Self-Hosting Memwyre
Run Memwyre entirely on your own infrastructure. This guide covers deploying the backend services via Docker Compose and configuring local language models using Ollama for a completely private, offline memory vault.Architecture Overview
A self-hosted Memwyre deployment consists of four primary components:- Backend API (
memwyre-backend): The core FastAPI/Python service handling ingestion, search pipelines, and client requests. - Celery Worker (
celery-worker): Resolves background tasks such as parsing web documents, downloading YouTube transcripts, and rebuilding relationship edges. - Database (
postgreswithpgvector): Stores relational structured data and high-dimensional semantic vector embeddings. - Cache & Queue (
redis): Serves as the message broker for Celery and caching layer for rapid API queries.
Docker Compose Quickstart
Create adocker-compose.yml file in your installation directory:
Environment Variables
Configure these variables inside your.env file or docker-compose block:
Local LLM Setup (Ollama)
To run a zero-cloud setup, route all embedding and extraction tasks to Ollama.Step 1: Install & Launch Ollama
Install Ollama on your host system (macOS, Windows, or Linux). Ensure Ollama runs locally by verifying:Step 2: Download Required Models
Memwyre requires a dense vector embedding model and an inference model. Pull them using the CLI:Step 3: Run Docker Containers
Ensureextra_hosts is mapped to host.docker.internal:host-gateway in your docker-compose.yml so that Docker containers can contact your hostβs Ollama port (11434).
Spin up your cluster:
Step 4: Verify Local Connection
Inspect logs to verify that Memwyre connects to Ollama successfully during startup:nomic-embed-text, your offline memory vault is up and running! π