How to Build A Rag Pipeline with Semantic Kernel (Step by Step)

Building a RAG Pipeline with Semantic Kernel

We’re building a RAG pipeline that actually handles messy PDFs — not the clean-text demos you see everywhere. Managing the complexities of various data sources can be a pain, and that’s where the Semantic Kernel shines. The aim here is to help you build a solid semantic kernel build a RAG pipeline that gets the job done right.

Prerequisites

• Python 3.11+
• pip install langchain>=0.2.0
• Microsoft Semantic Kernel: microsoft/semantic-kernel (Stars: 27,569, Forks: 4,526, Open Issues: 495, License: MIT, Last Updated: March 26, 2026)

Step 1: Set Up Your Environment

Before we jump into the actual code, you need to set up your Python environment. Here’s how you do it. Create a virtual environment to keep things tidy. This is the best way to manage dependencies. Trust me, it’s saved my skin more than once.

# Create a new directory
mkdir rag_pipeline
cd rag_pipeline
# Set up the virtual environment
python3 -m venv venv
source venv/bin/activate
# Install the necessary packages
pip install langchain>=0.2.0
pip install microsoft-semantic-kernel

Now, run pip list to confirm everything is in order. You’ll see langchain and microsoft-semantic-kernel among the installed packages.

Step 2: Create Your Semantic Kernel Instance

Now we’ll create an instance of the Semantic Kernel. This is crucial because this instance will be one of the primary components of your RAG pipeline. If you fail here, your whole setup will crumble like a poorly made soufflé.

from semantic_kernel import SemanticKernel

# Create a kernel instance
kernel = SemanticKernel()

Don’t forget, if you make a typo, you’ll see an ImportError, which is a clear sign that either the installation failed or the module name was misspelled. Double-check your package names.

Step 3: Load Your Data Sources

It’s time to load the data sources that you want to query. For this tutorial, let’s assume we’re dealing with messy PDFs. You’ll need to parse these into a format that can be indexed. This is where semantic kernel build a RAG pipeline starts to show its strength.

import pdfplumber

def load_pdf(file_path):
 text = ""
 with pdfplumber.open(file_path) as pdf:
 for page in pdf.pages:
 text += page.extract_text() + "\n"
 return text

# Load your PDF document
data = load_pdf("path_to_your_file.pdf")
print(data[:200]) # Print first 200 characters for verification

Running this code without a valid PDF filepath will cause a FileNotFoundError. Make sure the file exists. And yes, I’ve had a moment where I spent way too long trying to understand why my path was invalid. So, check your directories!

Step 4: Indexing Your Data

Once you have your data loaded, the next step is indexing it so it can be quickly queried. We’ll use the capabilities of the Semantic Kernel to build a memory index.

from semantic_kernel.indexing import create_memory_index

# Create a memory index and add the PDF data
memory_index = create_memory_index()
memory_index.add_document("your_doc_id", data)

If you’ve overlooked the memory index setup, you might see an error related to the indexing function. It’s easy to forget to import what you need. Just ensure you’re importing the correct classes.

Step 5: Querying Your Index

With your data indexed, you can now run queries against it. This is where the magic happens! You’ll want to perform queries that return relevant information based on what you need. Here’s how to query your memory index effectively.

query = "What are the main points discussed in the document?"
results = memory_index.query(query)
print(results)

Watch out for overly broad queries. They’ll yield tons of data that might not be relevant. You don’t want to sift through a mountain of text just to find that one nugget of information!

Step 6: Implementing RAG Strategy

Now that you’ve loaded, indexed, and queried your data, it’s time to implement the RAG strategy. You need to fetch the right documents, generate a response based on your query, and finally, return a condensed answer that encompasses the relevant data from the indexed sources.

from semantic_kernel.rag import generate_response

# Fetch relevant documents based on your query
relevant_docs = memory_index.fetch_relevant_documents(query)
response = generate_response(relevant_docs)
print(response)

This step can throw errors if there are no relevant documents found. Make sure your query is well-tailored to your indexed data to avoid empty responses. I’ve been there, querying for something that just wasn’t in the documents. Lesson learned: query with purpose!

The Gotchas

Here are a few pitfalls that you might stumble upon when deploying this RAG pipeline in a production environment:

• Performance Issues: Large PDF files may slow down the extraction and indexing process. Ensure you’re using asynchronous operations where necessary.
• Data Drift: The content of your PDFs may change over time. Set up a process to re-index or update your pipeline periodically.
• Ambiguous Queries: Users may not phrase queries in a way that matches the indexed documents. Implement a fallback mechanism for rephrasing queries.
• Resource Management: Monitor the memory usage of your application. Indexing large datasets can consume a lot of resources and lead to crashes if not handled properly.

Full Code Example

from semantic_kernel import SemanticKernel
import pdfplumber
from semantic_kernel.indexing import create_memory_index
from semantic_kernel.rag import generate_response

# Create a kernel instance
kernel = SemanticKernel()

def load_pdf(file_path):
 text = ""
 with pdfplumber.open(file_path) as pdf:
 for page in pdf.pages:
 text += page.extract_text() + "\n"
 return text

# Load and index the PDF document
data = load_pdf("path_to_your_file.pdf")
memory_index = create_memory_index()
memory_index.add_document("your_doc_id", data)

# Run queries
query = "What are the main points discussed in the document?"
results = memory_index.query(query)
print(results)

# Implementing RAG
relevant_docs = memory_index.fetch_relevant_documents(query)
response = generate_response(relevant_docs)
print(response)

What’s Next

Your next concrete step is to implement logging in your application. Understanding how your pipeline performs over time will yield insights for optimizations. Plus, it’s great for debugging!

FAQ

Q: Can I use this with other types of documents?
A: Absolutely! Just modify the loading function to accommodate different file types. However, beware of formats that don’t easily convert to text.

Q: Is there a way to optimize querying speed?
A: Yes, indexing smaller chunks of text may be more efficient, especially with large documents. Experiment with chunk sizes.

Q: How do I handle different languages in my PDFs?
A: Ensure your models can recognize different languages. You may need to adjust parameters based on the text’s language.

Data Sources

• Microsoft Semantic Kernel
• LangChain Documentation

Last updated March 27, 2026. Data sourced from official docs and community benchmarks.

🕒 Published: March 26, 2026