I built a cold-tier vector memory index that fits 1 billion conversation turns in 200 GB — pip installable
Been working on the memory problem for long-running local AI assistants. When your agent has been running for months, you can't keep everything in context and you can't afford to store float32 embeddings forever.
I wrote SSE (Sparse Spectral Encoding) — it compresses dense embeddings by keeping only the dominant Fourier coefficients per vector, quantizing magnitude and phase. One tuning knob (K) trades recall for storage across a wide range.
**Benchmarked against BEIR and LoCoV1 with real sentence encoders:**
**Method**
**Bytes/chunk**
**nDCG@10**
**vs int8**
ScalarInt8
384
0.646
1.0×
**Spectral K=64**
**192**
**0.581**
**2× smaller**
**Spectral K=128**
**384**
**0.650**
**same size, slightly better**
K=64 clears a 70% recall floor at half the bytes. K=128 matches or beats int8 at equal storage across scifact, fiqa, arguana, and LoCoV1.
**Try it:**
pip install spectraltm
No GPU needed. No transformer inference at index time. Works with any encoder you already have (MiniLM, BGE, E5 — drop in your vectors, SSE handles the rest).
Paper on Zenodo with full benchmark tables: [https://zenodo.org/records/21015380\](https://zenodo.org/records/21015380)
Happy to answer questions about the compression math or the benchmark methodology.