Tencent SkillHub · Developer Tools

Voyage AI CLI

Voyage AI embedding and reranking CLI integrated with MongoDB Atlas Vector Search. Use for: generating text embeddings, reranking search results, storing embeddings in Atlas, performing vector similarity search, creating vector search indexes, listing available models, comparing text similarity, bulk ingestion, interactive demos, and learning about AI concepts. Triggers: embed text, generate embeddings, vector search, rerank documents, voyage ai, semantic search, similarity search, store embeddings, atlas vector search, embedding models, cosine similarity, bulk ingest, explain embeddings.

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Requirements

Target platform: OpenClaw
Install method: Manual import
Extraction: Extract archive
Prerequisites: OpenClaw
Primary doc: SKILL.md

Package facts

Download mode: Yavira redirect
Package format: ZIP package
Source platform: Tencent SkillHub
What's included: SKILL.md, references/vector-search.md, references/models.md

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Release facts

Source: Tencent SkillHub
Verification: Indexed source record
Version: 1.4.0

Provenance

Publisher: mrlynn
Source page: View original listing
Canonical URL: Open canonical page

Documentation

ClawHub primary doc Primary doc: SKILL.md 22 sections Open source page

🧭 Voyage AI Skill

Uses the vai CLI (voyageai-cli) for Voyage AI embeddings, reranking, and MongoDB Atlas Vector Search. Pure Node.js — no Python required.

Setup

npm install -g voyageai-cli

Environment Variables

VariableRequired ForDescriptionVOYAGE_API_KEYembed, rerank, store, search, similarity, ingest, pingModel API key from MongoDB AtlasMONGODB_URIstore, search, index, ingest, ping (optional)Atlas connection string Get your API key: MongoDB Atlas → AI Models → Create model API key

embed — Generate embeddings

vai embed "What is MongoDB?" vai embed "search query" --model voyage-4-large --input-type query --dimensions 512 vai embed --file document.txt --input-type document cat texts.txt | vai embed vai embed "hello" --output-format array

rerank — Rerank documents

vai rerank --query "database performance" --documents "MongoDB is fast" "SQL is relational" vai rerank --query "best database" --documents-file candidates.json --top-k 3

store — Embed and store in Atlas

vai store --db mydb --collection docs --field embedding \ --text "MongoDB Atlas is a cloud database" \ --metadata '{"source": "docs"}' # Batch from JSONL vai store --db mydb --collection docs --field embedding --file documents.jsonl

search — Vector search

vai search --query "cloud database" --db mydb --collection docs \ --index vector_index --field embedding # With pre-filter vai search --query "performance" --db mydb --collection docs \ --index vector_index --field embedding --filter '{"category": "guides"}' --limit 5

index — Manage vector search indexes

vai index create --db mydb --collection docs --field embedding \ --dimensions 1024 --similarity cosine --index-name my_index vai index list --db mydb --collection docs vai index delete --db mydb --collection docs --index-name my_index

models — List available models

vai models vai models --type embedding vai models --type reranking vai models --json

ping — Test connectivity

vai ping vai ping --json

config — Manage persistent configuration

vai config set api-key "pa-your-key" echo "pa-your-key" | vai config set api-key --stdin vai config get vai config delete api-key vai config path vai config reset

demo — Interactive guided walkthrough

vai demo vai demo --no-pause vai demo --skip-pipeline vai demo --keep

explain — Learn about AI concepts

vai explain # List all topics vai explain embeddings vai explain reranking vai explain vector-search vai explain rag vai explain cosine-similarity vai explain two-stage-retrieval vai explain input-type vai explain models vai explain api-keys vai explain api-access vai explain batch-processing

similarity — Compare text similarity

vai similarity "MongoDB is a document database" "MongoDB Atlas is a cloud database" vai similarity "database performance" --against "MongoDB is fast" "PostgreSQL is relational" vai similarity --file1 doc1.txt --file2 doc2.txt vai similarity "text A" "text B" --json

ingest — Bulk import with progress

vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding vai ingest --file data.csv --db myapp --collection docs --field embedding --text-column content vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding \ --model voyage-4 --batch-size 100 --input-type document vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding --dry-run

completions — Shell completion scripts

vai completions bash # Output bash completion script vai completions zsh # Output zsh completion script # Install bash completions vai completions bash >> ~/.bashrc && source ~/.bashrc # Install zsh completions vai completions zsh > ~/.zsh/completions/_vai

help — Display help

vai help vai help embed vai embed --help

Embed → Store → Search Pipeline

# 1. Store documents vai store --db myapp --collection articles --field embedding \ --text "MongoDB Atlas provides a fully managed cloud database" \ --metadata '{"title": "Atlas Overview"}' # 2. Create index vai index create --db myapp --collection articles --field embedding \ --dimensions 1024 --similarity cosine --index-name article_search # 3. Search vai search --query "how does cloud database work" \ --db myapp --collection articles --index article_search --field embedding

Two-Stage Retrieval (Embed + Rerank)

# 1. Get candidates via vector search vai search --query "database scaling" --db myapp --collection articles \ --index article_search --field embedding --limit 20 --json > candidates.json # 2. Rerank for precision vai rerank --query "database scaling" --documents-file candidates.json --top-k 5

Bulk Ingest Pipeline

# 1. Validate data (dry run) vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding --dry-run # 2. Ingest with progress vai ingest --file corpus.jsonl --db myapp --collection docs --field embedding # 3. Create index vai index create --db myapp --collection docs --field embedding \ --dimensions 1024 --similarity cosine

Global Flags

FlagDescription--jsonMachine-readable JSON output--quietSuppress non-essential output

References

Model Catalog — All models with pricing and specs Vector Search Patterns — Atlas Vector Search integration guide

Category context

Code helpers, APIs, CLIs, browser automation, testing, and developer operations.

Source: Tencent SkillHub

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Package contents

Included in package

3 Docs

SKILL.md Primary doc
references/models.md Docs
references/vector-search.md Docs