Ever wished you could chat with your documents like they’re your personal assistant? Imagine asking your collection of PDFs, research papers, and notes questions—and getting instant, accurate answers. That’s the power of a RAG pipeline, and you’re about to build one this weekend.
No PhD required. Just a curious mind and a couple of hours.
What's RAG, and Why Should You Care?
Here’s the problem with large language models: they’re brilliant, but they don’t know about your specific documents. They can’t access your company’s internal wikis or that research paper you downloaded last week. A RAG pipeline solves this problem elegantly.
Think of a RAG pipeline as giving an AI a smart filing cabinet. When you ask a question, the system follows three simple steps:
First, it searches through your documents to find relevant information. Second, it feeds that context to an AI model. Finally, it gives you an answer grounded in your actual content.
The result? No more hallucinations about your specific data. No more manually searching through dozens of files.
What You'll Build Today
By the end of this tutorial, you’ll have a personal AI document assistant that can:
- Ingest PDFs, text files, and markdown documents
- Answer questions based on your document content
- Cite which documents it’s pulling information from
- Run entirely on your local machine or cloud
This is perfect for researchers, students, and knowledge workers. Anyone drowning in documents will benefit.
Prerequisites: Your Toolkit
Before we dive in, make sure you have:
- Python 3.8+ installed
- A code editor (VS Code, PyCharm, or whatever you prefer)
- Basic Python knowledge (if you know what a function is, you’re ready)
- Ollama LLaMa3 or latest version
Total setup time: 10 minutes.
Step 1: Setting Up Your Environment
Let’s start fresh. First, open your terminal. Then, create a new project:
bash
mkdir rag-document-assistant
cd rag-document-assistant
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
Now install the essential libraries:
bash
pip install langchain openai chromadb pypdf sentence-transformers
Here’s what each library does:
- LangChain: The framework that ties everything together
- OpenAI: Powers our AI responses
- ChromaDB: Stores document embeddings (numerical representations)
- PyPDF: Reads PDF files
- Sentence-transformers: Creates embeddings from text
Step 2: Loading and Processing Documents
Create a file called rag_pipeline.py. Let’s start by loading documents:
python
from langchain.document_loaders import PyPDFLoader, TextLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
import os
def load_documents(folder_path):
"""Load all PDFs and text files from a folder."""
documents = []
for filename in os.listdir(folder_path):
file_path = os.path.join(folder_path, filename)
if filename.endswith('.pdf'):
loader = PyPDFLoader(file_path)
documents.extend(loader.load())
elif filename.endswith('.txt'):
loader = TextLoader(file_path)
documents.extend(loader.load())
return documents
def split_documents(documents):
"""Split documents into manageable chunks."""
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=1000,
chunk_overlap=200,
length_function=len,
)
chunks = text_splitter.split_documents(documents)
return chunks
Why Split Documents?
Large documents overwhelm the AI. We break them into 1000-character chunks. Additionally, we add 200-character overlap between chunks. This ensures relevant context isn’t split awkwardly.Step 3: Creating Your Vector Database
Now for the magic part. We’ll turn text into searchable vectors:
python
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
def create_vector_store(chunks):
"""Create a vector database from document chunks."""
embeddings = OpenAIEmbeddings()
vector_store = Chroma.from_documents(
documents=chunks,
embedding=embeddings,
persist_directory="./chroma_db"
)
return vector_store
Understanding Embeddings
This step creates numerical representations of your text. Similar concepts get similar numbers. Therefore, retrieval becomes accurate and fast.Think of it like GPS coordinates for ideas. Related concepts live close together in this number space.Step 4: Building the RAG Chain
Time to connect everything. This is where your RAG pipeline comes together:
python
from langchain.chat_models import ChatOpenAI
from langchain.chains import RetrievalQA
def create_rag_chain(vector_store):
"""Create the RAG question-answering chain."""
llm = ChatOpenAI(
model_name="gpt-3.5-turbo",
temperature=0
)
qa_chain = RetrievalQA.from_chain_type(
llm=llm,
chain_type="stuff",
retriever=vector_store.as_retriever(search_kwargs={"k": 3}),
return_source_documents=True
)
return qa_chain
Key Parameters Explained
The k=3 parameter retrieves the three most relevant chunks for each query. Meanwhile, temperature=0 keeps responses focused and consistent. This prevents creative but inaccurate answers.Step 5: Putting It All Together
Let’s create the main application:
python
def main():
# Create documents folder if it doesn't exist
docs_folder = "./documents"
if not os.path.exists(docs_folder):
os.makedirs(docs_folder)
print(f"Created {docs_folder}. Add your documents there!")
return
print("Loading documents...")
documents = load_documents(docs_folder)
print(f"Splitting {len(documents)} documents into chunks...")
chunks = split_documents(documents)
print(f"Creating vector store with {len(chunks)} chunks...")
vector_store = create_vector_store(chunks)
print("Building RAG chain...")
qa_chain = create_rag_chain(vector_store)
print("\n🎉 Your document assistant is ready!\n")
while True:
question = input("Ask a question (or 'quit' to exit): ")
if question.lower() == 'quit':
break
result = qa_chain({"query": question})
print(f"\nAnswer: {result['result']}\n")
print("Sources:")
for doc in result['source_documents']:
print(f"- {doc.metadata.get('source', 'Unknown')}")
print()
if __name__ == "__main__":
main()
Common Pitfalls (and How to Avoid Them)
Pitfall #1: Wrong Chunk Size
Problem: Too large and you’ll hit token limits. Too small and you’ll lose context.Solution: The sweet spot is 800-1200 characters for most use cases. Start with 1000 and adjust based on your documents.Pitfall #2: No Chunk Overlap
Problem: Without overlap, important information at chunk boundaries gets split.Solution: Always use 10-20% overlap. For 1000-character chunks, use 200 characters of overlap.Pitfall #3: Not Testing Edge Cases
Problem: Users ask questions your documents don’t answer.Solution: Test your RAG pipeline with questions it can’t answer. A good system should say “I don’t know” rather than hallucinate.Pitfall #4: Ignoring Metadata
Problem: Citations aren’t useful without context.Solution: Store source file names, page numbers, and dates in metadata. This makes your citations actionable.Pitfall #5: Default Retrieval Settings
Problem: Three chunks might be too few for complex questions.Solution: Experiment with different k values. Monitor which queries need more context. Adjust accordingly.Taking Your RAG Pipeline Further
Your weekend project is complete. However, here’s where you can go next:
Level Up Your Embeddings
Try different embedding models. For example,
sentence-transformers/all-MiniLM-L6-v2 offers faster, local embeddings. This reduces API costs significantly.
Add a Web Interface
Wrap your RAG pipeline in Streamlit. Create a beautiful UI your non-technical friends can use. Share your AI document assistant widely.
Support More File Types
Add loaders for Word docs, web pages, or audio transcripts. Expand your system’s capabilities gradually.
Implement Hybrid Search
Combine vector search with traditional keyword search. This approach improves retrieval even further.
Add Conversation Memory
Use LangChain’s conversation memory. Enable multi-turn conversations. Your assistant will remember context from previous questions.
Your RAG Journey Starts Now
You’ve just built something powerful: an AI document assistant grounded in your own knowledge. Whether you’re a researcher analyzing papers, a student organizing notes, or a professional managing documentation, you now have a tool that scales.
The best part? This is just the beginning. RAG pipeline technology evolves rapidly. New techniques and optimizations emerge constantly. You’re now equipped to explore this exciting space.
Therefore, grab some coffee and drop your documents in that folder. Start asking questions. Your personal AI document assistant is waiting.
Ready to Build Enterprise-Grade RAG Solutions?
Building a simple RAG pipeline is one thing. However, deploying production-ready, enterprise-scale AI document assistants requires expertise, infrastructure, and ongoing optimization.
At Cenango, our AI experts specialize in:
- Custom RAG pipeline development for enterprise needs
- Scalable AI document assistant deployment
- Integration with existing business systems
- Advanced retrieval optimization and fine-tuning
- Security-compliant AI solutions
Whether you’re exploring AI possibilities or ready to implement a production system, our team can help you navigate the journey.
Schedule a demo with Cenango's AI expert team today. Let's discuss how a custom RAG pipeline can transform your document workflows and unlock insights from your knowledge base.