0din-prompt-toolkit

odin-prompt-toolkit (Python)

Multi-language SDK for LSH (Locality-Sensitive Hashing) signature generation for AI prompt similarity detection.

This is the Python implementation of the odin-prompt-toolkit algorithm, also available in Rust and TypeScript.

Installation

0din-prompt-toolkit ships as two packages so you only ship compiled code when you want it:

# Pure Python — works everywhere, slower signature generation
pip install 0din-prompt-toolkit

# With native Rust acceleration — recommended for production
pip install "0din-prompt-toolkit[native]"

# Optional features
pip install "0din-prompt-toolkit[onnx]"        # local ONNX embeddings + SusFactor
pip install "0din-prompt-toolkit[openai]"      # OpenAI embeddings
pip install "0din-prompt-toolkit[cm-lsh]"      # Confidence Matrix LSH
pip install "0din-prompt-toolkit[threatfeed]"  # 0DIN threat feed
pip install "0din-prompt-toolkit[all]"         # everything, including native

From git (development):

pip install "0din-prompt-toolkit[native] @ git+https://github.com/0din-ai/prompt-toolkit#subdirectory=packages/python"

Native vs pure Python

Both paths produce bit-identical signatures (verified across all implementations via canonical test vectors). They differ only in how they install and how fast signature generation runs.

• Pure Python (0din-prompt-toolkit) — a single universal wheel with no compiled code. Installs anywhere with zero build tools. Signature generation runs in a Python loop.
• Native ([native] → 0din-prompt-toolkit-native) — a prebuilt compiled Rust extension. pip downloads a wheel matching your platform, so no Rust toolchain or compiler is required on Linux/macOS/Windows with CPython 3.10–3.13. On any platform without a matching wheel, the base package still installs and automatically falls back to pure Python — nothing breaks, it's just slower.

The accelerator is transparent — the same API uses native automatically when it's present:

from odin_prompt_toolkit import NATIVE_AVAILABLE
print("native" if NATIVE_AVAILABLE else "pure Python")

Force pure Python even when native is installed: export ODIN_PROMPT_TOOLKIT_NO_NATIVE=1.

Speed tradeoff

Native replaces the hot signature-generation loop with compiled, SIMD-optimized Rust:

The multiplier depends on embedding dimension: pure Python loops over every dimension, so the gap widens with larger vectors (~63× at 384-dim, ~590× at 1024-dim). Native throughput is roughly constant.

What it means end-to-end: in a real pipeline, embedding generation usually dominates. On the 3,714-prompt benchmark (local ONNX, CPU) embedding took 112.6 s; adding signature generation cost 0.7 s with native (+0.6%) vs 43.8 s pure Python (+38%). When embeddings are pre-computed or cached (e.g. real-time dedup), signature generation is the cost — and the full speedup applies.

Rule of thumb: use [native] in production; pure Python is a correct, always-available fallback for prototyping or unusual platforms.

Quick Start

Basic LSH Signatures

from odin_prompt_toolkit import simhash_lsh_multi, normalize_vector

# Your embedding vector (must be L2-normalized)
vector = [0.5, 0.5, 0.5, 0.5]
normalized = normalize_vector(vector)

# Generate LSH signatures (3 families, 256 bits, 16 bands)
families = simhash_lsh_multi(normalized)

print(f"Signature: {families[0].signature}")
print(f"Bands: {families[0].bands}")

Similarity Comparison

from odin_prompt_toolkit import simhash_lsh_multi, hamming_distance_hex, cosine_from_hamming

# Generate signatures for two vectors
families1 = simhash_lsh_multi(vector1)
families2 = simhash_lsh_multi(vector2)

# Compute Hamming distance
distance = hamming_distance_hex(families1[0].signature, families2[0].signature)

# Estimate cosine similarity
similarity = cosine_from_hamming(distance, 256)
print(f"Estimated cosine similarity: {similarity:.3f}")

Confidence Matrix LSH (CM-LSH)

from odin_prompt_toolkit.cm_lsh import create_default_cm_lsh

# Create CM-LSH hasher (1024 dimensions)
cm_lsh = create_default_cm_lsh(1024, family=0)

# Generate 512-bit signature with confidence matrix
hash1 = cm_lsh.hash(embedding1)
hash2 = cm_lsh.hash(embedding2)

# Compute calibrated similarity
similarity = cm_lsh.sim(hash1, hash2)
print(f"CM-LSH similarity: {similarity:.3f}")

# Check for duplicates
is_duplicate = cm_lsh.is_dup(hash1, hash2, threshold=0.85)

Signature Versions

• V0: OpenAI text-embedding-3-large (1536 dimensions, API-based)
• V1: 0din-jailbreak-embeddings-small ONNX (1024 dimensions, local)
• Latest: Resolves to V1

Important: V0 and V1 signatures are not comparable due to different embedding spaces.

Algorithm

SimHash via Random Hyperplane LSH (Charikar 2002):

• Deterministic hyperplanes via SplitMix64 PRNG
• Default: 3 families × 256 bits × 16 bands
• Hex-encoded signatures (64 hex chars = 256 bits)
• Hamming distance → cosine similarity via cos(π × d/n)

See the specification for complete algorithm details.

Development

Setup

cd python
pip install -e ".[dev]"

Run Tests

pytest tests/

Type Checking

mypy odin_prompt_toolkit/

Formatting

black odin_prompt_toolkit/ tests/
ruff check odin_prompt_toolkit/ tests/

License

Apache License 2.0