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paperus-ml

Machine learning service of paperus: text embeddings for search by meaning, reranking of search hits, and OCR of scanned pages, with ONNX Runtime on the CPU or an NVIDIA GPU. Models are downloaded from Hugging Face on first use and unloaded when idle.

pip install paperus-ml
PAPERUS_ML_TOKEN=… paperus-ml --host 0.0.0.0   # port 3003

Settings are environment variables:

Variable Default Meaning
PAPERUS_ML_TOKEN none Shared secret; requests need Authorization: Bearer <token>
PAPERUS_ML_CACHE_PATH ~/.cache/paperus-ml Downloaded models
PAPERUS_ML_PRELOAD [] Models to load at start and keep, e.g. ["arctic-embed-m-v2"] (paperus then sends the one in use)
PAPERUS_ML_MODEL_TTL 300 Unload other models after this many idle seconds (0: never)
PAPERUS_ML_INTRA_OP_THREADS 0 CPU cores per model at start (0: all it sees); paperus' administration sets it afterwards
PAPERUS_ML_MAX_BATCH_TEXTS / _TOKENS 32 / 16384 Batch size (memory grows with both)

paperus sends its settings with PUT /config (cores, models to keep loaded). One batch runs at a time per model, using all its cores; simultaneous requests take turns batch by batch.

Most installations use the paperus-ml container image instead. Status: early version (0.0.x); expect breaking changes.

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