Vector Search Performance Optimization
A vector search system that performs beautifully in a demo with a thousand test vectors can slow to a crawl once it's handling millions of embeddings and real production traffic. Getting vector search to perform well at scale isn't one fix — it's a set of levers spanning...
LangChain Async Operations
Introduction: The I/O Reality of LLM Applications Large Language Model API calls are slow. A single request to GPT-4 might take one to ten seconds. In a synchronous application, that time is wasted. The server sits idle, blocking the thread, waiting for a response from a...
Asyncio in LLM Applications
LLM APIs are high-latency, I/O-bound black holes. A single GPT-4 call takes 1–10 seconds. Do that synchronously in a loop, and you're burning wall-clock time watching network requests finish one by one. Asyncio fixes this by letting Python juggle hundreds of in-flight...
Python Async Programming for AI
AI workloads are I/O monsters. You're waiting on OpenAI's API, streaming tokens from Claude, fetching embeddings from a vector DB, or pulling training data from S3. Standard synchronous Python processes these one by one. Async lets you orchestrate thousands of these waits...