vector-engine 1.1.0

ML-first vector computation and retrieval engine.

Vector Engine v1.1.0

Reproducibility-first vector retrieval toolkit for local ML and IR workflows.

Vector Engine provides a clean Python API for vector indexing/search, evaluation, training utilities, and evidence-oriented benchmarking on a single machine.

Why Vector Engine

• ANN libraries are fast but often backend-specific and low-level.
• Vector databases focus on serving and infra, not local experimentation loops.
• ML teams still need one local toolkit for ingest, retrieval, evaluation, and reproducibility.

Vector Engine focuses on that local workflow and keeps evidence outputs machine-checkable.

Start Here

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip && python -m pip install vector-engine

Install Options

PyPI:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install vector-engine

Local development:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip setuptools wheel
python -m pip install -e ".[dev,ml]"
python -m pytest -q

macOS arm64 + Python 3.12 constrained setup:

python3.12 -m venv .venv312
source .venv312/bin/activate
python -m pip install --upgrade pip setuptools wheel
python -m pip install -c requirements/constraints-macos-arm64-py312.txt -e ".[dev,ml]"
python -m pytest -q

Optional FAISS extra:

python -m pip install -e ".[faiss]"

If you hit externally-managed-environment, use a virtual environment as shown above.

60-Second Quickstart

import numpy as np
from vector_engine import VectorArray, VectorIndex

xb = VectorArray.from_numpy(
    np.random.randn(1000, 384).astype("float32"),
    ids=[f"doc-{i}" for i in range(1000)],
    normalize=True,
)
xq = VectorArray.from_numpy(np.random.randn(2, 384).astype("float32"), normalize=True)

index = VectorIndex.create(xb, metric="cosine", backend="bruteforce")
results = index.search(xq, k=5)
print(results.ids[0], results.scores[0])

Colab Demos

• Demo A (basic): Google Colab
• Scaling expansion: Google Colab

v1.1.0 Surface

• Core: VectorArray, VectorIndex, Metric, SearchResult
• ML: knn_classify, knn_regress, kmeans, KMeansResult
• Training: mine_hard_negatives, TripletBatch
• Eval: precision_at_k, recall_at_k, ndcg_at_k, retrieval_report, retrieval_report_detailed, batch_metrics_summary, retrieval_cohort_report
• Ingest/connectors: load_numpy_bundle, load_jsonl_bundle, load_parquet_bundle, with_deterministic_splits, scripts/ingest_dataset.py

API Contract Highlights

• VectorArray requires non-empty 2D tensors (n, d) and unique int/str IDs.
• VectorIndex.search(..., k=...) requires positive integer k.
• Metadata lengths align with vector row counts.
• kmeans(..., random_state=...) validates finite vectors and deterministic seeds.
• Retrieval evaluation validates malformed ground truth with stable eval_error prefixes.

Data Ingest to Eval Recipe

1. Build a reproducible ingest bundle from JSONL:

python scripts/ingest_dataset.py \
  --input-jsonl artifacts/raw/source.jsonl \
  --output-dir artifacts/ingest_bundle \
  --id-field id \
  --text-field text \
  --embedding-dim 256 \
  --seed 7 \
  --label-field label \
  --split-field split \
  --query-group-field query_group \
  --ground-truth-field ground_truth

1. Run retrieval evaluation:

python scripts/rag_real_corpus_eval.py \
  --embeddings artifacts/ingest_bundle/embeddings.npy \
  --query-embeddings artifacts/repro_smoke/real_corpus_inputs/query_embeddings.npy \
  --ids artifacts/ingest_bundle/ids.json \
  --ground-truth artifacts/ingest_bundle/ground_truth.json \
  --metadata artifacts/ingest_bundle/metadata.json \
  --output artifacts/real_corpus_runs/run_1.json \
  --backend bruteforce \
  --k 6 \
  --ks 1,3,6 \
  --loops 5 \
  --threshold-recall 0.75 \
  --threshold-ndcg 0.70 \
  --threshold-p95-ms 120

Bundle outputs include:

• embeddings.npy, ids.json, metadata.json
• optional labels.json, splits.json, query_groups.json, ground_truth.json
• ingest_manifest.v1.json (contract-validated)

Backends

Backend	Search	Add	Save/Load	Custom Metric
bruteforce	yes	yes	yes	yes
faiss	yes	yes	yes	no

FAISS is optional. The required reproducibility path is bruteforce-safe.

Reproducibility and Evidence

Recommended release evidence flow:

python scripts/repro_smoke.py --output-dir artifacts/repro_smoke
python scripts/benchmark_matrix.py --mode exact --warmup 2 --loops 8 --seed 7 --output-dir artifacts/benchmark_matrix
python scripts/publishable_results.py --matrix-summary artifacts/benchmark_matrix/matrix_summary.json --stability-summary artifacts/testing_runs/stability_summary_bruteforce_200.json --output artifacts/benchmark_matrix/publishable_results.v1.json
python scripts/credibility_audit.py --matrix-summary artifacts/benchmark_matrix/matrix_summary.json --stability-summary artifacts/testing_runs/stability_summary_bruteforce_200.json --publishable-summary artifacts/benchmark_matrix/publishable_results.v1.json --output artifacts/audit/credibility_audit.v1.json

Examples

• examples/minimal_rag_integration.py
• examples/hard_negative_training_batch.py
• examples/cohort_eval_workflow.py
• notebooks/01_semantic_search.ipynb
• notebooks/02_knn_baseline.ipynb
• notebooks/03_recommender_similarity.ipynb

Troubleshooting

• externally-managed-environment: install inside a venv.
• No FAISS available: run bruteforce path and skip overlap-gated FAISS checks.
• Dimension mismatch: ensure query and index embeddings share the same dimension.
• NumPy segfault on macOS/Python 3.12: reinstall with requirements/constraints-macos-arm64-py312.txt and run python scripts/env_diagnostics.py.

Project Links

• docs/releases/v1.1.0.md
• docs/releases/v1.1.0-checklist.md
• docs/reproducibility.md
• docs/use_cases.md
• docs/api_stability.md
• docs/research_claims.md
• LICENSE
• CITATION.cff