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Complete implementation: REST API, MCP server, and documentation

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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>
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# Supabase Migrations for T6 Mem0 v2
## Overview
This directory contains SQL migrations for setting up the Supabase vector store used by T6 Mem0 v2.
## Migrations
### 001_init_vector_store.sql
Initial setup migration that creates:
- **pgvector extension**: Enables vector similarity search
- **t6_memories table**: Main storage for memory vectors and metadata
- **Indexes**: HNSW for vectors, B-tree for filters, GIN for JSONB
- **Functions**:
- `match_t6_memories()`: Vector similarity search with filters
- `get_t6_memory_stats()`: Memory statistics
- `update_t6_memories_updated_at()`: Auto-update timestamp
- **View**: `t6_recent_memories` for quick access to recent entries
## Applying Migrations
### Method 1: Supabase SQL Editor (Recommended)
1. Open your Supabase project dashboard
2. Navigate to SQL Editor
3. Create a new query
4. Copy and paste the contents of `001_init_vector_store.sql`
5. Click "Run" to execute
### Method 2: psql Command Line
```bash
# Connect to your Supabase database
psql "postgresql://supabase_admin:PASSWORD@172.21.0.12:5432/postgres"
# Run the migration
\i migrations/supabase/001_init_vector_store.sql
```
### Method 3: Programmatic Application
```python
import psycopg2
# Connect to Supabase
conn = psycopg2.connect(
"postgresql://supabase_admin:PASSWORD@172.21.0.12:5432/postgres"
)
# Read and execute migration
with open('migrations/supabase/001_init_vector_store.sql', 'r') as f:
migration_sql = f.read()
with conn.cursor() as cur:
cur.execute(migration_sql)
conn.commit()
conn.close()
```
## Verification
After applying the migration, verify the setup:
```sql
-- Check if pgvector extension is enabled
SELECT * FROM pg_extension WHERE extname = 'vector';
-- Check if table exists
\d t6_memories
-- Verify indexes
\di t6_memories*
-- Test the similarity search function
SELECT * FROM match_t6_memories(
'[0.1, 0.2, ...]'::vector(1536), -- Sample embedding
10, -- Match count
'test_user', -- User ID filter
NULL, -- Agent ID filter
NULL -- Run ID filter
);
-- Get memory statistics
SELECT * FROM get_t6_memory_stats();
```
## Rollback
If you need to rollback the migration:
```sql
-- Drop view
DROP VIEW IF EXISTS t6_recent_memories;
-- Drop functions
DROP FUNCTION IF EXISTS get_t6_memory_stats();
DROP FUNCTION IF EXISTS match_t6_memories(vector, INT, TEXT, TEXT, TEXT);
DROP FUNCTION IF EXISTS update_t6_memories_updated_at();
-- Drop trigger
DROP TRIGGER IF EXISTS t6_memories_updated_at_trigger ON t6_memories;
-- Drop table (WARNING: This will delete all data!)
DROP TABLE IF EXISTS t6_memories CASCADE;
-- Optionally remove extension (only if not used elsewhere)
-- DROP EXTENSION IF EXISTS vector CASCADE;
```
## Schema
### t6_memories Table
| Column | Type | Description |
|--------|------|-------------|
| id | UUID | Primary key |
| embedding | vector(1536) | OpenAI embedding vector |
| metadata | JSONB | Flexible metadata |
| user_id | TEXT | User identifier |
| agent_id | TEXT | Agent identifier |
| run_id | TEXT | Run identifier |
| memory_text | TEXT | Original memory text |
| created_at | TIMESTAMPTZ | Creation timestamp |
| updated_at | TIMESTAMPTZ | Last update timestamp |
| hash | TEXT | Deduplication hash (unique) |
### Indexes
- **t6_memories_embedding_idx**: HNSW index for fast vector search
- **t6_memories_user_id_idx**: B-tree for user filtering
- **t6_memories_agent_id_idx**: B-tree for agent filtering
- **t6_memories_run_id_idx**: B-tree for run filtering
- **t6_memories_created_at_idx**: B-tree for time-based queries
- **t6_memories_metadata_idx**: GIN for JSON queries
- **t6_memories_text_search_idx**: GIN for full-text search
## Notes
- The HNSW index provides O(log n) approximate nearest neighbor search
- Cosine distance is used for similarity (1 - cosine similarity)
- All timestamps are stored in UTC
- The hash column ensures deduplication of identical memories
- Metadata is stored as JSONB for flexible schema evolution
## Support
For issues or questions about migrations, refer to:
- [Supabase Vector Documentation](https://supabase.com/docs/guides/database/extensions/pgvector)
- [pgvector Documentation](https://github.com/pgvector/pgvector)
- Project Architecture: `../../ARCHITECTURE.md`