vibex-protocol 1.0.0
VIBE-X Protocol: Vector-Integrated Binary Extension for emotional and contextual metadata encoding.
License: MIT
Requires Python: >=3.10
Keywords: sentiment-analysis, metadata, affective-computing, nlp, text-encoding, protocol

# VIBE-X Protocol  
**Vector-Integrated Binary Extension — "Encode Once, Query Infinitely."**

VIBE-X is a lightweight binary encoding protocol that attaches sentiment and emotional metadata **directly to or alongside UTF-8 text streams**. By adding a compact 14-bit MetaBlock to sentiment-bearing spans, VIBE-X enables instant emotional queries **without repeatedly calling large NLP models**.

The goal is simple:

> Turn expensive, repeated model inference into a one-time encoding step,  
> then enable near-zero-cost emotional queries for the rest of the data’s life cycle.

---

## 1. What Is VIBE-X?

VIBE-X (Vector-Integrated Binary Extension) is a compact, token-level emotional vector that travels with the text:

- **V — Vector**: Multi-dimensional sentiment representation (polarity, intensity, emotion class, context, span, etc.)
- **I — Integrated**: Aligned with UTF-8 text and modern subword tokenizers (BPE, SentencePiece)
- **B — Binary**: Bit-level, storage- and compute-efficient representation
- **E — Extension**: Extends existing text systems; does not replace UTF-8
- **X — Extensible**: Designed for future modalities and fields (audio, video, emergency flags, etc.)

Traditional approach:  
`[TEXT] + [Separate Sentiment Database]`  

VIBE-X approach:  
`[TEXT   +   EMBEDDED / LINKED SENTIMENT BITS]`

Emotion becomes a **native attribute of data** – portable, queryable, and secure.

---

## 2. Core Design: The 14-bit SPICE-R MetaBlock

At the heart of VIBE-X is a 14-bit MetaBlock called **SPICE-R**, which encodes the emotional state of a token span.

High-level fields:

- **S – Span Flag + Length**  
  - 1 bit flag (`Has_SPAN`)  
  - Optional 3-bit span length (0–7 → up to 8 tokens total using 1+N logic)
- **P – Polarity (2 bits)**  
  - 00 = Neutral  
  - 01 = Negative  
  - 10 = Positive  
  - 11 = Ironic / sarcasm-aware
- **I – Intensity (3 bits)**  
  - 0–7 scale for emotional strength (from flat to maximum intensity)
- **C – Context (1 bit)**  
  - 0 = Static / literal  
  - 1 = Dynamic / context-dependent (sarcasm, rhetorical use, contrastive meaning)
- **E – Emotion Class (3 bits)**  
  - 8-way mapping (e.g., Joy, Trust, Fear, Surprise, Sadness, Disgust, Anger, Anticipation, etc.)
- **R – Reserved / Emergency (1 bit)**  
  - Reserved for forward compatibility  
  - Can be used as an **Emergency Flag** (0 = normal, 1 = urgent/critical)

Total: **14 bits per MetaBlock**

This vector is compact enough for **petabyte-scale storage**, yet expressive enough to cover nuanced, context-aware emotional states (including irony, multi-token phrases, and emergency cases).

---

## 3. Architecture: Inline vs Sidecar Modes

VIBE-X supports two complementary integration modes:

### 3.1 Inline Mode (Embedded in Text Stream)

MetaBlocks are embedded directly into the text near the anchor token, using **non-printable markers** (e.g., Unicode Private Use Area characters).

- Text + metadata are stored as a **single atomic stream**
- No risk of desynchronization between text and emotion
- Ideal for:
  - Messaging protocols
  - Embedded / IoT devices
  - Offline and portable files
  - Real-time streaming scenarios

### 3.2 Sidecar Mode (External Metadata Layer)

The original UTF-8 text remains untouched. Emotional metadata is stored in a separate **sidecar file or binary structure**, referencing token indices.

- 100% UTF-8 compatible
- Easy redaction (delete or rotate sidecar without touching original text)
- Ideal for:
  - Large-scale analytics
  - Archival / regulatory systems
  - Batch processing pipelines

### 3.3 Hybrid & Convertible

Tools can convert between **Inline** and **Sidecar** representations:

- Inline → Sidecar: strip markers, export all MetaBlocks
- Sidecar → Inline: inject markers for portable, single-file use

This flexibility lets integrators choose atomicity or strict UTF-8 compatibility per use case.

---

## 4. Encoding–Decoding Pipeline

### 4.1 Encoding: “Analyze Once, Encode Infinitely”

1. **Master Analyzer** (e.g., a transformer-based sentiment model) processes the text once.
2. It emits structured sentiment for each span: polarity, intensity, context, emotion class, span length, optional lifecycle info (timestamp, model version, confidence).
3. The VIBE-X compiler:
   - Packs these attributes into the 14-bit SPICE-R MetaBlock.
   - Either embeds it into the text (Inline Mode) or writes a compact sidecar file (Sidecar Mode).

From that moment on, **no further model inference is needed** for emotional queries.

### 4.2 Decoding: Instant Query-Time Retrieval

1. The VIBE-X decoder reads the text (and optionally sidecar).
2. It parses each 14-bit MetaBlock and reconstructs:
   - Emotional valence
   - Intensity level
   - Emotion class
   - Context flag (literal vs ironic / dynamic)
   - Span coverage
   - Optional Emergency flag
3. Applications can answer complex emotional queries using only cheap bit operations:
   - “Show all negatively intense comments with irony.”
   - “Find all emergency spans in this log stream.”
   - “Filter for high-intensity joy in the last 24 hours.”

No GPU, no large model required at query time.

---

## 5. Efficiency & Benchmark Highlights

A Grok-based benchmark on **100,000 real-world social media comments** (Turkish + English) shows:

- **Decoding latency:** ≈ 0.045 µs per MetaBlock
- **Query latency:** < 0.3 ms per query
- **Sentiment accuracy:**  
  - ~96% (Turkish)  
  - ~94% (English)  
  - Outperforms typical traditional NLP baselines (~85%)
- **Compute savings:** >92% reduction in compute cost for repeated queries
- **Storage savings:**  
  - >99% vs JSON-style sentiment metadata  
  - ≈ 1–3% overhead when stored inline with text
- **Energy & CO₂:**  
  - ≈ 99.9% lower energy use vs re-running BERT per query on the same data  
  - From ~15 kg to ~0.01 kg CO₂ per 10M queries (illustrative scenario)

At scale (e.g., 10M–1B queries), VIBE-X converts sentiment analysis from a **recurring OPEX cost** into a one-time **CAPEX encoding step**, then amortizes the benefit over all future reads.

---

## 6. Real-World Applications

Because VIBE-X is designed for **read-heavy workloads**, it is especially valuable wherever the same data is queried many times:

- **Content Moderation & Safety**  
  - Sub-ms detection of hostility, self-harm, hate, harassment  
  - Emergency flagging for critical events and distress signals

- **Personalization & Recommendation**  
  - Mood-aware feeds, playlists, and news ranking  
  - Filter by emotional fit instead of recomputing sentiment

- **Customer Support & CX**  
  - Instant escalation for angry or distressed customers  
  - Sentiment-aware agent routing and dashboards

- **Search & Information Retrieval**  
  - “Only positive reviews” filters for search results  
  - Emotion-aware legal, compliance, or e-discovery search

- **Business Intelligence & Analytics**  
  - Brand perception tracking  
  - Market/employee sentiment over time, at very low cost

- **Compliance & Archiving**  
  - Long-term archives searchable by emotional state  
  - GDPR/CCPA-aligned design via sidecar separation and anonymization

- **Emergency-Aware Systems**  
  - Call centers, IT incident management, smart cities, healthcare triage  
  - Bit-level emergency flag for fast prioritization

---

## 7. Extensibility: VIBE-A and VIBE-V

VIBE-X is defined as a **core text protocol**, but the same logic extends to other modalities:

- **VIBE-A (Audio)**  
  - Encodes prosody, stress, tempo, intensity, and emotional class from audio into a Meta-A* block.
  - Use cases: call centers, voice assistants, mental health monitoring.

- **VIBE-V (Video / Visual Media)**  
  - Attaches affective metadata to video frames or segments.
  - Use cases: content moderation, gaming, interactive narratives, surveillance analytics.

Together, VIBE-X, VIBE-A, and VIBE-V form a multi-modal emotional metadata layer for next-generation AI and analytics systems.

---

## 8. Status & Implementation Notes

- This repository hosts the **Python reference implementation** of the VIBE-X protocol (encoder/decoder and utilities).
- The protocol is designed to be:
  - Language-agnostic (C/C++/Rust/Go bindings are straightforward)
  - Tokenizer-agnostic (supports subword tokenization)
  - Storage-agnostic (inline, sidecar, or hybrid)

Production deployments are expected to:

- Use a **binary sidecar format** for maximum performance.
- Optionally support JSON/CBOR/Protobuf wrappers for debugging and interoperability.
- Maintain lifecycle metadata (timestamp, model version, confidence scores) outside the 14-bit MetaBlock to keep the core encoding compact.

---
### 9. Installation

VIBE-X can be installed locally as a Python package.

Install from source
git clone https://github.com/vibexcode/vibe-x
cd vibe-x
pip install -e .


This installs the package in editable mode, so any changes inside src/vibex/ become available immediately without reinstalling.

# ---------------------------------------------

### 10. Project Structure

vibe-x/
│
├── src/
│   └── vibex/
│       ├── __init__.py
│       ├── inline_encoder.py
│       ├── inline_decoder.py
│       ├── metablock.py
│       ├── tokenizer.py
│       └── exceptions.py
│
├── examples/
│   ├── basic_encode.py
│   ├── basic_decode.py
│   ├── multi_annotation.py
│   └── error_handling.py
│
├── tests/
│   └── test_basic_flow.py
│
├── docs/
│   └── (overview or specifications)
│
├── LICENSE
├── README.md
└── pyproject.toml


## 11. USAGE


# ---------------------------------------------
# Basic Encoding Example
# ---------------------------------------------
from vibex.inline_encoder import InlineEncoder, SentimentAnnotation
from vibex.tokenizer import Tokenizer

# Initialize encoder with default tokenizer
encoder = InlineEncoder(tokenizer=Tokenizer())

text = "The movie was absolutely amazing"

# Create an annotation:
# anchor = token index
# length = number of tokens spanned
annotation = SentimentAnnotation(
    anchor=3,      # "absolutely"
    length=2,      # covers "absolutely amazing"
    polarity=2,    # positive
    intensity=5,   # strong emotion
    context=0,     # literal
    emotion=1      # Joy
)

encoded_text = encoder.encode(text, [annotation])
print("Encoded:", encoded_text)

# ---------------------------------------------
# Basic Decoding Example
# ---------------------------------------------
from vibex.inline_decoder import InlineDecoder
from vibex.tokenizer import Tokenizer

decoder = InlineDecoder(tokenizer=Tokenizer())

decoded = decoder.decode(encoded_text)

print("Clean text:", decoded.clean_text)
print("Clean tokens:", decoded.clean_tokens)
print("\nDecoded MetaBlocks:")
for block in decoded.blocks:
    print(" - HEX:", block.block.to_hex())
    print("   Span:", block.span)
    print("   Polarity:", block.block.polarity)
    print("   Intensity:", block.block.intensity)
    print("   Emotion:", block.block.emotion)
# ---------------------------------------------
# Multiple Annotation Example
# ---------------------------------------------
from vibex.inline_encoder import InlineEncoder, SentimentAnnotation
from vibex.tokenizer import Tokenizer

text = "I loved the performance but the ending felt rushed"

encoder = InlineEncoder(Tokenizer())

annotations = [
    SentimentAnnotation(
        anchor=1,      # "loved"
        length=1,
        polarity=2,    # positive
        intensity=6,
        context=0,
        emotion=1
    ),
    SentimentAnnotation(
        anchor=7,      # "felt"
        length=2,      # "felt rushed"
        polarity=1,    # negative
        intensity=5,
        context=1,     # context-dependent
        emotion=4
    ),
]

encoded = encoder.encode(text, annotations)
print("Encoded:", encoded)
# ---------------------------------------------
# Error Handling Example
# ---------------------------------------------
from vibex.inline_encoder import InlineEncoder, SentimentAnnotation
from vibex.exceptions import MetaBlockEncodingError
from vibex.tokenizer import Tokenizer

encoder = InlineEncoder(Tokenizer())
text = "This is a short text"

try:
    # Intentional mistake: anchor index out of token range
    annotation = SentimentAnnotation(
        anchor=50,
        length=1,
        polarity=1,
        intensity=3,
        context=0,
        emotion=2
    )
    encoder.encode(text, [annotation])
except MetaBlockEncodingError as e:
    print("Encoding error caught:", e)


## 12. License

This project is released under the **MIT License**.  
See the `LICENSE` file for details.

---

## 13. Citation

If you use VIBE-X in academic work or production systems, please cite:

```text
Kandemiş, U. (2025). VIBE-X: Vector-Integrated Binary Extension for Sentiment-Aware Communication Systems.
"Encode Once, Query Infinitely."
Zenodo. DOI: <insert DOI here>


## Contributing

Contributions, issues, and feature requests are welcome.
Feel free to open an issue or submit a pull request.

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