A 10 million document vector corpus stored as standard float32 embeddings demands roughly 31 GB of raw RAM. For engineers running memory-constrained environments, edge hardware, or localized RAG architectures, that footprint is a non-starter. You either pay exorbitant cloud bills for high-memory instances or accept the heavy maintenance tax of traditional Product Quantization (PQ) indices like FAISS IndexPQ, which force you through upfront codebook training, slow vector-repacking removes, and complex parameter tuning.

TurboVec upends this trade-off. Built in Rust with Python bindings, TurboVec implements Google Research’s TurboQuant algorithm to squash that same 31 GB dataset down to 4 GB. It does this without requiring a offline training phase, without rebuilds as your corpus grows, and while outperforming FAISS IndexPQFastScan search latency across both ARM and x86 architectures.

```
+-----------------------------------------------------------------------+
|                       10M Document FP32 Corpus                        |
|                                31 GB                                  |
+-----------------------------------------------------------------------+
                                   |
                                   v
+-----------------------------------------------------------------------+
|                      TurboVec Quantized Index                         |
|                             4 GB (16x)                                |
+-----------------------------------------------------------------------+
```

## The Quantization Trap: Why Traditional PQ Hinders Real-Time Ingest

Standard Product Quantization algorithms like FAISS IndexPQ achieve high compression by clustering vector sub-spaces using algorithms such as k-means++. However, this approach introduces three major systemic bottlenecks:

1. **Training Dependencies**: You cannot index vectors incrementally without first training codebooks on a representative sample. If your domain shifts, your quantization quality degrades until you rebuild the entire index.
2. **Dynamic Ingest Cost**: Adding or removing vectors on a live FAISS index requires expensive layout modifications. Removing an ID via `IndexIDMap` over `IndexPQFastScan` forces a complete repacking of stored codes, turning a simple deletion into an operational block that can take anywhere from 0.19 to 1.02 seconds for a 100K vector index.
3. **Hardware Inefficiencies**: Standard PQ search relies heavily on multi-byte Look-Up Table (LUT) sweeps that struggle to fully saturate SIMD vector registers across heterogeneous hardware.

TurboVec bypasses data-dependent codebook training entirely by leveraging data-oblivious quantization.

## Inside the TurboQuant Pipeline: Zero Training, Optimal Distortion

Instead of inspecting your dataset to find optimal cluster centers, TurboQuant exploits a key mathematical property of high-dimensional space: after a random rotation, any coordinate distribution converges to a predictable shape regardless of the input data.

```
Raw Vector (float32)
       │
       ▼
1. Normalize Lengths ──────────────► Store ||v|| as float32
       │
       ▼
2. Random Rotation ───────────────► Multiply by Random Orthogonal Matrix
       │                            (Coordinates convert to Beta / Gaussian)
       ▼
3. Per-Coordinate Calibration ────► TQ+ fits shift/scale scalars via index.calibrate()
       │
       ▼
4. Lloyd-Max Quantization ────────► Map coordinates to fixed theoretical buckets
       │                            (2-bit: 4 buckets | 4-bit: 16 buckets)
       ▼
5. Nibble Bit-Packing ────────────► Compress 1536 dims into 384 bytes
       │
       ▼
6. Length-Renormalized Scoring ───► Correct inner product bias using ||v|| / ⟨u, x̂⟩
```

### Step 1: Length Normalization
The vector's original magnitude (norm) is extracted and saved as an isolated float32 scalar. The remaining directional component is normalized onto a unit hypersphere.

### Step 2: Data-Oblivious Random Rotation
The unit vector is multiplied by a fixed random orthogonal matrix. This rotation redistributes energy across all dimensions. Regardless of whether your raw input consists of sparse text embeddings or dense audio vectors, the rotated coordinates independently follow a Beta distribution that converges to a Gaussian $N(0, 1/d)$ in high dimensions.

### Step 3: Per-Coordinate Calibration (TQ+)
While the Beta distribution holds asymptotically, finite-dimensional embeddings (especially low-bit regimes or word-vector styles) experience subtle coordinate drift. The TQ+ variant solves this by fitting two scalars per coordinate (a shift and a scale) to align empirical quantiles with the codebook's outermost centroids. Calling `index.calibrate(sample)` on a small draw (~1024 vectors) permanently locks in this state without triggering full-index retraining or rebuilds. TQ+ yields recall gains of up to +2.2 percentage points at R@1 on drifting datasets like GloVe d=200.

### Step 4: Mathematical Lloyd-Max Quantization
Because the post-rotation coordinate distribution is known mathematically, optimal bucket boundaries and centroids are calculated directly from probability theory using the Lloyd-Max algorithm. For 2-bit index configurations, coordinates fall into 4 buckets; for 4-bit, 16 buckets. No k-means, no iterations over data rows. The codebook achieves a distortion rate within a factor of 2.7x of Shannon's theoretical distortion-rate limit.

### Step 5: High-Density Bit-Packing
Quantized coordinate indices (0-3 for 2-bit, 0-15 for 4-bit) are packed directly into byte streams. A 1536-dimensional vector drops from 6,144 bytes in FP32 down to 384 bytes in 2-bit mode, yielding a 16x compression ratio.

### Step 6: Length-Renormalized Scoring
Scalar quantization inherently contracts vector magnitudes, causing inner products to be systematically underestimated. Adapted from the RaBitQ framework, TurboVec computes a scalar correction factor during encoding:

$$\frac{\|v\|}{\langle u, \hat{x} \rangle}$$

Where $u$ is the rotated unit direction and $\hat{x}$ is the centroid reconstruction. Multiplying candidate scores by this factor during search removes downward estimator bias at zero query-time cost and zero extra storage overhead.

## Low-Level SIMD Execution on ARM and x86

TurboVec writes kernel execution paths natively for target hardware using explicit SIMD intrinsics rather than relying on autovectorization. 

```
                       Query Input Vector
                               │
                               ▼
                   Query Random Rotation
                               │
                               ▼
                 +---------------------------+
                 |  SIMD Kernel Dispatch     |
                 +---------------------------+
                               │
            ┌──────────────────┴──────────────────┐
            ▼                                     ▼
     ARM Architecture                       x86 Architecture
  (NEON SDOT / SMMLA)                  (AVX-512 VNNI / vpermb)
            │                                     │
            └──────────────────┬──────────────────┘
                               ▼
                32-Vector Block Filtering Check
                               │
                 ┌─────────────┴─────────────┐
                 ▼                           ▼
          [Block Allowed?]            [Block Excluded?]
                 │                           │
                 ▼                           ▼
       Nibble-Split LUT Scan           Short-Circuit Block
                 │                           (0 SIMD ops)
                 ▼
     Unbiased Score Accumulation
                 │
                 ▼
       Min-Heap Top-K Insert
```

### ARM Architectures (Google Axion / GCP c4a)
On ARM64, search queries utilize hand-written NEON kernels leveraging `SDOT` (Signed Dot Product) and `SMMLA` (Matrix Multiply Accumulate) instructions. The vector-major memory layout enables direct dot-product accumulation across packed byte structures.

### x86 Architectures (Intel Sapphire Rapids / Xeon)
Modern x86 chips utilize AVX-512 VNNI (Vector Neural Network Instructions) combined with `vpermb` for fast byte-level lookup table (LUT) evaluations. The kernel falls back dynamically to AVX2 implementations or scalar routines on older CPUs based on runtime feature detection via `is_x86_feature_detected!`.

### Short-Circuiting Block-Level Filtering
Unlike post-filtering architectures that score candidate items only to discard forbidden items later, TurboVec handles filtering directly inside its SIMD execution loop. Operations run over contiguous 32-vector blocks:

* If an allowlist bitmask indicates that zero slots inside a 32-vector block are permitted, the entire SIMD block is short-circuited before performing any LUT lookups or scoring logic.
* If a block is partially allowed, invalid slots are filtered out during final min-heap insertion.

This ensures selective filters eliminate computational work proportionally to their selectivity.

## Hardware Benchmarks: TurboVec vs FAISS

The following benchmark metrics reflect 100K vector indices queried with 1K input vectors at $k=64$ (median of 5 runs).

### Search Latency Metrics
In throughput tests against FAISS `IndexPQFastScan` (with sub-quantizer counts matched identically to TurboVec bit rates), TurboVec dominates across both ARM and x86 hardware.

| Architecture | CPU Target | Bit Depth | FAISS Latency | TurboVec Latency | Speedup Factor |
| :--- | :--- | :--- | :--- | :--- | :--- |
| **ARM** | Google Axion (8 vCPU) | 4-bit | Baseline | **3.5x Faster** | 3.5x |
| **ARM** | Google Axion (8 vCPU) | 2-bit | Baseline | **1.26x Faster** | 1.26x |
| **x86** | Sapphire Rapids (8 vCPU)| 4-bit | Baseline | **3.4x Faster** | 3.4x |
| **x86** | Sapphire Rapids (8 vCPU)| 2-bit | Baseline | **1.20x Faster** | 1.20x |

### Dynamic Mutability: Ingest & Removal Performance
Where TurboVec sets an engineering milestone is dynamic index modification. FAISS incurs heavy penalties when modifying indexed structures post-creation.

```
Single Removal Latency (100K Vector Index)
FAISS IndexIDMap:  [========================================] ~1,020,000 µs (Repack)
TurboVec IdMap:    [=] 1.22 µs (O(1) Swap-and-Pop)
```

| Operation | Target Config | FAISS Performance | TurboVec Performance | Efficiency Gain |
| :--- | :--- | :--- | :--- | :--- |
| **Single Add ($n=1$)** | Warm Index | High Overhead | **6.3 - 19.7 µs** | 7.6x - 13.9x Faster |
| **Batch Add ($n=100$)** | Warm Index | High Overhead | **4.6 - 16.3 µs / vec** | 4.6x - 15.1x Faster |
| **Delete by ID ($n=1$)**| `IdMapIndex` | 0.19 - 1.02 seconds | **0.44 - 1.22 µs** | ~100,000x Faster |

FAISS relies on memory repacking during vector removals, causing second-long spikes. TurboVec's `IdMapIndex` executes deletions using an $O(1)$ swap-and-pop technique paired with index metadata tracking, resulting in sub-microsecond latency.

## Crash-Safe Incremental Persistence

Standard in-memory engines require full file dumps to persist updates to disk. TurboVec introduces incremental state syncing:

```python
from turbovec import IdMapIndex
import numpy as np

index = IdMapIndex(dim=1536, bit_width=4)
index.add_with_ids(vectors, ids)

# Full file snapshot
index.write("index.tvim")

# Make small updates
index.remove(1002)

# Incremental update: Flushes only modified bytes via single fsync
index.sync("index.tvim")
```

The `sync(path)` call isolates modified byte ranges, issues a single atomic `fsync`, and guarantees crash safety across arbitrary byte boundaries. Appending or deleting vectors on multi-gigabyte indices completes in milliseconds.

## Pragmatic Integration Guide

TurboVec provides native bindings for both Python and Rust environments, along with direct drop-in integration classes for common orchestration frameworks.

### Python Native API
Python arrays must be standard `float32` 2D contiguous arrays.

```python
import numpy as np
from turbovec import TurboQuantIndex, IdMapIndex

# Initialize uncalibrated or calibrate on sample
vectors = np.random.randn(10000, 1536).astype(np.float32)

index = TurboQuantIndex(dim=1536, bit_width=4)
index.add(vectors)

# Perform SIMD search
queries = np.random.randn(5, 1536).astype(np.float32)
scores, indices = index.search(queries, k=10)

# Stable ID tracking with dynamic filters
id_index = IdMapIndex(dim=1536, bit_width=4)
id_index.add_with_ids(vectors, np.arange(10000, dtype=np.uint64))

# Filtered Search: Executed directly inside the 32-vector SIMD block
allowed_ids = np.array([10, 25, 99, 402], dtype=np.uint64)
scores, ids = id_index.search(queries, k=10, allowlist=allowed_ids)
```

### Rust Native API
Add `turbovec` to your `Cargo.toml`:

```rust
use turbovec::{TurboQuantIndex, IdMapIndex};

fn main() -> Result<(), Box<dyn std.error::Error>> {
    let mut index = TurboQuantIndex::new(1536, 4)?;
    
    // Pass vector slices directly
    index.add(&vectors);
    let results = index.search(&queries, 10);
    
    // Persistent disk storage
    index.write("index.tv")?;
    let loaded = TurboQuantIndex::load("index.tv")?;
    
    Ok(())
}
```

### Framework Drop-In Integration
Swap out standard heavy vector stores in your existing application pipeline without rewriting pipeline abstractions:

```bash
pip install turbovec[langchain]
pip install turbovec[llama-index]
pip install turbovec[haystack]
pip install turbovec[agno]
```

* **LangChain**: Replaces `langchain_core.vectorstores.InMemoryVectorStore`
* **LlamaIndex**: Replaces `llama_index.core.vector_stores.SimpleVectorStore`
* **Haystack**: Replaces `haystack.document_stores.in_memory.InMemoryDocumentStore`
* **Agno**: Replaces `agno.vectordb.lancedb.LanceDb`

## Architecting Air-Gapped Local Vector Pipelines

By combining pure local execution, low memory overhead, and SIMD hardware acceleration, TurboVec provides a lean foundation for high-performance vector retrieval. Eliminating external API calls, background training routines, and managed database dependencies allows you to run millions of vector embeddings locally on a single machine.

## References

* https://github.com/RyanCodrai/turbovec

Discover how TurboVec leverages Google Research's TurboQuant algorithm to compress 31 GB vector indices into 4 GB of RAM without sacrificing search throughput.

FAISS Is Eating Your RAM: How TurboVec Quantizes 31 GB Vector Corpora to 4 GB While Outrunning SIMD Scans

Reggi

Every engineer inherits that one 50,000-line legacy monolith where tracing execution flow means running recursive `grep` chains until mental fatigue sets in. Feeding that same codebase into an AI coding assistant rarely solves the issue. Most coding agents lean on dense vector retrieval or classic RAG patterns that optimize for textual proximity instead of architectural topology. The retrieval mechanism fetches semantically related snippets, misses critical downstream caller logic, and feeds the LLM an incomplete context window that breeds hallucinations. 

Treating interconnected systems like arbitrary blocks of prose is fundamentally broken. **Graphify** fixes this structural mismatch by ditching raw vector stores in favor of a locally parsed, deterministic knowledge graph that links source code, system documentation, PDFs, and auxiliary media into an explicit dependency network.

## The Vector Bottleneck vs. Deterministic AST Parsing

Vector similarity algorithms calculate mathematical distances between text embeddings. That approach works fine for conversational search, but code execution paths are precise graphs, not vibes. When a database pool is initialized in a framework core, an edge-case handler three directories deep does not share textual similarity with it. It shares an architectural dependency.

Instead of burning tokens on fuzzy LLM passes, Graphify operates on your local machine using **tree-sitter AST (Abstract Syntax Tree)** parsing. The parser runs deterministically across your source code to extract classes, functions, imports, and cross-module relationships (`calls`, `inherits`, `mixes_in`).

The engineering win here is twofold:

1. **Zero LLM Token Consumption for Code**: Structural extraction is performed entirely by native AST traversal. You run zero API calls to map your code architecture.
2. **Complete Data Privacy**: Your source files never leave local memory or disk during graph construction.

To maintain strict data integrity, Graphify explicitly classifies every edge in the graph into two distinct categories:

* **EXTRACTED**: Hard facts verified directly by the AST parser, such as explicit imports, class extensions, and standard method calls.
* **INFERRED**: Relationships resolved via Graphify's downstream dependency resolution heuristics.

```bash
$ graphify explain "APIRouter"
Node: APIRouter
  Source:    routing.py L2210
  Community: 2
  Degree:    47

Connections (47):
  --> RequestValidationError [uses] [INFERRED]
  --> Dependant [uses] [INFERRED]
  --> .get() [method] [EXTRACTED]
  <-- __init__.py [imports] [EXTRACTED]
```

When an agent or developer executes an inspection command, the output does not guess. It outlines exact node degrees, module community clusters, and transparently flags whether an edge was hard-extracted or analytically inferred.

## Under the Hood: Pipeline, Community Detection, and Artifacts

Graphify distributes via PyPI under the package name `graphifyy` (double 'y' to prevent namespace collisions) while maintaining the CLI command `graphify`. Setup takes roughly 30 seconds:

```bash
uv tool install graphifyy
graphify install
```

Running `/graphify .` through a supported assistant indexes the working tree and generates three core artifacts inside the `graphify-out/` directory:

* `graph.html`: A standalone interactive visualization. It renders the entire dependency map directly in the browser, letting developers visually trace system boundaries and subsystem clusters.
* `GRAPH_REPORT.md`: A markdown summary highlighting "god nodes" (components with disproportionately high connectivity degrees), anomalous cross-boundary links, and tailored diagnostic prompts.
* `graph.json`: The raw graph state machine. Downstream tools and agents query this artifact directly without reparsing the entire file tree.

Behind the scenes, Graphify identifies architectural boundaries using the **Leiden algorithm**. This community detection method groups highly coupled modules into unified subsystems, applying automated color-coded clustering to make modular silos obvious at a glance.

```
+-----------------------------------------------------------------------+
|                         SOURCE REPOSITORY                             |
|  +--------------------+   +-------------------+   +----------------+  |
|  | Source Code Files  |   | Markdown & Docs   |   | PDF / Media    |  |
|  +---------+----------+   +---------+---------+   +--------+-------+  |
+------------|------------------------|----------------------|----------+
             |                        |                      |           
    [Tree-sitter AST]        [Semantic LLM Pass]    [faster-whisper]     
   (0 LLM Tokens / Local)   (Ollama, Claude, etc.) (Local Transcription) 
             |                        |                      |           
             +------------------------+----------------------+           
                                      |                                  
                                      v                                  
                       +-----------------------------+                   
                       |      LEIDEN ALGORITHM       |                   
                       |    (Community Detection)    |                   
                       +--------------+--------------+                   
                                      |                                  
                                      v                                  
                         [ graphify-out/ Artifacts ]                     
                     +---------------------------------+                 
                     | * graph.html    (Visual Map)    |                 
                     | * GRAPH_REPORT.md (God Nodes)   |                 
                     | * graph.json    (Full Topology) |                 
                     +----------------+----------------+                 
                                      |                                  
                                      v                                  
                          [ System Integrations ]                        
                     +---------------------------------+                 
                     | * Git Hooks & Merge Drivers     |                 
                     | * MCP Server (stdio / HTTP)     |                 
                     | * 20+ Assistant Guidance Rules  |                 
                     +---------------------------------+                 
```

Graphify also indexes unstructured project assets. Markdown documentation, technical PDFs, and media assets pass through ingestion pipelines, including local audio transcription via `faster-whisper` when optional dependencies are installed. In-line code comments containing `# NOTE:`, `# WHY:`, or `# HACK:` are converted into distinct graph nodes linked directly to the parent source code, capturing contextual intent alongside functional architecture.

## Empirical Performance Benchmarks

Graphify’s structured indexing model holds distinct advantages over vector-only and pure memory solutions across standardized benchmarks:

| Benchmark | Target Metric | Graphify | Alternative Systems |
| :--- | :--- | :--- | :--- |
| **LOCOMO (n=300)** | *recall@10* | **0.497** | mem0 (0.048), supermemory (0.149) |
| **LOCOMO (n=300)** | *QA Accuracy* | **45.3%** | supermemory (49.7%), mem0 (27.3%) |
| **LongMemEval-S (n=50)** | *QA Accuracy* | **76%** | On par with *dense RAG* |
| **Graph Generation** | *LLM Token Cost* | **0** | High recurring per-token cost |

The benchmark results highlight a decisive technical reality. Graphify achieves roughly 10x the recall@10 performance of mem0 and more than triples supermemory on LOCOMO benchmarks, matches dense RAG QA accuracy on LongMemEval-S, and completely eliminates the LLM token tax required for AST code parsing.

## Integration Protocols: Git Automation and MCP Serving

To prevent agent context drift, Graphify injects auto-guidance hooks across more than 20 development tools, including Claude Code, Cursor, Codex, GitHub Copilot CLI, Gemini CLI, Aider, Trae, and Kilo Code:

```bash
graphify cursor install    # Generates .cursor/rules/graphify.mdc
graphify claude install    # Registers a PreToolUse hook inside Claude Code
graphify codex install     # Provisions AGENTS.md rules for Codex
```

These configuration rules force the AI assistant to read `graph.json` before executing unguided, brute-force file searches.

For engineering teams, the `graphify-out/` directory is designed to be committed to version control. When one engineer generates an updated graph and pushes to main, teammates inherit the parsed system map on pull without spending compute cycles rebuilding it.

Running `graphify hook install` sets up repository hooks equipped with a **custom merge driver**. If two developers commit structural changes concurrently, the merge driver resolves `graph.json` diffs natively, eliminating manual conflict resolution.

For centralized architectures, Graphify operates as a Model Context Protocol (MCP) server over standard input/output or HTTP transports:

```bash
# Spin up an HTTP-accessible MCP instance
python -m graphify.serve graphify-out/graph.json --transport http --port 8080 --api-key "$SECRET"
```

## Production Tooling and CI/CD Setup

When implementing Graphify in continuous integration pipelines or developer workstations, keep these package nuances in mind:

### Package Distribution Details
The PyPI package is named `graphifyy`, but it exposes the binary `graphify`. Installing via `uvx graphify` directly will fail. Use `uv tool install graphifyy` instead.

### Extended Dependencies
For processing non-code assets, install the corresponding target extras:

```bash
uv tool install "graphifyy[pdf]"  # Adds document parsing capabilities
uv tool install "graphifyy[all]"  # Provisions the complete extraction engine
```

### Headless CI/CD Extraction
For headless automated builds in CI environments, Graphify supports multiple backend providers to parse non-code docs: Gemini, Claude, OpenAI, DeepSeek, and local inference with **Ollama**.

```bash
# Execute fully offline document processing
GRAPHIFY_OLLAMA_NUM_CTX=32768 graphify extract ./docs --backend ollama
```

## The Architectural Verdict

Generic vector search fails on source code because software systems are strict directed graphs, not unstructured prose. By decoupling code extraction into deterministic, zero-token AST parsing while reserving semantic analysis for unstructured documentation, Graphify provides a balanced, robust indexing model. 

It slashes API costs, fixes context retrieval hallucinations, and hands both developers and AI assistants an accurate map of complex codebases.

## References
* Graphify Repository: https://github.com/Graphify-Labs/graphify

Graphify replaces vector search with local tree-sitter AST parsing, mapping massive codebases into deterministic, hallucination-free knowledge graphs with zero LLM token cost.

Why Vector Search Keeps Failing on Large Codebases (And How AST Knowledge Graphs Fix It)

A user types "laptop" into your search bar and gets an empty state. Your database contains hundreds of articles about "notebooks". Different strings, identical intent, zero matches. This is the hard ceiling of lexical search: traditional token matching evaluates character sequences rather than meaning. It cannot infer that "buy" and "purchase" denote identical operations, or that "shipping" and "delivery" refer to the same logistical state.

Semantic search bypasses this lexical limit by comparing conceptual vectors directly. Using Transformers.js, you can execute this entire pipeline inside the browser runtime: zero servers, zero API tokens, and zero outbound network traffic once the model weights load.

## What Sentence Embeddings Represent

Transformer models cannot compute over raw strings. Text must first be transformed into a numerical representation. An embedding is the final output of this conversion: a sequence mapped into a dense array of floating-point values known as a vector.

The underlying mechanism relies on geometric proximity. Sentences with similar conceptual semantics are mapped to coordinates that sit physically close within a shared vector space.

The default model for this pipeline, `all-MiniLM-L6-v2`, projects any input string into a 384-dimensional vector space. Fine-tuned on the SNLI and MultiNLI datasets, the network optimizes these geometric coordinates so that phrases like "fast shipping" and "cheap delivery options" become immediate spatial neighbors. Conversely, an unrelated phrase like "car engine repair" is projected far across the space.

```
"fast shipping"           --> [ 0.042, -0.121, ...,  0.089 ] \
                                                              Spatial Neighbors (High Similarity)
"cheap delivery options"  --> [ 0.039, -0.118, ...,  0.095 ] /

"car engine repair"       --> [-0.512,  0.801, ..., -0.344 ] --> Distant Vector
```

Individual dimensions are not human-interpretable; dimension 47 does not correspond to an isolated property. For information retrieval, only the distance between vectors matters. A short vector distance indicates semantic alignment, while a large distance indicates unrelated content.

## Mean Pooling and Unit Normalization

By default, raw Transformer architectures emit one vector per token. Every word or subword receives its own coordinate. To perform sentence-level retrieval, you must collapse those token vectors into a single sentence vector.

Mean pooling achieves this by calculating the average of all token vectors, weighted against the attention mask to ensure padding tokens do not alter the calculation. Unit normalization scales the resulting composite vector to a magnitude of 1, drastically simplifying subsequent similarity math.

In Transformers.js, passing `{ pooling: 'mean', normalize: true }` handles both operations automatically:

```javascript
import { pipeline } from '@xenova/transformers';

// Downloads and initializes the model on first run (cached by the browser thereafter)
const extractor = await pipeline(
    'feature-extraction',
    'Xenova/all-MiniLM-L6-v2',
    { progress_callback: console.log }
);

// Generates a single, normalized 384-dimensional vector
const output = await extractor(
    'This is a sample sentence.',
    { pooling: 'mean', normalize: true }
);

const embedding = output.data; // Float32Array of 384 values
```

Without these options, the pipeline returns unpooled, token-level tensors. Those are useful for named entity recognition, but they cannot be used for sentence-level semantic search.

## Tensor Outputs and Batched Ingestion

The `Tensor` instance returned by the `feature-extraction` pipeline exposes three primary properties:

* `dims`: `[1, 384]` for a single sentence, or `[N, 384]` for an $N$-length batch. The second dimension is locked to 384 for this model architecture.
* `type`: `'float32'`, confirming that each value is a standard 32-bit float.
* `data`: The underlying linear buffer stored in row-major order. A batch of 3 sentences forms a contiguous array of $3 \times 384 = 1,152$ numbers.

Calling `output.tolist()` transforms the tensor into nested JavaScript arrays, while `output[0].data` isolates the 384-number buffer for the first sentence.

Batching inputs is critical for throughput. Rather than issuing serial extractor invocations, passing an array of strings processes the entire set inside a single forward pass:

```javascript
const outputs = await extractor(
    [
        'I want to buy a new laptop.',
        'Where can I get a car?',
        'My package delivery is delayed.',
        'How do I make a purchase online?'
    ],
    { pooling: 'mean', normalize: true }
);
```

Because the Transformer architecture is optimized for parallel matrix math, embedding ten sentences in a single batched pass takes nearly the same time as embedding one. When indexing a document corpus, batching yields massive speedups over serial execution.

## Cosine Similarity: The Math Behind Search

With your documents and search queries converted into coordinates, you score semantic alignment using cosine similarity. This measures the cosine of the angle between two vectors: 1.0 represents identical direction (equivalent meaning), while 0 represents orthogonality (unrelated concepts).

Because we enforced `normalize: true`, every vector already has a magnitude of 1. This reduces the cosine similarity formula to a simple dot product, requiring only element-wise multiplication and accumulation:

$$\text{Cosine Similarity} = \mathbf{A} \cdot \mathbf{B} = \sum_{i=1}^{n} A_i B_i$$

```javascript
function cosineSimilarity(vec1, vec2) {
    let sum = 0;
    for (let i = 0; i < vec1.length; i++) {
        sum += vec1[i] * vec2[i];
    }
    // Clamping guards against float precision artifacts yielding values like -0.000000001
    return Math.max(0, sum);
}
```

Scores for normalized sentence embeddings generally follow these ranges:

| Score Range | Similarity Meaning |
| :--- | :--- |
| **0.8 - 1.0** | Near-duplicate meaning |
| **0.6 - 0.8** | Very similar |
| **0.4 - 0.6** | Related |
| **0.2 - 0.4** | Loosely related |
| **0.0 - 0.2** | Unrelated |

## The SemanticSearch Architecture

Production semantic retrieval decouples ingestion from search time:

1. **`indexDocuments(documents)`**: Accepts an array of objects containing a `text` property, batches the text through the extractor, and stores the resulting vectors alongside the document metadata.
2. **`search(query)`**: Runs one single forward pass to generate the query vector (typically under 100ms), iterates across the cached index, runs `cosineSimilarity`, and sorts the result set.
3. **`save()` and `load()`**: Serializes vector arrays to avoid re-generating static data across browser sessions.

Because scoring cached vectors is pure linear algebra in JavaScript, the retrieval loop completes in sub-millisecond time.

```
[In-Memory Index] (Cached 384-dim Vectors)
       │
       ├─ Cosine Scoring Loop ◄── Query Vector (Generated in ~100ms)
       │  (Sub-millisecond)
       ▼
[Ranked Search Results]
```

## Offloading Execution to a Web Worker

Executing Transformer inference directly on the main thread is a non-starter for production applications. Heavy tensor operations block UI updates, freezing inputs, scrolling, and CSS animations.

Web Workers isolate model execution in a background thread. Communication is coordinated through message passing and correlation identifiers:

```javascript
// main.js
const worker = new Worker('worker.js', { type: 'module' });
const promises = new Map();
let requestId = 0;

worker.onmessage = (event) => {
    const { id, type, data } = event.data;
    if (type === 'progress') {
        console.log(data); // Feed progress directly to the UI
    } else if (type === 'result') {
        promises.get(id)?.resolve(data);
        promises.delete(id);
    }
};

async function embedInWorker(sentences) {
    const id = requestId++;
    return new Promise((resolve, reject) => {
        promises.set(id, { resolve, reject });
        worker.postMessage({ id, type: 'embed', data: sentences });
    });
}
```

```javascript
// worker.js
import { pipeline } from '@xenova/transformers';

let extractor = null;

self.onmessage = async (event) => {
    const { id, type, data } = event.data;

    if (type === 'embed') {
        if (!extractor) {
            extractor = await pipeline('feature-extraction', 'Xenova/all-MiniLM-L6-v2', {
                progress_callback: (status) => self.postMessage({ id, type: 'progress', data: status })
            });
        }
        const output = await extractor(data, { pooling: 'mean', normalize: true });
        self.postMessage({ id, type: 'result', data: output.data });
    }
};
```

This singleton pattern ensures the pipeline is initialized only once. The message `id` acts as a correlation key, ensuring concurrent async inference calls resolve to their corresponding promises.

## Storage and Scale Profiles

Generating embeddings is computationally expensive, so persistent caching is essential for repeat visits:

* **`localStorage`**: Stores roughly 5 MB depending on the engine. A 12-document collection with 384-dimensional floating-point vectors consumes roughly 200 KB when serialized to JSON.
* **`IndexedDB`**: Provides high-capacity, structured client-side storage for larger datasets without strict 5 MB boundaries.

Linear scans using JavaScript loops operate smoothly up to a few hundred documents. When scaling beyond that threshold, brute-force scoring introduces latency. For larger client-side corpora, running an in-browser PostgreSQL instance with the `pg_embedding` extension provides approximate nearest neighbor indexing entirely within the browser sandbox.

## Model Selection Matrix

Choosing a model depends on your target language coverage and performance requirements:

| Model | Profile | Use Case |
| :--- | :--- | :--- |
| `Xenova/all-MiniLM-L6-v2` | Fast, lightweight footprint | Best default for English retrieval workflows |
| `Xenova/multilingual-e5-base` | Multilingual support, cross-lingual alignment | Mixed-language corpora (e.g., matching English queries to French or German documents) |

With `Xenova/multilingual-e5-base`, cross-lingual queries work out of the box. The model maps shared concepts across languages into the same vector space, so an English query naturally surfaces matching French or German documents without a separate translation step.

The entire architecture requires just four clear stages: initialize the pipeline, batch-embed the index, compute the query vector, and rank using cosine similarity. The math and mechanics you use here form the foundation for client-side recommendations, deduplication systems, and retrieval-augmented generation.

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