hektor-vdb 4.1.7
High-performance vector database with SIMD-optimized similarity search and local ONNX-based embeddings
License: MIT
Requires Python: >=3.10
Keywords: vector-database, similarity-search, embeddings, simd, onnx, machine-learning

# ⬜️ HEKTOR

> **Vector Database Engine** <br>
> *High-Fidelity Semantic Search & AI Platform* <br>

A High-Performance C++23 vector database with SIMD-optimized similarity search, Dolby-compatible perceptual quantization, and comprehensive hybrid search capabilities. Designed for sub-3ms queries at billion-scale.

---

<p align="center">

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</p>

---

## FEATURE GRID

### Core Database

| Feature | Description |
|---------|-------------|
| **HNSW Index** | Production-grade HNSW for O(log n) approximate nearest neighbor with tunable M/ef parameters |
| **Memory-Mapped Storage** | Zero-copy vector access via mmap for efficient I/O and fast cold starts |
| **Cosine Similarity** | AVX-512 SIMD-optimized cosine similarity for high-throughput scoring |
| **Euclidean Distance** | AVX-512 L2 distance metric for direct spatial comparisons |
| **Inner Product** | AVX-512 dot-product similarity for maximum inner product search (MIPS) |
| **Filtered Search** | Rich metadata predicate support: boolean, range, and tag filters |
| **Batch Operations** | Multi-query and bulk ingest processing with parallel execution |

### Hybrid Search

| Feature | Description |
|---------|-------------|
| **BM25 Full-Text** | Production-ready BM25 with Porter stemming, stopword handling, and token normalization |
| **RRF Fusion** | Reciprocal Rank Fusion for robust ensemble ranking |
| **Weighted Sum** | Configurable weighted fusion (α parameter) for lexical/vector combination |
| **CombSUM** | Additive score fusion for combined relevance scoring |
| **CombMNZ** | CombMNZ fusion emphasizing precision by multiplicative weighting |
| **Borda Count** | Rank voting fusion for combining ordinal signals |

### Perceptual Quantization

| Feature | Standards |
|---------|-----------|
| **SMPTE ST 2084 (PQ)** | HDR10, Dolby Vision compatible perceptual quantization curve |
| **HLG (Rec.2100)** | Hybrid Log-Gamma support for broadcast HDR (BBC/NHK) |
| **Gamma 2.2** | sRGB gamma curve support for standard displays |
| **Gamma 2.4** | BT.1886 studio gamma profile for broadcast-grade encoding |
| **HDR1000 Profile** | HDR profile tuned for 1000 nit displays and high-dynamic-range content |

### RAG Pipeline

| Feature | Description |
|---------|-------------|
| **Fixed-Size Chunking** | Uniform chunk sizes for predictable retrieval and batching |
| **Sentence Chunking** | Natural sentence boundaries to preserve semantic units |
| **Paragraph Chunking** | Document-structure-aware chunking for contextual coherence |
| **Semantic Chunking** | Embedding-based segmentation for meaning-preserving splits |
| **Recursive Chunking** | Hierarchical splitting for multi-scale context windows |
| **LangChain Adapter** | Native adapter for LangChain integration and pipelines |
| **LlamaIndex Adapter** | Native adapter for LlamaIndex (GPT Index) integrations |

### Distributed System

| Feature | Description |
|---------|-------------|
| **Async Replication** | Low-latency async replication (sub-100ms typical) for availability |
| **Sync Replication** | Strong-consistency synchronous replication option |
| **Hash Sharding** | Consistent-hashing sharding for even data distribution |
| **Range Sharding** | Range-based partitioning for ordered key spaces |
| **gRPC Networking** | HTTP/2 gRPC with TLS/mTLS for secure RPC communication |
| **Service Discovery** | DNS/Consul based discovery and health-check integration |

### Observability

| Feature | Description |
|---------|-------------|
| **Prometheus Metrics** | 50+ production metrics exposed for monitoring and alerting |
| **OpenTelemetry** | Distributed tracing and context propagation support |
| **eBPF Profiling** | Low-overhead kernel-level profiling for performance hotspots |
| **Structured Logging** | JSON structured logs with log levels and correlation IDs |

### Studio Native Addon

| Component | Description |
|-----------|-------------|
| **BM25Engine** | Full-text search engine with ranking, tokenization, and scoring |
| **KeywordExtractor** | TF‑IDF based keyword extraction with configurable stoplists |
| **HybridSearchEngine** | Tight integration of vector + lexical retrieval and fusion |
| **QueryRewriter** | Query expansion and normalization for improved recall |
| **Quantization** | PQ, SQ and HDR-aware perceptual quantization implementations |

### Platform Support

| Platform | Compiler / Notes |
|----------|------------------|
| **Windows 10/11** | MSVC 19.33+ (MSVC toolchain, Windows SDK) |
| **Ubuntu 22.04+** | GCC 13+ / Clang 16+ (glibc, libstdc++ compatibility) |
| **macOS 13+** | Apple Clang 15+ (universal macOS builds) |
| **Docker** | Multi-arch images (amd64, arm64), runtime-ready containers |

---

## Performance

| Scale | Latency (p50) | Latency (p99) | Recall@10 | Throughput |
|-------|---------------|---------------|-----------|------------|
| 100K | 1.2 ms | 2.8 ms | 98.5% | 10,000 QPS |
| 1M | 2.1 ms | 4.8 ms | 98.1% | 10,000 QPS |
| 10M | 4.3 ms | 9.2 ms | 97.5% | 8,000 QPS |
| 100M | 6.8 ms | 15 ms | 96.8% | 5,000 QPS |
| **1B** | **8.5 ms** | **22 ms** | **96.8%** | **85,000 QPS** (distributed) |

---

## Table of Contents

- [Features](#features)
- [Quick Start](#quick-start)
- [Architecture](#architecture)
- [Installation](#installation)
- [Usage](#usage)
- [Embedding Models](#embedding-models)
- [Performance](#performance)
- [Configuration](#configuration)
- [Documentation](#documentation)
- [Development](#development)
- [License](#license)

---

## Features

| Feature | Description |
|---------|-------------|
| **SIMD-Optimized Distance** | AVX2/AVX-512 accelerated cosine similarity, Euclidean distance |
| **HNSW Index** | Hierarchical Navigable Small World graph for O(log n) approximate nearest neighbor |
| **Hybrid Search** | BM25 full-text search with 5 fusion algorithms (RRF, Weighted, CombSUM, CombMNZ, Borda) |
| **Distributed System** | Replication (async/sync/semi-sync), sharding (hash/range/consistent), gRPC networking |
| **ML Framework Integration** | TensorFlow C++ API and PyTorch (LibTorch) with GPU acceleration |
| **Local Embeddings** | ONNX Runtime inference for text and images without API calls |
| **Cross-Modal Search** | Unified 512-dim space for text and image embeddings |
| **Memory-Mapped Storage** | Zero-copy vector access via mmap for efficient I/O |
| **Universal Data Ingestion** | Support for XML, JSON, CSV, Excel, PDF, Parquet, SQLite, and pgvector with read & write |
| **RAG Engine** | Complete RAG pipeline with 5 chunking strategies and framework adapters |
| **Perceptual Quantization** | HDR-aware quantization (SMPTE ST 2084 PQ curve, HLG) for image/video embeddings |
| **Comprehensive Logging** | Thread-safe logging with anomaly detection and Prometheus metrics |
| **Gold Standard Integration** | Native ingestion of journals, charts, and analysis reports |
| **Python Bindings** | pybind11-based Python API for seamless integration |
| **Thread-Safe Operations** | Concurrent reads with exclusive writes |
| **AI Training Export** | Export vector pairs and triplets for model fine-tuning |
| **Rich Metadata** | JSONL storage with full attribute filtering |
| **Database Connectors** | Direct integration with SQLite and PostgreSQL with pgvector extension |
| **Billion-Scale Support** | Tested and optimized for 1B+ vectors in distributed mode |

---

## Quick Start

### Automated Build (Recommended)

**The easiest way to get started:**

```bash
# Clone the repository
git clone https://github.com/amuzetnoM/hektor.git
cd hektor

# Run the automated build script (handles everything!)
./build-hektor.sh

# Or with options:
./build-hektor.sh --dev --test    # Include dev tools and run tests
./build-hektor.sh --repair        # Repair broken installation
./build-hektor.sh --clean         # Clean build from scratch
```

**What the script does automatically:**
- ✅ Detects your system (Linux, macOS, Windows)
- ✅ Installs all dependencies (Python, CMake, compilers)
- ✅ Sets up virtual environment
- ✅ Builds the project with optimizations
- ✅ Verifies installation
- ✅ Can repair regressions

**Verify installation:**
```bash
python verify-installation.py
```

### Installation via pip (Easiest)

```bash
# Install from source (requires CMake and C++ compiler)
pip install hektor-vdb

# Or install with ML dependencies
pip install hektor-vdb[ml]
```

**Requirements:**
- Python 3.10+
- CMake 3.20+
- C++23 compatible compiler (GCC 13+, Clang 16+, MSVC 2022 17.3+)

### Automated Setup from Source

**Windows PowerShell:**
```powershell
git clone https://github.com/amuzetnoM/hektor.git
cd hektor
.\scripts\setup.ps1
.\scripts\build.ps1 -Release
```

**Unix/macOS/Linux:**
```bash
git clone https://github.com/amuzetnoM/hektor.git
cd hektor
chmod +x scripts/setup.sh
./scripts/setup.sh
mkdir build && cd build
cmake .. -G Ninja -DCMAKE_BUILD_TYPE=Release
ninja
```

> **Detailed Installation:** See **[docs/02_INSTALLATION.md](docs/02_INSTALLATION.md)** for complete setup instructions, system requirements, and troubleshooting.
> **Docker/Kubernetes:** See **[docs/03_QUICKSTART.md](docs/03_QUICKSTART.md)** for quick deployment options.

### First Database

```python
import pyvdb

# Create database optimized for Gold Standard
db = pyvdb.create_gold_standard_db("./my_vectors")

# Add a document
db.add_text("Gold broke above $4,200 resistance with strong volume", {
    "type": "Journal",
    "date": "2025-12-01",
    "bias": "BULLISH"
})

# Semantic search
results = db.search("gold breakout bullish momentum", k=5)
for r in results:
    print(f"{r.score:.4f}: {r.metadata.date} - {r.metadata.type}")
```

---

## Architecture

```
+-----------------------------------------------------------------------------+
|                              VECTOR STUDIO                                   |
+-----------------------------------------------------------------------------+
|                                                                             |
|  +---------------+   +---------------+   +--------------------------+       |
|  |  Text Encoder |   | Image Encoder |   |   Gold Standard Ingest   |       |
|  |  MiniLM-L6-v2 |   |  CLIP ViT-B32 |   | Journals | Charts | Rpts |       |
|  +-------+-------+   +-------+-------+   +------------+--------------+       |
|          |                   |                        |                     |
|          +--------+----------+-----------+------------+                     |
|                   |                                                         |
|          +--------v--------+                                                |
|          |   Projection    |   384-dim -> 512-dim unified space             |
|          +--------+--------+                                                |
|                   |                                                         |
|  +----------------v----------------+   +--------------------------+         |
|  |          HNSW Index             |   |     Metadata Store       |         |
|  |  M=16, ef_construction=200      |   |   JSONL, rich filters    |         |
|  +----------------+----------------+   +------------+-------------+         |
|                   |                                 |                       |
|  +----------------v---------------------------------v-------------+         |
|  |                   Memory-Mapped Storage                        |         |
|  |          vectors.bin | index.hnsw | metadata.jsonl             |         |
|  +----------------------------------------------------------------+         |
|                                                                             |
+-----------------------------------------------------------------------------+
```

### Component Overview

| Component | Technology | Purpose |
|-----------|------------|---------|
| **Core Engine** | C++23 | Vector operations, HNSW index, storage |
| **Distance Functions** | AVX2/AVX-512 SIMD | Optimized similarity computation |
| **Text Embeddings** | ONNX (MiniLM-L6-v2) | Sentence embeddings (384-dim) |
| **Image Embeddings** | ONNX (CLIP ViT-B/32) | Visual embeddings (512-dim) |
| **Python Bindings** | pybind11 | High-level Python API |
| **CLI** | C++ | Command-line database operations |

---

## Installation

> **For detailed installation instructions, see [docs/02_INSTALLATION.md](docs/02_INSTALLATION.md).**

### System Requirements

| Component | Minimum | Recommended |
|-----------|---------|-------------|
| OS | Windows 10 (1903+) / Linux | Windows 11 / Ubuntu 24.04 |
| CPU | x64 with SSE4.1 | Intel 11th gen+ / AMD Zen3+ (AVX-512) |
| RAM | 8 GB | 16+ GB |
| Storage | 5 GB | SSD with 20+ GB |
| Python | 3.10 | 3.12+ |
| CMake | 3.20 | 3.28+ |
| Compiler | GCC 13+ / Clang 16+ / MSVC 19.33+ | GCC 14+ / Clang 18+ / MSVC Latest |

### Dependencies

The setup scripts automatically install required dependencies. See [docs/02_INSTALLATION.md](docs/02_INSTALLATION.md) for manual installation instructions.

### Manual Installation

See [docs/02_INSTALLATION.md](docs/02_INSTALLATION.md) for detailed manual installation steps for each operating system.

---

## Usage

### Python API

```python
import pyvdb

# Create or open database
db = pyvdb.create_database("./vectors")
# or: db = pyvdb.open_database("./vectors")

# Add text document with metadata
metadata = pyvdb.Metadata()
metadata.type = pyvdb.DocumentType.Journal
metadata.date = "2025-12-01"
metadata.bias = "BULLISH"
metadata.gold_price = 4220.50

doc_id = db.add_text("Gold analysis content here", metadata)

# Add chart image
chart_id = db.add_image("charts/GOLD.png", {"type": "Chart", "asset": "GOLD"})

# Basic search
results = db.search("gold bullish momentum", k=10)

# Filtered search
options = pyvdb.QueryOptions()
options.k = 10
options.type_filter = pyvdb.DocumentType.Journal
options.date_from = "2025-11-01"
options.date_to = "2025-12-31"
options.min_score = 0.7

results = db.query_text("resistance breakout", options)

# Cross-modal: search images with text
results = db.search("bullish flag pattern chart", k=5)

# Database management
stats = db.stats()
db.sync()
db.optimize()
db.close()
```

### CLI Usage

```bash
# Initialize database
vdb init ./my_database

# Add documents
vdb add ./my_database --text document.txt --type Journal --date 2025-12-01
vdb add ./my_database --image chart.png --type Chart --asset GOLD

# Ingest Gold Standard outputs
vdb ingest ./my_database ../gold_standard/output

# Search
vdb search ./my_database "gold breakout" --k 10 --min-score 0.7

# Statistics
vdb stats ./my_database

# Export for training
vdb export ./my_database ./training_data.jsonl
```

### Gold Standard Integration

```python
from pyvdb import create_gold_standard_db, GoldStandardIngest, IngestConfig

# Create database configured for Gold Standard
db = create_gold_standard_db("./gold_vectors")

# Ingest all outputs
ingest = GoldStandardIngest(db)
config = IngestConfig()
config.gold_standard_output = "../gold_standard/output"
config.incremental = True  # Only add new documents

stats = ingest.ingest(config)
print(f"Added: {stats.journals} journals, {stats.charts} charts")
```

---

## Embedding Models

Vector Studio uses local ONNX models for embedding generation:

| Model | Type | Dimensions | Size | Latency (CPU) |
|-------|------|------------|------|---------------|
| all-MiniLM-L6-v2 | Text | 384 | 23 MB | ~5 ms |
| CLIP ViT-B/32 | Image | 512 | 340 MB | ~50 ms |

Text embeddings are projected from 384 to 512 dimensions to enable unified cross-modal search.

### Download Models

```powershell
# Automatic download to ~/.cache/vector_studio/models/
python scripts/download_models.py
```

---

## Document Types

Vector Studio recognizes these Gold Standard document types:

| Type | Description | Source |
|------|-------------|--------|
| Journal | Daily analysis with bias | `output/Journal_*.md` |
| Chart | Asset price charts | `output/charts/*.png` |
| CatalystWatchlist | 11-category catalyst matrix | `output/reports/catalysts_*.md` |
| InstitutionalMatrix | Bank forecasts and scenarios | `output/reports/inst_matrix_*.md` |
| EconomicCalendar | Fed/NFP/CPI events | `output/reports/economic_calendar_*.md` |
| WeeklyRundown | Weekly technical summary | `output/reports/weekly_rundown_*.md` |
| ThreeMonthReport | Tactical 1-3 month outlook | `output/reports/3m_*.md` |
| OneYearReport | Strategic 12-24 month view | `output/reports/1y_*.md` |

---

## Metadata Fields

Each vector stores rich metadata extracted from Gold Standard:

| Field | Type | Description |
|-------|------|-------------|
| `id` | int | Unique vector ID |
| `type` | enum | DocumentType classification |
| `date` | string | YYYY-MM-DD format |
| `source_file` | string | Original file path |
| `asset` | string | GOLD, SILVER, DXY, etc. |
| `bias` | string | BULLISH, BEARISH, NEUTRAL |
| `gold_price` | float | Price at time of document |
| `silver_price` | float | Silver spot price |
| `gsr` | float | Gold/Silver ratio |
| `dxy` | float | Dollar index value |
| `vix` | float | Volatility index |
| `yield_10y` | float | 10Y Treasury yield |

---

## Performance

### Benchmarks

*Intel Core i7-12700H, 32GB RAM, NVMe SSD*

| Operation | Dataset Size | Time | Throughput |
|-----------|-------------|------|------------|
| Add text | 1 document | 8 ms | 125/sec |
| Add image | 1 image | 55 ms | 18/sec |
| Search (k=10) | 100,000 vectors | 2.1 ms | 476 qps |
| Search (k=10) | 1,000,000 vectors | 2.9 ms | 345 qps |
| Search (k=10) | 10,000,000 vectors | 4.3 ms | 233 qps |
| Search (k=10) | 100,000,000 vectors | 6.8 ms | 147 qps |
| Batch ingest | 1,000 documents | 12 s | 83/sec |

### HNSW Index Quality

| ef_search | Recall@10 | Latency (1M vectors) |
|-----------|-----------|----------------------|
| 50 | 95.2% | 2.9 ms |
| 100 | 98.1% | 4.8 ms |
| 200 | 99.4% | 8.5 ms |
| 500 | 99.9% | 19.3 ms |

### Quantization Performance (See System Snapshot v4.0.0)

| Method | Memory | Recall@10 | Query Time | Compression |
|--------|--------|-----------|------------|-------------|
| Uncompressed | 2048 MB | 100.0% | 2.9 ms | 1x |
| Scalar Quant | 512 MB | 96.5% | 2.8 ms | 4x |
| **SQ + PQ Curve** | **512 MB** | **97.8%** | **3.0 ms** | **4x** |
| **Display-Aware** | **512 MB** | **98.1%** | **3.1 ms** | **4x** |
| Product Quant | 64 MB | 88.2% | 1.8 ms | 32x |

**Key**: Perceptual quantization adds **+1-3% recall** for image/video content with minimal overhead.

### Billion-Scale Performance

| Metric | Value | Configuration |
|--------|-------|---------------|
| **Total Vectors** | **1 billion** | 10-node cluster |
| **Query Latency (p99)** | **8.5 ms** | With sharding |
| **Recall@10** | **96.8%** | Maintained at scale |
| **Throughput** | **85,000 QPS** | Distributed |

### Memory Usage

| Component | Size per Vector |
|-----------|-----------------|
| Vector storage (512-dim float32) | 2,048 bytes |
| HNSW index | ~200 bytes |
| Metadata (avg) | ~100 bytes |
| **Total** | **~2.4 KB** |

---

## Configuration

### Database Configuration

```python
config = pyvdb.DatabaseConfig()
config.dimension = 512                      # Vector dimensionality
config.metric = pyvdb.DistanceMetric.Cosine # Distance function
config.hnsw_m = 16                          # HNSW connections per node
config.hnsw_ef_construction = 200           # Build quality
config.hnsw_ef_search = 50                  # Search quality (default)
config.max_elements = 1_000_000             # Maximum capacity
config.provider = pyvdb.ExecutionProvider.Auto  # CPU/CUDA/DirectML
```

### Environment Variables

| Variable | Description | Default |
|----------|-------------|---------|
| `VDB_MODELS_DIR` | ONNX model directory | `~/.cache/vector_studio/models` |
| `VDB_LOG_LEVEL` | Logging verbosity | `INFO` |
| `VDB_NUM_THREADS` | Thread pool size (0=auto) | `0` |
| `VDB_SIMD` | SIMD level (avx512/avx2/sse4/none) | `auto` |

---

## Documentation

Comprehensive documentation is available in the `docs/` directory:

| # | Document | Description |
|---|----------|-------------|
| 00 | [Documentation Index](00_INDEX.md) | Comprehensive navigation hub for all documentation |
| 01 | [Introduction](01_INTRODUCTION.md) | System overview, key features, and quick start guide |
| 02 | [Installation](02_INSTALLATION.md) | System requirements, installation steps, and configuration |
| 03 | [Quick Start](03_QUICKSTART.md) | Create your first database and perform basic operations |
| 04 | [User Guide](04_USER_GUIDE.md) | Complete user guide covering all features |
| 05 | [Architecture](05_ARCHITECTURE.md) | System design, data flow, component diagrams |
| 06 | [Data Formats](06_DATA_FORMATS.md) | Supported data types and format specifications |
| 07 | [Data Ingestion](07_DATA_INGESTION.md) | Data adapters, batch processing, ingestion patterns |
| 08 | [Embeddings & Models](08_EMBEDDINGS_MODELS.md) | Text and image encoders, model specifications |
| 09 | [Vector Operations](09_VECTOR_OPERATIONS.md) | HNSW algorithm, distance metrics, mathematical foundations |
| 10 | [Hybrid Search](10_HYBRID_SEARCH.md) | BM25 full-text search, fusion methods, RAG toolkit |
| 11 | [Distributed System](11_DISTRIBUTED_SYSTEM.md) | Replication, sharding, gRPC networking |
| 12 | [ML Framework Integration](12_ML_FRAMEWORK.md) | TensorFlow and PyTorch C++ API integration |
| 13 | [LLM Engine](13_LLM_ENGINE.md) | Local text generation with llama.cpp |
| 14 | [Quantization](14_QUANTIZATION.md) | Vector compression and quantization techniques |
| 15 | [Logging & Monitoring](15_LOGGING_MONITORING.md) | Logging system, Prometheus metrics, observability |
| 16 | [Deployment Guide](16_DEPLOYMENT.md) | Docker, Kubernetes, production deployment |
| 17 | [Performance Tuning](17_PERFORMANCE_TUNING.md) | Benchmarks, optimization techniques, best practices |
| 18 | [Security](18_SECURITY.md) | Security best practices and guidelines |
| 19 | [Real-World Applications](19_REAL_WORLD_APPLICATIONS.md) | Production use cases and success stories |
| 20 | [API Reference](20_API_REFERENCE.md) | Complete C++ API documentation |
| 21 | [Python Bindings](21_PYTHON_BINDINGS.md) | Python API reference and examples |
| 22 | [Custom Development](22_CUSTOM_DEVELOPMENT.md) | Developing custom adapters and extensions |
| 23 | [Contributing Guide](23_CONTRIBUTING.md) | How to contribute to Vector Studio |

---

## Development

### Building from Source

```powershell
# Debug build with tests
.\scripts\build.ps1 -Debug

# Release build
.\scripts\build.ps1 -Release

# Check dependencies only
.\scripts\build.ps1 -CheckOnly

# Auto-install missing dependencies
.\scripts\build.ps1 -AutoInstall

# With GPU support
.\scripts\build.ps1 -Release -GPU
```

### Running Tests

```powershell
# C++ tests
cd build
ctest --output-on-failure

# Python tests
pytest tests/ -v
```

### Code Quality

```powershell
# Format C++ code
clang-format -i src/**/*.cpp include/**/*.hpp

# Format Python code
black scripts/ bindings/python/
isort scripts/ bindings/python/
```

---

## Project Structure

```
vector_database/
+-- CMakeLists.txt          # Build configuration
+-- README.md               # This file
+-- LICENSE                 # MIT License
+-- CONTRIBUTING.md         # Contribution guidelines
+-- CHANGELOG.md            # Version history
+-- requirements.txt        # Python runtime dependencies
+-- requirements-dev.txt    # Python development dependencies
+-- .gitignore              # Git ignore rules
+-- .gitattributes          # Git attributes
+-- include/
|   +-- vdb/                # Public C++ headers
+-- src/                    # C++ implementation
|   +-- core/               # Distance, threading, vector ops
|   +-- index/              # HNSW implementation
|   +-- storage/            # Memory-mapped persistence
|   +-- embeddings/         # ONNX encoder wrappers
|   +-- cli/                # Command-line tool
+-- bindings/
|   +-- python/             # pybind11 Python bindings
+-- scripts/
|   +-- setup.ps1           # Windows setup script
|   +-- setup.sh            # Unix setup script
|   +-- build.ps1           # Windows build script
|   +-- download_models.py  # ONNX model downloader
+-- tests/                  # Unit tests
+-- docs/                   # Documentation
```

---

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

## Acknowledgments

- [HNSW Paper](https://arxiv.org/abs/1603.09320) - Malkov and Yashunin
- [Sentence-Transformers](https://www.sbert.net/) - MiniLM models
- [OpenAI CLIP](https://openai.com/research/clip) - Vision-language models
- [ONNX Runtime](https://onnxruntime.ai/) - Cross-platform inference

---

<p align="center">
<sub>Part of the <a href="../gold_standard/README.md">Gold Standard</a> precious metals intelligence system.</sub>
</p>