX related queries. This chatbot will retrieve information from IBM AIX documentation, process it using FAISS for vector search, and generate responses using OpenAI's LLM. We will also cover automating AIX documentation collection and deploying the chatbot using Docker.
đź“Ś System Design Overview
Architecture
- Data Collection Layer: Scrapes IBM AIX documentation and extracts text from PDFs.
- Data Processing Layer: Splits text and converts it into vector embeddings using FAISS.
- Retrieval-Augmented Generation (RAG) Layer: Uses a vector database to fetch relevant documents and feed them to OpenAI’s LLM.
- User Interface: A Streamlit-based UI for chatbot interactions.
- Deployment: Docker containerization for easy deployment and scalability.
đź“Ś Step 1: Setting Up the Project
Install Required Dependencies
Ensure you have Python 3.9+ installed, then install the required libraries:
pip install streamlit langchain openai faiss-cpu bs4 requests PyMuPDF
Create a new Python script Aix_Rag_Chatbot.py and add the following code.
đź“Ś Step 2: Collecting IBM AIX Documentation
We will automate the process of gathering IBM AIX documentation from IBM Knowledge Center, IBM Redbooks (PDFs), and online resources.
🔹 Web Scraping IBM Docs
import requests
from bs4 import BeautifulSoup
def scrape_aix_docs(url):
"""
Fetches IBM AIX documentation from a given URL and extracts text content.
"""
response = requests.get(url)
soup = BeautifulSoup(response.text, "html.parser")
text_content = "\n".join([p.get_text() for p in soup.find_all("p")])
with open("aix_docs.txt", "w", encoding="utf-8") as file:
file.write(text_content)
print("AIX documentation scraped and saved!")
Call this function with:
scrape_aix_docs("https://www.ibm.com/docs/en/aix/7.3?topic=commands")
⏳ Estimated Time: 10-20 seconds
🔹 Extracting Text from AIX PDFs
import fitz # PyMuPDF
def extract_text_from_pdf(pdf_path):
"""
Extracts text from a given PDF file and appends it to aix_docs.txt.
"""
output_text = ""
with fitz.open(pdf_path) as doc:
for page in doc:
output_text += page.get_text()
with open("aix_docs.txt", "a", encoding="utf-8") as file:
file.write(output_text)
print("Extracted text from PDF saved!")
Call this function with:
extract_text_from_pdf("aix_redbook.pdf")
⏳ Estimated Time: 20-60 seconds
đź“Ś Step 3: Creating a FAISS Vector Database
We will now split the extracted text, convert it into embeddings, and store it using FAISS.
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.document_loaders import TextLoader
def build_vector_db():
"""
Processes AIX documentation, splits it into smaller chunks,
generates embeddings, and stores them in FAISS vector database.
"""
with open("aix_docs.txt", "r", encoding="utf-8") as file:
aix_text = file.read()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
split_texts = text_splitter.split_text(aix_text)
with open("aix_docs_cleaned.txt", "w", encoding="utf-8") as file:
for chunk in split_texts:
file.write(chunk + "\n\n")
loader = TextLoader("aix_docs_cleaned.txt")
documents = loader.load()
embedding_model = OpenAIEmbeddings()
vector_db = FAISS.from_documents(documents, embedding_model)
vector_db.save_local("aix_vector_db")
print("Vector database built successfully!")
Call this function once:
build_vector_db()
⏳ Estimated Time: 1-2 minutes
đź“Ś Step 4: Implementing the AIX Expert Chatbot
Now, let’s create a Streamlit UI for interacting with our chatbot.
import streamlit as st
from langchain.llms import OpenAI
from langchain.chains import RetrievalQA
# Load FAISS vector DB
embedding_model = OpenAIEmbeddings()
vector_db = FAISS.load_local("aix_vector_db", embedding_model)
retriever = vector_db.as_retriever()
# Setup RAG Chain
llm = OpenAI()
qa_chain = RetrievalQA.from_chain_type(llm=llm, retriever=retriever)
# Streamlit UI
st.title("IBM AIX Expert Chatbot")
st.write("Ask me anything about IBM AIX!")
user_input = st.text_input("Enter your question:")
if st.button("Submit"):
if user_input:
response = qa_chain.run(user_input)
st.write(response)
else:
st.write("Please enter a question.")
⏳ Estimated Time: 30-60 seconds
Run the chatbot with:
streamlit run Aix_Rag_Chatbot.py
đź“Ś Step 5: Deploying with Docker
🔹 Build & Run Docker Container
# Build the Docker image
docker build -t aix-rag-chatbot .
# Run the container
docker run -p 8501:8501 aix-rag-chatbot
Access your chatbot at http://localhost:8501
Now the chatbot is ready for action, deploy it on your favourite on-prem/cloud environment and scale it as per need.
This content originally appeared on DEV Community and was authored by kaustubh yerkade
kaustubh yerkade | Sciencx (2025-02-19T20:49:36+00:00) Building an IBM AIX Expert Chatbot using RAG and FAISS. Retrieved from https://www.scien.cx/2025/02/19/building-an-ibm-aix-expert-chatbot-using-rag-and-faiss/
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