rankops-python
Python bindings for rankops (Rust) -- fusion and reranking for hybrid search.
Installation
pip install rankops
From source
# Using uv (recommended)
cd rankops-python
uv venv
source .venv/bin/activate
uv tool install maturin
maturin develop --uv
# Or using pip
pip install maturin
maturin develop --release
Quick Start
import rankops
# BM25 results (keyword search)
bm25 = [("doc_1", 87.5), ("doc_2", 82.3), ("doc_3", 78.1)]
# Dense embedding results (semantic search)
dense = [("doc_2", 0.92), ("doc_1", 0.88), ("doc_4", 0.85)]
# RRF finds consensus: doc_2 appears high in both lists
fused = rankops.rrf(bm25, dense, k=60)
# [("doc_2", 0.033), ("doc_1", 0.032), ("doc_3", 0.016), ("doc_4", 0.016)]
Why RRF? BM25 scores are 0-100, dense scores are 0-1. RRF ignores scores and uses only rank positions, so no normalization needed.
API Reference
Rank-based Fusion (ignores scores)
These methods work with any score scale because they only use rank positions:
RRF (Reciprocal Rank Fusion)
# Two lists
fused = rankops.rrf(bm25, dense, k=60, top_k=10)
# Multiple lists
fused = rankops.rrf_multi([bm25, dense, sparse], k=60, top_k=10)
When to use: Different score scales (BM25: 0-100, dense: 0-1), zero-configuration needs.
ISR (Inverse Square Rank)
fused = rankops.isr(bm25, dense, k=1, top_k=10)
fused = rankops.isr_multi([bm25, dense], k=1, top_k=10)
When to use: When lower ranks should contribute more relative to top positions.
Borda Count
fused = rankops.borda(bm25, dense, top_k=10)
fused = rankops.borda_multi([bm25, dense], top_k=10)
When to use: Simple voting-based fusion.
Score-based Fusion (uses scores)
These methods require scores on compatible scales:
CombSUM
fused = rankops.combsum(bm25, dense, top_k=10)
fused = rankops.combsum_multi([bm25, dense], top_k=10)
When to use: Scores on the same scale (both 0-1, both cosine similarity).
CombMNZ
fused = rankops.combmnz(bm25, dense, top_k=10)
fused = rankops.combmnz_multi([bm25, dense], top_k=10)
When to use: Same scale as CombSUM, but want to reward documents appearing in multiple lists.
Weighted Fusion
fused = rankops.weighted(
bm25, dense,
weight_a=0.7, # Weight for first list
weight_b=0.3, # Weight for second list
normalize=True, # Normalize scores to [0,1] before combining
top_k=10
)
When to use: Trust one retriever more than another.
DBSF (Distribution-Based Score Fusion)
fused = rankops.dbsf(bm25, dense, top_k=10)
fused = rankops.dbsf_multi([bm25, dense], top_k=10)
When to use: Score distributions differ significantly between retrievers.
Standardized Fusion (ERANK-style)
# Two lists with default clipping [-3.0, 3.0]
fused = rankops.standardized(bm25, dense, clip_range=(-3.0, 3.0), top_k=10)
# Multiple lists
fused = rankops.standardized_multi(
[bm25, dense],
clip_min=-3.0,
clip_max=3.0,
top_k=10
)
When to use: Different score distributions, need configurable outlier handling, have negative scores.
Additive Multi-Task Fusion (ResFlow-style)
# E-commerce example: CTR + CTCVR with 1:20 weight ratio
fused = rankops.additive_multi_task(
ctr_scores, # Click-through rate predictions
ctcvr_scores, # Click-to-conversion rate predictions
weight_a=1.0, # Weight for first task
weight_b=20.0, # Weight for second task (20x more important)
normalization="minmax", # Normalization method: "zscore", "minmax", "sum", "rank", "none"
top_k=10
)
When to use: Combining multiple ranking tasks (e.g., CTR + CTCVR in e-commerce), tasks have different scales but you know relative importance.
Explainability
Get detailed provenance information showing which retrievers contributed to each result:
import rankops
bm25 = [("doc_1", 87.5), ("doc_2", 82.3)]
dense = [("doc_2", 0.92), ("doc_3", 0.88)]
# Get results with full provenance
explained = rankops.rrf_explain(
[bm25, dense],
["bm25", "dense"],
k=60
)
# Inspect first result
result = explained[0]
print(f"Document: {result.id}")
print(f"Score: {result.score}")
print(f"Rank: {result.rank}")
print(f"Consensus: {result.explanation.consensus_score}")
# See which retrievers contributed
for source in result.explanation.sources:
print(f" {source.retriever_id.id}: rank={source.original_rank}, contribution={source.contribution}")
Available explainability functions:
rrf_explain()- RRF with explainabilitycombsum_explain()- CombSUM with explainabilitycombmnz_explain()- CombMNZ with explainabilitydbsf_explain()- DBSF with explainability
Result Validation
Validate fusion results to ensure they meet expected properties:
import rankops
fused = rankops.rrf(bm25, dense, k=60)
# Comprehensive validation
result = rankops.validate(fused, check_non_negative=False, max_results=10)
if not result.is_valid:
print(f"Validation errors: {result.errors}")
if result.warnings:
print(f"Warnings: {result.warnings}")
# Individual checks
sorted_check = rankops.validate_sorted(fused)
dup_check = rankops.validate_no_duplicates(fused)
finite_check = rankops.validate_finite_scores(fused)
non_neg_check = rankops.validate_non_negative_scores(fused)
bounds_check = rankops.validate_bounds(fused, max_results=10)
Validation checks:
- Sorted: Results are sorted by score (descending)
- No duplicates: Each document ID appears only once
- Finite scores: No NaN or Infinity values
- Non-negative (optional): Warns on negative scores when
check_non_negative=True - Max results (optional): Warns if result count exceeds
max_results
Configuration Objects
For advanced usage, you can use configuration objects:
config = rankops.RrfConfigPy(k=100)
# Other config types available:
# - FusionConfigPy (for CombSUM/CombMNZ/Borda/DBSF)
# - WeightedConfigPy (for weighted fusion)
# - StandardizedConfigPy (for standardized fusion)
# - AdditiveMultiTaskConfigPy (for additive multi-task fusion)
Complete Example: RAG Pipeline
import rankops
def rag_pipeline(query: str):
# Step 1: Retrieve from multiple sources
bm25_results = [
("doc_123", 87.5),
("doc_456", 82.3),
("doc_789", 78.1),
]
dense_results = [
("doc_456", 0.92),
("doc_123", 0.88),
("doc_999", 0.85),
]
# Step 2: Fuse results (RRF finds consensus)
fused = rankops.rrf_multi(
[bm25_results, dense_results],
k=60,
top_k=100 # Top 100 for reranking
)
# Step 3: Rerank with cross-encoder (expensive, so only top 100)
# reranked = cross_encoder_rerank([id for id, _ in fused], query)
# Step 4: Return top 10 for LLM context
return [id for id, _ in fused[:10]]
Type Hints and IDE Support
The package includes type stubs (.pyi files) for full type checking support with:
- mypy:
mypy your_code.py - pyright: Built into VS Code and other editors
- PyCharm: Automatic type checking
All functions are fully typed:
from typing import List, Tuple
RankedList = List[Tuple[str, float]]
fused: RankedList = rankops.rrf(bm25, dense, k=60)
Error Handling
# Most functions don't error - they return empty list for edge cases
fused = rankops.rrf([], []) # Returns []
# Validation functions return ValidationResult with errors/warnings
result = rankops.validate(fused, check_non_negative=False, max_results=10)
if not result.is_valid:
for error in result.errors:
print(f"Error: {error}")
Performance
Python bindings add minimal overhead over native Rust:
- RRF (100 items): ~15us (vs 13us in Rust)
- RRF (1000 items): ~180us (vs 159us in Rust)
Overhead comes from Python object conversion and result serialization (~1-2us per call).
See Also
- rankops (Rust) README -- Algorithm overview and quickstart
- API Documentation -- Full Rust API reference
License
MIT OR Apache-2.0
1 maintainer
Release files for rankops 0.1.23
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