GrepAI Storage with Qdrant

This skill covers using Qdrant as the storage backend for GrepAI, offering high-performance vector search.

When to Use This Skill

• Need fastest possible search performance

• Very large codebases (50K+ files)

• Already using Qdrant infrastructure

• Want advanced vector search features

What is Qdrant?

Qdrant is a purpose-built vector database offering:

• ⚡ Extremely fast vector similarity search

• 📏 Excellent scalability

• 🔧 Advanced filtering capabilities

• 🐳 Easy Docker deployment

Prerequisites

• Qdrant server running

• Network access to Qdrant

Advantages

Benefit

Description

⚡ Performance

Fastest vector search

📏 Scalability

Handles millions of vectors

🔍 Advanced

Filtering, payloads, sharding

🐳 Easy deploy

Docker-ready

☁️ Cloud option

Qdrant Cloud available

Setting Up Qdrant

Option 1: Docker (Recommended)

# Run Qdrant with persistent storage

docker run -d \

  --name grepai-qdrant \

  -p 6333:6333 \

  -p 6334:6334 \

  -v qdrant_storage:/qdrant/storage \

  qdrant/qdrant

Ports:

• 6333: REST API

• 6334: gRPC API (used by GrepAI)

Option 2: Docker Compose

# docker-compose.yml

version: '3.8'

services:

  qdrant:

    image: qdrant/qdrant

    ports:

      - "6333:6333"

      - "6334:6334"

    volumes:

      - qdrant_storage:/qdrant/storage

    environment:

      - QDRANT__SERVICE__GRPC_PORT=6334

volumes:

  qdrant_storage:

docker-compose up -d

Option 3: Qdrant Cloud

• Sign up at cloud.qdrant.io

• Create a cluster

• Get your endpoint and API key

Configuration

Basic Configuration (Local)

# .grepai/config.yaml

store:

  backend: qdrant

  qdrant:

    endpoint: localhost

    port: 6334

With TLS (Production)

store:

  backend: qdrant

  qdrant:

    endpoint: qdrant.company.com

    port: 6334

    use_tls: true

With API Key (Qdrant Cloud)

store:

  backend: qdrant

  qdrant:

    endpoint: your-cluster.aws.cloud.qdrant.io

    port: 6334

    use_tls: true

    api_key: ${QDRANT_API_KEY}

Set the environment variable:

export QDRANT_API_KEY="your-api-key"

Configuration Options

Option

Default

Description

endpoint

localhost

Qdrant server hostname

port

6334

gRPC port

use_tls

false

Enable TLS encryption

api_key

none

Authentication key

Verifying Setup

Check Qdrant is Running

# REST API health check

curl http://localhost:6333/health

# Expected: {"status":"ok"}

Check Collections (after indexing)

# List collections

curl http://localhost:6333/collections

# Get collection info

curl http://localhost:6333/collections/grepai

From GrepAI

grepai status

# Should show Qdrant backend info

Qdrant Dashboard

Access the web dashboard at http://localhost:6333/dashboard:

• View collections

• Browse vectors

• Execute queries

• Monitor performance

Performance Characteristics

Search Latency

Codebase Size

Vectors

Search Time

Small (1K files)

5,000

<10ms

Medium (10K files)

50,000

<20ms

Large (100K files)

500,000

<50ms

Memory Usage

Qdrant loads vectors into memory for fast search:

Vectors

Dimensions

Memory

10,000

768

~60 MB

100,000

768

~600 MB

1,000,000

768

~6 GB

Advanced Configuration

Qdrant Server Configuration

Create config/production.yaml:

storage:

  storage_path: /qdrant/storage

service:

  grpc_port: 6334

  http_port: 6333

  max_request_size_mb: 32

optimizers:

  memmap_threshold_kb: 200000

  indexing_threshold_kb: 50000

Mount in Docker:

docker run -d \

  -v ./config:/qdrant/config \

  -v qdrant_storage:/qdrant/storage \

  qdrant/qdrant

Collection Settings

GrepAI creates a collection named grepai with:

• Vector size: matches your embedding dimensions

• Distance: Cosine similarity

• On-disk storage for large datasets

Clustering (Advanced)

For very large deployments, Qdrant supports distributed mode:

# qdrant config

cluster:

  enabled: true

  p2p:

    port: 6335

Backup and Restore

Snapshot Creation

# Create snapshot via REST API

curl -X POST 'http://localhost:6333/collections/grepai/snapshots'

Restore Snapshot

# Restore from snapshot

curl -X PUT 'http://localhost:6333/collections/grepai/snapshots/recover' \

  -H 'Content-Type: application/json' \

  -d '{"location": "/path/to/snapshot"}'

Migrating from GOB

• Start Qdrant:

docker run -d --name qdrant -p 6333:6333 -p 6334:6334 qdrant/qdrant

• Update configuration:

store:

  backend: qdrant

  qdrant:

    endpoint: localhost

    port: 6334

• Delete old index:

rm .grepai/index.gob

• Re-index:

grepai watch

Migrating from PostgreSQL

• Start Qdrant

• Update configuration to use Qdrant

• Re-index (embeddings must be regenerated)

Common Issues

❌ Problem: Connection refused

✅ Solution: Ensure Qdrant is running:

docker ps | grep qdrant

docker start grepai-qdrant

❌ Problem: gRPC connection failed

✅ Solution: Check port 6334 is exposed:

docker run -p 6334:6334 ...

❌ Problem: Authentication failed

✅ Solution: Check API key:

echo $QDRANT_API_KEY

❌ Problem: Out of memory

✅ Solutions:

• Enable on-disk storage in Qdrant config

• Increase Docker memory limit

• Use Qdrant Cloud for managed scaling

❌ Problem: Slow initial indexing

✅ Solution: This is normal; Qdrant optimizes in background. Searches will be fast after indexing completes.

Qdrant vs PostgreSQL

Feature

Qdrant

PostgreSQL

Search speed

⚡⚡⚡

⚡⚡

Setup complexity

Easy (Docker)

Medium

SQL queries

❌

✅

Scalability

Excellent

Good

Memory efficiency

Excellent

Good

Team familiarity

Lower

Higher

Recommendation: Use Qdrant for large codebases or maximum performance. Use PostgreSQL if you need SQL integration or team is familiar with it.

Best Practices

• Use persistent volume: Mount /qdrant/storage

• Enable TLS in production: Set use_tls: true

• Secure API key: Use environment variables

• Monitor memory: Vector search is memory-intensive

• Regular snapshots: Backup before major changes

Output Format

Qdrant storage status:

✅ Qdrant Storage Configured

   Backend: Qdrant

   Endpoint: localhost:6334

   TLS: disabled

   Collection: grepai

   Contents:

   - Files: 5,000

   - Vectors: 25,000

   - Dimensions: 768

   Performance:

   - Connection: OK

   - Indexed: Yes

   - Search latency: ~15ms