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[![Node.js](https://img.shields.io/node/v/voyageai-cli.svg)](https://nodejs.org) [![GitHub release](https://img.shields.io/github/v/release/mrlynn/voyageai-cli?label=Desktop%20App)](https://github.com/mrlynn/voyageai-cli/releases)\n\nThe fastest path from documents to semantic search. Chunk files, generate [Voyage AI](https://docs.vaicli.com/) embeddings, store in [MongoDB Atlas](https://www.mongodb.com/docs/atlas/atlas-vector-search/), and query with two-stage retrieval — from the terminal, your browser, or a desktop app.\n\n> **⚠️ Disclaimer:** This is an independent, community-built tool — **not** an official product of MongoDB, Inc. or Voyage AI. See [Disclaimer](#disclaimer) for details.\n\n---\n\n## Why Voyage AI?\n\nVoyage AI provides **state-of-the-art embedding models** with the best quality-to-cost ratio in the industry. Here's why developers choose Voyage AI:\n\n| Advantage | What It Means |\n|-----------|---------------|\n| **🎯 #1 on RTEB** | Voyage 4 ranks first on retrieval benchmarks, outperforming OpenAI, Cohere, and other providers |\n| **💰 Up to 83% Cost Savings** | Asymmetric retrieval: embed docs with `voyage-4-lite`, query with `voyage-4-large`, same quality, fraction of the cost |\n| **🔗 Shared Embedding Space** | All Voyage 4 models produce compatible embeddings, so you can mix and match for optimal cost-quality tradeoffs |\n| **🏢 Domain-Specific Models** | Specialized models for code, finance, law, and multilingual content that beat general-purpose alternatives |\n| **⚡ Two-Stage Retrieval** | Rerank-2.5 boosts search precision by re-scoring candidates with a powerful cross-encoder |\n\n**Get started:**\n```bash\n# Get a free API key at https://dash.voyageai.com\nvai quickstart    # Interactive tutorial — zero to semantic search in 2 minutes\n```\n\n**Learn more:** [Voyage AI Docs](https://docs.voyageai.com) · [Pricing](https://voyageai.com/pricing) · [Blog](https://blog.voyageai.com)\n\n---\n\n## Three Ways to Use It\n\n<table>\n<tr>\n<td align=\"center\" width=\"25%\">\n<h3>🖥️ CLI</h3>\n<code>vai</code><br/><br/>\n22 commands · 5 chunking strategies<br/>\nEnd-to-end RAG pipeline from your terminal<br/><br/>\n<code>curl -fsSL https://vaicli.com/install.sh | sh</code>\n</td>\n<td align=\"center\" width=\"25%\">\n<h3>🌐 Web Playground</h3>\n<code>vai playground</code><br/><br/>\n7 interactive tabs + Workflow Store<br/>\nwith 20+ installable workflow packages<br/><br/>\n<em>Launches in your browser</em>\n</td>\n<td align=\"center\" width=\"25%\">\n<h3>💻 Desktop App</h3>\nStandalone Electron app<br/><br/>\nSecure keychain storage, dark/light themes,<br/>\nMongoDB LeafyGreen design system<br/><br/>\n<a href=\"https://github.com/mrlynn/voyageai-cli/releases\">Download from GitHub Releases</a>\n</td>\n<td align=\"center\" width=\"25%\">\n<h3>🏪 Workflow Store</h3>\n<code>vai store</code> or Playground<br/><br/>\nBrowse, install, and run pre-built<br/>\nRAG workflows from npm<br/><br/>\n<em>Official + community packages</em>\n</td>\n</tr>\n</table>\n\n---\n\n## Table of Contents\n\n- [Why Voyage AI?](#why-voyage-ai)\n- [Desktop App](#desktop-app)\n- [Web Playground](#web-playground)\n- [CLI — Quick Start](#cli--quick-start)\n  - [Install](#install)\n  - [5-Minute RAG Pipeline](#5-minute-rag-pipeline)\n  - [Project Config](#project-config)\n  - [Core Workflow](#core-workflow)\n  - [Individual Commands](#individual-commands)\n  - [Models & Benchmarks](#models--benchmarks)\n  - [Local Inference](#local-inference)\n  - [Benchmarking Your Data](#benchmarking-your-data)\n  - [Learn](#learn)\n  - [Environment & Auth](#environment--auth)\n  - [Shell Completions](#shell-completions)\n  - [All Commands](#all-commands)\n- [Workflow Store](#workflow-store)\n- [MCP Server](#mcp-server)\n- [Screenshots](#screenshots)\n- [Requirements](#requirements)\n- [Author](#author)\n- [Disclaimer](#disclaimer)\n- [License](#license)\n\n---\n\n## Desktop App\n\nA standalone desktop application built with Electron and the MongoDB LeafyGreen design system. Everything the CLI and playground can do — in a native app experience.\n\n[![Download Latest Release](https://img.shields.io/github/v/release/mrlynn/voyageai-cli?label=Download&style=for-the-badge)](https://github.com/mrlynn/voyageai-cli/releases)\n\n### Key Features\n\n- **🔐 Secure API Key Storage** — Stores your Voyage AI API key and MongoDB URI in the OS keychain (macOS Keychain, Windows Credential Vault, Linux Secret Service). No plaintext config files.\n- **🎨 Dark & Light Themes** — Full theme support with automatic system detection, built on MongoDB's LeafyGreen design tokens.\n- **🍃 MongoDB LeafyGreen UI** — Native MongoDB look & feel with LeafyGreen components and iconography throughout.\n- **📱 Sidebar Navigation** — Clean, collapsible sidebar for quick access to all features: Embed, Compare, Search, Benchmark, Explore, Settings, and more.\n- **⚡ All Playground Features** — Every tab from the web playground, plus desktop-native conveniences like system tray integration.\n\n### Installation\n\nDownload the latest release for your platform from [GitHub Releases](https://github.com/mrlynn/voyageai-cli/releases):\n\n| Platform | Download |\n|----------|----------|\n| macOS (Apple Silicon) | `.dmg` |\n| macOS (Intel) | `.dmg` |\n| Windows | `.exe` installer |\n| Linux | `.AppImage` / `.deb` |\n\n> **Prefer the CLI?** Install with `curl -fsSL https://vaicli.com/install.sh | sh` or `brew install mrlynn/vai/vai`\n\n---\n\n## Web Playground\n\nAn interactive, browser-based interface for exploring Voyage AI embeddings without writing code. Ships with the CLI — just run:\n\n```bash\nvai playground\n```\n\nYour default browser opens with a full-featured UI:\n\n| Tab | What It Does |\n|-----|-------------|\n| **Embed** | Generate embeddings for any text, inspect vectors, adjust dimensions and models |\n| **Compare** | Side-by-side similarity comparison of two or more texts with cosine similarity scores |\n| **Search** | Connect to MongoDB Atlas and run vector similarity searches with filters and reranking |\n| **Benchmark** | Compare model latency, cost, and quality across the Voyage 4 family on your own data |\n| **Explore** | Visualize embedding spaces with dimensionality reduction (PCA/t-SNE) and clustering |\n| **Workflow Store** | Browse, install, and run 20+ official and community workflow packages |\n| **About** | Project info, links, and version details |\n| **Settings** | Configure API keys, MongoDB URI, default model, and preferences |\n\nThe playground connects to the same backend as the CLI. Any API keys or MongoDB URIs you've configured via `vai config` are available automatically.\n\n---\n\n## CLI — Quick Start\n\n**22 commands · 1,000+ tests · 5 chunking strategies · End-to-end RAG pipeline**\n\n### Install\n\n```bash\n# Fastest — one command, no dependencies\ncurl -fsSL https://vaicli.com/install.sh | sh\n\n# Via npm\nnpm install -g voyageai-cli\n\n# Via Homebrew\nbrew install mrlynn/vai/vai\n```\n\n### 5-Minute RAG Pipeline\n\nGo from a folder of documents to a searchable vector database:\n\n```bash\n# Set credentials\nexport VOYAGE_API_KEY=\"your-key\"\nexport MONGODB_URI=\"mongodb+srv://user:pass@cluster.mongodb.net/\"\n\n# Initialize project\nvai init --yes\n\n# Chunk → embed → store (one command)\nvai pipeline ./docs/ --db myapp --collection knowledge --create-index\n\n# Search with two-stage retrieval\nvai query \"How do I configure replica sets?\" --db myapp --collection knowledge\n```\n\nThat's it. Documents chunked, embedded with `voyage-4-large`, stored in Atlas with metadata, vector index created, and searchable with reranking.\n\n### Project Config\n\nStop typing `--db myapp --collection docs` on every command:\n\n```bash\nvai init\n```\n\nCreates `.vai.json` with your defaults — model, database, collection, chunking strategy. Every command reads it automatically. CLI flags override when needed.\n\n```json\n{\n  \"model\": \"voyage-4-large\",\n  \"db\": \"myapp\",\n  \"collection\": \"knowledge\",\n  \"field\": \"embedding\",\n  \"dimensions\": 1024,\n  \"chunk\": {\n    \"strategy\": \"recursive\",\n    \"size\": 512,\n    \"overlap\": 50\n  }\n}\n```\n\n### Code Generation & Scaffolding\n\n#### `vai generate` — Production code snippets\n\nGenerate ready-to-use code from your `.vai.json` config:\n\n```bash\n# List available components\nvai generate --list\n\n# Generate and pipe to files\nvai generate client > lib/voyage.js\nvai generate retrieval > lib/retrieval.js\nvai generate search-api > routes/search.js\n\n# Different targets\nvai generate client --target python    # Flask\nvai generate retrieval --target nextjs # Next.js + MUI\n```\n\nComponents: `client`, `connection`, `retrieval`, `ingest`, `search-api`\n\nTargets: `vanilla` (Node.js/Express), `nextjs` (Next.js + MUI), `python` (Flask)\n\n#### `vai scaffold` — Complete starter projects\n\nCreate a full project directory with all files pre-configured:\n\n```bash\n# Node.js + Express API (9 files)\nvai scaffold my-rag-api\n\n# Next.js + Material UI (13 files)\nvai scaffold my-app --target nextjs\n\n# Python + Flask (8 files)\nvai scaffold flask-api --target python\n\n# Preview without creating files\nvai scaffold my-app --dry-run\n```\n\nEach project includes: server, API routes, Voyage AI client, MongoDB connection, retrieval module, ingestion pipeline, `.env.example`, and README.\n\n### Data Lifecycle\n\n#### `vai purge` — Remove stale embeddings\n\nRemove embeddings from MongoDB based on criteria:\n\n```bash\n# Remove docs embedded with an old model\nvai purge --model voyage-3.5\n\n# Remove docs whose source files no longer exist\nvai purge --stale\n\n# Remove docs older than a date\nvai purge --before 2026-01-01\n\n# Filter by source pattern\nvai purge --source \"docs/old/*.md\"\n\n# Preview before deleting\nvai purge --model voyage-3.5 --dry-run\n```\n\n#### `vai refresh` — Re-embed with new settings\n\nRe-embed documents in-place with a new model, dimensions, or chunk settings:\n\n```bash\n# Upgrade to a new model\nvai refresh --model voyage-4-large\n\n# Change dimensions for cost savings\nvai refresh --model voyage-4-large --dimensions 256\n\n# Re-chunk with a better strategy, then re-embed\nvai refresh --rechunk --strategy markdown --chunk-size 1024\n\n# Preview what would change\nvai refresh --model voyage-4-large --dry-run\n```\n\n### Core Workflow\n\n#### `vai pipeline` — Chunk → embed → store\n\nThe end-to-end command. Takes files or directories, chunks them, embeds in batches, stores in MongoDB Atlas.\n\n```bash\n# Directory of docs\nvai pipeline ./docs/ --db myapp --collection knowledge --create-index\n\n# Single file\nvai pipeline whitepaper.pdf --db myapp --collection papers\n\n# Preview without API calls\nvai pipeline ./docs/ --dry-run\n\n# Custom chunking\nvai pipeline ./docs/ --strategy markdown --chunk-size 1024 --overlap 100\n```\n\nSupports: `.txt`, `.md`, `.html`, `.json`, `.jsonl`, `.pdf` (optional `pdf-parse` dependency). Auto-detects markdown files for heading-aware chunking.\n\n#### `vai query` — Search + rerank\n\nTwo-stage retrieval in one command: embed query → vector search → rerank → results.\n\n```bash\n# Search with reranking (default)\nvai query \"How does authentication work?\" --db myapp --collection knowledge\n\n# Vector search only (skip rerank)\nvai query \"auth setup\" --no-rerank\n\n# With pre-filter\nvai query \"performance tuning\" --filter '{\"category\": \"guides\"}' --top-k 10\n```\n\n#### `vai chunk` — Document chunking\n\nStandalone chunking for when you need control over the pipeline.\n\n```bash\n# Chunk a directory, output JSONL\nvai chunk ./docs/ --output chunks.jsonl --stats\n\n# Specific strategy\nvai chunk paper.md --strategy markdown --chunk-size 1024\n\n# Preview\nvai chunk ./docs/ --dry-run\n```\n\nFive strategies: `fixed`, `sentence`, `paragraph`, `recursive` (default), `markdown`.\n\n#### `vai estimate` — Cost estimator\n\nCompare symmetric vs. asymmetric embedding strategies before committing.\n\n```bash\nvai estimate --docs 10M --queries 100M --months 12\n```\n\nShows cost breakdown for every Voyage 4 model combination, including asymmetric retrieval (embed docs with `voyage-4-large`, query with `voyage-4-lite` — same quality, fraction of the cost).\n\n### Individual Commands\n\nFor when you need fine-grained control:\n\n```bash\n# Embed text\nvai embed \"What is MongoDB?\" --model voyage-4-large --dimensions 512\n\n# Rerank documents\nvai rerank --query \"database performance\" \\\n  --documents \"MongoDB is fast\" \"PostgreSQL is relational\" \"Redis is cached\"\n\n# Compare similarity\nvai similarity \"MongoDB is a database\" \"Atlas is a cloud database\"\n\n# Store a single document\nvai store --db myapp --collection docs --field embedding \\\n  --text \"MongoDB Atlas provides managed cloud databases\"\n\n# Bulk import from file\nvai ingest --file corpus.jsonl --db myapp --collection docs --field embedding\n\n# Vector search (raw)\nvai search --query \"cloud database\" --db myapp --collection docs\n\n# Manage indexes\nvai index create --db myapp --collection docs --field embedding\nvai index list --db myapp --collection docs\n```\n\n### Models & Benchmarks\n\n```bash\n# List models with architecture and shared space info\nvai models --wide\n\n# Show RTEB benchmark scores\nvai models --benchmarks\n```\n\n#### Voyage 4 Family\n\n| Model | Architecture | Price/1M tokens | RTEB Score | Best For |\n|-------|-------------|----------------|------------|----------|\n| voyage-4-large | **MoE** | $0.12 | **71.41** | Best quality — first production MoE embedding model |\n| voyage-4 | Dense | $0.06 | 70.07 | Balanced quality/cost |\n| voyage-4-lite | Dense | $0.02 | 68.10 | High-volume, budget |\n| voyage-4-nano | Dense | Free (open-weight) | — | Local dev, edge, [HuggingFace](https://huggingface.co/voyageai/voyage-4-nano) |\n\n**Shared embedding space:** All Voyage 4 models produce compatible embeddings. Embed docs with `voyage-4-large`, query with `voyage-4-lite` — no re-vectorization needed.\n\n#### Competitive Landscape (RTEB NDCG@10)\n\n| Model | Score |\n|-------|-------|\n| **voyage-4-large** | **71.41** |\n| voyage-4 | 70.07 |\n| Gemini Embedding 001 | 68.66 |\n| voyage-4-lite | 68.10 |\n| Cohere Embed v4 | 65.75 |\n| OpenAI v3 Large | 62.57 |\n\nAlso available: `voyage-code-3` (code), `voyage-finance-2` (finance), `voyage-law-2` (legal), `rerank-2.5` / `rerank-2.5-lite`.\n\n### Local Inference\n\nRun embeddings locally with `voyage-4-nano` -- no API key, no network, no cost.\nNano shares the same embedding space as the Voyage 4 API models, so you can\nprototype locally and upgrade to the API when ready.\n\n**Prerequisites:** Python 3.10+ and ~700MB disk space for the model.\n\n#### Setup (one-time)\n\n```bash\nvai nano setup      # Creates venv, installs deps, downloads model\nvai nano status     # Verify everything is ready\n```\n\n#### Usage\n\n```bash\n# Embed text locally\nvai embed \"What is MongoDB?\" --local\n\n# Run the full pipeline locally\nvai pipeline ./docs/ --local --db myapp --collection knowledge\n\n# Bulk ingest with local embeddings\nvai ingest --file corpus.jsonl --local --db myapp --collection docs\n```\n\n#### Interactive Demo\n\n```bash\nvai demo nano       # Zero-dependency guided walkthrough\n```\n\nCovers similarity matrices, MRL dimension comparison, and interactive REPL --\nall without an API key or MongoDB connection.\n\n#### Nano Commands\n\n| Command | Description |\n|---------|-------------|\n| `vai nano setup` | Set up Python venv, install deps, download model |\n| `vai nano status` | Check local inference readiness |\n| `vai nano test` | Smoke-test local inference |\n| `vai nano info` | Show model details and cache location |\n| `vai nano clear-cache` | Remove cached model files |\n\n#### Upgrade Path\n\nSince nano shares the Voyage 4 embedding space, your local embeddings are\ncompatible with `voyage-4`, `voyage-4-lite`, and `voyage-4-large`. No\nre-vectorization needed when you add an API key.\n\n### Benchmarking Your Data\n\nPublished benchmarks measure average quality across standardized datasets. `vai benchmark` measures what matters for **your** use case:\n\n```bash\n# Compare model latency and cost\nvai benchmark embed --models voyage-4-large,voyage-4,voyage-4-lite --rounds 5\n\n# Test asymmetric retrieval on your data\nvai benchmark asymmetric --file your-corpus.txt --query \"your actual query\"\n\n# Validate shared embedding space\nvai benchmark space\n\n# Compare quantization tradeoffs\nvai benchmark quantization --model voyage-4-large --dtypes float,int8,ubinary\n\n# Project costs at scale\nvai benchmark cost --tokens 500 --volumes 100,1000,10000,100000\n```\n\n### Evaluation\n\nMeasure and compare your retrieval quality:\n\n```bash\n# Evaluate retrieval pipeline\nvai eval --test-set test.jsonl --db myapp --collection docs\n\n# Save results for later comparison\nvai eval --test-set test.jsonl --save baseline.json\n\n# Compare against a baseline (shows deltas)\nvai eval --test-set test.jsonl --baseline baseline.json\n\n# Compare multiple configurations\nvai eval compare --test-set test.jsonl --configs baseline.json,experiment.json\n\n# Evaluate reranking in isolation\nvai eval --mode rerank --test-set rerank-test.jsonl\n\n# Compare rerank models\nvai eval --mode rerank --models \"rerank-2.5,rerank-2.5-lite\" --test-set test.jsonl\n```\n\n**Metrics:** MRR, nDCG@K, Recall@K, MAP, Precision@K\n\n**Test set format (JSONL):**\n```json\n{\"query\": \"What is vector search?\", \"relevant\": [\"doc_id_1\", \"doc_id_2\"]}\n```\n\n### Learn\n\nInteractive explanations of key concepts:\n\n```bash\nvai explain embeddings        # What are vector embeddings?\nvai explain moe               # Mixture-of-experts architecture\nvai explain shared-space      # Shared embedding space & asymmetric retrieval\nvai explain rteb              # RTEB benchmark scores\nvai explain quantization      # Matryoshka dimensions & quantization\nvai explain two-stage         # The embed → search → rerank pattern\nvai explain nano              # voyage-4-nano open-weight model\nvai explain models            # How to choose the right model\n```\n\n17 topics covering embeddings, reranking, vector search, RAG, and more.\n\n### Environment & Auth\n\n| Variable | Required For | Description |\n|----------|-------------|-------------|\n| `VOYAGE_API_KEY` | All embedding/reranking | [Model API key](https://docs.vaicli.com/management/api-keys/) from MongoDB Atlas |\n| `MONGODB_URI` | store, search, query, pipeline, index | MongoDB Atlas connection string |\n\nCredentials resolve in order: environment variables → `.env` file → `~/.vai/config.json`.\n\n```bash\n# Or use the built-in config store\necho \"your-key\" | vai config set api-key --stdin\nvai config set mongodb-uri \"mongodb+srv://...\"\n```\n\n#### All Config Keys\n\n| CLI Key | Description | Example |\n|---------|-------------|---------|\n| `api-key` | Voyage AI API key | `vai config set api-key pa-...` |\n| `mongodb-uri` | MongoDB Atlas connection string | `vai config set mongodb-uri \"mongodb+srv://...\"` |\n| `base-url` | Override API endpoint (Atlas AI or Voyage) | `vai config set base-url https://ai.mongodb.com/v1` |\n| `default-model` | Default embedding model | `vai config set default-model voyage-3` |\n| `default-dimensions` | Default output dimensions | `vai config set default-dimensions 512` |\n| `default-db` | Default MongoDB database for workflows/commands | `vai config set default-db my_knowledge_base` |\n| `default-collection` | Default MongoDB collection for workflows/commands | `vai config set default-collection documents` |\n| `llm-provider` | LLM provider for chat/generate (`anthropic`, `openai`, `ollama`) | `vai config set llm-provider anthropic` |\n| `llm-api-key` | LLM provider API key | `vai config set llm-api-key sk-...` |\n| `llm-model` | LLM model override | `vai config set llm-model claude-sonnet-4-5-20250929` |\n| `llm-base-url` | LLM endpoint override (e.g. for Ollama) | `vai config set llm-base-url http://localhost:11434` |\n| `show-cost` | Show cost estimates after operations | `vai config set show-cost true` |\n| `telemetry` | Enable/disable anonymous usage telemetry | `vai config set telemetry false` |\n\nConfig is stored in `~/.vai/config.json`. Use `vai config get` to see all values (secrets are masked) or `vai config get <key>` for a specific value. The desktop app's Settings → Database page also reads and writes this file.\n\n#### Telemetry\n\nvai collects anonymous usage telemetry for the CLI and desktop app. On first launch, vai shows a one-time notice before any telemetry is sent. The CLI and desktop app share the same telemetry preference and notice state via `~/.vai/config.json`.\n\nUse the built-in telemetry controls:\n\n```bash\nvai telemetry\nvai telemetry off\nvai telemetry on\nvai telemetry status\nvai telemetry reset\n```\n\nYou can also disable telemetry with environment variables:\n\n```bash\nexport VAI_TELEMETRY=0\nexport DO_NOT_TRACK=1\n```\n\nFor local auditing, set:\n\n```bash\nexport VAI_TELEMETRY_DEBUG=1\n```\n\nThis prints telemetry payloads to `stderr` instead of sending them.\n\n### Shell Completions\n\n```bash\n# Bash\nvai completions bash >> ~/.bashrc\n\n# Zsh\nmkdir -p ~/.zsh/completions\nvai completions zsh > ~/.zsh/completions/_vai\n```\n\nCovers all 22 commands, subcommands, flags, model names, and explain topics.\n\n### All Commands\n\n| Command | Description |\n|---------|-------------|\n| **Project Setup** | |\n| `vai init` | Initialize project with `.vai.json` |\n| `vai generate` | Generate code snippets (retrieval, ingest, client) |\n| `vai scaffold` | Create complete starter projects |\n| **RAG Pipeline** | |\n| `vai pipeline` | Chunk → embed → store (end-to-end) |\n| `vai query` | Search + rerank (two-stage retrieval) |\n| `vai chunk` | Chunk documents (5 strategies) |\n| `vai estimate` | Cost estimator (symmetric vs asymmetric) |\n| **Embeddings** | |\n| `vai embed` | Generate embeddings |\n| `vai rerank` | Rerank documents by relevance |\n| `vai similarity` | Compare text similarity |\n| **Data Management** | |\n| `vai store` | Embed and store single documents |\n| `vai ingest` | Bulk import with progress |\n| `vai search` | Vector similarity search |\n| `vai index` | Manage Atlas Vector Search indexes |\n| `vai purge` | Remove embeddings by criteria |\n| `vai refresh` | Re-embed with new model/settings |\n| **Evaluation** | |\n| `vai eval` | Evaluate retrieval quality (MRR, nDCG, Recall) |\n| `vai eval compare` | Compare configurations side-by-side |\n| `vai benchmark` | 8 subcommands for model comparison |\n| **Workflow Store** | |\n| `vai store list` | Browse available workflows (official + community) |\n| `vai store install` | Install a workflow package from npm |\n| `vai store run` | Run an installed workflow |\n| `vai store uninstall` | Remove an installed workflow |\n| **MCP Server** | |\n| `vai mcp` | Start the MCP server (expose vai tools to AI agents) |\n| `vai mcp install` | Install vai into AI tool configs (Claude, Cursor, etc.) |\n| `vai mcp uninstall` | Remove vai from AI tool configs |\n| `vai mcp status` | Show installation status across all tools |\n| `vai mcp generate-key` | Generate API key for HTTP server auth |\n| **Tools & Learning** | |\n| `vai models` | List models, benchmarks, architecture |\n| `vai explain` | 25 interactive concept explainers |\n| `vai config` | Manage persistent configuration |\n| `vai ping` | Test API and MongoDB connectivity |\n| `vai playground` | Interactive web playground |\n| `vai demo` | Guided walkthrough |\n| `vai completions` | Shell completion scripts |\n| `vai about` | About this tool |\n| `vai version` | Print version |\n\n---\n\n## Workflow Store\n\nBrowse, install, and run pre-built RAG workflows — from the Playground UI or the CLI. The Workflow Store features **20 official workflows** and a growing ecosystem of community packages published on npm.\n\n### Browse & Install\n\n```bash\n# Open the visual Workflow Store in the Playground\nvai playground    # Click the grid icon → Store\n\n# Or from the CLI\nvai store list                    # Browse available workflows\nvai store install model-shootout  # Install a workflow\nvai store run model-shootout      # Run it\n```\n\n### Official Workflows\n\n| Workflow | Category | What It Does |\n|----------|----------|-------------|\n| **model-shootout** | Utility | Compare voyage-4-large, voyage-4, and voyage-4-lite side-by-side on your data |\n| **asymmetric-search** | Retrieval | Embed with voyage-4-large, query with voyage-4-lite — ~83% cost reduction |\n| **cost-optimizer** | Utility | Quantify exact cost savings of asymmetric retrieval |\n| **question-decomposition** | Retrieval | Break complex questions into sub-queries, search in parallel, merge & rerank |\n| **knowledge-base-bootstrap** | Integration | End-to-end onboarding: ingest → verify → test query → status report |\n| **hybrid-precision-search** | Retrieval | Three retrieval strategies in parallel, merged and reranked |\n| **embedding-drift-detector** | Analysis | Monitor embedding quality over time |\n| **multilingual-search** | Retrieval | Translate queries into multiple languages, search each in parallel |\n\nPlus 12 more covering code migration, financial risk scanning, clinical protocol matching, meeting action items, and more.\n\n### Community Packages\n\nAnyone can publish a workflow to npm. Tag your package with `vai-workflow` and add a `vai-workflow` field to your `package.json`:\n\n```json\n{\n  \"name\": \"vai-workflow-my-pipeline\",\n  \"keywords\": [\"vai-workflow\"],\n  \"vai-workflow\": {\n    \"category\": \"retrieval\",\n    \"tags\": [\"custom\", \"my-use-case\"],\n    \"tools\": [\"query\", \"rerank\", \"generate\"]\n  }\n}\n```\n\nCommunity workflows appear automatically in the Store alongside official packages.\n\n---\n\n## MCP Server\n\nExpose vai's RAG tools to any MCP-compatible AI agent — Claude Desktop, Claude Code, Cursor, Windsurf, VS Code, and more. **11 tools** for embedding, retrieval, reranking, ingestion, and learning — all accessible without writing code.\n\n### One-Command Setup\n\n```bash\n# Install into your AI tool of choice\nvai mcp install claude\nvai mcp install cursor\nvai mcp install all          # all supported tools at once\n\n# Check what's configured\nvai mcp status\n```\n\nThe install command **merges** into existing configs — it won't touch your other MCP servers.\n\n### Supported Tools\n\n| Target | AI Tool |\n|--------|---------|\n| `claude` | Claude Desktop |\n| `claude-code` | Claude Code |\n| `cursor` | Cursor |\n| `windsurf` | Windsurf |\n| `vscode` | VS Code |\n\n### What Your Agent Gets\n\nOnce installed, your AI agent can use these tools:\n\n| Tool | What It Does |\n|------|-------------|\n| `vai_query` | Full RAG: embed → vector search → rerank |\n| `vai_search` | Raw vector similarity search |\n| `vai_rerank` | Rerank documents against a query |\n| `vai_embed` | Generate embedding vectors |\n| `vai_similarity` | Cosine similarity between texts |\n| `vai_ingest` | Chunk, embed, and store documents |\n| `vai_collections` | List MongoDB collections with vector indexes |\n| `vai_models` | List models with pricing and benchmarks |\n| `vai_topics` | Browse educational topics |\n| `vai_explain` | Get detailed concept explanations |\n| `vai_estimate` | Estimate embedding costs |\n\n### Transport Modes\n\n```bash\nvai mcp                                    # stdio (default, local)\nvai mcp --transport http --port 3100       # HTTP (remote, multi-client)\n```\n\n📖 **Full documentation:** [docs/mcp-server.md](docs/mcp-server.md)\n\n---\n\n## Screenshots\n\n### Desktop App — Dark Theme\n![Vai - Embed Tab (Dark)](screenshots/vai-dark-embed.png)\n\n### Desktop App — Settings\n![Vai - Settings (Dark)](screenshots/vai-dark-settings.png)\n\n### Desktop App — Light Theme\n![Vai - Embed Tab (Light)](screenshots/vai-light-embed.png)\n\n### Search & Reranking\n![Vai - Search Tab](screenshots/vai-dark-search.png)\n\n### Benchmark\n![Vai - Benchmark Tab](screenshots/vai-dark-benchmark.png)\n\n---\n\n## Project Structure\n\nThis is a **monorepo-lite** that separates the CLI from the desktop app:\n\n```\nvoyageai-cli/\n├── src/            ← Core library + CLI + web playground (npm package)\n├── electron/       ← Desktop app (distributed via GitHub Releases)\n├── docs/           ← Shared documentation\n├── test/           ← Test suites\n└── .github/\n    └── workflows/\n        ├── ci.yml           ← Tests + npm publish for CLI\n        └── release-app.yml  ← Electron builds + GitHub Releases\n```\n\n### Distribution Channels\n\n| Product | Channel | What users get |\n|---------|---------|----------------|\n| **CLI** (`vai`) | `npm install -g voyageai-cli` | Terminal tool, 22 commands, RAG pipeline |\n| **Web Playground** | `vai playground` | Runs locally, no install beyond the CLI |\n| **Desktop App** | [GitHub Releases](https://github.com/mrlynn/voyageai-cli/releases) | Standalone app, no Node required |\n\n### Development Scripts\n\n```bash\n# CLI development\nnpm test                    # Run test suite\nnpm run test:watch         # Watch mode\n\n# Electron app development  \nnpm run app:install        # Install electron dependencies\nnpm run app:start          # Launch electron app\nnpm run app:dev            # Launch with DevTools\nnpm run app:build          # Build for all platforms\n```\n\n## Requirements\n\n- Node.js 18+\n- [MongoDB Atlas](https://www.mongodb.com/atlas) account (free tier works)\n- [Voyage AI model API key](https://docs.vaicli.com/management/api-keys/) (created in Atlas)\n\n## Author\n\nBuilt by [Michael Lynn](https://github.com/mrlynn), Principal Staff Developer Advocate at [MongoDB](https://www.mongodb.com).\n\n## Disclaimer\n\nThis is a community tool and is not affiliated with, endorsed by, or supported by MongoDB, Inc. or Voyage AI. All trademarks belong to their respective owners.\n\nFor official documentation and support:\n- **MongoDB:** [mongodb.com](https://www.mongodb.com) | [Atlas](https://www.mongodb.com/atlas) | [Support](https://support.mongodb.com)\n- **Voyage AI:** [MongoDB Voyage AI Docs](https://docs.vaicli.com/)\n\n## License\n\n[MIT](LICENSE) © [Michael Lynn](https://github.com/mrlynn)\n","readmeFilename":"README.md"}