CLI Reference

# npm (recommended)
npm install -g memvid-cli

# Verify installation
memvid --version

# Increase logging verbosity
memvid --verbose find memory.mv2 --query "test"
memvid -vvv find memory.mv2 --query "test"  # Maximum verbosity

# Set default embedding model
memvid --model bge-small find memory.mv2 --query "test"
memvid -m openai find memory.mv2 --query "test"

# JSON output (most commands support this)
memvid find memory.mv2 --query "test" --json

# Create memory and add documents
memvid create project.mv2

# Add a text file
cat notes.txt | memvid put project.mv2 --title "Notes"

# Add a PDF
memvid put project.mv2 --input report.pdf --title "Report"

# Add with metadata
memvid put project.mv2 --input doc.txt \
  --title "Meeting Notes" \
  --track meetings \
  --tag "date=2024-01-15" \
  --tag "attendees=alice,bob" \
  --label important

# Simple search
memvid find project.mv2 --query "budget projections"

# Semantic search with embeddings
memvid find project.mv2 --query "financial outlook" --mode sem

# Ask questions with LLM
memvid ask project.mv2 --question "What was decided about the budget?" --use-model openai

# Time-filtered search
memvid find project.mv2 --query "status" --as-of-ts 1704067200

# Extract facts (fast, offline)
memvid enrich project.mv2 --engine rules

# Extract facts (LLM, more accurate)
memvid enrich project.mv2 --engine openai

# View all facts
memvid memories project.mv2

# Query specific entity
memvid state project.mv2 "Alice"

# Export facts
memvid export project.mv2 -o facts.json --format json

# Extract tables from PDF
memvid tables import project.mv2 --input financial.pdf

# List extracted tables
memvid tables list project.mv2

# Export table to CSV
memvid tables export project.mv2 --table-id tbl_001 -o table.csv

# Human-readable (default)
memvid find project.mv2 --query "test"

# JSON output
memvid find project.mv2 --query "test" --json

# Parse with jq
memvid find project.mv2 --query "test" --json | jq '.hits[0].title'