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Building Your Own Search Infrastructure

Before diving into the architecture diagram, let’s understand the key components and challenges when building a general-purpose indexing tool for high-cardinality systems like S3:

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Core Components

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Feature Extraction & Processing

• Custom feature extraction configurations for different content types
• Model versioning and embedding updates
• Handling multiple modalities (text, image, video)

Implementation: Model inference using open-source models (BERT, ResNet, etc.) deployed on Amazon SageMaker, with automated model registry and versioning

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Queuing & Caching

• Distributed queuing to handle high-volume processing
• Cache management to ensure performance
• Ensuring all content gets processed reliably

Implementation: Celery workers for distributed task processing, Redis for caching and task queues, with automatic retry mechanisms

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Hybrid Storage

• Vector databases for similarity search (KNN)
• Transactional databases for metadata
• Cold storage for original content
• Cache layer for frequent access

Implementation: MongoDB for metadata storage, Qdrant for vector similarity search, S3 for cold storage, Redis for hot cache

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Synchronization

• Real-time sync with source object stores
• Handling streaming data
• Maintaining consistency across storage types

Implementation: S3 Event Notifications → SQS → Lambda for real-time updates, scheduled polling jobs for consistency checks, DynamoDB for state management

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Search Capabilities

• Multi-vector search across modalities
• Simplified retrieval syntax
• Personalization through:
  • Reranking
  • Learning to Rank (LTR)
  • Model fine-tuning

Implementation: Query parity across vector stores, custom ranking algorithms, A/B testing framework for search optimization

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Performance Optimization

• Query analytics
• Dynamic indexing
• Scale management
• System stability

Implementation: ClickHouse for query analytics and performance tracking, automated index creation based on query patterns, horizontal scaling with Kubernetes

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System Architecture

Below is a visualization of how these components interact:

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DIY Implementation Challenges

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1. Infrastructure Components

• Feature Extraction Pipeline

  • Multiple model deployments
  • Version management
  • Embedding updates
  • Custom extraction configurations

• Storage & Queuing

  • Distributed queue management
  • Multi-database synchronization
  • Cache invalidation
  • Cold storage optimization

• Search Implementation

  • Vector similarity search
  • Keyword search
  • Metadata filtering
  • Result merging logic

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2. Operational Challenges

• High availability across all components
• Scaling with data growth
• Error handling and recovery
• Model versioning and updates
• Data consistency across stores
• Performance optimization
• Security implementation

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Mixpeek Simplification

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Two API Calls

1. Index API

# Single call to index any content
response = mixpeek.index(
    url="your-content-url",
    collection_id="your-collection",
    feature_extractors=["text", "image", "video"]
)

2. Search API

# Single call to search across all modalities
results = mixpeek.search(
    queries=["your search query"],
    collection_ids=["your-collection"],
    vector_index="multimodal"
)

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Key Benefits

• No infrastructure management
• Automatic scaling
• Built-in version management
• Pre-optimized performance
• Unified API for all modalities
• Automatic synchronization
• Built-in security and compliance