A fast RAG tool that indexes documents using your choice of embedding provider and stores them in LanceDB for efficient similarity search.

# Create a config
$ quickrag init

# Index documents
$ quickrag index gutenberg/ --output gutenberg.rag
✔ Parsing documents from gutenberg/... (using recursive-token chunker)
✔ Detecting embedding dimensions
✔ Initializing database
✔ Finding files to index
✔ Removing deleted files from index
✔ Preparing for indexing
✔ Indexing files
✔ Finalizing
Indexing complete! Processed 622 chunks across 2 files. Removed 1 deleted file.
Added 619 new chunks (3 already existed). Total chunks in database: 619

# Search
$ quickrag query gutenberg.rag "Who is Sherlock Holmes?"

• Multiple embedding providers (VoyageAI, OpenAI, Ollama)
• Token-based recursive chunking (default) or character-based chunking
• LanceDB vector storage with persistent .rag files
• Idempotent indexing (tracks indexed files, skips unchanged)
• Automatic cleanup of deleted files from index
• UTF-8 sanitization for PDF conversions
• TypeScript & Bun

brew install statico/quickrag/quickrag

# macOS (Apple Silicon)
curl -L https://github.com/statico/quickrag/releases/latest/download/quickrag-darwin-arm64 -o /usr/local/bin/quickrag
chmod +x /usr/local/bin/quickrag

# macOS (Intel)
curl -L https://github.com/statico/quickrag/releases/latest/download/quickrag-darwin-x64 -o /usr/local/bin/quickrag
chmod +x /usr/local/bin/quickrag

# Linux (ARM64)
curl -L https://github.com/statico/quickrag/releases/latest/download/quickrag-linux-arm64 -o /usr/local/bin/quickrag
chmod +x /usr/local/bin/quickrag

# Linux (x64)
curl -L https://github.com/statico/quickrag/releases/latest/download/quickrag-linux-x64 -o /usr/local/bin/quickrag
chmod +x /usr/local/bin/quickrag

Note: macOS binaries are not codesigned. You may need to run xattr -d com.apple.quarantine /usr/local/bin/quickrag to bypass Gatekeeper.

Requires Bun.

git clone https://github.com/statico/quickrag.git
cd quickrag
bun install
bun run dev --help

quickrag init

This creates ~/.config/quickrag/config.yaml:

provider: ollama
model: nomic-embed-text
baseUrl: http://localhost:11434
chunking:
  strategy: recursive-token
  chunkSize: 500
  chunkOverlap: 50
  minChunkSize: 50

Edit ~/.config/quickrag/config.yaml to set API keys and preferences:

provider: openai
apiKey: sk-your-key-here
model: text-embedding-3-small
chunking:
  strategy: recursive-token
  chunkSize: 500
  chunkOverlap: 50
  minChunkSize: 50

quickrag index ./documents --output my-docs.rag

quickrag query my-docs.rag "What is the main topic?"

Configuration Options:

• provider: Embedding provider (openai, voyageai, or ollama)
• apiKey: API key (can also use environment variables)
• model: Model name for the embedding provider
• baseUrl: Base URL for Ollama (default: http://localhost:11434)
• chunking.strategy: recursive-token (default) or simple
• chunking.chunkSize: Tokens (for recursive-token, default: 500) or characters (for simple, default: 1000)
• chunking.chunkOverlap: Tokens (for recursive-token, default: 50) or characters (for simple, default: 200)
• chunking.minChunkSize: Minimum chunk size in tokens (default: 50). Chunks smaller than this are filtered out to prevent tiny fragments.

Token-based splitting that respects semantic boundaries. Splits at paragraph breaks, line breaks, sentence endings, then word boundaries. Chunks are sized by estimated tokens (default: 500), aligning with embedding model expectations. Maintains configurable overlap (default: 50 tokens, ~10%).

Character-based chunking for backward compatibility. Chunks are sized by characters (default: 1000) with sentence boundary detection. Overlap is character-based (default: 200).

Benchmarked on test corpus (2 files: sherlock-holmes.txt, frankenstein.txt):

Recommendation: Use recursive-token for production. The indexing time difference is negligible compared to improved retrieval quality.

Most Use Cases:

• strategy: recursive-token
• chunkSize: 400-512 (tokens) - Research-backed sweet spot for 85-90% recall
• chunkOverlap: 50-100 (tokens, ~10-20%)

Technical Documentation:

• strategy: recursive-token
• chunkSize: 500-600 (tokens)
• chunkOverlap: 75-100 (tokens)

Narrative Text:

• strategy: recursive-token
• chunkSize: 400-500 (tokens)
• chunkOverlap: 50-75 (tokens)

Academic Papers:

• strategy: recursive-token
• chunkSize: 600-800 (tokens)
• chunkOverlap: 100-150 (tokens)

# Basic indexing
quickrag index ./documents --output my-docs.rag

# Override chunking parameters
quickrag index ./documents --chunker recursive-token --chunk-size 500 --chunk-overlap 50 --min-chunk-size 50 --output my-docs.rag

# Use different provider
quickrag index ./documents --provider openai --model text-embedding-3-small --output my-docs.rag

# Clear existing index
quickrag index ./documents --clear --output my-docs.rag

Note: QuickRAG automatically detects and removes deleted files from the index. If a file was previously indexed but no longer exists in the source directory, it will be removed from the database during the next indexing run.

quickrag query my-docs.rag "What is the main topic?"

quickrag interactive my-docs.rag

provider: voyageai
apiKey: your-voyage-api-key
model: voyage-3

provider: openai
apiKey: sk-your-openai-key
model: text-embedding-3-small

provider: ollama
model: nomic-embed-text
baseUrl: http://localhost:11434

• .txt - Plain text files
• .md - Markdown files
• .markdown - Markdown files

bun install
bun run dev index ./documents --provider ollama --output test.rag
bun run build
bun run typecheck

• Bun >= 1.0.0
• TypeScript >= 5.0.0
• For Ollama: A running Ollama instance with an embedding model installed (e.g., ollama pull nomic-embed-text)

This is free and unencumbered software released into the public domain.

For more information, see UNLICENSE or visit https://unlicense.org