memvid

A command-line tool for building and querying AI memory files. Store documents, search with BM25 + vector ranking, and run RAG queries from a single portable .mv2 file.

Built in Rust. No database required.

Install

``bash
npm install -g memvid-cli
`

Or run directly without installing:

`bash
npx memvid-cli --help
`

Quick Start

`bash


Create a memory and add some documents

memvid create research.mv2
memvid put research.mv2 --text "Rust achieves memory safety without garbage collection"
memvid put research.mv2 --text "Python excels at rapid prototyping and data analysis"
memvid put research.mv2 --input ./papers/

Search

memvid find research.mv2 --query "memory safety"

Ask questions (requires OPENAI_API_KEY for synthesis)

memvid ask research.mv2 --question "Compare Rust and Python for systems programming"

Check stats

memvid stats research.mv2
`

Commands

$3

`bash


Create a new memory file

memvid create notes.mv2

Add text directly

memvid put notes.mv2 --text "Your content here"

Add from file (supports PDF, DOCX, TXT, MD, HTML, and more)

memvid put notes.mv2 --input document.pdf

Add entire folder recursively

memvid put notes.mv2 --input ./documents/

Batch ingest with embeddings for semantic search

memvid put-many notes.mv2 --input ./corpus/ --embedding bge-small
`

$3

`bash


Lexical search (BM25)

memvid find notes.mv2 --query "machine learning"

Semantic search (requires embeddings)

memvid find notes.mv2 --query "ML algorithms" --mode sem

Hybrid search (lexical + semantic reranking)

memvid find notes.mv2 --query "neural networks" --mode auto

Limit results

memvid find notes.mv2 --query "data" --k 5
`

$3

`bash


Basic RAG query

memvid ask notes.mv2 --question "What are the key findings?"

Use a specific model

memvid ask notes.mv2 --question "Summarize the main points" --model openai:gpt-4o

Get context only (no LLM synthesis)

memvid ask notes.mv2 --question "What is discussed?" --context-only
`

$3

`bash


View stats

memvid stats notes.mv2

View timeline of recent additions

memvid timeline notes.mv2 --limit 20

View a specific frame

memvid view notes.mv2 --frame 42

Verify file integrity

memvid verify notes.mv2

Repair indexes

memvid doctor notes.mv2 --rebuild-lex-index
`

Embedding Models

For semantic search, you need to generate embeddings during ingestion:

`bash


Local models (fast, no API key needed)

memvid put notes.mv2 --input doc.pdf --embedding bge-small
memvid put notes.mv2 --input doc.pdf --embedding nomic

OpenAI models (requires OPENAI_API_KEY)

memvid put notes.mv2 --input doc.pdf --embedding openai-small
`

Available local models: bge-small, bge-base, nomic, gte-large

Available OpenAI models: openai-small, openai-large, openai-ada

Windows users: Local embedding models are not available on Windows due to ONNX runtime limitations. Use OpenAI embeddings instead:

`bash
set OPENAI_API_KEY=sk-...
memvid put notes.mv2 --input doc.pdf --embedding openai-small
`

Environment Variables

| Variable | Description |
|----------|-------------|
| OPENAI_API_KEY | Required for OpenAI embeddings and LLM synthesis |
| OPENAI_BASE_URL | Custom OpenAI-compatible endpoint |
| NVIDIA_API_KEY | For NVIDIA NIM embeddings |
| MEMVID_MODELS_DIR | Where to cache local embedding models |
| MEMVID_API_KEY | For capacity beyond the free tier |

Supported Platforms

| Platform | Architecture | Local Embeddings |
|----------|--------------|------------------|
| macOS | ARM64 (Apple Silicon) | Yes |
| macOS | x64 (Intel) | Yes |
| Linux | x64 (glibc) | Yes |
| Windows | x64 | No (use OpenAI) |

Document Formats

The CLI uses Apache Tika for document extraction and supports:

- PDF, DOCX, XLSX, PPTX
- HTML, XML, Markdown
- Plain text, CSV, JSON
- Images (with OCR when available)
- And many more

Examples

$3

`bash
memvid create papers.mv2
memvid put-many papers.mv2 --input ~/Downloads/arxiv/ --embedding bge-small
memvid ask papers.mv2 --question "What are recent advances in transformer architectures?"
`

$3

`bash
memvid create docs.mv2
memvid put docs.mv2 --input ./docs/ --label documentation
memvid put docs.mv2 --input ./README.md --label readme
memvid find docs.mv2 --query "authentication" --k 10
`

$3

`bash
memvid create notes.mv2
memvid put notes.mv2 --text "Meeting with Alice: discussed Q4 roadmap" --label meeting
memvid put notes.mv2 --text "Idea: use vector search for semantic dedup" --label idea
memvid timeline notes.mv2 --limit 50
``

More Information

- Documentation: https://docs.memvid.com
- GitHub: https://github.com/memvid/memvid
- Discord: https://discord.gg/2mynS7fcK7
- Website: https://memvid.com

License

Apache-2.0