How to Keep Embeddings Up to Date Without Full Reindexing

Keeping embeddings fresh is one of the hardest problems in production RAG systems. Most teams default to full reindexing, but there's a better way: incremental updates through change tracking. This guide shows you how to implement delta sync for your vector database.

The Incremental Update Challenge

Traditional approaches to keeping embeddings current fall into two categories, both flawed:

The Cron Job Approach

Many teams set up scheduled jobs that periodically reindex their entire dataset. This seems simple, but it has major problems:

• Wasteful: Processes unchanged data repeatedly
• Stale windows: Data can be outdated between runs
• Resource spikes: Creates predictable load spikes
• No real-time updates: Changes wait for the next scheduled run

The Manual Trigger Approach

Some teams reindex manually when they notice stale data. This is even worse:

• Reactive, not proactive: Problems are discovered by users
• Inconsistent: Updates happen irregularly
• High operational overhead: Requires constant monitoring

Neither approach scales or maintains true freshness.

The Solution: Change Tracking

The correct way to keep embeddings updated is to track changes at the source and process only what's modified. This is called delta sync or incremental updates.

Step 1: Identify Change Sources

Your data changes come from specific sources:

• Database writes: INSERT, UPDATE, DELETE operations
• File system changes: New files, modified files, deleted files
• API updates: External systems pushing changes
• Streaming data: Real-time event streams

Each source requires different change detection mechanisms.

Step 2: Implement Change Detection

#### Database Change Tracking

For SQL databases, use change data capture (CDC) or transaction logs:

-- Example: Track updates with a modified_at timestamp
SELECT * FROM documents 
WHERE modified_at > last_sync_timestamp

For NoSQL databases, leverage native change streams:

// MongoDB change stream example
const changeStream = db.collection('documents').watch();
changeStream.on('change', (change) => {
  processChange(change);
});

#### File System Monitoring

Use file system watchers or polling with checksums:

import hashlib
import os
def get_file_hash(filepath):
    with open(filepath, 'rb') as f:
        return hashlib.md5(f.read()).hexdigest()
Compare hashes to detect changes

#### API and Stream Monitoring

For external APIs, use webhooks or polling with ETags/Last-Modified headers. For streams, process events as they arrive.

Step 3: Process Changes Incrementally

Once you detect changes, process them selectively:

1. Filter relevant changes: Not all changes need embedding updates 2. Batch efficiently: Group small changes into batches 3. Handle dependencies: Some changes may require related updates 4. Maintain consistency: Ensure atomic updates

Step 4: Update Vector Database

Apply changes to your vector database:

• Insert new embeddings: Add vectors for new documents
• Update existing embeddings: Replace vectors for modified documents
• Delete stale embeddings: Remove vectors for deleted documents

Most vector databases support upsert operations for this purpose.

Implementation Patterns

Pattern 1: Event-Driven Updates

Process changes as they occur:

def handle_document_change(event):
    if event.type == 'INSERT':
        embedding = generate_embedding(event.document)
        vector_db.upsert(event.document.id, embedding)
    elif event.type == 'UPDATE':
        embedding = generate_embedding(event.document)
        vector_db.upsert(event.document.id, embedding)
    elif event.type == 'DELETE':
        vector_db.delete(event.document.id)

Pattern 2: Batch Processing

Collect changes and process in batches:

def process_batch(changes):
    embeddings = []
    for change in changes:
        embedding = generate_embedding(change.document)
        embeddings.append({
            'id': change.document.id,
            'vector': embedding,
            'metadata': change.document.metadata
        })
    vector_db.upsert_batch(embeddings)

Pattern 3: Hybrid Approach

Combine real-time critical updates with batched bulk updates:

• Critical documents: Update immediately
• Bulk changes: Batch and process periodically
• Background maintenance: Full validation runs

Best Practices

1. Idempotency

Ensure your update process is idempotent—running the same update multiple times should produce the same result. This prevents issues from retries or duplicate events.

2. Error Handling

Implement robust error handling:

• Retry failed updates with exponential backoff
• Log failures for manual review
• Maintain a dead letter queue for problematic records

3. Monitoring

Track key metrics:

• Update latency: Time from change to embedding update
• Update success rate: Percentage of successful updates
• Cost per update: Embedding API costs for incremental updates

4. Validation

Periodically validate your incremental updates:

• Compare sample embeddings with full reindex results
• Check for missing or stale embeddings
• Verify metadata consistency

The Bottom Line

Updating vectors correctly requires tracking changes, not running cron jobs. Incremental embedding updates through delta sync provide:

• Cost efficiency: Only process what changed
• Real-time freshness: Updates happen as changes occur
• Scalability: Grows with change volume, not total data size
• Reliability: Fewer moving parts than scheduled full reindexes

Stop reindexing everything. Start tracking changes.

The path to fresh embeddings isn't more frequent full rebuilds—it's intelligent change tracking and incremental updates.