Building an IBM AIX Expert Chatbot using RAG and FAISS

[MyrinNew]

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.





📌 System Design Overview

Architecture

1. Data Collection Layer: Scrapes IBM AIX documentation and extracts text from PDFs.
2. Data Processing Layer: Splits text and converts it into vector embeddings using FAISS.
3. Retrieval-Augmented Generation (RAG) Layer: Uses a vector database to fetch relevant documents and feed them to OpenAI’s LLM.
4. User Interface: A Streamlit-based UI for chatbot interactions.
5. Deployment: Docker containerization for easy deployment and scalability.

📌 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.





📌 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





📌 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





📌 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










📌 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.




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