Vector Search Architecture¶

System Overview¶

The YAMS Vector Search system provides semantic search capabilities through a modular architecture that processes text into vector embeddings and enables similarity-based retrieval.

┌─────────────────────────────────────────────────────────────┐
│                     Application Layer                         │
├─────────────────────────────────────────────────────────────┤
│                    Semantic Search API                        │
├────────────┬────────────┬────────────┬────────────┬─────────┤
│  Document  │ Embedding  │   Vector   │   Model    │ Hybrid  │
│  Chunker   │ Generator  │  Database  │  Registry  │ Search  │
├────────────┴────────────┴────────────┴────────────┴─────────┤
│                    Vector Index Manager                       │
├───────────────────────────────────────────────────────────────┤
│               Storage Layer (LanceDB/Arrow)                   │
└───────────────────────────────────────────────────────────────┘

Core Components¶

1. Document Chunker¶

Responsible for segmenting documents into semantically meaningful chunks.

Key Features: - Multiple chunking strategies (sliding window, semantic, markdown-aware) - Configurable chunk size and overlap - Sentence boundary preservation - Metadata preservation

Data Flow:

Document → Chunker → Text Chunks → Embedding Generator

2. Embedding Generator¶

Converts text into high-dimensional vector representations.

Key Features: - ONNX Runtime integration - Batch processing support - Model-agnostic interface - Normalization and preprocessing

Architecture:

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│   Text       │────▶│  Tokenizer   │────▶│    ONNX      │
│   Input      │     │              │     │   Runtime    │
└──────────────┘     └──────────────┘     └──────────────┘
                                                  │
                                                  ▼
                                          ┌──────────────┐
                                          │  Embedding   │
                                          │   Vector     │
                                          └──────────────┘

3. Vector Database¶

Persistent storage and retrieval of vector embeddings.

Key Features: - LanceDB integration for columnar storage - Apache Arrow format support - ACID compliance - Metadata filtering

Schema:

VectorTable {
    chunk_id: STRING (PRIMARY KEY)
    document_hash: STRING
    embedding: FLOAT32[]
    content: STRING
    metadata: MAP<STRING, STRING>
    model_id: STRING
    model_version: STRING
    embedding_version: INT32
    created_at: TIMESTAMP
    is_stale: BOOLEAN
}

4. Vector Index Manager¶

Manages different index types for efficient similarity search.

Supported Index Types:

Index Type	Description	Use Case
FLAT	Brute force search	Small datasets (<10K)
HNSW	Hierarchical graph	Medium datasets (10K-1M)
IVF_FLAT	Inverted file index	Large datasets (>100K)
IVF_PQ	Product quantization	Very large datasets (>1M)

Index Selection Logic:

if (num_vectors < 10000) {
    return IndexType::FLAT;
} else if (num_vectors < 100000) {
    return IndexType::HNSW;
} else if (memory_constraint) {
    return IndexType::IVF_PQ;
} else {
    return IndexType::IVF_FLAT;
}

5. Model Registry¶

Manages embedding models and their lifecycle.

Key Features: - Model versioning - Performance tracking - Compatibility matrix - Hot-swapping support

Model Lifecycle:

Register → Load → Cache → Use → Monitor → Update/Replace

6. Hybrid Search Engine¶

Combines vector and keyword search for improved results.

Architecture:

┌─────────────┐     ┌─────────────┐
│   Vector    │     │   Keyword   │
│   Search    │     │    Search   │
└──────┬──────┘     └──────┬──────┘
       │                    │
       └────────┬───────────┘
                │
         ┌──────▼──────┐
         │   Result    │
         │   Fusion    │
         └──────┬──────┘
                │
         ┌──────▼──────┐
         │   Ranked    │
         │   Results   │
         └─────────────┘

Data Flow¶

Indexing Pipeline¶

graph LR
    A[Document] --> B[Chunker]
    B --> C[Text Chunks]
    C --> D[Embedding Generator]
    D --> E[Vectors]
    E --> F[Index Manager]
    F --> G[Vector Database]

    H[Metadata] --> G

Document Input: Raw documents enter the system
Chunking: Documents are split into processable chunks
Embedding: Each chunk is converted to a vector
Indexing: Vectors are added to the search index
Storage: Indexed vectors are persisted to database

Search Pipeline¶

graph LR
    A[Query Text] --> B[Embedding Generator]
    B --> C[Query Vector]
    C --> D[Index Search]
    D --> E[Candidate Vectors]
    E --> F[Similarity Scoring]
    F --> G[Ranking]
    G --> H[Results]

    I[Filters] --> D

Query Processing: User query is converted to vector
Index Search: Find candidate vectors using index
Scoring: Calculate similarity scores
Filtering: Apply metadata and threshold filters
Ranking: Sort results by relevance

Scalability Design¶

Horizontal Scaling¶

Sharding Strategy:

Documents → Hash(doc_id) → Shard Assignment

Shard Distribution: - Consistent hashing for balanced distribution - Replication factor of 3 for availability - Cross-shard query aggregation

Vertical Scaling¶

Resource Optimization: - Memory-mapped indices for large datasets - Lazy loading of embeddings - Compression with Product Quantization - GPU acceleration for embedding generation

Caching Strategy¶

Multi-Level Cache:

L1: Query Result Cache (LRU, 1GB)
L2: Embedding Cache (LFU, 4GB)
L3: Model Cache (TTL, 8GB)

Performance Characteristics¶

Latency Targets¶

Operation	P50	P95	P99
Embedding Generation	10ms	25ms	50ms
Vector Search (10K)	5ms	15ms	30ms
Vector Search (1M)	20ms	50ms	100ms
Batch Insert (100)	50ms	100ms	200ms

Throughput Targets¶

Embedding Generation: 1000 docs/sec
Vector Search: 5000 QPS
Index Updates: 10000 vectors/sec
Batch Operations: 100K vectors/batch

High Availability¶

Fault Tolerance¶

Component Redundancy: - Multiple embedding service instances - Replicated vector indices - Database replication - Load balancer failover

Recovery Mechanisms:

1. Health checks every 10s
2. Automatic failover < 30s
3. Data consistency verification
4. Incremental recovery

Backup Strategy¶

Backup Schedule: - Incremental: Every hour - Full: Daily at 02:00 UTC - Retention: 30 days

Backup Components: - Vector embeddings - Index structures - Model checkpoints - Configuration

Security Considerations¶

Data Protection¶

Encryption at Rest: AES-256 for stored vectors
Encryption in Transit: TLS 1.3 for API calls
Access Control: Role-based permissions
Audit Logging: All operations logged

Privacy¶

PII Handling: Automatic redaction in logs
Data Retention: Configurable TTL for vectors
Right to Deletion: Support for GDPR compliance
Anonymization: Hash-based document IDs

Monitoring and Observability¶

Key Metrics¶

System Metrics: - CPU/Memory utilization - Disk I/O rates - Network throughput - Queue depths

Application Metrics: - Embedding generation rate - Search latency distribution - Index size and growth - Cache hit rates

Business Metrics: - Query volume - Result relevance scores - User satisfaction metrics - API usage patterns

Alerting Rules¶

alerts:
  - name: high_search_latency
    condition: p95_latency > 100ms
    severity: warning

  - name: low_cache_hit_rate
    condition: cache_hit_rate < 0.7
    severity: info

  - name: index_corruption
    condition: index_health_check == failed
    severity: critical

Integration Points¶

External Systems¶

Supported Integrations: - Elasticsearch (keyword search) - PostgreSQL (metadata storage) - Redis (caching layer) - Kafka (event streaming) - Prometheus (monitoring)

API Interfaces¶

REST API:

POST /api/v1/vectors/search
POST /api/v1/vectors/index
GET  /api/v1/vectors/{id}
DELETE /api/v1/vectors/{id}

gRPC API:

service VectorSearch {
    rpc Search(SearchRequest) returns (SearchResponse);
    rpc Index(IndexRequest) returns (IndexResponse);
    rpc GetVector(GetRequest) returns (VectorRecord);
}

Deployment Architecture¶

Container Architecture¶

services:
  embedding-service:
    image: kronos/embedding:latest
    replicas: 3
    resources:
      memory: 4GB
      cpu: 2

  vector-database:
    image: kronos/vector-db:latest
    replicas: 3
    volumes:
      - vector-data:/data

  index-manager:
    image: kronos/index-manager:latest
    replicas: 2
    resources:
      memory: 8GB

Kubernetes Deployment¶

apiVersion: apps/v1
kind: Deployment
metadata:
  name: vector-search
spec:
  replicas: 3
  template:
    spec:
      containers:
      - name: vector-search
        image: kronos/vector-search:latest
        resources:
          requests:
            memory: "4Gi"
            cpu: "2"
          limits:
            memory: "8Gi"
            cpu: "4"

Future Enhancements¶

Planned Features¶

Multi-Modal Search: Support for image and audio embeddings
Cross-Lingual Search: Multi-language support
Federated Search: Distributed search across regions
AutoML: Automatic model selection and tuning
Graph Embeddings: Knowledge graph integration

Research Areas¶

Neural architecture search for optimal models
Quantum computing for similarity calculations
Homomorphic encryption for private search
Continual learning for model updates

Appendix¶

Configuration Reference¶

vector_search:
  embedding:
    model: all-MiniLM-L6-v2
    dimension: 384
    batch_size: 32

  index:
    type: HNSW
    parameters:
      m: 16
      ef_construction: 200
      ef_search: 50

  database:
    path: /data/vectors
    cache_size: 4GB
    max_connections: 100

  search:
    default_k: 10
    max_k: 100
    similarity_threshold: 0.7

Performance Tuning Matrix¶

Parameter	Small Dataset	Medium Dataset	Large Dataset
Index Type	FLAT	HNSW	IVF_PQ
Batch Size	16	32	64
Cache Size	1GB	4GB	16GB
Shards	1	4	16
Replicas	1	2	3