---
id: vectors
title: Vectors Command
sidebar_position: 10
---
# Vectors Command
Manage vector databases for semantic search and RAG (Retrieval-Augmented Generation) applications. The vectors command provides database lifecycle management including creation, deletion, and inspection.
## Overview
Vector databases store embeddings generated from text documents, enabling semantic search capabilities. The vectors command helps manage these databases, view their contents, and maintain storage efficiency.
## Usage
```bash
# List all vector databases
lc vectors list
# Show database information
lc vectors info <database>
# Delete a database
lc vectors delete <database>
# Using aliases
lc v list
lc v info docs
lc v delete old-db
```
## Subcommands
| `list` | `l` | List all vector databases |
| `delete` | `d` | Delete a vector database |
| `info` | `i` | Show information about a database |
## Options
| `-h` | `--help` | Print help | False |
## Examples
### Database Management
**List Databases**
```bash
lc vectors list
# Output:
# • project-docs (1,247 vectors)
# • knowledge-base (523 vectors)
# • research-papers (89 vectors)
# Short form
lc v l
```
**Database Information**
```bash
lc vectors info project-docs
# Output:
# Database: project-docs
# Vectors: 1,247
# Dimensions: 1536
# Model: text-embedding-3-small
# Size: 12.3 MB
# Created: 2024-01-15
# Last updated: 2024-01-20
lc v i project-docs
```
**Delete Database**
```bash
lc vectors delete old-project
# Will prompt for confirmation
lc v d old-project
```
### Complete RAG Workflow
```bash
# Step 1: Create embeddings
lc embed -f "docs/*.md" --vectordb project-docs
# Step 2: Verify database
lc vectors info project-docs
# Step 3: Search similar content
lc similar -v project-docs "deployment process"
# Step 4: Use in chat
lc -v project-docs "How do I deploy the application?"
# Step 5: Cleanup when done
lc vectors delete project-docs
```
## See Also
- [Embed Command](embed.md)
- [Similar Command](similar.md)
## Troubleshooting
### Common Issues
#### "Database not found"
- **Error**: Specified vector database doesn't exist
- **Solution**: Use `lc vectors list` to see available databases
- **Create**: Use `lc embed -v <name>` to create new database
#### "Permission denied"
- **Error**: Cannot access vector database files
- **Solution**: Check file permissions on database directory
- **Fix**: `chmod 755 ~/.config/lc/vectors/`
#### "Database corrupt"
- **Error**: Vector database file is corrupted
- **Solution**: Delete and recreate the database
- **Backup**: Export important data before deletion
### Best Practices
1. **Consistent Models**: Use the same embedding model for all vectors in a database
2. **Regular Cleanup**: Remove unused databases to save disk space
3. **Meaningful Names**: Use descriptive names for databases
4. **Size Management**: Monitor database sizes for performance
### Performance Considerations
- Large databases (>10k vectors) may have slower search times
- Consider splitting large databases by topic or date
- Regular maintenance improves query performance
- Monitor disk space usage
```bash
# Check database sizes
du -sh ~/.config/lc/vectors/*
# Performance monitoring
lc vectors list # Shows vector counts
lc vectors info <db> # Shows detailed stats
```
### Database Location
Vector databases are stored locally:
- **Linux/macOS**: `~/.config/lc/vectors/`
- **Windows**: `%APPDATA%\lc\vectors\`
Each database is a directory containing:
- Vector data files
- Metadata and configuration
- Search indices
### Security and Backup
```bash
# Backup vector databases
tar -czf vectors-backup.tar.gz ~/.config/lc/vectors/
# Restore from backup
cd ~/.config/lc/
tar -xzf vectors-backup.tar.gz
# Secure database directory
chmod 700 ~/.config/lc/vectors/
```