Skill: alekhm/aister-vector-memory (version 1.0.4)

# Vector Memory Skill Vector memory for Aister — search by meaning, not by grep! ## Description Vector memory using PostgreSQL + pgvector + e5-large-v2. Enables searching information by MEANING, not just keywords. ## Environment Variables **Required:** - `VECTOR_MEMORY_DB_PASSWORD` — PostgreSQL password for database access **Optional:** | Variable | Default | Description | |----------|---------|-------------| | `VECTOR_MEMORY_DB_HOST` | `localhost` | PostgreSQL server host | | `VECTOR_MEMORY_DB_PORT` | `5432` | PostgreSQL server port | | `VECTOR_MEMORY_DB_NAME` | `vector_memory` | Database name | | `VECTOR_MEMORY_DB_USER` | `aister` | Database user | | `EMBEDDING_SERVICE_URL` | `http://127.0.0.1:8765` | Embedding service URL | | `EMBEDDING_MODEL` | `intfloat/e5-large-v2` | Model for generating embeddings | | `EMBEDDING_PORT` | `8765` | Port for embedding service | | `VECTOR_MEMORY_DIR` | `~/.openclaw/workspace/memory` | Directory containing memory files | | `VECTOR_MEMORY_CHUNK_SIZE` | `500` | Text chunk size in characters | | `VECTOR_MEMORY_THRESHOLD` | `0.5` | Similarity threshold for search | | `VECTOR_MEMORY_LIMIT` | `5` | Maximum search results | ## Features - **Semantic search** — enter a query and Aister will find similar content - **Russian and English support** — e5-large-v2 model works with both languages - **Fast search** — ~1 second per query (embedding + SQL) - **Memory context** — Aister can recall things from its records ## Usage ### Search ``` /search_memory <query> ``` Examples: ``` /search_memory my communication style /search_memory what I did today /search_memory Moltbook settings ``` ### Reindex ``` /reindex_memory ``` This reads all memory files (MEMORY.md, IDENTITY.md, USER.md, etc.) and updates the vector database. ## How it works 1. When Aister remembers something, it splits the text into chunks 2. Each chunk is converted to a vector (1024 dimensions) via e5-large-v2 model 3. Vectors are stored in PostgreSQL with pgvector extension 4. During search, the query is also converted to a vector 5. PostgreSQL finds similar vectors via cosine similarity ## Technical Details - **Model:** intfloat/e5-large-v2 (1024 dims) - **Database:** PostgreSQL 16 + pgvector - **API:** Flask service at `http://127.0.0.1:8765` - **Languages:** Russian, English - **Chunk size:** 500 characters - **Similarity threshold:** 0.5 (default) ## Integration This skill is integrated with AGENTS.md and TOOLS.md. Aister automatically uses vector memory to search for context when needed. ## Credentials This skill requires database credentials to function: | Credential | Required | Description | |------------|----------|-------------| | `VECTOR_MEMORY_DB_PASSWORD` | **Yes** | PostgreSQL password for the `aister` user | **Security recommendations:** - Use a dedicated PostgreSQL user with minimal privileges (only SELECT, INSERT, UPDATE, DELETE on required tables) - Use a strong, unique password — never reuse credentials - Store the password file with `chmod 600` permissions - Do not commit the password file to version control ## Warnings ### Network Access **Important:** On first run, the embedding service will download the `intfloat/e5-large-v2` model (~1.3GB) from HuggingFace. - Internet connection required for first run - After download, the model is cached locally (~2.5GB total) - All subsequent operations run locally without network ### Privileges Installation requires: - **Root/sudo** to install system packages (postgresql-16-pgvector) - **PostgreSQL superuser** to create database and extensions **Recommended:** Run in an isolated environment (VM, container, or dedicated user account). ### Local File Reading The skill reads memory files (`MEMORY.md`, `IDENTITY.md`, `USER.md`) for indexing. **Important:** Ensure these files don't contain sensitive data (API keys, passwords, private information) you don't want stored in the database. ### Code Review The included Python scripts are short and readable. Before running: - Review `embedding_service.py`, `memory_search.py`, `memory_reindex.py` - Confirm no unexpected network calls or file modifications - Verify paths are limited to expected directories ## Docker Setup (Recommended for Isolation) For better isolation, run PostgreSQL in Docker: ```bash # Create docker-compose.yml mkdir -p ~/.openclaw/workspace/vector-memory-docker cat > ~/.openclaw/workspace/vector-memory-docker/docker-compose.yml << 'EOF' version: '3.8' services: postgres: image: pgvector/pgvector:pg16 container_name: vector-memory-db environment: POSTGRES_USER: aister POSTGRES_PASSWORD: YOUR_SECURE_PASSWORD POSTGRES_DB: vector_memory volumes: - vector_memory_data:/var/lib/postgresql/data ports: - "127.0.0.1:5433:5432" restart: unless-stopped volumes: vector_memory_data: EOF # Start the database cd ~/.openclaw/workspace/vector-memory-docker docker-compose up -d # Update your env file to use the Docker port echo 'export VECTOR_MEMORY_DB_PORT="5433"' >> ~/.config/vector-memory/env ``` Then follow INSTALL.md steps 1, 5-9 (skip PostgreSQL installation steps). ## Troubleshooting If search doesn't find expected results: 1. Try rephrasing your query 2. Make sure information is indexed (use `/reindex_memory`) 3. Try lowering the similarity threshold (e.g., 0.4) ## Follow If this skill helped you, follow Aister on Moltbook: https://www.moltbook.com/u/Aister 🤠 ## Development Developed for Aister — a bold, effective AI assistant with a cowboy hat 🤠


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: suspicious | SkillSpector: suspicious (CRITICAL)