Standalone command-line tool for creating, inspecting, querying, and managing RVF vector stores. Runs on Windows, macOS, and Linux with zero runtime dependencies.
Download from GitHub Releases:
# macOS (Apple Silicon)
curl -L -o rvf https://github.com/ruvnet/ruvector/releases/latest/download/rvf-darwin-arm64
chmod +x rvf && sudo mv rvf /usr/local/bin/
# macOS (Intel)
curl -L -o rvf https://github.com/ruvnet/ruvector/releases/latest/download/rvf-darwin-x64
chmod +x rvf && sudo mv rvf /usr/local/bin/
# Linux x64
curl -L -o rvf https://github.com/ruvnet/ruvector/releases/latest/download/rvf-linux-x64
chmod +x rvf && sudo mv rvf /usr/local/bin/
# Linux ARM64
curl -L -o rvf https://github.com/ruvnet/ruvector/releases/latest/download/rvf-linux-arm64
chmod +x rvf && sudo mv rvf /usr/local/bin/Windows (PowerShell):
Invoke-WebRequest -Uri https://github.com/ruvnet/ruvector/releases/latest/download/rvf-windows-x64.exe -OutFile rvf.exeRequires Rust:
cargo install --git https://github.com/ruvnet/ruvector.git rvf-cliOr clone and build:
git clone https://github.com/ruvnet/ruvector.git
cd ruvector
cargo build -p rvf-cli --release
# Binary: target/release/rvf (or rvf.exe on Windows)# Create a 128-dimensional vector store with cosine distance
rvf create mydb.rvf --dimension 128 --metric cosine
# Ingest vectors from JSON
rvf ingest mydb.rvf --input vectors.json
# Search for nearest neighbors
rvf query mydb.rvf --vector "0.1,0.2,0.3,..." --k 10
# Check store status
rvf status mydb.rvfThe repo includes 48 pre-built .rvf example stores in examples/rvf/output/. Use the CLI to inspect, query, and manipulate them:
# List all example stores
ls examples/rvf/output/*.rvf
# Inspect a store
rvf status examples/rvf/output/basic_store.rvf
rvf inspect examples/rvf/output/basic_store.rvf
# Query the semantic search example (500 vectors, 384 dimensions)
rvf status examples/rvf/output/semantic_search.rvf
rvf inspect examples/rvf/output/semantic_search.rvf --json
# Inspect the RAG pipeline store
rvf status examples/rvf/output/rag_pipeline.rvf
# Look at COW lineage (parent -> child)
rvf inspect examples/rvf/output/lineage_parent.rvf
rvf inspect examples/rvf/output/lineage_child.rvf
# Check financial signals store
rvf status examples/rvf/output/financial_signals.rvf
# View the compacted store
rvf status examples/rvf/output/compacted.rvf
# Inspect agent memory store
rvf inspect examples/rvf/output/agent_memory.rvf
# View all stores at once (JSON)
for f in examples/rvf/output/*.rvf; do
echo "--- $(basename $f) ---"
rvf status "$f" 2>/dev/null
done| Store | Vectors | Dim | Description |
|---|---|---|---|
basic_store.rvf |
100 | 384 | Basic vector store |
semantic_search.rvf |
500 | 384 | Semantic search embeddings |
rag_pipeline.rvf |
300 | 256 | RAG pipeline embeddings |
embedding_cache.rvf |
500 | 384 | Embedding cache |
filtered_search.rvf |
200 | 256 | Filtered search with metadata |
financial_signals.rvf |
100 | 512 | Financial signal vectors |
recommendation.rvf |
100 | 256 | Recommendation engine |
medical_imaging.rvf |
100 | 768 | Medical imaging features |
multimodal_fusion.rvf |
100 | 2048 | Multimodal fusion vectors |
legal_discovery.rvf |
100 | 768 | Legal document embeddings |
progressive_index.rvf |
1000 | 384 | Progressive HNSW index |
quantization.rvf |
1000 | 384 | Quantized vectors |
swarm_knowledge.rvf |
100 | 128 | Swarm intelligence KB |
agent_memory.rvf |
50 | 128 | Agent conversation memory |
experience_replay.rvf |
50 | 64 | RL experience replay buffer |
lineage_parent.rvf |
— | — | COW parent (lineage demo) |
lineage_child.rvf |
— | — | COW child (lineage demo) |
compacted.rvf |
— | — | Post-compaction store |
Create a new empty RVF store.
rvf create store.rvf --dimension 128 --metric cosine
rvf create store.rvf -d 384 -m l2 --profile 1 --jsonOptions:
-d, --dimension— Vector dimensionality (required)-m, --metric— Distance metric:l2,ip(inner product),cosine(default:l2)-p, --profile— Hardware profile 0-3 (default:0)--json— Output as JSON
Import vectors from a JSON file.
rvf ingest store.rvf --input data.json
rvf ingest store.rvf -i data.json --batch-size 500 --jsonInput JSON format:
[
{"id": 1, "vector": [0.1, 0.2, 0.3, ...]},
{"id": 2, "vector": [0.4, 0.5, 0.6, ...]}
]Search for k nearest neighbors.
rvf query store.rvf --vector "1.0,0.0,0.5,0.3" --k 10
rvf query store.rvf -v "0.5,0.5,0.0,0.0" -k 5 --jsonWith filters:
rvf query store.rvf -v "1.0,0.0" -k 10 \
--filter '{"eq":{"field":0,"value":{"string":"category_a"}}}'Delete vectors by ID or filter.
rvf delete store.rvf --ids 1,2,3
rvf delete store.rvf --filter '{"gt":{"field":0,"value":{"u64":100}}}'Show store status.
rvf status store.rvf
rvf status store.rvf --jsonInspect store segments and lineage.
rvf inspect store.rvf
rvf inspect store.rvf --jsonReclaim dead space from deleted vectors.
rvf compact store.rvf
rvf compact store.rvf --strip-unknown --jsonCreate a derived child store (COW branching).
rvf derive parent.rvf child.rvf --derivation-type clone
rvf derive parent.rvf child.rvf -t snapshot --jsonDerivation types: clone, filter, merge, quantize, reindex, transform, snapshot
Snapshot-freeze the current state.
rvf freeze store.rvfVerify the tamper-evident witness chain.
rvf verify-witness store.rvfVerify kernel binding and attestation.
rvf verify-attestation store.rvfStart an HTTP server (requires serve feature).
cargo build -p rvf-cli --features serve
rvf serve store.rvf --port 8080Boot an RVF file in a QEMU microVM (requires launch feature).
cargo build -p rvf-cli --features launch
rvf launch store.rvf --port 8080 --memory-mb 256All commands support --json for machine-readable output:
rvf status store.rvf --json | jq '.total_vectors'
rvf query store.rvf -v "1,0,0,0" -k 5 --json | jq '.results[].id'Platform-specific quickstart scripts are in examples/rvf/scripts/:
# Linux / macOS
bash examples/rvf/scripts/rvf-quickstart.sh
# Windows PowerShell
.\examples\rvf\scripts\rvf-quickstart.ps1MIT OR Apache-2.0