Integrate self-hosted Supabase with mem0 system

- 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>

docs/backup/what-is-mem0.mdx

@@ -0,0 +1,114 @@
+---
+title: What is Mem0?
+icon: "brain"
+iconType: "solid"
+---
+
+<Snippet file="async-memory-add.mdx" />
+
+Mem0 is a memory layer designed for modern AI agents. It acts as a persistent memory layer that agents can use to:
+
+- Recall relevant past interactions
+- Store important user preferences and factual context
+- Learn from successes and failures
+
+It gives AI agents memory so they can remember, learn, and evolve across interactions. Mem0 integrates easily into your agent stack and scales from prototypes to production systems.
+
+
+## Stateless vs. Stateful Agents
+
+Most current agents are stateless: they process a query, generate a response, and forget everything. Even with huge context windows, everything resets the next session.
+
+Stateful agents, powered by Mem0, are different. They retain context, recall what matters, and behave more intelligently over time.
+
+<Frame>
+  <img src="../images/stateless-vs-stateful-agent.png" />
+</Frame>
+
+
+## Where Memory Fits in the Agent Stack
+
+Mem0 sits alongside your retriever, planner, and LLM. Unlike retrieval-based systems (like RAG), Mem0 tracks past interactions, stores long-term knowledge, and evolves the agent’s behavior.
+
+<Frame>
+  <img src="../images/memory-agent-stack.png" />
+</Frame>
+
+Memory is not about pushing more tokens into a prompt but about intelligently remembering context that matters. This distinction matters:
+
+| Capability       | Context Window        | Mem0 Memory                |
+|------------------|------------------------|-----------------------------|
+| Retention        | Temporary              | Persistent                  |
+| Cost             | Grows with input size  | Optimized (only what matters) |
+| Recall           | Token proximity        | Relevance + intent-based   |
+| Personalization  | None                   | Deep, evolving profile     |
+| Behavior         | Reactive               | Adaptive                   |
+
+
+## Memory vs. RAG: Complementary Tools
+
+RAG (Retrieval-Augmented Generation) is great for fetching facts from documents. But it’s stateless. It doesn’t know who the user is, what they’ve asked before, or what failed last time.
+
+Mem0 provides continuity. It stores decisions, preferences, and context—not just knowledge.
+
+| Aspect             | RAG                          | Mem0 Memory                   |
+|--------------------|-------------------------------|-------------------------------|
+| Statefulness       | Stateless                     | Stateful                      |
+| Recall Type        | Document lookup               | Evolving user context         |
+| Use Case           | Ground answers in data        | Guide behavior across time    |
+
+Together, they’re stronger: RAG informs the LLM; Mem0 shapes its memory.
+
+
+## Types of Memory in Mem0
+
+Mem0 supports different kinds of memory to mimic how humans store information:
+
+- **Working Memory**: short-term session awareness
+- **Factual Memory**: long-term structured knowledge (e.g., preferences, settings)
+- **Episodic Memory**: records specific past conversations
+- **Semantic Memory**: builds general knowledge over time
+
+
+## Why Developers Choose Mem0
+
+Mem0 isn’t a wrapper around a vector store. It’s a full memory engine with:
+
+- **LLM-based extraction**: Intelligently decides what to remember
+- **Filtering & decay**: Avoids memory bloat, forgets irrelevant info
+- **Costs Reduction**: Save compute costs with smart prompt injection of only relevant memories
+- **Dashboards & APIs**: Observability, fine-grained control
+- **Cloud and OSS**: Use our platform version or our open-source SDK version
+
+You plug Mem0 into your agent framework, it doesn’t replace your LLM or workflows. Instead, it adds a smart memory layer on top.
+
+
+## Core Capabilities
+
+- **Reduced token usage and faster responses**: sub-50 ms lookups
+- **Semantic memory**: procedural, episodic, and factual support
+- **Multimodal support**: handle both text and images
+- **Graph memory**: connect insights and entities across sessions
+- **Host your way**: either a managed service or a self-hosted version
+
+
+## Getting Started
+Mem0 offers two powerful ways to leverage our technology: our [managed platform](/platform/overview) and our [open source solution](/open-source/overview).
+
+
+<CardGroup cols={3}>
+  <Card title="Quickstart" icon="rocket" href="/quickstart">
+    Integrate Mem0 in a few lines of code
+  </Card>
+  <Card title="Playground" icon="play" href="https://app.mem0.ai/playground">
+    Mem0 in action
+  </Card>
+  <Card title="Examples" icon="lightbulb" href="/examples">
+  See what you can build with Mem0
+  </Card>
+</CardGroup>
+
+## Need help?
+If you have any questions, please feel free to reach out to us using one of the following methods:
+
+<Snippet file="get-help.mdx"/>