Vinkura Memory Layer


A multi-tenant, production-grade memory engine for AI applications. Built on PostgreSQL and pgvector to solve the complex problem of data isolation and semantic retrieval in enterprise SaaS environments.

---

The Problem

Most AI frameworks (LangChain, LlamaIndex) assume a single-tenant environment. In real-world SaaS, enterprise, and government applications, you have:
- Multiple organizations.
- Distinct users with hierarchical roles.
- Strict data residency and isolation requirements.
- The need for full auditability.

Vinkura Memory Layer provides the infrastructure to build secure AI products where multi-tenancy is enforced by architecture, not just developer discipline.

Key Features

- Architectural Isolation: Every query is implicitly scoped to an organization through service-level context.
- Namespacing (v0.2.0): Organize memory within orgs into logical namespaces (e.g., 'staging', 'production', 'project-x').
- pgvector Integration: Native support for high-performance HNSW vector search.
- Strict Integrity: Database-level composite foreign keys prevent cross-tenant writes.
- Granular Visibility: Support for private, org, and public memory scopes.
- Traceable Audit Logs: Full history of who queried what and when.
- Drizzle ORM: Lightweight, type-safe database interactions.

Quick Start

$3

- PostgreSQL with pgvector extension.
- Node.js v18+.

$3

``bash
npm install vinkura-memory
`

$3

`typescript
import { MemoryService, AccessControl, MockEmbeddingProvider, createDb } from 'vinkura-memory';

const { db } = createDb(process.env.DATABASE_URL);

// 1. Validate the user context (typically from a session/JWT)
const security = new AccessControl(db);
const context = await security.validateContext('user_uuid', 'org_uuid');

// 2. Instantiate the scoped service
const memory = new MemoryService(db, context, new MockEmbeddingProvider());

// 3. Store knowledge in a specific namespace
await memory.store({
content: "The Q3 roadmap focuses on multi-cloud expansion.",
visibility: 'org',
namespace: 'internal-wiki',
metadata: { priority: 'high' }
});

// 4. Semantic Query within a namespace
const results = await memory.query({
text: "What are our roadmap priorities?",
namespace: 'internal-wiki',
limit: 5
});
`

Documentation

- Architecture & Design
- Security & Isolation
- API Reference
- Database Schema
- Changelog

Development

To run tests locally, ensure you have a standard Postgres instance with pgvector running:
`bash
docker-compose up -d
npm test
``

Authors

- Akshat Shukla (@binarybardakshat)
- VinkuraAI

License

MIT