symrank 0.1.5

High-performance cosine similarity ranking for Retrieval-Augmented Generation (RAG) pipelines, vector search, and information retrieval, with a Python interface powered by a Rust backend.

Similarity ranking for Retrieval-Augmented Generation

Meta

✨ What is SymRank?

SymRank is a blazing-fast Python library for top-k cosine similarity ranking, designed for vector search, retrieval-augmented generation (RAG), and embedding-based matching.

Built with a Rust + SIMD backend, it offers the speed of native code with the ease of Python.

🚀 Why SymRank?

⚡ Fast: SIMD-accelerated cosine scoring with adaptive parallelism

🧠 Smart: Automatically selects serial or parallel mode based on workload

🔢 Top-K optimized: Efficient inlined heap selection (no full sort overhead)

🐍 Pythonic: Easy-to-use Python API

🦀 Powered by Rust: Safe, high-performance core engine

📉 Memory Efficient: Supports batching for speed and to reduce memory footprint

📦 Installation

You can install SymRank with 'uv' or alternatively using 'pip'.

Recommended (with uv):

uv pip install symrank

Alternatively (using pip):

pip install symrank

🧪 Usage

Basic Example (using python lists)

import symrank as sr

query = [0.1, 0.2, 0.3, 0.4]  
candidates = [
    ("doc_1", [0.1, 0.2, 0.3, 0.5]),
    ("doc_2", [0.9, 0.1, 0.2, 0.1]),
    ("doc_3", [0.0, 0.0, 0.0, 1.0]),
]

results = sr.cosine_similarity(query, candidates, k=2)
print(results)

Output

[{'id': 'doc_1', 'score': 0.9939991235733032}, {'id': 'doc_3', 'score': 0.7302967309951782}]

Basic Example (using numpy arrays)

import symrank as sr
import numpy as np

query = np.array([0.1, 0.2, 0.3, 0.4], dtype=np.float32)
candidates = [
    ("doc_1", np.array([0.1, 0.2, 0.3, 0.5], dtype=np.float32)),
    ("doc_2", np.array([0.9, 0.1, 0.2, 0.1], dtype=np.float32)),
    ("doc_3", np.array([0.0, 0.0, 0.0, 1.0], dtype=np.float32)),
]

results = sr.cosine_similarity(query, candidates, k=2)
print(results)

Output

[{'id': 'doc_1', 'score': 0.9939991235733032}, {'id': 'doc_3', 'score': 0.7302967309951782}]

🧩 API: cosine_similarity(...)

cosine_similarity(
    query_vector,              # List[float] or np.ndarray
    candidate_vectors,         # List[Tuple[str, List[float] or np.ndarray]]
    k=5,                       # Number of top results to return
    batch_size=None            # Optional: set for memory-efficient batching
)

'cosine_similarity(...)' Parameters

Parameter	Type	Default	Description
query_vector	list[float] or np.ndarray	required	The query vector you want to compare against the candidate vectors.
candidate_vectors	list[tuple[str, list[float] or np.ndarray]]	required	List of (id, vector) pairs. Each vector can be a list or NumPy array.
k	int	5	Number of top results to return, sorted by descending similarity.
batch_size	int or None	None	Optional batch size to reduce memory usage. If None, uses SIMD directly.

Returns

List of dictionaries with id and score (cosine similarity), sorted by descending similarity:

[{"id": "doc_42", "score": 0.8763}, {"id": "doc_17", "score": 0.8451}, ...]

📄 License

This project is licensed under the Apache License 2.0.