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Overview
Decompressed is the control layer for your vector data lifecycle. Version, rollback, and share embeddings across every RAG pipeline.
News
Stop Embedding Your Entire Corpus Blindly Decompressed Learn
Most teams pick an embedding model, chunk arbitrarily, embed everything, and hope. That loop costs real money every time it breaks. Here's why sample-first RAG design is the only rational way to stop paying for failed experiments.
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Why Your Pinecone Index Keeps Breaking (and the Vector Ops Fix) Decompressed Learn
You have CI/CD for your frontend, backend, and infrastructure. Why is your AI data still a manual upsert-and-pray process? Introducing Vector Ops: deployments for your vector database.
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I Updated My Embedding Model and My RAG Broke: A Post-Mortem Decompressed Learn
Upgrading from text-embedding-ada-002 to text-embedding-3-small looks simple, until your search results turn to garbage. Here's why embedding model migrations silently break RAG, and how to do them safely.
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Detecting Embedding Drift: The Silent Killer of RAG Accuracy Decompressed Learn
Your RAG pipeline shipped fine. Then answers started slipping. The problem is upstream, not the LLM. Here's how embedding drift breaks retrieval and what to do about it.
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