Complete implementation: REST API, MCP server, and documentation

Implementation Summary:
- REST API with FastAPI (complete CRUD operations)
- MCP Server with Python MCP SDK (7 tools)
- Supabase migrations (pgvector setup)
- Docker Compose orchestration
- Mintlify documentation site
- Environment configuration
- Shared config module

REST API Features:
- POST /v1/memories/ - Add memory
- GET /v1/memories/search - Semantic search
- GET /v1/memories/{id} - Get memory
- GET /v1/memories/user/{user_id} - User memories
- PATCH /v1/memories/{id} - Update memory
- DELETE /v1/memories/{id} - Delete memory
- GET /v1/health - Health check
- GET /v1/stats - Statistics
- Bearer token authentication
- OpenAPI documentation

MCP Server Tools:
- add_memory - Add from messages
- search_memories - Semantic search
- get_memory - Retrieve by ID
- get_all_memories - List all
- update_memory - Update content
- delete_memory - Delete by ID
- delete_all_memories - Bulk delete

Infrastructure:
- Neo4j 5.26 with APOC/GDS
- Supabase pgvector integration
- Docker network: localai
- Health checks and monitoring
- Structured logging

Documentation:
- Introduction page
- Quickstart guide
- Architecture deep dive
- Mintlify configuration

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>

docs/introduction.mdx

@@ -0,0 +1,168 @@
+---
+title: Introduction
+description: 'Welcome to T6 Mem0 v2 - Memory System for LLM Applications'
+---
+
+<img
+  className="block dark:hidden"
+  src="/images/hero-light.svg"
+  alt="Hero Light"
+/>
+<img
+  className="hidden dark:block"
+  src="/images/hero-dark.svg"
+  alt="Hero Dark"
+/>
+
+## What is T6 Mem0 v2?
+
+T6 Mem0 v2 is a comprehensive memory system for LLM applications built on **mem0.ai**, featuring:
+
+- 🔌 **MCP Server Integration** - Native Model Context Protocol support for Claude Code and AI tools
+- 🌐 **REST API** - Full HTTP API for memory operations
+- 🗄️ **Hybrid Storage** - Supabase (vector) + Neo4j (graph) for optimal performance
+- 🤖 **AI-Powered** - OpenAI embeddings with 26% accuracy improvement
+- 📊 **Graph Visualization** - Explore memory relationships in Neo4j Browser
+- 🐳 **Docker-Native** - Fully containerized deployment
+
+## Key Features
+
+<CardGroup cols={2}>
+  <Card
+    title="Semantic Memory Search"
+    icon="magnifying-glass"
+    href="/api-reference/memories/search"
+  >
+    Find relevant memories using AI-powered semantic similarity
+  </Card>
+  <Card
+    title="MCP Integration"
+    icon="plug"
+    href="/mcp/introduction"
+  >
+    Use as MCP server with Claude Code, Cursor, and other AI tools
+  </Card>
+  <Card
+    title="Graph Relationships"
+    icon="diagram-project"
+    href="/setup/neo4j"
+  >
+    Visualize and explore memory connections with Neo4j
+  </Card>
+  <Card
+    title="Multi-Agent Support"
+    icon="users"
+    href="/api-reference/introduction"
+  >
+    Isolate memories by user, agent, or run identifiers
+  </Card>
+</CardGroup>
+
+## Architecture
+
+T6 Mem0 v2 uses a **hybrid storage architecture** for optimal performance:
+
+```
+┌──────────────────────────────────┐
+│  Clients (Claude, N8N, Apps)    │
+└──────────────┬───────────────────┘
+               │
+┌──────────────┴───────────────────┐
+│  MCP Server (8765) + REST (8080) │
+└──────────────┬───────────────────┘
+               │
+┌──────────────┴───────────────────┐
+│      Mem0 Core Library           │
+└──────────────┬───────────────────┘
+               │
+    ┌──────────┴──────────┐
+    │                     │
+┌───┴──────┐      ┌──────┴─────┐
+│ Supabase │      │   Neo4j    │
+│ (Vector) │      │   (Graph)  │
+└──────────┘      └────────────┘
+```
+
+### Storage Layers
+
+- **Vector Store (Supabase)**: Semantic similarity search with pgvector
+- **Graph Store (Neo4j)**: Relationship modeling between memories
+- **Key-Value Store (PostgreSQL JSONB)**: Flexible metadata storage
+
+## Performance
+
+Based on mem0.ai research:
+
+- **26% higher accuracy** compared to baseline OpenAI
+- **91% lower latency** than full-context approaches
+- **90% token cost savings** through selective retrieval
+
+## Use Cases
+
+<AccordionGroup>
+  <Accordion icon="comment" title="Conversational AI">
+    Maintain context across conversations, remember user preferences, and provide personalized responses
+  </Accordion>
+  <Accordion icon="robot" title="AI Agents">
+    Give agents long-term memory, enable learning from past interactions, and improve decision-making
+  </Accordion>
+  <Accordion icon="headset" title="Customer Support">
+    Remember customer history, track issues across sessions, and provide consistent support
+  </Accordion>
+  <Accordion icon="graduation-cap" title="Educational Tools">
+    Track learning progress, adapt to user knowledge level, and personalize content delivery
+  </Accordion>
+</AccordionGroup>
+
+## Quick Links
+
+<CardGroup cols={2}>
+  <Card
+    title="Quickstart"
+    icon="rocket"
+    href="/quickstart"
+  >
+    Get up and running in 5 minutes
+  </Card>
+  <Card
+    title="Architecture Deep Dive"
+    icon="sitemap"
+    href="/architecture"
+  >
+    Understand the system design
+  </Card>
+  <Card
+    title="API Reference"
+    icon="code"
+    href="/api-reference/introduction"
+  >
+    Explore the REST API
+  </Card>
+  <Card
+    title="MCP Integration"
+    icon="link"
+    href="/mcp/introduction"
+  >
+    Connect with Claude Code
+  </Card>
+</CardGroup>
+
+## Technology Stack
+
+- **Core**: mem0ai library
+- **Vector DB**: Supabase with pgvector
+- **Graph DB**: Neo4j 5.x
+- **LLM**: OpenAI API (Phase 1), Ollama (Phase 2)
+- **REST API**: FastAPI + Pydantic
+- **MCP**: Python MCP SDK
+- **Container**: Docker & Docker Compose
+
+## Support & Community
+
+- **Repository**: [git.colsys.tech/klas/t6_mem0_v2](https://git.colsys.tech/klas/t6_mem0_v2)
+- **mem0.ai**: [Official mem0 website](https://mem0.ai)
+- **Issues**: Contact maintainer
+
+---
+
+Ready to get started? Continue to the [Quickstart Guide](/quickstart).