Memory Layer Architecture Deep Dive

# Memory Layer Architecture Deep Dive

The Memory Layer is the foundation of CognitiveX. This article explores its technical architecture.

Database Design

We use PostgreSQL with pgvector extension:

CREATE TABLE memories (
  id UUID PRIMARY KEY,
  content TEXT,
  embedding vector(1536),
  metadata JSONB,
  created_at TIMESTAMP
);

CREATE INDEX ON memories USING ivfflat (embedding vector_cosine_ops); ```

Embedding Strategy

Multiple embedding approaches:

• **OpenAI text-embedding-3-large**: Best quality
• **Sentence Transformers**: Self-hosted option
• **Custom Fine-tuned**: Domain-specific

Retrieval Pipeline

1. **Query Embedding**: Convert search to vector 2. **Similarity Search**: Find top-k candidates 3. **Reranking**: Use cross-encoder for precision 4. **Citation Extraction**: Link to sources

Optimization Techniques

Caching

Embeddings are cached aggressively:

• **Redis**: Recent embeddings
• **Local Cache**: Frequently used patterns
• **CDN**: Static embeddings

Batching

Process multiple items together:

const embeddings = await batchEmbed(texts, { batchSize: 100 });

Quantization

Reduce vector size without losing quality:

• **PCA Dimensionality Reduction**: 1536 → 768 dimensions
• **Product Quantization**: 10x size reduction
• **Binary Quantization**: Ultimate compression

Performance Numbers

• **Query Latency**: <50ms for most searches
• **Throughput**: 10,000+ queries/second
• **Accuracy**: 95%+ retrieval precision

Future Enhancements

• Multi-modal embeddings (images, code, audio)
• Temporal awareness (time-based relevance)
• Hierarchical memory (different detail levels)

Check our GitHub for implementation details.