Embedding Pipeline Build — Text to Vector at Scale

I will build a production embedding pipeline for your text data — covering text preprocessing, batch embedding generation with rate limit management, embedding storage, incremental update capability for new documents, and embedding quality validation. Embedding pipelines built without batch processing and rate limit management fail on large corpora — hitting API rate limits mid-ingestion with no resumption capability, losing progress and requiring a full restart; a properly engineered pipeline processes in appropriately sized batches, respects rate limits, checkpoints progress, and resumes from the last successful batch after any interruption.

The pipeline covers text preprocessing and cleaning, batched embedding generation, rate limit management with exponential backoff, checkpoint-based progress tracking for resumable processing, embedding storage integration, and a validation check confirming embedding quality.