encypher-ai 2.2.0

Embed invisible metadata in AI-generated text using zero-width characters.

EncypherAI Core

A Python package for embedding and extracting metadata in text using Unicode variation selectors without affecting readability.

Overview

EncypherAI Core provides tools for invisibly encoding metadata (such as model information, timestamps, and custom data) into text generated by AI models. This enables:

• Provenance tracking: Identify which AI model generated a piece of text
• Timestamp verification: Know when text was generated
• Custom metadata: Embed any additional information you need
• Tamper detection using digital signatures: Verify text integrity using digital signatures
• Streaming support: Works with both streaming and non-streaming LLM outputs
• LLM integrations: Ready-to-use integrations with OpenAI, Google Gemini, Anthropic Claude, and more
• Modular architecture: Clean separation of key management, payload handling, and signing operations

The encoding is done using Unicode variation selectors, which are designed to specify alternative forms of characters without affecting text appearance or readability.

Relationship with C2PA

EncypherAI's manifest format is inspired by the Coalition for Content Provenance and Authenticity (C2PA) standard, adapted specifically for plain-text environments. While C2PA focuses on embedding provenance information in rich media file formats, EncypherAI extends these concepts to text-only content where traditional file embedding methods aren't applicable.

Key alignments include:

• Structured provenance manifests with claim generators and actions
• Cryptographic integrity through digital signatures
• Shared mission of improving content transparency and trust

Learn more about EncypherAI's relationship with C2PA in our documentation.

LLM Integrations

EncypherAI seamlessly integrates with popular LLM providers:

• OpenAI: GPT-3.5, GPT-4o, and other OpenAI models
• Google Gemini: Gemini 2.0 Flash, Pro, and other Gemini models
• Anthropic Claude: Claude 3 Opus, Sonnet, Haiku, and other Claude models
• LiteLLM: For unified access to multiple LLM providers

Check our documentation for detailed integration examples and code snippets for each provider.

Demo Video

Watch our demo video to see EncypherAI in action, demonstrating how to embed and verify metadata in AI-generated content.

Interactive Colab Notebook

Try EncypherAI directly in your browser with our interactive Google Colab notebook. The notebook demonstrates all the core features including metadata embedding, extraction, digital signature verification, and tampering detection.

Local Jupyter Notebook Example

For a local demonstration, check out the detailed Jupyter Notebook example included in the repository: encypher/examples/encypher_v2_demo.ipynb

This notebook covers key generation, basic and manifest format usage, and tamper detection using the latest version (v2.2.0+).

Installation

First, install the uv package manager if you don't have it already:

# Install uv (recommended)
pip install uv

# Then install EncypherAI
uv pip install encypher-ai

Quick Start

Note: Digital signatures require managing a private/public key pair. You can use the helper script encypher/examples/generate_keys.py to create your first key pair and get setup instructions, or generate keys programmatically as shown below.

Basic Encoding and Verification

from encypher.core.unicode_metadata import UnicodeMetadata
from encypher.core.keys import generate_ed25519_key_pair # Updated to specific key type
from cryptography.hazmat.primitives.asymmetric.types import Ed25519PublicKey, Ed25519PrivateKey
from typing import Optional, Dict, Union # Added Union
import time
from encypher.core.payloads import BasicPayload, ManifestPayload # For type hinting verified_payload

# --- Key Management (Replace with your actual key management) ---
# Generate a new Ed25519 key pair
private_key: Ed25519PrivateKey
public_key: Ed25519PublicKey
private_key, public_key = generate_ed25519_key_pair()
signer_id_example = "readme-signer-001" # Using signer_id

# Store public keys (e.g., in a database or secure store)
public_keys_store: Dict[str, Ed25519PublicKey] = { signer_id_example: public_key }

# Create a provider function to look up public keys by ID
def public_key_provider(signer_id: str) -> Optional[Ed25519PublicKey]: # Renamed and uses signer_id
    return public_keys_store.get(signer_id)
# -----------------------------------------------------------------

# Core information for embedding
current_timestamp = int(time.time())  # Current Unix timestamp (seconds since epoch)

# Custom metadata payload (user-defined data)
custom_payload = {
    "model_id": "gpt-4o-2024-05-13",
    "source_script": "README_quickstart",
    "user_defined_version": "2.2.0" # Updated version
}

# Embed metadata and sign
# The 'metadata_format' and 'version' (EncypherAI spec version) parameters for embed_metadata
# default to "basic" and the latest spec version respectively.
encoded_text = UnicodeMetadata.embed_metadata(
    text="This is a sample text generated by an AI model.",
    private_key=private_key,         # Private key for signing
    signer_id=signer_id_example,     # Identifier for the key pair
    timestamp=current_timestamp,     # Integer Unix timestamp
    custom_metadata=custom_payload   # Your arbitrary metadata
)

# Extract metadata (without verification - returns the raw payload if successful)
# This is useful for quick inspection but does not guarantee authenticity or integrity.
extracted_unverified_payload = UnicodeMetadata.extract_metadata(encoded_text)
print(f"Extracted (unverified) payload: {extracted_unverified_payload}")

# Verify the signature and extract metadata using the public key provider
# This is the recommended way to get trusted metadata.
is_valid: bool
extracted_signer_id: Optional[str]
verified_payload: Union[BasicPayload, ManifestPayload, None] # Type hint for clarity
is_valid, extracted_signer_id, verified_payload = UnicodeMetadata.verify_metadata(
    text=encoded_text,
    public_key_provider=public_key_provider
)

print(f"\nSignature valid: {is_valid}")
if is_valid and verified_payload:
    print(f"Verified Signer ID: {extracted_signer_id}")
    print(f"Verified Timestamp: {verified_payload.timestamp}")
    print(f"Verified Custom Metadata: {verified_payload.custom_metadata}")
    print(f"Verified Format: {verified_payload.format}")
    print(f"Verified EncypherAI Spec Version: {verified_payload.version}")
else:
    print("Metadata could not be verified or extracted.")

Streaming Example

from encypher.streaming.handlers import StreamingHandler
from encypher.core.unicode_metadata import UnicodeMetadata # Added for verification
from encypher.core.keys import generate_ed25519_key_pair # Updated to specific key type
from cryptography.hazmat.primitives.asymmetric.types import Ed25519PublicKey, Ed25519PrivateKey
from typing import Optional, Dict, Union # Added Union
import time
from encypher.core.payloads import BasicPayload, ManifestPayload # For type hinting verified_payload

# --- Assuming key setup from the 'Basic Encoding and Verification' example ---

# Custom metadata for streaming example
stream_timestamp = int(time.time())
stream_custom_payload = {
    "model_id": "gpt-4o-2024-05-13",
    "source_script": "README_streaming_example",
    "user_defined_version": "2.2.0" # Updated version
}

# Create a streaming handler
handler = StreamingHandler(
    private_key=private_key,
    signer_id=signer_id_example,
    timestamp=stream_timestamp,
    custom_metadata=stream_custom_payload,
    # metadata_format defaults to "basic"
    # encode_first_chunk_only defaults to True, which is common for streaming
)

chunks = [
    "This is ",
    "a sample ",
    "text generated ",
    "by an AI model, delivered in chunks."
]

full_response_from_stream = ""
print("\nSimulating stream output:")
for chunk in chunks:
    processed_chunk = handler.process_chunk(chunk) # Aligned to process_chunk
    if processed_chunk: # process_chunk might return None if it only buffers
        print(processed_chunk, end="")
        full_response_from_stream += processed_chunk

# Complete the stream (important for final metadata embedding if not all chunks were processed)
final_chunk = handler.finalize_stream()
if final_chunk:
    print(final_chunk, end="")
    full_response_from_stream += final_chunk
print("\n--- End of Stream ---")

# Verify the full streamed text
print(f"\nVerifying full streamed text: '{full_response_from_stream[:50]}...' ({len(full_response_from_stream)} chars)")
is_stream_valid: bool
stream_signer_id: Optional[str]
stream_payload: Union[BasicPayload, ManifestPayload, None]
is_stream_valid, stream_signer_id, stream_payload = UnicodeMetadata.verify_metadata(
    text=full_response_from_stream,
    public_key_provider=public_key_provider # Using the provider from basic example
)

print(f"\nStream signature valid: {is_stream_valid}")
if is_stream_valid and stream_payload:
    print(f"Stream Verified Signer ID: {stream_signer_id}")
    print(f"Stream Verified Timestamp: {stream_payload.timestamp}")
    print(f"Stream Verified Custom Metadata: {stream_payload.custom_metadata}")
    print(f"Stream Verified Format: {stream_payload.format}")
    print(f"Stream Verified EncypherAI Spec Version: {stream_payload.version}")
else:
    print("Stream metadata could not be verified or extracted.")

Tamper Detection

EncypherAI's digital signatures enable tamper detection. Any modification to the text after signing will cause verification to fail:

# Create tampered text by modifying a character in the encoded text
tampered_text = encoded_text.replace("sample", "simple")

# Verification will fail for tampered_text:
# is_valid, signer_id, payload = UnicodeMetadata.verify_metadata(
#    tampered_text, public_key_provider=public_key_provider
# )
# print(f"Is tampered text valid? {is_valid}") # Expected: False

Key Features

• Invisible Metadata: Embed metadata in text without affecting its visible appearance or readability.
• Digital Signatures: Cryptographically sign metadata to ensure authenticity and detect tampering.
• Streaming Support: Process and embed metadata in real-time as text is generated or streamed.
• Customizable Metadata: Embed any JSON-serializable information relevant to your application.
• Modular Architecture: Clean separation of key management, payload handling, and signing operations.

Command Line Interface

EncypherAI includes a command-line interface for quick encoding and decoding tasks.

First, ensure you have generated a key pair. You can use the generate-keys command for this:

# Generate a new Ed25519 key pair, saving public key as my_signer_id.pem
python -m encypher.examples.cli_example generate-keys --output-dir ./keys --signer-id my_signer_id

This will create private_key.pem and keys/my_signer_id.pem (if --output-dir ./keys was used and my_signer_id was the signer ID).

Encoding Text with Metadata

To encode text with metadata:

# Basic encoding
python -m encypher.examples.cli_example encode \
  --text "This is a sample text generated by an AI model." \
  --private-key ./keys/private_key.pem \
  --signer-id my_signer_id

Additional options:

• --output-file: Optional. File to save encoded text; otherwise, prints to stdout.
• --custom-metadata: Optional. A JSON string for your custom data (e.g., '{\"key\": \"value\"}').
• --timestamp: Optional. Integer Unix timestamp. Defaults to current time.
• --model-id: Optional. Convenience for adding a model ID to custom metadata.

Decoding and Verifying Metadata

To extract and verify metadata from text:

# Basic decoding
python -m encypher.examples.cli_example decode \
  --text "Text with embedded metadata..." \
  --public-key-dir ./keys

Additional options:

• --output-file: Optional. File to save decoded metadata; otherwise, prints to stdout.
• --verify: Optional. Verify the signature (default: True).

Development Setup

To set up the project for development:

# Install uv (if not already installed)
pip install uv

# Install EncypherAI in editable mode for development
uv pip install -e .

Migration Guide

If you're upgrading from a previous version, check out our Migration Guide to learn about the new modular structure introduced in version 2.2.0.

License

This project is licensed under the GNU Affero General Public License v3.0 - see the LICENSE file for details.