Managing Embeddings in Amazon Aurora with Amazon Bedrock

Introduction

Embeddings convert unstructured data (text, images, etc.) into numeric vectors that capture semantic meaning, enabling search, recommendations, clustering, and RAG pipelines. In production, generating and keeping these embeddings synchronized with your database is important.

This post outlines practical patterns for managing embeddings in Amazon Aurora (PostgreSQL-compatible) using Amazon Bedrock. It highlights design choices, trade-offs, example schemas, and operational tips to help you choose the right approach for your latency, cost, and consistency requirements.

Key Features

• Automatic change detection
• Pluggable embedding provider
• Multiple integration patterns
• Vector storage & indexing
• Retry, batching, and backoff strategies

Benefits

• Freshness: Keep embeddings aligned with the latest data changes.
• Flexibility: Choose real-time or eventual-consistent pipelines to match requirements.
• Scalability: Move heavy work to asynchronous, batched processors to scale cheaply
• Simplicity in model management: Use Amazon Bedrock to avoid owning embedding model infrastructure and to switch models easily.
• Better app performance: Store vectors beside data to reduce lookup and synchronization complexity.

Use Cases

• Semantic document search and retrieval.
• Recommendations and content personalization.
• RAG knowledge bases for LLM augmentation.
• Clustering, similarity analytics, and anomaly detection.
• Indexing chat transcripts, product descriptions, or support tickets for downstream ML.
• Multi-Model Orchestration

Getting Started

Prereqs: Amazon Aurora PostgreSQL cluster, pgvector extension, Amazon Bedrock access + AWS IAM, and (if needed) Amazon SQS/AWS Lambda/pg_cron and Amazon VPC config for AWS Lambdas.

Example schema (conceptual):

Implementation Patterns (pick one by SLA & scale)

1. In-transaction synchronous (direct):
   The database trigger directly calls the embedding generator during the transaction.

• Pros: Ensures the embedding is always current at the moment of commit.
• Cons: Slows down inserts/updates because the transaction must wait for the embedding request. Potential risk of timeouts if the external service responds slowly.
• Best suited for: Low-traffic scenarios where strict real-time consistency is essential.

2. Synchronous trigger → AWS Lambda:
   Instead of invoking Amazon Bedrock directly, the database trigger synchronously calls an AWS Lambda function. The AWS Lambda then requests the embedding from Amazon Bedrock and returns it.

• Pros: Keeps AI logic separate from the database, improving modularity.
• Cons: The transaction still pauses until the embedding is completed.
• Best suited for: Small to mid-sized workloads that require synchronous consistency but prefer to decouple database operations from AI processing.

3. Event-driven asynchronous (recommended default):
   The trigger simply marks a row as PENDING and sends out a lightweight event (e.g., using LISTEN/NOTIFY, an event table, or SQS). A worker process (Lambda or container) then consumes the event, generates the embedding via Bedrock, and updates the record.

• Pros: Keeps writing fast, allows worker processes to scale independently, and makes retries/error handling easier.
• Cons: Embeddings aren’t immediately available; a short delay exists.
• Best suited for: Most production workloads where near-real-time updates are acceptable.

4. Queue + batched processing (Amazon SQS + AWS Lambda or AWS Batch):
   IDs of new documents are pushed into a queue and processed in groups. The worker fetches multiple rows at once, requests embeddings from Amazon Bedrock in batch mode, and updates the database.

• Pros: Significantly reduces costs at scale (fewer API calls) and improves throughput.
• Cons: Adds some latency since items may wait to be processed in a batch.
• Best suited for: Large-scale systems where cost efficiency and throughput are more important than instant updates.

5. Scheduled batch jobs (pg_cron or cron):
   A scheduled task periodically checks for records without embeddings and processes them in bulk (e.g., hourly or nightly).

• Pros: Straightforward approach with minimal overhead, avoids constant event handling.
• Cons: Embeddings may be outdated for hours, unsuitable for real-time needs.
• Best suited for: Data that changes infrequently or analytical workloads where freshness is not critical.

Minimal End-to-End (async)

• Insert/update document → DB trigger marks PENDING and publishes event/SQS message.
• Worker (AWS Lambda) retrieves document(s), calls Amazon Bedrock for embeddings (batch when possible), writes vectors, and updates status.
• App queries vectors using pgvector similarity searches.

Technical Challenges and Optimizations

1. API Rate Limits & Throttling — Amazon Bedrock enforces quotas. Use batching, exponential backoff, and throttling logic on the client
2. Transaction Latency — Avoid long-running calls inside database transactions; prefer async where possible.
3. Input Size & Chunking — Long documents may need chunking before embedding. Keep a consistent chunking strategy for indexing and retrieval.
4. Cost Control — Batch requests reduce per-item overhead; monitor model usage and choose model sizes wisely.
5. Vector Dimensionality & Indexing — Pick a vector size that matches your model; use appropriate indexes

Conclusion

Embedding generation for production systems requires balancing freshness, cost, and scalability. Amazon Bedrock provides a managed path to produce high-quality embeddings, while Amazon Aurora PostgreSQL (with pgvector) keeps vectors close to your data.

Pick the pattern that matches your SLAs: synchronous for immediate consistency, asynchronous for throughput and resilience. Add batching, retries, monitoring, and idempotent workers to turn a prototype into a reliable production system.

Drop a query if you have any questions regarding Amazon Bedrock and we will get back to you quickly.

About CloudThat