The Problem
Building a RAG (Retrieval-Augmented Generation) pipeline is easy; scaling it to ingest 100,000+ documents is hard. Naive Python scripts often crash due to Out-Of-Memory (OOM) errors or get banned by upstream APIs for hitting rate limits (429 Too Many Requests).
The Solution
I engineered XLR8 (Accelerate), a High-Throughput Ingestion Engin in Golang specifically designed to saturate network bandwidth without breaking API quotas, for speed and stability.
- Architecture: Implemented a Fan-Out/Fan-In Worker Pool pattern to process documents in parallel while maintaining constant memory usage.
- Traffic Control: Built a custom Token Bucket Rate Limiter to strictly adhere to API quotas regardless of concurrency levels.
- Observability: Integrated a TUI (Terminal UI) using Bubble Tea to visualize throughput (docs/sec) and error rates in real-time.
Key Metrics
- Throughput: Capable of processing 10,000+ documents without memory spikes.
- Reliability: Zero “429 Too Many Requests” errors due to strict rate limiting.
- Tech Stack: Golang, Weaviate (Vector DB), Concurrency Patterns (Fan-Out/an-In), Bubble Tea, OpenAI API.