Technical Insights & Architecture Papers

Deep-dives on high-throughput backend architecture, Python & Django performance, real-time Voice AI, and resilient database modeling.

/
Clear
Active Topic: #pgvector
Clear Topic

Semantic Caching for LLMs with Redis & `pgvector`: Slashing API Costs & Sub-20ms Latency

Identical and semantically equivalent LLM queries waste massive API budgets and introduce 1.5s+ latency. Build a high-throughput semantic caching layer using embeddings, cosine distance thresholds, and Redis vector indexing for sub-20ms instant responses.

Read Publication devManue

Hybrid Search in PostgreSQL: Combining Full-Text Search with pgvector via Reciprocal Rank Fusion

Pure vector cosine distance misses exact alphanumeric SKU/ID matches, while keyword search misses semantic intent. Learn how to architect a native hybrid search engine inside PostgreSQL using tsvector, pgvector, and Reciprocal Rank Fusion (RRF) in a single CTE query.

Read Publication devManue

Want Technical Consulting or Architecture Reviews?

We collaborate with engineering teams to audit database performance, optimize Python/Django ASGI architectures, and design real-time AI pipelines.

Chat on WhatsApp