copyforward 0.2.1

Fast similarity search using forward-looking LSH

copyforward

Fast copy-forward compression for message threads. Detects repeated substrings across messages and replaces them with references to earlier occurrences, reducing storage requirements by 50-90%.

Perfect for chat logs, document histories, dataframes with missing values, and any sequence of texts with repeated content.

Quick Start

Python

import copyforward

# Basic usage with text messages
messages = ["Hello world", "Hello world, how are you?", "Hello world today"]

# Text API (exact by default)
cf = copyforward.CopyForwardText.from_texts(messages)
print(f"Compression ratio: {cf.compression_ratio():.2f}")
# Render with replacement text to visualize references
visualized = cf.render("[REF]")  # ['Hello world', '[REF], how are you?', '[REF] today']

# Handle missing values (perfect for dataframes!)
messages_with_none = ["Hello world", None, "Hello world again"]
cf_none = copyforward.CopyForwardText.from_texts(messages_with_none)
result = cf_none.render("[REF]")  # ['Hello world', None, '[REF] again']

# Approximate (faster) text compression
cf_fast = copyforward.CopyForwardText.from_texts(messages, exact_mode=False)
approx_result = cf_fast.render("[REF]")  # May find different compression patterns

# Token API with missing values
toks = [[10, 11, 12], None, [10, 11, 12, 13]]
cf_tok = copyforward.CopyForwardTokens.from_tokens(toks, exact_mode=True)
# Render with replacement tokens - only non-None entries returned
rendered_toks = cf_tok.render([999])  # [[10, 11, 12], [999, 13]]

# Tokenizer opt-in: build token-mode directly from texts and keep tokenizer for decoding
repeated_messages = ["Hello world from Alice", "Hello world from Alice again", "Alice says hi"]
cf_tok2 = copyforward.CopyForwardTokens.from_texts_with_tokenizer(repeated_messages, tokenizer="whitespace", exact_mode=True)
token_ids = cf_tok2.render([9999])         # List[List[int]] with replacement tokens
decoded = cf_tok2.render_texts("[REF]")    # Decoded text with replacements

Rust

use copyforward::{exact, approximate, Config};

// Basic usage
let messages = &["Hello world", "Hello world, how are you?"];
let compressed = exact(messages, Config::default());

// Handle missing values (Option types work seamlessly!)
let messages_with_none = &[Some("Hello world"), None, Some("Hello world again")];
let compressed = exact(messages_with_none, Config::default());

// Fast approximate compression - 2x speed for large texts
let compressed = approximate(messages, Config::default()); 

// Render back to original
let original = compressed.render_with(|_, _, _, text| text.to_string());

Algorithm Selection

Choose between two optimized algorithms:

Algorithm	Best for	Speed	Accuracy
Exact	< 1MB total text, perfect compression needed	Slower	Perfect
Approximate	> 1MB text, speed matters	~2x faster	Excellent

The approximate algorithm may split some long references but still achieves excellent compression ratios.

Missing Value Support

Both Python and Rust APIs seamlessly handle missing/None values, making them perfect for dataframe compression:

Python

import copyforward

# DataFrame-like data with missing values
messages = [
    "User logged in",
    None,  # Missing log entry
    "User logged in successfully", 
    None,
    "User logged out"
]

cf = copyforward.CopyForwardText.from_texts(messages)
compressed = cf.render("[REF]")
# Result: ['User logged in', None, '[REF] successfully', None, 'User logged out']

# Token data with missing values
tokens = [[1, 2, 3], None, [1, 2, 3, 4]]
cf_tok = copyforward.CopyForwardTokens.from_tokens(tokens)

Rust

use copyforward::{exact, exact_tokens, Config};

// Mixed Option types work seamlessly
let messages = &[
    Some("User logged in"),
    None,
    Some("User logged in successfully")
];
let compressed = exact(messages, Config::default());

// Token sequences with None values
let tokens = &[
    Some(vec![1u32, 2u32, 3u32]),
    None,
    Some(vec![1u32, 2u32, 3u32, 4u32])
];
let compressed = exact_tokens(tokens, Config::default());

Installation

Python

pip install maturin
# Build the wheel with Python bindings enabled
maturin develop --features python

# If you want named tokenizers (e.g., whitespace / HF), enable the bundle:
# maturin develop --features python-tokenizers

Rust

[dependencies]
copyforward = "0.2"

Advanced Usage

Python

import copyforward

# Custom configuration (text)
cf = copyforward.CopyForwardText.from_texts(
    messages,
    exact_mode=True,      # Perfect compression
    min_match_len=8,      # Only create refs for 8+ char matches
    lookback=100          # Only search previous 100 messages
)

# Get detailed segment information
segments = cf.segments()
for msg_segments in segments:
    for segment in msg_segments:
        if segment['type'] == 'reference':
            print(f"Reference to message {segment['message']}")
        else:
            print(f"Literal text: {segment['text']}")

# Render with custom replacement (useful for debugging and visualization)
redacted = cf.render("[REFERENCE]")  # Shows where references occur

# Tokenization (opt-in)
cf_tok = copyforward.CopyForwardTokens.from_texts_with_tokenizer(
    messages,
    tokenizer="whitespace",   # or feature-gated 'hf:<model>' / 'file:<path>'
    exact_mode=True,
)
token_ids = cf_tok.render([9999])  # Replace references with token 9999
texts = cf_tok.render_texts("[REF]")  # Decoded text with "[REF]" replacements

Viewing generated Python docs

After building the Python extension with maturin, the PyO3 docstrings are available via Python's help system:

# Build and install the extension into your active venv
maturin develop --features python

python -c "import copyforward; help(copyforward.CopyForwardText)"
python -c "import copyforward; help(copyforward.CopyForwardTokens)"

This prints the docstring and usage information emitted by the PyO3 bindings.

Rust

use copyforward::{exact, Config, CopyForward};

// Custom configuration
let config = Config {
    min_match_len: 8,
    lookback: Some(100),  
    ..Config::default()
};

let compressed = exact(&messages, config);

// Get compression details
let segments = compressed.segments();
for (i, msg_segments) in segments.iter().enumerate() {
    println!("Message {}: {} segments", i, msg_segments.len());
}

// Custom rendering
let redacted = compressed.render_with_static("[REF]");

How It Works

Copy-forward compression works in two phases:

1. Analysis: Scan messages to find repeated substrings using rolling hash indexing
2. Compression: Replace repeated text with references to first occurrence

Example:

Input:  ["Hello world", "Hello world today"]
Output: [Literal("Hello world"), [Reference(0,0,11), Literal(" today")]]

This represents the second message as a reference to the entire first message plus the literal text " today".

Missing Value Handling: None/null values are preserved in their original positions but skipped during compression analysis, ensuring perfect round-trip fidelity for dataframe-like data.

Performance

Typical compression ratios:

• Chat logs: 60-80% space savings
• Code diffs: 70-90% space savings
• Document versions: 50-80% space savings
• Dataframes with missing values: 50-85% space savings (None values don't affect compression)

Speed comparison on 1MB of message data:

• Exact: ~50ms, perfect compression
• Approximate: ~25ms, 95% of perfect compression

Missing values add minimal overhead - compression speed remains constant regardless of None density.

Repository Structure

• src/ — Rust library implementation
• tests/ — Integration tests
• benches/ — Performance benchmarks

Changelog

See CHANGELOG.md for detailed release notes.

License

MIT License - see LICENSE file for details.

Features and Builds

• Default build has no Python or tokenizer dependencies, keeping Rust users lean.
• Cargo features:
  • python: enables PyO3 and numpy for Python bindings.
  • tokenizers: enables integration with the tokenizers crate for named tokenizers.
  • hf-hub: adds optional support wiring for Hub-based tokenizers; current crate version does not implement hub loading at runtime.
  • Bundles: python-tokenizers, python-tokenizers-hub for convenience.

Python wheels

• Build Python extension (bindings only):
  • maturin develop --features python
• Build Python extension with tokenizer support:
  • maturin develop --features python-tokenizers
• Hub bundle (compiles but hub loading not implemented in tokenizers v0.15):
  • maturin develop --features python-tokenizers-hub

Notes

• Using tokenizer="whitespace" requires only the python feature.
• Using tokenizer="hf:<model>" or tokenizer="file:<path>" requires tokenizers; hub loading by name is not implemented for the current tokenizers version. Load from a local tokenizer JSON using file:<path>.