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@peteski22/pci-context-store

v0.1.0

Published

Layer 1: Encrypted local-first data storage with CRDT sync for PCI

Readme

PCI Context Store

Layer 1: Encrypted local-first data storage with CRDT sync for Personal Context Infrastructure.

Overview

The Context Store provides:

  • Encrypted Vaults - AES-256-GCM encryption at rest
  • CRDT Sync - Conflict-free replication across devices
  • Vector Embeddings - Semantic search over personal context
  • Local-First - Data stays on your devices

Installation

pnpm add @peteski22/pci-context-store

Quick Start

import { ContextStore, Vault } from "pci-context-store";

// Initialize the context store
const store = new ContextStore({
  encryption: {
    algorithm: "aes-256-gcm",
  },
});

// Create a vault for health data
const healthVault = await store.createVault("health");

// Store encrypted data
await healthVault.put("allergies", {
  items: ["penicillin", "peanuts"],
  lastUpdated: new Date(),
});

// Retrieve and decrypt
const allergies = await healthVault.get("allergies");

Architecture

flowchart TB
    subgraph Store["Context Store"]
        subgraph Vaults["Vaults"]
            HV["Health Vault"]
            FV["Financial Vault"]
            CV["Custom Vaults"]
        end

        EL["Encryption Layer"]

        HV --> EL
        FV --> EL
        CV --> EL

        subgraph Services["Services"]
            SE["Sync Engine"]
            VE["Vector Embedding"]
            IS["Index Store"]
        end

        EL --> SE
        EL --> VE
        EL --> IS
    end

Storage Options

In-Memory (default)

const vault = new EncryptedVault({
  name: "my-vault",
  storage: { type: "memory" },
});

SQLite Persistence

const vault = new EncryptedVault({
  name: "my-vault",
  storage: {
    type: "sqlite",
    path: "./data/my-vault.db"
  },
});

Vector Search

Use SQLiteVectorStore for semantic similarity search with sqlite-vec:

import { SQLiteVectorStore } from "pci-context-store";

const vectorStore = new SQLiteVectorStore({
  path: ":memory:",
  dimensions: 384,  // e.g., all-MiniLM-L6-v2
  distanceMetric: "cosine",
});

// Add embeddings
await vectorStore.add("doc1", embedding, { source: "notes" });

// Search for similar
const results = await vectorStore.search(queryEmbedding, 10);

Development

# Install dependencies
pnpm install

# Run tests
pnpm test

# Build
pnpm build

# Type check
pnpm lint

Related Packages

License

Apache 2.0