glyphh 0.9.3

Hyperdimensional Computing SDK and Runtime

Glyphh Runtime

Hyperdimensional computing runtime for deterministic, explainable AI.

Glyphh encodes natural language into high-dimensional vector representations using Vector Symbolic Architecture (VSA). No LLM in the loop — just math. Same input, same output, every time.

Features

• MCP Server — Model Context Protocol interface for LLM sidecar integration
• GraphQL API — Query knowledge graphs and fact trees with confidence scores
• CLI — Manage models, deploy runtimes, and interact with the Glyphh Hub
• Deterministic — Auditable, reproducible results grounded in cosine similarity

Install

Setup your python environment:

python3 -m venv venv
source venv/bin/activate

Glyphh ships as a single package with different install profiles:

Profile	Command	What you get
SDK	pip install glyphh	Encoder, similarity, CLI, model packaging. Lightweight — just numpy, pyyaml, click, httpx.
Runtime	pip install glyphh[runtime]	Everything in SDK + FastAPI server, SQLAlchemy, pgvector, Alembic, Pydantic. For running the runtime locally.
Dev	pip install glyphh[dev]	Everything in SDK + pytest, hypothesis, black, ruff, mypy. For contributing to Glyphh.

Most users want either SDK (build and package models) or Runtime (deploy and serve them).

Quick Start

The runtime requires PostgreSQL with pgvector. Pick whichever option fits your setup:

Option 1 — Docker Compose (recommended)

Requires Docker Desktop (or Docker Engine + Compose plugin).

The CLI can scaffold the Docker files for you:

pip install glyphh[runtime]
glyphh docker init
docker pull ghcr.io/glyphh-ai/glyphh-runtime:latest
docker compose up -d

glyphh docker init writes a docker-compose.yml and init.sql into your current directory. The compose file runs PostgreSQL with pgvector and the published runtime image — no build step needed.

Verify it's running:

curl http://localhost:8002/health

Option 2 — Docker (manual)

Run the database and runtime as individual containers:

docker run -d --name glyphh-db \
  -e POSTGRES_USER=postgres \
  -e POSTGRES_PASSWORD=postgres \
  -e POSTGRES_DB=glyphh_runtime \
  -p 5432:5432 \
  pgvector/pgvector:pg16

docker pull ghcr.io/glyphh-ai/glyphh-runtime:latest

docker run -p 8002:8002 \
  -e DATABASE_URL=postgresql+asyncpg://postgres:[e-mail]:5432/glyphh_runtime \
  ghcr.io/glyphh-ai/glyphh-runtime:latest

Or with an existing database:

docker run -p 8002:8002 \
  -e DATABASE_URL=postgresql+asyncpg://user:pass@your-db-host:5432/glyphh \
  ghcr.io/glyphh-ai/glyphh-runtime:latest

Option 3 — pip install (bring your own Postgres)

If you already have PostgreSQL with pgvector running:

pip install glyphh[runtime]
export DATABASE_URL=postgresql+asyncpg://postgres:postgres@localhost:5432/glyphh_runtime
glyphh serve

Query a deployed model

glyphh query "What is the refund policy?"

How It Works

1. Your LLM sends a natural language query via MCP
2. Glyphh encodes it into a high-dimensional vector using stored procedures
3. The encoded query resolves against a knowledge graph via GraphQL
4. Fact trees with confidence scores are returned to ground the LLM's response

License

Glyphh AI Community License — Copyright (c) 2026 Glyphh AI LLC. All rights reserved.

See LICENSE for full terms. Patent pending (Application No. 63/969,729).