Listen to this Post
Serverless relational databases have gained significant attention recently, with Neon being one of the pioneering mostly serverless RDBMS options. The integration of AWS Lambda and Neon enables the creation of scalable, performant search applications without the need for continuously running services. Now, with advancements like DSQL and Aurora Serverless v2, comparisons with Neon are becoming more relevant.
Check out the example by szymon-szym demonstrating a serverless semantic search using:
– AWS Lambda for compute
– AWS Bedrock for AI-powered search
– Neon as the serverless database
🔗 Serverless semantic search – AWS Lambda, AWS Bedrock, Neon
You Should Know:
1. Setting Up AWS Lambda for Serverless Search
Deploy a Lambda function with Python for semantic search:
import json
import boto3
from pgvector.psycopg2 import register_vector
import psycopg2
def lambda_handler(event, context):
<h1>Connect to Neon DB</h1>
conn = psycopg2.connect(
host="your-neon-host",
database="testdb",
user="user",
password="password"
)
register_vector(conn)
cursor = conn.cursor()
<h1>Perform vector search</h1>
cursor.execute("SELECT id, content FROM documents ORDER BY embedding <=> %s LIMIT 5", (event['query_vector'],))
results = cursor.fetchall()
return {
'statusCode': 200,
'body': json.dumps(results)
}
2. Configuring Neon as a Serverless PostgreSQL Database
Neon provides a fully managed Postgres with autoscaling:
<h1>Install psycopg2 and pgvector for Python</h1> pip install psycopg2-binary pgvector <h1>Create a table with vector support in Neon</h1> CREATE EXTENSION vector; CREATE TABLE documents ( id SERIAL PRIMARY KEY, content TEXT, embedding VECTOR(1536) # For OpenAI embeddings );
3. Integrating AWS Bedrock for AI-Powered Search
Use Amazon Bedrock to generate embeddings:
import boto3
bedrock = boto3.client('bedrock-runtime')
def get_embedding(text):
response = bedrock.invoke_model(
body=json.dumps({"inputText": text}),
modelId="amazon.titan-embed-text-v1"
)
return json.loads(response['body'].read())['embedding']
4. Deploying with AWS SAM
Define resources in `template.yml`:
Resources: SearchFunction: Type: AWS::Serverless::Function Properties: CodeUri: lambda_function/ Handler: app.lambda_handler Runtime: python3.9 Environment: Variables: DB_HOST: !Ref NeonHost
5. Performance Optimization
- Use Aurora Serverless v2 for comparison:
-- Aurora supports pgvector too! CREATE EXTENSION IF NOT EXISTS vector;
- Enable Lambda Cold Start mitigation with Provisioned Concurrency.
What Undercode Say:
Serverless databases like Neon and Aurora v2 are transforming cloud architectures by eliminating manual scaling. Combining them with AWS Lambda and Bedrock unlocks AI-driven search at scale. Key takeaways:
– Use pgvector for PostgreSQL-based vector search.
– Optimize Lambda with layers for dependencies like psycopg2.
– Compare Neon vs. Aurora v2 for cost/performance trade-offs.
Expected Output:
A scalable, serverless semantic search system powered by AWS Lambda, Bedrock, and Neon, with minimal operational overhead.
🔗 Relevant URLs:
References:
Reported By: Darryl Ruggles – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
