AI article
Why Production RAG Pipelines Need More Than a Vector Database on AWS
Community description: My first RAG prototype did exactly what the tutorials suggested. The API received a document, split...
Dev.to | Sep 15, 2026 | Pasindu Lanka
Automated excerpt
The API received a document, split it into chunks, generated embeddings, and inserted them into a vector database. The vector database is just a derived index. If a vector was missing, the document was effectively lost. The upload handler writes a PROCESSING status. It handles the S3 ingestion, chunking, embedding, and vector storage internally.
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