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Enables searching information by MEANING, not just keywords.\n\n## Environment Variables\n\n**Required:**\n- `VECTOR_MEMORY_DB_PASSWORD` — PostgreSQL password for database access\n\n**Optional:**\n| Variable | Default | Description |\n|----------|---------|-------------|\n| `VECTOR_MEMORY_DB_HOST` | `localhost` | PostgreSQL server host |\n| `VECTOR_MEMORY_DB_PORT` | `5432` | PostgreSQL server port |\n| `VECTOR_MEMORY_DB_NAME` | `vector_memory` | Database name |\n| `VECTOR_MEMORY_DB_USER` | `aister` | Database user |\n| `EMBEDDING_SERVICE_URL` | `http://127.0.0.1:8765` | Embedding service URL |\n| `EMBEDDING_MODEL` | `intfloat/e5-large-v2` | Model for generating embeddings |\n| `EMBEDDING_PORT` | `8765` | Port for embedding service |\n| `VECTOR_MEMORY_DIR` | `~/.openclaw/workspace/memory` | Directory containing memory files |\n| `VECTOR_MEMORY_CHUNK_SIZE` | `500` | Text chunk size in characters |\n| `VECTOR_MEMORY_THRESHOLD` | `0.5` | Similarity threshold for search |\n| `VECTOR_MEMORY_LIMIT` | `5` | Maximum search results |\n\n## Features\n\n- **Semantic search** — enter a query and Aister will find similar content\n- **Russian and English support** — e5-large-v2 model works with both languages\n- **Fast search** — ~1 second per query (embedding + SQL)\n- **Memory context** — Aister can recall things from its records\n\n## Usage\n\n### Search\n\n```\n/search_memory <query>\n```\n\nExamples:\n```\n/search_memory my communication style\n/search_memory what I did today\n/search_memory Moltbook settings\n```\n\n### Reindex\n\n```\n/reindex_memory\n```\n\nThis reads all memory files (MEMORY.md, IDENTITY.md, USER.md, etc.) and updates the vector database.\n\n## How it works\n\n1. When Aister remembers something, it splits the text into chunks\n2. Each chunk is converted to a vector (1024 dimensions) via e5-large-v2 model\n3. Vectors are stored in PostgreSQL with pgvector extension\n4. During search, the query is also converted to a vector\n5. PostgreSQL finds similar vectors via cosine similarity\n\n## Technical Details\n\n- **Model:** intfloat/e5-large-v2 (1024 dims)\n- **Database:** PostgreSQL 16 + pgvector\n- **API:** Flask service at `http://127.0.0.1:8765`\n- **Languages:** Russian, English\n- **Chunk size:** 500 characters\n- **Similarity threshold:** 0.5 (default)\n\n## Integration\n\nThis skill is integrated with AGENTS.md and TOOLS.md. Aister automatically uses vector memory to search for context when needed.\n\n## Credentials\n\nThis skill requires database credentials to function:\n\n| Credential | Required | Description |\n|------------|----------|-------------|\n| `VECTOR_MEMORY_DB_PASSWORD` | **Yes** | PostgreSQL password for the `aister` user |\n\n**Security recommendations:**\n- Use a dedicated PostgreSQL user with minimal privileges (only SELECT, INSERT, UPDATE, DELETE on required tables)\n- Use a strong, unique password — never reuse credentials\n- Store the password file with `chmod 600` permissions\n- Do not commit the password file to version control\n\n## Warnings\n\n### Network Access\n\n**Important:** On first run, the embedding service will download the `intfloat/e5-large-v2` model (~1.3GB) from HuggingFace.\n\n- Internet connection required for first run\n- After download, the model is cached locally (~2.5GB total)\n- All subsequent operations run locally without network\n\n### Privileges\n\nInstallation requires:\n\n- **Root/sudo** to install system packages (postgresql-16-pgvector)\n- **PostgreSQL superuser** to create database and extensions\n\n**Recommended:** Run in an isolated environment (VM, container, or dedicated user account).\n\n### Local File Reading\n\nThe skill reads memory files (`MEMORY.md`, `IDENTITY.md`, `USER.md`) for indexing. \n\n**Important:** Ensure these files don't contain sensitive data (API keys, passwords, private information) you don't want stored in the database.\n\n### Code Review\n\nThe included Python scripts are short and readable. Before running:\n- Review `embedding_service.py`, `memory_search.py`, `memory_reindex.py`\n- Confirm no unexpected network calls or file modifications\n- Verify paths are limited to expected directories\n\n## Docker Setup (Recommended for Isolation)\n\nFor better isolation, run PostgreSQL in Docker:\n\n```bash\n# Create docker-compose.yml\nmkdir -p ~/.openclaw/workspace/vector-memory-docker\ncat > ~/.openclaw/workspace/vector-memory-docker/docker-compose.yml << 'EOF'\nversion: '3.8'\nservices:\n  postgres:\n    image: pgvector/pgvector:pg16\n    container_name: vector-memory-db\n    environment:\n      POSTGRES_USER: aister\n      POSTGRES_PASSWORD: YOUR_SECURE_PASSWORD\n      POSTGRES_DB: vector_memory\n    volumes:\n      - vector_memory_data:/var/lib/postgresql/data\n    ports:\n      - \"127.0.0.1:5433:5432\"\n    restart: unless-stopped\n\nvolumes:\n  vector_memory_data:\nEOF\n\n# Start the database\ncd ~/.openclaw/workspace/vector-memory-docker\ndocker-compose up -d\n\n# Update your env file to use the Docker port\necho 'export VECTOR_MEMORY_DB_PORT=\"5433\"' >> ~/.config/vector-memory/env\n```\n\nThen follow INSTALL.md steps 1, 5-9 (skip PostgreSQL installation steps).\n\n## Troubleshooting\n\nIf search doesn't find expected results:\n1. 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