Get started with VectorDB Observability

This guide demonstrates production-ready VectorDB observability setup with OpenTelemetry-native auto-instrumentations. Get enterprise-grade AI monitoring with zero code changes using our CLI or minimal SDK integration for complete vector database performance tracking.

Learn how to implement real-time vector operations monitoring, embedding performance tracking, similarity search optimization, and cost analysis for your VectorDB applications with OpenTelemetry traces and metrics.

flowchart TB;
    subgraph " "
        direction LR;
        subgraph " "
            direction LR;
            Shield360_SDK[Shield360 SDK] -->|Sends Traces & Metrics| OTC[OpenTelemetry Collector];
            OTC -->|Stores Data| ClickHouseDB[ClickHouse];
        end
        subgraph " "
            direction RL;
            Shield360_UI[Shield360] -->|Pulls Data| ClickHouseDB;
        end
    end

Deploy Shield360

Git clone Shield360 repository

git clone git@github.com:ThinkfleetAI/Shield360.git

Start Docker Compose

From the root directory of the Shield360 distribution, Run the below command:

docker compose up -d

Install Shield360 SDK

Python
Typescript

pip install shield360

npm install shield360

Instrument your application

Python
Typescript

Zero-Code instrumentation
Manual instrumentation

Via CLI arguments
Via environment variables

# Install Shield360
pip install shield360

# Start VectorDB monitoring instantly
shield360-instrument --service-name my-vectordb-app python your_vectordb_app.py

# With custom settings for VectorDB applications
shield360-instrument \
  --otlp-endpoint http://127.0.0.1:4318 \
  --service-name my-vectordb-app \
  --environment production \
  python your_vectordb_app.py

# Install Shield360
pip install shield360

# Set environment variables
export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export OTEL_SERVICE_NAME=my-vectordb-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production

# Run your VectorDB application
shield360-instrument python your_vectordb_app.py

Via function parameters
Via environment variables

Add the following two lines to your VectorDB application code:

import shield360

shield360.init(
  otlp_endpoint="http://127.0.0.1:4318",
  service_name="my-vectordb-app",
  environment="production"
)

# Your existing VectorDB code works the same
# Vector operations are traced automatically

Configure your OTLP endpoint using environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export OTEL_SERVICE_NAME=my-vectordb-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production

Add the following two lines to your VectorDB application code:

import shield360

shield360.init()

Manual instrumentation

Parameters
Environment Variables

Add the following two lines to your VectorDB application code:

import { Shield360 } from 'shield360';

await Shield360.init({
  otlpEndpoint: 'http://127.0.0.1:4318',
  serviceName: 'my-vectordb-app',
  environment: 'production'
});

// Your existing VectorDB code works the same
// Vector operations are traced automatically

Configure your OTLP endpoint using environment variables:

export OTEL_EXPORTER_OTLP_ENDPOINT="http://127.0.0.1:4318"
export OTEL_SERVICE_NAME=my-vectordb-app
export OTEL_DEPLOYMENT_ENVIRONMENT=production

Add the following two lines to your VectorDB application code:

import { Shield360 } from 'shield360';

await Shield360.init();

Monitor, debug and test the quality of your vector database

Navigate to Shield360 at 127.0.0.1:3000 to start monitoring your VectorDB applications.

You should see VectorDB-specific traces and metrics including:

Vector Operations: Track insert, update, delete, and query operations performance
Similarity Search Metrics: Monitor search latency, relevance scores, and result quality
Embedding Performance: Analyze embedding generation and storage efficiency
Index Operations: Monitor index building, updates, and optimization processes
Resource Utilization: Track memory usage, disk I/O, and computational costs
Database Performance: Monitor connection pooling, query optimization, and throughput

Send Observability telemetry to other OpenTelemetry backends

flowchart TB;
    subgraph " "
        direction LR;
        ApplicationCode[Application Code] -->|Instrumented with| Shield360_SDK[Shield360 SDK];
        Shield360_SDK -->|Sends Traces & Metrics| OT_Backend[OpenTelemetry Backend];
    end

If you wish to send telemetry directly from the SDK to another backend, you can stop the current Docker services by using the command below. For more details on sending the data to your existing OpenTelemetry backends, checkout our Supported Destinations guide.

docker compose down

If you have any questions or need support, reach out to our community.

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Integrations

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Destinations

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