41cd78207a
- Configure mem0 to use self-hosted Supabase instead of Qdrant for vector storage - Update docker-compose to connect containers to localai network - Install vecs library for Supabase pgvector integration - Create comprehensive test suite for Supabase + mem0 integration - Update documentation to reflect Supabase configuration - All containers now connected to shared localai network - Successful vector storage and retrieval tests completed 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
117 lines
3.3 KiB
Plaintext
117 lines
3.3 KiB
Plaintext
---
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title: Introduction
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description: 'Welcome to the Mem0 Memory System - A comprehensive memory layer for AI agents'
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---
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<img
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className="block dark:hidden"
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src="/images/hero-light.svg"
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alt="Hero Light"
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/>
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<img
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className="hidden dark:block"
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src="/images/hero-dark.svg"
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alt="Hero Dark"
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/>
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## What is Mem0 Memory System?
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The Mem0 Memory System is a comprehensive, self-hosted memory layer designed for AI agents and applications. Built on top of the open-source mem0 framework, it provides persistent, intelligent memory capabilities that enhance AI interactions through contextual understanding and knowledge retention.
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<CardGroup cols={2}>
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<Card
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title="Local-First Architecture"
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icon="server"
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href="/essentials/architecture"
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>
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Complete local deployment with Ollama, Neo4j, and Supabase for maximum privacy and control
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</Card>
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<Card
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title="Multi-Provider Support"
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icon="plug"
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href="/llm/configuration"
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>
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Seamlessly switch between OpenAI, Ollama, and other LLM providers
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</Card>
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<Card
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title="Graph Memory"
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icon="project-diagram"
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href="/database/neo4j"
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>
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Advanced relationship mapping with Neo4j for contextual memory connections
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</Card>
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<Card
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title="MCP Integration"
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icon="link"
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href="/guides/mcp-integration"
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>
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Model Context Protocol server for Claude Code and other AI tools
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</Card>
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</CardGroup>
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## Key Features
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<AccordionGroup>
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<Accordion title="Vector Memory Storage">
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High-performance vector search using Supabase with pgvector for semantic memory retrieval and similarity matching.
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</Accordion>
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<Accordion title="Graph Relationships">
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Neo4j-powered knowledge graph for complex entity relationships and contextual memory connections.
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</Accordion>
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<Accordion title="Local LLM Support">
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Full Ollama integration with 20+ local models including Llama, Qwen, and specialized embedding models.
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</Accordion>
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<Accordion title="API-First Design">
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RESTful API with comprehensive memory operations, authentication, and rate limiting.
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</Accordion>
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<Accordion title="Self-Hosted Privacy">
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Complete local deployment ensuring your data never leaves your infrastructure.
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</Accordion>
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</AccordionGroup>
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## Architecture Overview
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The system consists of several key components working together:
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```mermaid
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graph TB
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A[AI Applications] --> B[MCP Server]
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B --> C[Memory API]
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C --> D[Mem0 Core]
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D --> E[Vector Store - Supabase]
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D --> F[Graph Store - Neo4j]
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D --> G[LLM Provider]
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G --> H[Ollama Local]
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G --> I[OpenAI/Remote]
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```
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## Current Status: Phase 1 Complete ✅
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<Note>
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**Foundation Ready**: All core infrastructure components are operational and tested.
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</Note>
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| Component | Status | Description |
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|-----------|--------|-------------|
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| **Neo4j** | ✅ Ready | Graph database running on localhost:7474 |
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| **Supabase** | ✅ Ready | Self-hosted database with pgvector on localhost:8000 |
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| **Ollama** | ✅ Ready | 21+ local models available on localhost:11434 |
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| **Mem0 Core** | ✅ Ready | Memory management system v0.1.115 |
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## Getting Started
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<CardGroup cols={1}>
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<Card
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title="Quick Start Guide"
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icon="rocket"
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href="/quickstart"
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>
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Get your memory system running in under 5 minutes
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</Card>
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</CardGroup>
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Ready to enhance your AI applications with persistent, intelligent memory? Let's get started! |