From 1eb5e81ac7de395ef94d4928c69168f4d6ef87b1 Mon Sep 17 00:00:00 2001 From: zTgx <747674262@qq.com> Date: Wed, 8 Apr 2026 20:10:27 +0800 Subject: [PATCH 1/2] chore(release): bump workspace and Python package versions - Update workspace package version from 0.1.19 to 0.1.20 in Cargo.toml - Update Python package version from 0.1.0 to 0.1.1 in pyproject.toml --- Cargo.toml | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/Cargo.toml b/Cargo.toml index 9da12c6c..cfa82590 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -3,7 +3,7 @@ members = ["rust", "python"] resolver = "2" [workspace.package] -version = "0.1.19" +version = "0.1.20" edition = "2024" authors = ["zTgx "] license = "Apache-2.0" diff --git a/pyproject.toml b/pyproject.toml index 3a77c43c..bee82a1f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -4,7 +4,7 @@ build-backend = "maturin" [project] name = "vectorless" -version = "0.1.0" +version = "0.1.1" description = "Hierarchical document intelligence without vectors" readme = "README.md" requires-python = ">=3.9" From 1ef9dab2763beae7089701dab307eb860184be9e Mon Sep 17 00:00:00 2001 From: zTgx <747674262@qq.com> Date: Wed, 8 Apr 2026 20:17:40 +0800 Subject: [PATCH 2/2] docs(README): simplify explanation of Vectorless approach - Remove redundant bullet points about hybrid algorithm + LLM approach - Streamline description to focus on core concept of using LLM to navigate document tree structure - Update navigation step to remove unnecessary parenthetical explanation - Remove backtracking mention from verification step for clarity --- README.md | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index e96095ad..3365eb7d 100644 --- a/README.md +++ b/README.md @@ -17,11 +17,7 @@ **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: - -- **Pilot (LLM)** handles "where to go" -- **Algorithm** handles "how to walk" - +Instead of chunking documents into vectors, Vectorless preserves the document's tree structure and uses LLM to navigate it — like how a human reads a table of contents. ## How It Works @@ -45,9 +41,9 @@ Each node gets an AI-generated summary, enabling fast navigation. When you ask "How do I reset the device?": 1. **Analyze** — Understand query intent and complexity -2. **Navigate** — LLM guides tree traversal (like reading a TOC) +2. **Navigate** — LLM guides tree traversal 3. **Retrieve** — Return the exact section with context -4. **Verify** — Check if more information is needed (backtracking) +4. **Verify** — Check if more information is needed ## Traditional RAG vs Vectorless