ragdrift-py 0.1.2

Five-dimensional drift detection for production RAG systems. Rust core, Python bindings.

ragdrift

Five-dimensional drift detection for production RAG systems. Rust core, Python bindings.

The problem

Your RAG system is in production. The retriever silently starts returning slightly worse results because your corpus drifted. The model gets more confident but no better calibrated. The mix of intents your users send shifts week-over-week. None of this surfaces as a 5xx, none of it fails your tests, none of it fires your latency alerts.

By the time someone notices in a Slack thread, it has been broken for weeks.

ragdrift watches five dimensions at once and gives you a single threshold-able report. The numerical core is Rust; the Python wheel is a thin PyO3 binding that releases the GIL on the heavy paths.

Install

pip install ragdrift-py

The PyPI distribution is ragdrift-py (the bare ragdrift name on PyPI was taken by an unrelated project). The Python import is still import ragdrift, same convention as pyyaml/yaml or opencv-python/cv2. The Rust crates on crates.io are unaffected — cargo add ragdrift works.

Optional extras:

pip install 'ragdrift-py[opensearch,aws]'        # adapter + CloudWatch exporter
pip install 'ragdrift-py[pgvector,prometheus]'
pip install 'ragdrift-py[pinecone,datadog]'

30-second quickstart

import numpy as np
from ragdrift import RagDriftMonitor

baseline = np.load("baseline_embeddings.npy")  # (n, 768) float32
current  = np.load("current_embeddings.npy")

monitor = RagDriftMonitor(embedding_threshold=0.05)
report = monitor.evaluate(
    baseline_embeddings=baseline,
    current_embeddings=current,
)
if report.any_exceeded():
    print(report.to_json())

The five dimensions

Dimension	Method	What it catches
Embedding	MMD² (RBF) + sliced Wasserstein	corpus or model embedding distribution shift
Data	per-feature KS + PSI	tabular feature drift (latency, retrieval count, etc.)
Response	KS on lengths, optional SW	response length / semantic shift
Confidence	KS, optional ECE delta	confidence score collapse, calibration breakage
Query	k-means + symmetric KL	intent-mix shift in incoming queries

See docs for the math and citations.

Why not X?

• Arize Phoenix is great for embedding visualization and notebook-style exploration. It does not give you a single Rust-fast scalar you can alert on from Lambda.
• Evidently is excellent for tabular drift and report generation. It does not have a sliced-Wasserstein-on-embeddings primitive in the hot path.
• WhyLabs / NannyML are mature monitoring platforms — useful, vendor-tied, not embeddable as a library inside your service.
• Roll your own: most teams write the first 80% in 200 lines of numpy and then hit a wall when MMD on 10k×768 takes 8 seconds in pure Python.

ragdrift is the library you reach for when you want the math right, the runtime tight, and a single dependency that handles all five dimensions.

Architecture

                       +----------------------+
                       |  RagDriftMonitor     |
                       |  (Python facade)     |
                       +----------+-----------+
                                  |
            +---------+-----------+-----------+--------+
            |         |           |           |        |
     EmbeddingDrift  DataDrift  ResponseDrift  ConfidenceDrift  QueryDrift
            |         |           |           |        |
            +---------+-----------+-----------+--------+
                                  |
                           ragdrift._native
                          (PyO3, GIL-released)
                                  |
                          ragdrift-core (Rust)
                          KS  PSI  MMD  SW  k-means

Status

0.1.0 — alpha. API may change in 0.x. Semver respected within 0.x.y; minor versions are allowed to break the surface. Core math is well-tested (45+ Rust tests, 25+ Python tests); production deployments should pin an exact version.

Contributing

See CONTRIBUTING.md. All quality gates must pass on Linux, macOS, and Windows for Python 3.10–3.13 before merge.

License

Dual-licensed under MIT or Apache-2.0 (Rust convention). Pick whichever suits.

LICENSE-MIT · LICENSE-APACHE