Dev

.gitignore

@@ -53,4 +53,32 @@ config.toml
 .vectorless.toml
 
 # Test fixtures
-test_workspace/
+test_workspace/
+
+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+.pytest_cache/
+.mypy_cache/
+.ruff_cache/
+.venv/
+venv/
+ENV/

Cargo.toml

@@ -1,19 +1,17 @@
-[package]
-name = "vectorless"
+[workspace]
+members = ["rust", "python"]
+resolver = "2"
+
+[workspace.package]
 version = "0.1.19"
 edition = "2024"
 authors = ["zTgx <beautifularea@gmail.com>"]
-description = "Hierarchical, reasoning-native document intelligence engine"
 license = "Apache-2.0"
 repository = "https://github.com/vectorlessflow/vectorless"
 homepage = "https://vectorless.dev"
 documentation = "https://docs.rs/vectorless"
-keywords = ["rag", "document", "retrieval", "indexing", "llm"]
-categories = ["text-processing", "data-structures", "algorithms"]
-readme = "README.md"
-exclude = ["samples/", "docs/", ".*"]
 
-[dependencies]
+[workspace.dependencies]
 # Async runtime
 tokio = { version = "1", features = ["full"] }
 async-trait = "0.1"
@@ -26,7 +24,7 @@ toml = "0.8"
 
 # Error handling
 thiserror = "2"
-anyhow = { version = "1", optional = true }
+anyhow = "1"
 
 # OpenAI-compatible API client
 async-openai = { version = "0.34", features = ["chat-completion"] }
@@ -62,18 +60,17 @@ lru = "0.12"
 # Checksum
 sha2 = "0.10"
 
-# BLAKE2b hashing for fingerprints
+# BLAKE2b hashing
 blake2 = "0.10"
 base64 = "0.22"
 
-# Synchronization primitives (for memo store)
+# Synchronization primitives
 parking_lot = "0.12"
 
 # Compression
 flate2 = "1.0"
 
 # File locking (Unix)
-[target.'cfg(unix)'.dependencies]
 libc = "0.2"
 
 # PDF processing
@@ -84,7 +81,7 @@ lopdf = "0.34"
 zip = "2.2"
 roxmltree = "0.20"
 
-# Random number generation (for sampling)
+# Random number generation
 rand = "0.8"
 
 # BM25 scoring
@@ -93,11 +90,23 @@ bm25 = { version = "2.3.2", features = ["parallelism"] }
 # HTML parsing
 scraper = "0.22"
 
-[dev-dependencies]
+# Python bindings
+pyo3 = { version = "0.22", features = ["extension-module"] }
+
+# Dev dependencies
 tempfile = "3.10"
 tokio-test = "0.4"
 tracing-subscriber = { version = "0.3", features = ["env-filter"] }
 
+[workspace.lints.rust]
+missing_docs = "warn"
+unsafe_code = "warn"
+
+[workspace.lints.clippy]
+all = "warn"
+pedantic = "warn"
+
+# Profile settings (must be at root level, not under workspace)
 [profile.release]
 opt-level = 3
 lto = "thin"
@@ -115,11 +124,3 @@ debug = true
 
 [profile.release.package."*"]
 opt-level = 3
-
-[lints.rust]
-missing_docs = "warn"
-unsafe_code = "warn"
-
-[lints.clippy]
-all = "warn"
-pedantic = "warn"

README.md

@@ -1,18 +1,21 @@
 <div align="center">
 
-![Vectorless](docs/design/logo-horizontal.svg)
+<img src="https://raw.githubusercontent.com/vectorlessflow/vectorless/main/docs/design/logo-horizontal.svg" alt="Vectorless">
 
+[![PyPI](https://img.shields.io/pypi/v/vectorless.svg)](https://pypi.org/project/vectorless/)
+[![Python](https://img.shields.io/pypi/pyversions/vectorless.svg)](https://pypi.org/project/vectorless/)
+[![PyPI Downloads](https://static.pepy.tech/badge/vectorless/month)](https://pepy.tech/projects/vectorless)
 [![Crates.io](https://img.shields.io/crates/v/vectorless.svg)](https://crates.io/crates/vectorless)
-[![Downloads](https://img.shields.io/crates/d/vectorless.svg)](https://crates.io/crates/vectorless)
-[![Documentation](https://docs.rs/vectorless/badge.svg)](https://docs.rs/vectorless)
+[![Crates.io Downloads](https://img.shields.io/crates/d/vectorless.svg)](https://crates.io/crates/vectorless)
+[![Docs](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/)
 
 </div>
 
 ## What is Vectorless?
 
-**Vectorless** is a Rust library for querying structured documents using natural language — without vector databases or embedding models.
+**Vectorless** is a library for querying structured documents using natural language — without vector databases or embedding models. Core engine written in Rust, with Python bindings.
 
 Instead of chunking documents into vectors, Vectorless preserves the document's tree structure and uses a **hybrid algorithm + LLM approach** to navigate it — like how a human reads a table of contents:
 
@@ -22,7 +25,7 @@ Instead of chunking documents into vectors, Vectorless preserves the document's
 
 ## How It Works
 
-![How it works](docs/design/how-it-works.svg)
+<img src="https://raw.githubusercontent.com/vectorlessflow/vectorless/main/docs/design/how-it-works.svg" alt="How it works">
 
 ### 1. Index: Build a Navigable Tree
 
@@ -48,7 +51,7 @@ When you ask "How do I reset the device?":
 
 ## Traditional RAG vs Vectorless
 
-![Traditional RAG vs Vectorless](docs/design/comparison.svg)
+<img src="https://raw.githubusercontent.com/vectorlessflow/vectorless/main/docs/design/comparison.svg" alt="Traditional RAG vs Vectorless">
 
 | Aspect | Traditional RAG | Vectorless |
 |--------|----------------|------------|
@@ -90,44 +93,65 @@ Source: Chapter 4 > Section 4.2 > Reset Procedure
 
 ## Quick Start
 
-### Installation
+<details open>
+<summary><b>Python</b></summary>
+
+```bash
+pip install vectorless
+```
+
+```python
+from vectorless import Engine, IndexContext
+
+# Create engine (uses OPENAI_API_KEY env var)
+engine = Engine(workspace="./data")
+
+# Index a document
+ctx = IndexContext.from_file("./report.pdf")
+doc_id = engine.index(ctx)
+
+# Query
+result = engine.query(doc_id, "What is the total revenue?")
+print(f"Answer: {result.content}")
+```
+
+</details>
+
+<details>
+<summary><b>Rust</b></summary>
 
 ```toml
 [dependencies]
 vectorless = "0.1"
 ```
 
-### Configuration
-
 ```bash
 cp vectorless.example.toml ./vectorless.toml
 ```
 
-### Usage
-
 ```rust
 use vectorless::Engine;
 
 #[tokio::main]
 async fn main() -> vectorless::Result<()> {
-    // Create client
     let client = Engine::builder()
         .with_workspace("./workspace")
         .build()?;
 
-    // Index a document (PDF, Markdown, DOCX, HTML)
     let doc_id = client.index("./document.pdf").await?;
 
-    // Query with natural language
-    let result = client.query(&doc_id, "What are the system requirements?").await?;
+    let result = client.query(&doc_id,
+        "What are the system requirements?").await?;
 
     println!("Answer: {}", result.content);
-    println!("Source: {}", result.path); // e.g., "Chapter 2 > Section 2.1"
+    println!("Source: {}", result.path);
 
     Ok(())
 }
 ```
 
+</details>
+
 ## Features
 
 | Feature | Description |
@@ -142,7 +166,7 @@ async fn main() -> vectorless::Result<()> {
 
 ## Architecture
 
-![Architecture](docs/design/architecture.svg)
+<img src="https://raw.githubusercontent.com/vectorlessflow/vectorless/main/docs/design/architecture.svg" alt="Architecture">
 
 ### Core Components