vectorless 0.1.9

Hierarchical, reasoning-native document intelligence engine
Documentation 
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<div align="center">

![Vectorless](docs/assets/brand/logo-horizontal.svg)

[![Crates.io](https://img.shields.io/crates/v/vectorless.svg)](https://crates.io/crates/vectorless)
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[![Documentation](https://docs.rs/vectorless/badge.svg)](https://docs.rs/vectorless)
[![License](https://img.shields.io/badge/license-Apache--2.0-blue.svg)](LICENSE)
[![Rust](https://img.shields.io/badge/rust-1.85%2B-orange.svg)](https://www.rust-lang.org/)

**A hierarchical, reasoning-native document intelligence engine.**


</div>

## Why Vectorless?

Traditional RAG systems have a fundamental problem: **they lose document structure.**

When you chunk a document into vectors, you lose:
- The hierarchical relationship between sections
- The context of where information lives
- The ability to navigate based on reasoning

**Vectorless takes a different approach:**

It preserves your document's tree structure and uses an LLM to navigate it — just like a human would skim a table of contents, then drill into relevant sections.

**Result:** More accurate retrieval with zero infrastructure complexity.

## How It Works

![Architecture](docs/design/how-it-works.svg)

**Vectorless** preserves your document's hierarchical structure and uses a multi-stage pipeline for intelligent retrieval:

### Index Pipeline

Transforms documents into a navigable tree structure:

1. **Parse** — Parse documents (Markdown, PDF, DOCX, HTML) into structured content
2. **Build** — Construct document tree with metadata
3. **Enhance** — Add table of contents and section detection
4. **Enrich** — Generate AI summaries for tree nodes
5. **Optimize** — Optimize tree structure for efficient retrieval

### Retrieval Pipeline

Uses adaptive, multi-stage retrieval with backtracking:

1. **Analyze** — Detect query complexity, extract keywords
2. **Plan** — Select optimal strategy (keyword/semantic/LLM) and algorithm
3. **Search** — Execute tree traversal (greedy/beam/MCTS)
4. **Judge** — Evaluate sufficiency, trigger backtracking if needed

This mimics how humans navigate documentation: skim the TOC, drill into relevant sections, and backtrack when needed.

## Comparison

| Aspect | Vectorless | Traditional RAG |
|--------|-----------|-----------------|
| **Infrastructure** | Zero | Vector DB + Embedding Model |
| **Setup Time** | Minutes | Hours to Days |
| **Reasoning** | Native navigation | Similarity search only |
| **Document Structure** | Preserved | Lost in chunking |
| **Incremental Updates** | Supported | Full re-index required |
| **Debugging** | Traceable navigation path | Black box similarity scores |
| **Best For** | Structured documents | Unstructured text |

## Installation

Add to your `Cargo.toml`:

```toml
[dependencies]
vectorless = "0.1"
```

## Quick Start

Create a configuration file `vectorless.toml` in your project root:

```bash
cp templates/template.toml ./vectorless.toml
```

Basic usage:

```rust
use vectorless::client::{Engine, EngineBuilder};

#[tokio::main]
async fn main() -> vectorless::domain::Result<()> {
    // Create client
    let client = EngineBuilder::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(())
}
```

## Examples

See the [examples/](examples/) directory for complete working examples:

## Architecture

![Architecture](docs/design/architecture-v2.svg)

## Contributing

Contributions are welcome!

If you find this project useful, please consider giving it a star on [GitHub](https://github.com/vectorlessflow/vectorless) — it helps others discover it and supports ongoing development.

## Star History

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## License

Licensed under the Apache License, Version 2.0. See [LICENSE](LICENSE) for details.