# Process multiple files concurrently
memvid put memory.mv2 --input ./large-folder/ --parallel-segments

# Combine with embedding skip for fastest ingestion
memvid put memory.mv2 --input ./logs/ --embedding-skip --parallel-segments

# No vector embeddings (lexical only)
memvid create memory.mv2 --no-vec
memvid put memory.mv2 --input docs/

# Or skip per-ingestion
memvid put memory.mv2 --input logs.txt --embedding-skip

# Use smaller model for speed
memvid -m bge-small put memory.mv2 --input docs/

# Use larger model for quality
memvid -m gte-large put memory.mv2 --input docs/

# Lexical only (fastest)
memvid find memory.mv2 --query "handleAuth" --mode lex

# Semantic only
memvid find memory.mv2 --query "authentication logic" --mode sem

# Hybrid (default)
memvid find memory.mv2 --query "auth" --mode auto

# Fixed result count (faster, predictable)
memvid find memory.mv2 --query "term" --no-adaptive --top-k 10

# Adaptive (may return fewer, but higher quality)
memvid find memory.mv2 --query "term"  # Default

# Search only in specific directory
memvid find memory.mv2 --query "config" --scope "src/config/"

# Search specific document
memvid find memory.mv2 --query "api key" --uri "docs/security.md"

# Build sketch index
memvid sketch build memory.mv2 --variant medium

# Check sketch status
memvid sketch info memory.mv2

# Small memory for quick projects
memvid create notes.mv2 --size 10MB

# Large memory for document archives
memvid create archive.mv2 --size 50MB

# Compact storage
memvid doctor memory.mv2 --vacuum

# Full optimization
memvid doctor memory.mv2 --vacuum --rebuild-lex-index --rebuild-vec-index

# No vector index (lexical only)
memvid create code.mv2 --no-vec

# No lexical index (semantic only)
memvid create semantic.mv2 --no-lex

# Fast local synthesis
memvid ask memory.mv2 --question "..." --use-model tinyllama

# Fast API synthesis
memvid ask memory.mv2 --question "..." --use-model groq

# Get relevant context without LLM synthesis
memvid ask memory.mv2 --question "What are the config options?" --context-only

# Rebuild all indexes
memvid doctor memory.mv2 --rebuild-lex-index --rebuild-vec-index --rebuild-time-index

# Rebuild specific index
memvid doctor memory.mv2 --rebuild-vec-index

# Quick check
memvid verify memory.mv2

# Deep check
memvid verify memory.mv2 --deep

from memvid import use

# Reuse memory instance
mem = use('basic', 'memory.mv2')

# Batch operations
texts = [...]
for text in texts:
    mem.put(text)  # Batched internally

# Async for better throughput
import asyncio
from memvid import use_async

async def main():
    mem = await use_async('basic', 'memory.mv2')
    results = await asyncio.gather(*[
        mem.find(q) for q in queries
    ])

import { use } from '@anthropics/memvid'

// Reuse memory instance
const mem = await use('basic', 'memory.mv2')

// Parallel searches
const results = await Promise.all(
  queries.map(q => mem.find(q))
)

// Stream large results
for await (const chunk of mem.findStream(query)) {
  process.stdout.write(chunk)
}

# View usage statistics
memvid plan show

# JSON format for monitoring
memvid stats memory.mv2 --json

# Detailed stats
memvid stats memory.mv2

# Output example:
# Frames: 10,234
# Size: 45.2 MB
# Vector index: 23.1 MB
# Lexical index: 8.4 MB
# Avg query time: 12ms