Air-Gapped Hybrid Neural Search: Running Vector & FTS5 Pipelines Without Cloud LLMs

White Paper ID: WP-02

Author: AlaskaVault Machine Intelligence Group

Classification: Public Enterprise Specification

Architecture: Pure-Local Hybrid Search (FTS5 BM25 + Dense Vector Embeddings)

Executive Summary

The rapid enterprise adoption of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) has introduced a catastrophic security vulnerability: corporate intellectual property egress. Modern cloud-based AI search engines require sending proprietary source code, patient health records, internal memos, and legal contracts to third-party cloud APIs (such as OpenAI, Microsoft Azure AI, or Google Cloud Vertex). Once transmitted, this data is exposed to API logging, employee snooping, model provider retraining, and foreign subpoena requests.

AlaskaVault eliminates this trade-off by introducing Air-Gapped Hybrid Neural Search. By fusing quantized local embedding models with high-performance SQLite FTS5 lexical indexing (BM25), AlaskaVault executes millisecond-latency semantic and exact-match search entirely inside a local, air-gapped enclave. Not a single byte or packet leaves the host machine. This paper details the mathematical foundation, quantization mechanics, and Reciprocal Rank Fusion (RRF) pipeline that powers AlaskaVault's offline search engine.

1. The Cloud AI Security Trap

1.1 The RAG Data Egress Surface

A typical enterprise cloud RAG architecture introduces multiple attack vectors:

 Traditional Cloud RAG (Vulnerable):
 [Classified Document] ──────(Internet HTTPS)──────> [Cloud API Provider]
                                                     [Multi-Tenant Vector DB]
                                                     [Risk: Egress / Subpoena]

 AlaskaVault Air-Gapped Search (Sovereign):
 [Classified Document] ───(Local Bus 300 MB/s)───> [Local Quantized Model]
                                                   [SQLite FTS5 + Local HNSW]
                                                   [Zero Packet Egress]

2. Dual-Engine Architecture: Sparse + Dense Hybrid Retrieval

Single-mode search architectures fail in enterprise environments:

AlaskaVault unifies both engines into a concurrent, local-first pipeline:

                                  [Search Query: "Q"]
                                           │
                 ┌─────────────────────────┴─────────────────────────┐
                 ▼                                                   ▼
       [Dense Vector Path]                                 [Sparse Lexical Path]
   Local Quantized Transformer                           SQLite FTS5 BM25 Engine
 (384-dim normalized vector)                               (Term frequency index)
                 │                                                   │
      Cosine Similarity Search                             BM25 Ranking Function
                 │                                                   │
                 └─────────────────────────┬─────────────────────────┘
                                           │
                            [Reciprocal Rank Fusion (RRF)]
                                           │
                         [Unified Ranked Document Results]

3. Mathematical Foundations

3.1 Sparse Ranking: SQLite FTS5 BM25

For exact-token and keyword retrieval, AlaskaVault utilizes an optimized SQLite FTS5 engine implementing the Okapi BM25 ranking function:

Where:

3.2 Dense Semantic Ranking: Cosine Similarity in Local Vector Space

Simultaneously, the query is passed through a local, CPU-optimized transformer model to generate a dense 384-dimensional unit vector ec{u}. Semantic similarity to indexed chunk vector ec{v} is evaluated via dot product:

3.3 Fusion: Reciprocal Rank Fusion (RRF)

To merge the disparate scoring distributions of BM25 (unbounded positive floats) and Cosine Similarity ([-1, 1] floats), AlaskaVault applies Reciprocal Rank Fusion:

Where r_m(d) is the ordinal rank of document d in engine m, and k = 60 is a smoothing constant preventing top-ranked outliers from skewing the combined result.

4. Local Execution & Zero-Network Guarantee

5. Performance Benchmarks

6. Conclusion

AlaskaVault's Air-Gapped Hybrid Neural Search proves that enterprises do not need to sacrifice security for artificial intelligence. By combining local vector embeddings with deterministic BM25 lexical indexing, organizations maintain absolute data sovereignty, eliminate cloud subscription fees, and unlock sub-second search speeds on ordinary workstation hardware.