# Vectorless
A hierarchical, reasoning-native document intelligence engine.
## Features
- **Tree-based indexing** — Documents are organized as hierarchical trees, not flat vectors
- **LLM-driven retrieval** — Uses reasoning to navigate document structure
- **Multi-format support** — Markdown, PDF, HTML, DOCX (planned)
- **Workspace persistence** — LRU-cached storage with lazy loading
- **Configurable retrieval** — Pluggable retriever strategies (LLM navigate, beam search, MCTS)
## Quick Start
```rust
use vectorless::client::{Vectorless, VectorlessBuilder};
#[tokio::main]
async fn main() -> vectorless::core::Result<()> {
// Create client
let mut client = VectorlessBuilder::new()
.with_workspace("./workspace")
.build()?;
// Index a document
let doc_id = client.index("./document.md").await?;
// Query
let result = client.query(&doc_id, "What is this about?").await?;
println!("{}", result.content);
Ok(())
}
```
## Configuration
Create `config.toml` in your project root:
```toml
[summary]
model = "gpt-4o-mini"
endpoint = "https://api.openai.com/v1"
api_key = "sk-..."
[retrieval]
model = "gpt-4o"
retriever_type = "llm_navigate"
top_k = 3
[storage]
workspace_dir = "./workspace"
```
## Status
Early development. Core functionality works:
- ✅ Markdown indexing with LLM summaries
- ✅ Tree-based retrieval via LLM navigation
- ✅ Workspace persistence with LRU cache
- 🚧 PDF/HTML/DOCX parsing
- 🚧 Additional retriever strategies
## License
Apache-2.0