🏛️ Nexus Enterprise AI (veloxs-nexus)

A high-performance, headless, layered data intelligence, format-aware chunking, PII sanitization, and 3072-dimensional vector projection engine for enterprise AI applications.

📦 Installation

# Standard in-memory installation
pip install veloxs-nexus

# With PostgreSQL + pgvector support
pip install veloxs-nexus[postgres]

# With YAML configuration support
pip install veloxs-nexus[yaml]

⚡ Quick Start (import nexus)

import nexus

# 1. Initialize client in pure in-memory mode (zero disk I/O, serverless safe)
client = nexus.NexusClient(tenant_id="org-acme", in_memory_only=True)

# 2. Process tabular CSV, JSON, Markdown, or Text into 3072D vectors + PII-masked chunks
doc = client.process_document(
    document_id="doc-001",
    name="department_budgets.csv",
    text="""department,quarter,budget_usd,status
Engineering,Q3 2025,1250000,Completed
Security,Q3 2025,350000,Completed""",
    file_type="csv"
)

print(f"Total chunks: {len(doc.chunks)}")
print(f"First chunk text: {doc.chunks[0].text}")
print(f"Is Tabular: {doc.chunks[0].metadata['is_tabular']}")
print(f"Embedding length: {len(doc.chunks[0].embedding)}")  # 3072 normalized floats

# 3. Ingest and execute fail-closed grounded guardrail Q&A
client.index_document(doc)
response = client.ask("What is the engineering budget?")
print(f"Decision: {response.decision}")
print(f"Answer: {response.answer}")

🏛️ Modular Sub-Layer Architecture

veloxs-nexus exports 7 decoupled sub-layers under the nexus.* namespace:

🗄️ PostgreSQL + pgvector Schema

For production database persistence, use the provided schema:

CREATE EXTENSION IF NOT EXISTS vector;
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";

CREATE TABLE knowledge_documents (
    document_id         VARCHAR(128) PRIMARY KEY,
    name                VARCHAR(255) NOT NULL,
    file_type           VARCHAR(32) NOT NULL,
    file_size_bytes     BIGINT NOT NULL,
    content_hash        VARCHAR(64) NOT NULL,
    classification      VARCHAR(64) DEFAULT 'general',
    created_at          TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP,
    updated_at          TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

CREATE TABLE knowledge_chunks (
    chunk_id            VARCHAR(128) PRIMARY KEY,
    document_id         VARCHAR(128) NOT NULL REFERENCES knowledge_documents(document_id) ON DELETE CASCADE,
    source_job          VARCHAR(64) NOT NULL,
    chunk_index         INTEGER NOT NULL,
    chunk_text          TEXT NOT NULL,
    metadata            JSONB DEFAULT '{}'::jsonb,
    embedding           VECTOR(3072) NOT NULL,
    created_at          TIMESTAMP WITH TIME ZONE DEFAULT CURRENT_TIMESTAMP
);

CREATE INDEX idx_knowledge_chunks_embedding_hnsw 
ON knowledge_chunks 
USING hnsw (embedding vector_cosine_ops)
WITH (m = 16, ef_construction = 64);

🔐 Multi-Tenant Cryptographic Isolation

Each tenant's encryption and tokenization uses dynamic salt derivation: 38524\text{salt} = \text{HKDF-SHA256}(\text{"nexus-salt-"} \parallel \text{tenant_id} \parallel \text{"-"} \parallel \text{key_id})38524

This guarantees that two different tenants processing the same sensitive data produce cryptographically distinct ciphertexts.

📄 License

Proprietary and confidential software. Copyright © 2026 Veloxs AI Inc. All rights reserved. See LICENSE for license terms.