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Store documents, search with BM25 + vector ranking, and run RAG queries from a single portable `.mv2` file.\n\nBuilt in Rust. No database required.\n\n## Install\n\n```bash\nnpm install -g memvid-cli\n```\n\nOr run directly without installing:\n\n```bash\nnpx memvid-cli --help\n```\n\n## Quick Start\n\n```bash\n# Create a memory and add some documents\nmemvid create research.mv2\nmemvid put research.mv2 --text \"Rust achieves memory safety without garbage collection\"\nmemvid put research.mv2 --text \"Python excels at rapid prototyping and data analysis\"\nmemvid put research.mv2 --input ./papers/\n\n# Search\nmemvid find research.mv2 --query \"memory safety\"\n\n# Ask questions (requires OPENAI_API_KEY for synthesis)\nmemvid ask research.mv2 --question \"Compare Rust and Python for systems programming\"\n\n# Check stats\nmemvid stats research.mv2\n```\n\n## Commands\n\n### Creating and Ingesting\n\n```bash\n# Create a new memory file\nmemvid create notes.mv2\n\n# Add text directly\nmemvid put notes.mv2 --text \"Your content here\"\n\n# Add from file (supports PDF, DOCX, TXT, MD, HTML, and more)\nmemvid put notes.mv2 --input document.pdf\n\n# Add entire folder recursively\nmemvid put notes.mv2 --input ./documents/\n\n# Batch ingest with embeddings for semantic search\nmemvid put-many notes.mv2 --input ./corpus/ --embedding bge-small\n```\n\n### Searching\n\n```bash\n# Lexical search (BM25)\nmemvid find notes.mv2 --query \"machine learning\"\n\n# Semantic search (requires embeddings)\nmemvid find notes.mv2 --query \"ML algorithms\" --mode sem\n\n# Hybrid search (lexical + semantic reranking)\nmemvid find notes.mv2 --query \"neural networks\" --mode auto\n\n# Limit results\nmemvid find notes.mv2 --query \"data\" --k 5\n```\n\n### Question Answering\n\n```bash\n# Basic RAG query\nmemvid ask notes.mv2 --question \"What are the key findings?\"\n\n# Use a specific model\nmemvid ask notes.mv2 --question \"Summarize the main points\" --model openai:gpt-4o\n\n# Get context only (no LLM synthesis)\nmemvid ask notes.mv2 --question \"What is discussed?\" --context-only\n```\n\n### Inspection and Maintenance\n\n```bash\n# View stats\nmemvid stats notes.mv2\n\n# View timeline of recent additions\nmemvid timeline notes.mv2 --limit 20\n\n# View a specific frame\nmemvid view notes.mv2 --frame 42\n\n# Verify file integrity\nmemvid verify notes.mv2\n\n# Repair indexes\nmemvid doctor notes.mv2 --rebuild-lex-index\n```\n\n## Embedding Models\n\nFor semantic search, you need to generate embeddings during ingestion:\n\n```bash\n# Local models (fast, no API key needed)\nmemvid put notes.mv2 --input doc.pdf --embedding bge-small\nmemvid put notes.mv2 --input doc.pdf --embedding nomic\n\n# OpenAI models (requires OPENAI_API_KEY)\nmemvid put notes.mv2 --input doc.pdf --embedding openai-small\n```\n\nAvailable local models: `bge-small`, `bge-base`, `nomic`, `gte-large`\n\nAvailable OpenAI models: `openai-small`, `openai-large`, `openai-ada`\n\n**Windows users:** Local embedding models are not available on Windows due to ONNX runtime limitations. Use OpenAI embeddings instead:\n\n```bash\nset OPENAI_API_KEY=sk-...\nmemvid put notes.mv2 --input doc.pdf --embedding openai-small\n```\n\n## Environment Variables\n\n| Variable | Description |\n|----------|-------------|\n| `OPENAI_API_KEY` | Required for OpenAI embeddings and LLM synthesis |\n| `OPENAI_BASE_URL` | Custom OpenAI-compatible endpoint |\n| `NVIDIA_API_KEY` | For NVIDIA NIM embeddings |\n| `MEMVID_MODELS_DIR` | Where to cache local embedding models |\n| `MEMVID_API_KEY` | For capacity beyond the free tier |\n\n## Supported Platforms\n\n| Platform | Architecture | Local Embeddings |\n|----------|--------------|------------------|\n| macOS | ARM64 (Apple Silicon) | Yes |\n| macOS | x64 (Intel) | Yes |\n| Linux | x64 (glibc) | Yes |\n| Windows | x64 | No (use OpenAI) |\n\n## Document Formats\n\nThe CLI uses Apache Tika for document extraction and supports:\n\n- PDF, DOCX, XLSX, PPTX\n- HTML, XML, Markdown\n- Plain text, CSV, JSON\n- Images (with OCR when available)\n- And many more\n\n## Examples\n\n### Build a Research Knowledge Base\n\n```bash\nmemvid create papers.mv2\nmemvid put-many papers.mv2 --input ~/Downloads/arxiv/ --embedding bge-small\nmemvid ask papers.mv2 --question \"What are recent advances in transformer architectures?\"\n```\n\n### Index Code Documentation\n\n```bash\nmemvid create docs.mv2\nmemvid put docs.mv2 --input ./docs/ --label documentation\nmemvid put docs.mv2 --input ./README.md --label readme\nmemvid find docs.mv2 --query \"authentication\" --k 10\n```\n\n### Personal Note Archive\n\n```bash\nmemvid create notes.mv2\nmemvid put notes.mv2 --text \"Meeting with Alice: discussed Q4 roadmap\" --label meeting\nmemvid put notes.mv2 --text \"Idea: use vector search for semantic dedup\" --label idea\nmemvid timeline notes.mv2 --limit 50\n```\n\n## More Information\n\n- Documentation: https://docs.memvid.com\n- GitHub: https://github.com/memvid/memvid\n- Discord: https://discord.gg/2mynS7fcK7\n- Website: https://memvid.com\n\n## License\n\nApache-2.0\n","readmeFilename":"README.md"}