Documentation Index Fetch the complete documentation index at: https://docs.memvid.com/llms.txt
Use this file to discover all available pages before exploring further.
Learn how to create new memory files and ingest documents using the Memvid CLI.
Creating a Memory File Basic Usage Create a new .mv2 memory file:
memvid create my-knowledge.mv2
Options Option Description Default --tierCapacity tier (free, dev, enterprise) free--sizeCapacity override (e.g. 15MB, capped at 50MB) 50MB--no-lexDisable lexical/full-text index Enabled --no-vectorDisable vector index Enabled
Examples # Create a basic memory file memvid create research.mv2 # Create without lexical index memvid create notes.mv2 --no-lex # Create a smaller memory (capacity override) memvid create small.mv2 --size 512MB
memvid create is capped at 50MB. To go beyond 50MB, create the file and then apply a signed capacity ticket (see memvid tickets sync/apply).
JSON Output memvid create my-memory.mv2 --json
{ "path" : "my-memory.mv2" , "size_limit_bytes" : 536870912 , "lex_enabled" : true , "vec_enabled" : true , "created_at" : "2024-01-15T10:30:00Z" }
Inspecting a Memory File The open command shows metadata and manifests of an existing memory file.
Synopsis memvid open < FIL E > [OPTIONS]
Options Option Description --jsonEmit JSON output
Examples # Inspect a memory file memvid open my-memory.mv2 # Get JSON output for scripting memvid open my-memory.mv2 --json
Response Memory File: my-memory.mv2 Version: 2.1.0 Created: 2024-01-15T10:30:00Z Frames: 1,234 Size: 45.2 MB / 512 MB (8.8%) Indexes: Lexical: enabled (12,456 terms) Vector: enabled (1,234 vectors, 384d) Time: enabled (1,234 entries) Tracks: default: 890 frames meetings: 234 frames emails: 110 frames Memory Binding: Memory ID: mem_abc123 Bound at: 2024-01-15T10:30:00Z
JSON Output { "path" : "my-memory.mv2" , "version" : "2.1.0" , "created_at" : "2024-01-15T10:30:00Z" , "frame_count" : 1234 , "size_bytes" : 47395430 , "size_limit_bytes" : 536870912 , "indexes" : { "lex" : { "enabled" : true , "term_count" : 12456 }, "vec" : { "enabled" : true , "vector_count" : 1234 , "dimension" : 384 }, "time" : { "enabled" : true , "entry_count" : 1234 } }, "tracks" : { "default" : 890 , "meetings" : 234 , "emails" : 110 }, "binding" : { "memory_id" : "mem_abc123" , "bound_at" : "2024-01-15T10:30:00Z" } }
Ingesting Documents The put command adds documents to your memory file as frames.
Basic Usage # Ingest a single file (text-only) memvid put my-knowledge.mv2 --input document.pdf # Ingest a directory memvid put my-knowledge.mv2 --input ./documents/ # Ingest with semantic embeddings (+16x PQ compression) memvid put my-knowledge.mv2 --input document.pdf --embedding --vector-compression # Ingest from stdin (text-only by default) echo "Some text content" | memvid put my-knowledge.mv2
Core Options Option Description --input PATHPath to file or directory --uri URICustom URI for the frame --title TITLEDocument title --timestamp UNIX_TSPOSIX timestamp --track TRACKTrack/collection name --kind KINDContent type metadata --jsonOutput as JSON
Option Description --tag KEY=VALUEAdd tags (repeatable) --label LABELAdd labels (repeatable) --metadata JSONAdditional metadata as JSON --no-auto-tagDisable automatic tag extraction --no-extract-datesDisable date extraction
When the CLIP and NER models are installed, the CLI automatically enables visual embeddings for images/PDFs and entity extraction.
Option Description --clipExplicitly enable CLIP visual embeddings --no-clipDisable CLIP even when model is available --logic-meshExplicitly enable entity extraction --no-logic-meshDisable entity extraction even when model is available
Install models manually: memvid models install --clip mobileclip-s2 memvid models install --ner distilbert-ner
Embedding Options Option Description --embeddingEnable semantic embeddings -m, --embedding-model MODELChoose default embedding model (global flag; see below) --vector-compressionGenerate semantic embeddings with 16x compression --no-embeddingExplicitly disable embeddings
Embedding Model Options: Model Description bge-smallLocal fastembed default (384d) bge-baseLocal higher quality (768d) nomicLocal high accuracy (768d) gte-largeLocal best semantic depth (1024d) openai-smallOpenAI text-embedding-3-small (1536d) openai-largeOpenAI text-embedding-3-large (3072d) openaiAlias for openai-large openai-adaOpenAI text-embedding-ada-002 (1536d, legacy)
# Use built-in BGE (default, no API key needed) memvid put knowledge.mv2 --input docs/ --embedding # Use OpenAI embeddings export OPENAI_API_KEY = sk- ... memvid put knowledge.mv2 --input docs/ --embedding -m openai-small # Use OpenAI large model for higher quality memvid put knowledge.mv2 --input docs/ --embedding -m openai-large
Option Description --tablesExtract tables from PDF files --embed-rowsEmbed individual table rows for semantic search (default: true)
Duplicate Handling Option Description --update-existingReplace existing frame with same URI --allow-duplicateAllow multiple frames with same URI
Lock Control Option Description Default --lock-timeout MSWait time for lock 250ms --forceForce takeover of stale lock false
Ingesting Different File Types Memvid automatically detects and processes various file formats:
Text Files
Documents
Media
# Plain text memvid put knowledge.mv2 --input notes.txt --vector-compression # Markdown memvid put knowledge.mv2 --input README.md --vector-compression # HTML memvid put knowledge.mv2 --input page.html --vector-compression
# PDF files memvid put knowledge.mv2 --input report.pdf --vector-compression # PDF with table extraction memvid put knowledge.mv2 --input invoice.pdf --tables --vector-compression # Word documents memvid put knowledge.mv2 --input document.docx --vector-compression # Excel spreadsheets memvid put knowledge.mv2 --input data.xlsx --vector-compression # PowerPoint presentations memvid put knowledge.mv2 --input slides.pptx --vector-compression
# Images with EXIF extraction memvid put knowledge.mv2 --input photo.jpg # Audio files memvid put knowledge.mv2 --input recording.mp3 --audio # Video files (stored without transcoding) memvid put knowledge.mv2 --input video.mp4 --video
Organize your documents with tracks, tags, and timestamps:
# Add to a specific track memvid put knowledge.mv2 --input meeting-notes.md --vector-compression --track "meetings" # Add metadata tags memvid put knowledge.mv2 --input api-docs.md --vector-compression \ --tag "category=documentation" \ --tag "version=2.0" \ --tag "author=team" # Add labels memvid put knowledge.mv2 --input report.pdf --vector-compression \ --label "quarterly" \ --label "finance" # Set custom timestamp memvid put knowledge.mv2 --input old-report.pdf --vector-compression \ --timestamp 1686819000 # Combine options memvid put knowledge.mv2 --input quarterly-report.pdf --vector-compression \ --track "reports" \ --title "Q3 2024 Report" \ --tag "quarter=Q3" \ --tag "year=2024"
Parallel Ingestion For large datasets, enable multi-threaded processing:
# Enable parallel ingestion memvid put knowledge.mv2 --input ./large-dataset/ --vector-compression \ --parallel-segments \ --parallel-threads 8 # Fine-tune parallel settings memvid put knowledge.mv2 --input ./corpus/ --vector-compression \ --parallel-segments \ --parallel-seg-tokens 4000 \ --parallel-threads 4 \ --parallel-queue-depth 16
Option Description Default --parallel-segmentsEnable multi-threaded processing false --parallel-threadsNumber of worker threads CPU count - 1 --parallel-queue-depthQueue size for workers Auto --parallel-seg-tokensTarget tokens per segment Auto
Ingesting from Stdin Useful for piping data from other commands:
# Pipe text content echo "Important note to remember" | memvid put knowledge.mv2 --vector-compression # Pipe from curl curl -s https://api.example.com/data | memvid put knowledge.mv2 --vector-compression --title "API Response" # Pipe from another command cat log.txt | grep "ERROR" | memvid put knowledge.mv2 --vector-compression --track "errors"
Extract structured tables from PDFs (invoices, financial reports, pay stubs):
Basic Usage # Extract tables from a PDF memvid put knowledge.mv2 --input invoice.pdf --tables --vector-compression # Extract tables and embed individual rows for semantic search memvid put knowledge.mv2 --input financial-report.pdf --tables --embed-rows --vector-compression
Detection Methods The table extractor uses multiple detection methods:
Method Best For Stream Tables without visible borders, text-based layouts Lattice Tables with visible grid lines and borders LineBased Columnar data with clear alignment patterns
The extractor automatically tries each method and picks the best results.
After extraction, use the tables command to view and export:
# List all tables in a memory memvid tables list knowledge.mv2 # Output: # Found 3 tables: # - pdf_table_1_page1: 5 rows x 4 cols (LineBased) # - pdf_table_2_page1: 12 rows x 3 cols (Stream) # - pdf_table_3_page2: 8 rows x 5 cols (Lattice) # View a specific table memvid tables view knowledge.mv2 --table-id pdf_table_1_page1 # Export to CSV memvid tables export knowledge.mv2 --table-id pdf_table_1_page1 --format csv > data.csv # Export to JSON memvid tables export knowledge.mv2 --table-id pdf_table_1_page1 --format json
Example: Invoice Processing # Create memory for invoices memvid create invoices.mv2 # Ingest invoice with table extraction memvid put invoices.mv2 --input amazon-invoice.pdf --tables --vector-compression # Search for specific items memvid find invoices.mv2 --query "total" --json # List extracted tables memvid tables list invoices.mv2 # Export line items to CSV memvid tables export invoices.mv2 --table-id pdf_table_1_page1 --format csv
Updating Documents The update command modifies an existing frame.
Synopsis memvid update < FIL E > [OPTIONS]
Options Option Description --frame-id <ID>Target frame by ID --uri <URI>Target frame by URI --input <PATH>New payload from file --set-uri <URI>Update frame URI --title <TITLE>Update title --timestamp <TS>Update timestamp --track <TRACK>Update track --kind <KIND>Update kind --tag <KEY=VALUE>Add/update tags --label <LABEL>Add/update labels --metadata <JSON>Add/update metadata --embeddingsRecompute embeddings --jsonJSON output
Examples # Update title memvid update project.mv2 --frame-id 1234 --title "Updated Title" # Update content and recompute embeddings memvid update project.mv2 --uri "file:///doc.txt" \ --input updated-doc.txt \ --embeddings # Add new tags memvid update project.mv2 --frame-id 1234 \ --tag "status=reviewed" \ --label approved
Response Updated frame 1234 in project.mv2 Title: Updated Title Tags added: status=reviewed Labels added: approved Embeddings: recomputed
Deleting Documents The delete command removes a frame from the memory.
Synopsis memvid delete < FIL E > [OPTIONS]
Options Option Description --frame-id <ID>Target by frame ID --uri <URI>Target by frame URI --yesSkip confirmation prompt --jsonJSON output
Examples # Delete by frame ID memvid delete project.mv2 --frame-id 1234 # Delete by URI (skip confirmation) memvid delete project.mv2 --uri "file:///old-doc.txt" --yes
Response Deleted frame 1234 from project.mv2 URI: file:///old-doc.txt Title: Old Document
Remote API Ingestion The api-fetch command fetches remote content from APIs and ingests as frames.
Synopsis memvid api-fetch < FIL E > < CONFI G > [OPTIONS]
Options Option Description --dry-runPreview without writing --mode <MODE>Override configured ingest mode --uri <URI>Override base URI --jsonJSON output
{ "url" : "https://api.example.com/documents" , "method" : "GET" , "headers" : { "Authorization" : "Bearer ${API_TOKEN}" }, "pagination" : { "type" : "cursor" , "cursor_param" : "after" , "cursor_path" : "$.meta.next_cursor" }, "items_path" : "$.data" , "mapping" : { "title" : "$.name" , "text" : "$.content" , "uri" : "$.id" } }
Examples # Fetch from API memvid api-fetch project.mv2 ./fetch-config.json # Dry run to preview memvid api-fetch project.mv2 ./fetch-config.json --dry-run
Real-World Examples Documentation Knowledge Base # Create the memory memvid create docs.mv2 # Ingest documentation with embeddings memvid put docs.mv2 --input ./docs/ --vector-compression --track "documentation" # Add API reference memvid put docs.mv2 --input ./api-reference/ --vector-compression \ --track "api" \ --tag "type=reference"
Research Paper Archive # Create the memory memvid create papers.mv2 # Ingest papers with metadata for paper in ./papers/*.pdf ; do memvid put papers.mv2 --input " $paper " --vector-compression \ --track "research" \ --tag "source=arxiv" done
Code Repository # Create memory for codebase memvid create codebase.mv2 # Ingest with parallel processing memvid put codebase.mv2 --input ./src/ --vector-compression \ --parallel-segments \ --track "source" # Add tests and docs memvid put codebase.mv2 --input ./tests/ --vector-compression --track "tests" memvid put codebase.mv2 --input ./docs/ --vector-compression --track "docs"
Troubleshooting File Locked Error: File is locked by another process
Solutions: # Check who holds the lock memvid who knowledge.mv2 # Request release memvid nudge knowledge.mv2 # Find process on macOS/Linux lsof knowledge.mv2 # Wait longer for lock memvid put knowledge.mv2 --input doc.pdf --lock-timeout 5000 # Force takeover (only if previous writer crashed) memvid put knowledge.mv2 --input doc.pdf --force
Capacity Exceeded Solutions: # Check current usage memvid stats knowledge.mv2 # Delete unused frames memvid delete knowledge.mv2 --frame-id 42 --yes # Compact the file memvid doctor knowledge.mv2 --vacuum
Embedding Model Issues Error: Failed to load embedding model
Solution: # Set model directory export MEMVID_MODELS_DIR =~ /. memvid / models # Or use offline mode with pre-cached models export MEMVID_OFFLINE = 1
Next Steps
Search & Ask Query your memories with lexical, semantic, and hybrid search
Timeline & View Explore your memories chronologically
