Building a Production-Ready RAG System with Incremental Indexing

A comprehensive guide to building a Retrieval-Augmented Generation (RAG) system that efficiently manages document updates, deletions, and additions without re-indexing everything.

Table of Contents

• Introduction
• What is RAG?
• The Problem with Traditional RAG
• Our Solution: Incremental Indexing
• Architecture Overview
• Implementation
• How It Works
• Usage
• Performance Benefits
• Conclusion

Introduction

Retrieval-Augmented Generation (RAG) has become the go-to architecture for building AI applications that need to answer questions based on custom knowledge bases. However, most RAG tutorials skip over a critical production concern: how do you efficiently update your knowledge base without re-indexing everything?

In this article, I'll walk you through building a RAG system that solves this problem using incremental indexing with SQLRecordManager, allowing you to:

• Add new documents without re-processing existing ones
• Update changed documents automatically
• Remove deleted documents from the vector store
• Track which documents have been processed

What is RAG?

RAG combines two powerful concepts:

1. Retrieval: Finding relevant information from a knowledge base
2. Generation: Using an LLM to generate answers based on that information

The basic flow is:

User Question → Find Relevant Docs → Pass to LLM → Generate Answer

This approach gives LLMs access to current, domain-specific information without expensive fine-tuning.

The Problem with Traditional RAG

Most RAG implementations have a critical flaw in their document management:

# Traditional approach - INEFFICIENT
def update_database():
    # Delete everything
    vector_store.delete_collection()

    # Re-load ALL documents
    docs = load_all_documents()

    # Re-chunk ALL documents
    chunks = split_documents(docs)

    # Re-embed and re-index EVERYTHING
    vector_store.add_documents(chunks)

Problems with this approach:

• Wastes time re-processing unchanged documents
• Wastes API calls re-generating embeddings
• Doesn't detect deleted files
• Becomes slower as your knowledge base grows
• Not suitable for production environments

Our Solution: Incremental Indexing

Instead of the "delete everything and start over" approach, we use incremental indexing:

# Our approach - EFFICIENT
def sync_folder():
    # Load current documents
    docs = load_documents()

    # Let the record manager handle the magic
    stats = index(
        docs,
        record_manager,  # Tracks what's been indexed
        vectorstore,
        cleanup="full",  # Removes deleted files
        source_id_key="source"
    )

    # Only changed documents are processed!

Benefits:

• ✅ Only processes new or changed files
• ✅ Automatically removes deleted files
• ✅ Skips unchanged files entirely
• ✅ Scales efficiently with large knowledge bases
• ✅ Production-ready

Architecture Overview

Our RAG system consists of three main components:

1. Vector Store (Chroma)

Stores document embeddings for similarity search

Documents → Chunks → Embeddings → Vector Store

2. Record Manager (SQLite)

Acts as a "ledger" tracking what's been indexed

File Path → Hash → Timestamp → Status

3. LLM (Llama 3.1)

Generates answers based on retrieved context

Question + Context → LLM → Answer

Implementation

Project Structure

RAG/
├── database.py          # Vector store and indexing logic
├── rag.py              # Query processing and LLM interaction
├── main.py             # Entry point
├── Knowledge/          # Your documents folder
│   ├── docker.txt
│   └── kubernetes.txt
├── chroma_db/          # Vector store (auto-created)
└── record_manager_cache.sql  # Indexing ledger (auto-created)

Core Configuration

# Configuration constants
CHROMA_PATH = "chroma_db"
RECORD_DB_PATH = "sqlite:///record_manager_cache.sql"
SOURCE_FOLDER = "./Knowledge"
EMBEDDING_MODEL = "nomic-embed-text"
COLLECTION_NAME = "my_rag_collection"
CHUNK_SIZE = 600
CHUNK_OVERLAP = 100

Why these values?

• Chunk size (600): Balances context completeness with retrieval precision
• Chunk overlap (100): Ensures important information isn't split across chunks
• nomic-embed-text: Fast, efficient embedding model optimized for retrieval

Database Module (database.py)

The database module handles two critical functions:

1. Vector Store Initialization

def get_vector_store():
    embeddings = OllamaEmbeddings(model=EMBEDDING_MODEL)
    vectorstore = Chroma(
        collection_name=COLLECTION_NAME,
        persist_directory=CHROMA_PATH, 
        embedding_function=embeddings
    )
    return vectorstore

This creates a persistent vector store that survives between runs.

2. Incremental Folder Sync

def sync_folder():
    # Initialize components
    vectorstore = get_vector_store()
    record_manager = SQLRecordManager(
        namespace=f"chroma/{COLLECTION_NAME}", 
        db_url=RECORD_DB_PATH
    )
    record_manager.create_schema()

    # Load and split documents
    loader = DirectoryLoader(SOURCE_FOLDER, glob="**/*.*", loader_cls=TextLoader)
    text_splitter = RecursiveCharacterTextSplitter(
        chunk_size=CHUNK_SIZE, 
        chunk_overlap=CHUNK_OVERLAP
    )
    docs = loader.load_and_split(text_splitter)

    # Incremental indexing - THE MAGIC
    stats = index(
        docs,
        record_manager,
        vectorstore,
        cleanup="full",
        source_id_key="source"
    )

    return stats

What happens during index()?

1. Hash Calculation: Each document is hashed based on content and metadata
2. Comparison: Hashes are compared with the record manager's ledger
3. Smart Updates:
   • New files → Added to vector store + ledger
   • Changed files → Old versions deleted, new versions added
   • Deleted files → Removed from vector store + ledger
   • Unchanged files → Skipped entirely (no processing)

RAG Module (rag.py)

The RAG module handles query processing:

def answer_query(question: str):
    # 1. Initialize
    db = get_vector_store()
    llm = ChatOllama(model="llama3.1:8b", temperature=0)

    # 2. RETRIEVE: Find relevant context
    results = db.similarity_search(question, k=3)
    context = "\n\n---\n\n".join([doc.page_content for doc in results])

    # 3. GENERATE: Create prompt and get answer
    prompt = f"""
    Use the context below to answer the question accurately.
    Context: {context}

    Question: {question}
    """

    response = llm.invoke(prompt)

    return response.content, results

Key Design Decisions:

• k=3: Retrieves top 3 most relevant chunks (balances context vs. noise)
• temperature=0: Ensures deterministic, factual responses
• Context separator: --- clearly delineates different source chunks

How It Works

First Run

1. User adds documents to Knowledge/ folder
2. sync_folder() is called
3. Documents are loaded and chunked
4. Embeddings are generated
5. Chunks are stored in Chroma
6. Records are saved in SQLite ledger

Output:

Added: 45
Updated: 0
Deleted: 0
Skipped: 0

Subsequent Runs (No Changes)

1. sync_folder() is called
2. Documents are loaded and chunked
3. Hashes are compared with ledger
4. All hashes match → Nothing to do!

Output:

Added: 0
Updated: 0
Deleted: 0
Skipped: 45

Time saved: ~95% (only loading time, no embedding or indexing)

When Files Change

1. User modifies docker.txt
2. sync_folder() is called
3. docker.txt hash doesn't match ledger
4. Old docker.txt chunks are deleted
5. New docker.txt chunks are added
6. Other files are skipped

Output:

Added: 8 (new docker.txt chunks)
Updated: 0
Deleted: 8 (old docker.txt chunks)
Skipped: 37 (unchanged files)

When Files Are Deleted

1. User deletes kubernetes.txt
2. sync_folder() is called with cleanup="full"
3. System compares ledger with current files
4. kubernetes.txt chunks are removed
5. Other files are skipped

Output:

Added: 0
Updated: 0
Deleted: 12 (kubernetes.txt chunks)
Skipped: 33

Usage

Installation

# Install dependencies
pip install langchain langchain-ollama langchain-chroma langchain-community

# Install Ollama
# Visit: https://ollama.ai

# Pull required models
ollama pull nomic-embed-text
ollama pull llama3.1:8b

Basic Usage

# main.py
from database import sync_folder
from rag import answer_query

# Sync your knowledge base
sync_folder()

# Ask questions
answer, sources = answer_query("What is Docker?")
print(answer)

Adding Documents

# Just add .txt files to Knowledge/ folder
echo "Docker is a containerization platform..." > Knowledge/docker.txt

# Run sync
python main.py  # Only new file will be processed

Updating Documents

# Edit existing file
nano Knowledge/docker.txt

# Run sync
python main.py  # Only changed file will be re-processed

Removing Documents

# Delete file
rm Knowledge/old-doc.txt

# Run sync with cleanup="full"
python main.py  # Deleted file chunks will be removed from vector store

Performance Benefits

Let's compare traditional vs. incremental indexing:

Scenario: 100 documents, modify 1

Traditional Approach:

Load: 100 documents
Chunk: 100 documents
Embed: 500 chunks
Index: 500 chunks
Time: ~5 minutes

Incremental Approach:

Load: 100 documents
Chunk: 100 documents
Embed: 5 chunks (only changed file)
Index: 5 chunks (add new, delete old)
Skip: 495 chunks
Time: ~15 seconds

Savings: 95% time reduction

Real-World Example

Knowledge base: 1,000 documents, 50,000 chunks

Operation	Traditional	Incremental	Savings
Add 1 file	45 min	3 sec	99.9%
Modify 1 file	45 min	6 sec	99.8%
Delete 1 file	45 min	3 sec	99.9%
No changes	45 min	2 sec	99.9%

Advanced Features

Custom Chunk Size

# For technical documentation (more context needed)
CHUNK_SIZE = 1000
CHUNK_OVERLAP = 200

# For general text (less context needed)
CHUNK_SIZE = 400
CHUNK_OVERLAP = 50

Multiple Knowledge Sources

# Load from different folders
loaders = [
    DirectoryLoader("./docs", glob="**/*.txt"),
    DirectoryLoader("./manuals", glob="**/*.md"),
    DirectoryLoader("./code", glob="**/*.py")
]

all_docs = []
for loader in loaders:
    all_docs.extend(loader.load())

Custom Retrieval

# Increase context for complex questions
results = db.similarity_search(question, k=5)

# Use similarity scores
results_with_scores = db.similarity_search_with_score(question, k=3)
for doc, score in results_with_scores:
    print(f"Relevance: {score}")

Troubleshooting

Documents not being indexed

• Check file format (must be readable by TextLoader)
• Verify SOURCE_FOLDER path is correct
• Ensure files have content

Deletions not detected

• Make sure you're using cleanup="full"
• Verify record manager is properly initialized
• Check that source_id_key matches document metadata

Out of memory errors

• Reduce CHUNK_SIZE
• Process documents in batches
• Use a vector store with disk persistence (we already use Chroma)

Conclusion

Building a production-ready RAG system requires more than just connecting an LLM to a vector store. Efficient document management through incremental indexing is crucial for:

• Performance: Only process what's changed
• Cost: Minimize embedding API calls
• Scalability: Handle growing knowledge bases
• Maintenance: Easy updates without downtime

The combination of Chroma for vector storage and SQLRecordManager for tracking changes provides a robust foundation for production RAG applications.

Key Takeaways

1. Use incremental indexing instead of re-indexing everything
2. Track document state with a record manager
3. Set cleanup="full" to detect deleted files
4. Choose appropriate chunk sizes for your use case
5. Monitor statistics to understand system behavior

Next Steps

• Add support for more file types (PDF, DOCX, HTML)
• Implement batch processing for large knowledge bases
• Add caching for frequently asked questions
• Set up monitoring and logging
• Deploy with a web interface

Resources

• LangChain Documentation
• Chroma Vector Store
• Ollama Models

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Built with ❤️ using LangChain, Chroma, and Ollama