driftvane
Compose drift detectors for RAG and agent systems.
Most drift libraries are either tabular-only (Evidently, DataDrift) or are
platforms that want you to ship telemetry to their backend (Phoenix, Arize).
driftvane is a small Python library that lets you wire up multiple drift
signals — embedding, retrieval, response, latency — into one report. No
server, no UI, no telemetry. Plug it into a Lambda or Glue job, get a
pandas.DataFrame or a JSON dict back.
Install
pip install driftvane
# optional
pip install "driftvane[pandas]" # to_pandas()
pip install "driftvane[external-response]" # delegate response scoring to context-drift-detector-py
Quickstart
import numpy as np
from driftvane import (
DriftReport,
EmbeddingDrift,
RetrievalDrift,
ResponseDrift,
LatencyDrift,
)
from driftvane.detectors.response import Triple
ref_emb = np.load("reference_query_embeddings.npy") # (n, 768)
cur_emb = np.load("current_query_embeddings.npy")
report = DriftReport.from_signals([
EmbeddingDrift(threshold=0.1).compute(ref_emb, cur_emb),
RetrievalDrift(k=10, threshold=0.3).compute(ref_top_k, cur_top_k),
ResponseDrift(threshold=0.15).compute(ref_triples, cur_triples),
LatencyDrift(p_threshold=0.01).compute(ref_latencies, cur_latencies),
])
if report.any_drifted():
print(report.to_pandas())
Or fail a CI job when retrieval moves too much:
from driftvane import DriftAlert
try:
report.alert_if({"retrieval_jaccard_at_10": 0.2})
except DriftAlert as e:
sys.exit(f"drift gate failed: {e}")
Detectors
| Detector | Input | Statistic | Notes |
|---|---|---|---|
EmbeddingDrift |
two (n, d) arrays |
MMD with RBF kernel, median-heuristic sigma | numpy-only, O(n²) — subsample for n > a few thousand |
RetrievalDrift |
paired top-k id lists | 1 − mean Jaccard@k; reports RBO too | aligned queries required |
ResponseDrift |
(intent, context, answer) triples |
shift in mean answer-to-context grounding | uses context-drift-detector-py if installed |
LatencyDrift |
two 1-D arrays of floats | Kolmogorov–Smirnov D + asymptotic p-value | scipy-free |
Each detector returns a DriftSignal(name, value, threshold, drifted, metadata).
DriftReport collects them.
What it does NOT do
- No server. No UI. No telemetry shipping.
- No tabular feature drift — use DataDrift for KS/PSI on classical features.
- No live trace ingestion or OTel collection — point this at parquet/numpy arrays you already have.
- No causal root-cause analysis. It tells you that drift is there, not why.
- No model retraining triggers — emit your own when
report.any_drifted().
Why not Phoenix / Arize / Evidently / Ragas?
| driftvane | Phoenix | Arize | Evidently | Ragas | |
|---|---|---|---|---|---|
| Library-only (no server) | ✓ | ✗ | ✗ | partial | ✓ |
| RAG-shaped detectors | ✓ | ✓ | ✓ | ✗ | ✓ |
| Embedding MMD out of the box | ✓ | partial | ✓ | ✗ | ✗ |
| Retrieval rank-shift | ✓ | ✗ | partial | ✗ | ✗ |
| Run inside a 5s Lambda | ✓ | ✗ | ✗ | ✓ | partial |
| numpy-only core deps | ✓ | ✗ | ✗ | ✗ | ✗ |
Status
v0.1 — alpha. The four detectors above work and have tests. Public API may change before v1.0. Issues and PRs welcome.
Metadata
Release files for driftvane 0.1.0
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|---|---|---|---|---|
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Total release size: 25.4 kB
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