diff --git a/README.md b/README.md
index dd9ed5c..3fd73c5 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,15 @@
+
+> [!NOTE]
+> 本文档由 WeHub 基于上游 README 翻译整理，属于社区翻译，非官方中文文档。
+> [English](./README.en.md) · [原始项目](https://github.com/StarTrail-org/PixelRAG) · [上游 README](https://github.com/StarTrail-org/PixelRAG/blob/HEAD/README.md)
+> 原作者、版权与许可证归属以原始项目及本仓库 LICENSE 文件为准。
+
- 
+
- Official codebase for PIXELRAG: Web Screenshots Beat Text for
-Retrieval-Augmented Generation
+ PIXELRAG: Web Screenshots Beat Text for
+Retrieval-Augmented Generation 的官方代码库
Yichuan Wang*,
@@ -16,8 +22,8 @@ Retrieval-Augmented Generation
Joseph E. Gonzalez†,
Sewon Min†
-* Equal contribution † Equal advising
Work done at Berkeley SkyLab & BAIR & Berkeley NLP
-Search any document by how it looks, not just the text it contains.
+* 同等贡献 † 同等指导
工作完成于 Berkeley SkyLab & BAIR & Berkeley NLP
+按文档的外观搜索，而不仅仅看它包含的文字。
@@ -28,10 +34,10 @@ Retrieval-Augmented Generation
- What it is ·
- Give Claude eyes ·
- How it works ·
- Pipelines
+ 它是什么 ·
+ 给 Claude 一双「眼睛」 ·
+ 工作原理 ·
+ 流水线
---
@@ -40,7 +46,7 @@ Retrieval-Augmented Generation
pip install pixelrag
```
-The two core operations — **render** a page to screenshots, **search** a visual index:
+两大核心操作——将页面**渲染（render）**为截图，并在视觉索引中**搜索（search）**：
```bash
# Render any page or document to screenshot tiles
@@ -52,30 +58,19 @@ curl -X POST https://api.pixelrag.ai/search \
-d '{"queries": [{"text": "What is the capital of France?"}], "n_docs": 5}'
```
-> **Live, hosted endpoint** — [`https://api.pixelrag.ai`](https://api.pixelrag.ai/status) serves a
-> pre-built index of **8.28M Wikipedia pages**. No setup, no API key. It even takes an image as the query
-> ([visual search](https://pixelrag.ai/docs#search)) — see the **[API reference →](https://pixelrag.ai/docs)**.
+> **在线托管端点** — [`https://api.pixelrag.ai`](https://api.pixelrag.ai/status) 提供由 **8.28M** 篇 Wikipedia 页面构成的预构建索引。无需配置，无需 API 密钥。查询甚至可以是图片（[视觉搜索（visual search）](https://pixelrag.ai/docs#search)) — 详见 **[API 参考 →](https://pixelrag.ai/docs)**.
-Or try it in the browser at **[pixelrag.ai](https://pixelrag.ai)**, or run the demo notebook in
-Colab [](https://colab.research.google.com/github/StarTrail-org/PixelRAG/blob/main/demos/quickstart.ipynb) — it
-renders a page and searches the hosted index, with the images inline.
+或在浏览器中访问 **[pixelrag.ai](https://pixelrag.ai)**,，或在 Colab 中运行演示 notebook [](https://colab.research.google.com/github/StarTrail-org/PixelRAG/blob/main/demos/quickstart.ipynb) — 它会渲染页面并搜索托管索引，图像内联显示。
-## What it is
+## 它是什么
-PixelRAG renders documents — web pages, PDFs, images — as screenshots and retrieves over the
-images directly. Visual structure that HTML parsing throws away — tables, charts, layout,
-infographics — stays intact, so the reader model can actually answer questions about it.
-Wikipedia's 8.28M articles ship as a pre-built index; the pipeline itself is general-purpose.
+PixelRAG 将文档——网页、PDF、图像——渲染为截图，并直接在图像上进行检索。HTML 解析会丢弃的视觉结构——表格、图表、版式、信息图——得以保留，因此阅读模型能够真正回答相关问题。Wikipedia 的 8.28M 篇文章以预构建索引形式提供；流水线本身则是通用型的。
-## Give Claude eyes
+## 给 Claude 一双「眼睛」
-The renderer also ships as a Claude Code plugin — the **pixelbrowse** skill. Instead of fetching
-raw HTML, Claude screenshots a page with `pixelshot` and _reads the image_, so it sees
-charts, diagrams, tables, and layout the way a person does.
+渲染器还以 Claude Code 插件形式提供——**pixelbrowse** 技能。Claude 不再抓取原始 HTML，而是用 `pixelshot` 对页面截图并*阅读图像*，从而像人一样看到图表、示意图、表格和版式。
-Install it — no clone needed. Install the `pixelshot` CLI so it's on your `PATH`
-(use `uv tool` or `pipx` to keep it isolated yet always available to Claude — a
-plain `pip install` into a project venv may leave `pixelshot` off `PATH`):
+安装即可——无需克隆仓库。安装 `pixelshot` CLI，使其位于你的 `PATH` 上（使用 `uv tool` 或 `pipx`，既可隔离环境，又随时可供 Claude 使用——仅向项目 venv 执行普通 `pip install` 可能会使 `pixelshot` 不在 `PATH` 中）：
```bash
uv tool install pixelrag # pixelshot on PATH (or: pipx install pixelrag)
@@ -83,50 +78,45 @@ claude plugin marketplace add StarTrail-org/PixelRAG
claude plugin install pixelbrowse@pixelrag-plugins
```
-Then just ask Claude to look at a page:
+然后直接让 Claude 查看某个页面即可：
```bash
claude -p "screenshot https://news.ycombinator.com and summarize the top stories"
claude -p "screenshot https://arxiv.org/abs/2404.12387 and explain the key findings"
```
-Or use the slash command in an interactive session: `/screenshot https://example.com`.
-No MCP server, no backend: the skill just calls `pixelshot` (Playwright/CDP) on your machine.
+或在交互式会话中使用斜杠命令：`/screenshot https://example.com`。
+无需 MCP 服务器，也无需后端：该技能只在你本机调用 `pixelshot`（Playwright/CDP）。
-## How it works
+## 工作原理
- 
+
-Text-based RAG parses the page to text chunks and **loses the table** — the reader can't find the
-answer. PixelRAG renders the page to **screenshot tiles**, retrieves the right tile, and the reader
-reads the number straight off the image.
+基于文本的 RAG 将页面解析为文本块并**丢失表格**——阅读器找不到答案。PixelRAG 将页面渲染为**截图分块（screenshot tiles）**，检索到正确的分块后，阅读器可直接从图像中读取数字。
-Two pieces make this work: (1) rendering documents to images instead of parsing them to text, and
-(2) a `Qwen3-VL-Embedding` model, LoRA-fine-tuned on screenshot data, that embeds page images into
-a space where visual content is retrievable.
+实现这一点需要两个组件：(1) 将文档渲染为图像，而非解析为文本；(2) 一个在截图数据上经 LoRA 微调的 `Qwen3-VL-Embedding` 模型，将页面图像嵌入到可视内容可检索的空间中。
-## Pipelines
+## 流水线
-Capture is the standalone `pixelshot` command; the rest of the pipeline runs through the
-`pixelrag` umbrella — `pixelrag `. Install only the stages you need:
+Capture 是独立的 `pixelshot` 命令；流水线的其余部分通过 `pixelrag` 总入口运行——`pixelrag `。只需安装你需要的阶段：
-| Command | What it does | Install |
+| 命令 | 功能 | 安装 |
| ------------------------------------------ | --------------------------------------------------------------- | ------------------------------- |
-| `pixelshot` | Document → image tiles (Playwright CDP, PDF) | `pip install pixelrag` |
-| `pixelrag chunk` · `embed` · `build-index` | Tiles → vectors → FAISS index | `pip install 'pixelrag[embed]'` |
-| `pixelrag index` | Orchestrates the full pipeline: source → ingest → embed → index | `pip install 'pixelrag[index]'` |
-| `pixelrag serve` | FAISS search API (FastAPI, CPU or GPU) | `pip install 'pixelrag[serve]'` |
+| `pixelshot` | 文档 → 图像分块（Playwright CDP、PDF） | `pip install pixelrag` |
+| `pixelrag chunk` · `embed` · `build-index` | 分块 → 向量 → FAISS 索引 | `pip install 'pixelrag[embed]'` |
+| `pixelrag index` | 编排完整流水线：源 → 摄取 → 嵌入 → 索引 | `pip install 'pixelrag[index]'` |
+| `pixelrag serve` | FAISS 搜索 API（FastAPI，CPU 或 GPU） | `pip install 'pixelrag[serve]'` |
```
render ←── index ──→ embed serve (independent) train → serve (HTTP)
```
-**`train` is a separate uv project** with its own pinned env (`torch==2.9.1+cu129`,
-`transformers==4.57.1`, cuDNN 9.20) — install it from inside `train/`, not from the root.
+**`train` 是一个独立的 uv 项目**，拥有各自锁定的环境（`torch==2.9.1+cu129`、
+`transformers==4.57.1`、cuDNN 9.20）——请在 `train/` 内部安装，而非在仓库根目录。
-### Search a pre-built index
+### 搜索预构建索引
```bash
pip install 'pixelrag[serve]'
@@ -144,9 +134,9 @@ curl -X POST http://localhost:30001/search \
-d '{"queries": [{"text": "What is the capital of France?"}], "n_docs": 5}'
```
-### Build an index from your own documents
+### 用自己的文档构建索引
-Works on **Linux (CUDA)** and **macOS (Apple Silicon / MPS)** — `device: auto` picks the best backend.
+适用于 **Linux（CUDA）** 和 **macOS（Apple Silicon / MPS）**——`device: auto` 会自动选择最佳后端。
```bash
pip install 'pixelrag[index]'
@@ -170,9 +160,9 @@ pixelrag serve --index-dir ./my_index --port 30001
```
-Try it: index a PDF and search it locally
+试试看：索引 PDF 并在本地搜索
-No GPU required — runs on macOS (Apple Silicon) or any machine with Python 3.10+.
+无需 GPU — 可在 macOS（Apple Silicon）或任何装有 Python 3.10+ 的机器上运行。
```bash
pip install 'pixelrag[index]'
@@ -207,7 +197,7 @@ curl -X POST http://localhost:30001/search \
-### Render a page programmatically
+### 以编程方式渲染页面
```python
from pixelrag_render import render_url
@@ -216,7 +206,7 @@ from pixelrag_render import render_url
tiles = render_url("https://en.wikipedia.org/wiki/Python", "./tiles")
```
-The same rendering is available as a CLI — `pixelshot` ships with `pip install pixelrag`:
+同样的渲染能力也可通过 CLI 使用 — `pixelshot` 随 `pip install pixelrag` 一起提供：
```bash
# Web page → tiles (headless Chromium via CDP)
@@ -230,16 +220,11 @@ pixelshot paper.pdf -o ./tiles --dpi 200
pixelshot https://github.com/StarTrail-org/PixelRAG paper.pdf -o ./tiles
```
-> **Chrome on Windows/macOS** — the bundled turbo `headless_shell` auto-installs on
-> **linux-x64** only. Elsewhere, `pixelshot` uses your system Chrome/Chromium (or
-> Playwright's Chromium), auto-detected from the standard install locations. Point it at
-> a specific binary with `CHROME_PATH=/path/to/chrome` if it isn't found automatically.
-> Each render runs in an isolated, throwaway Chrome profile, so it works even while you
-> have Chrome open.
+> **Windows/macOS 上的 Chrome** — 捆绑的 turbo `headless_shell` 仅在 **linux-x64** 上自动安装。在其他平台上，`pixelshot` 会使用你系统中的 Chrome/Chromium（或 Playwright 的 Chromium），从标准安装位置自动检测。若无法自动找到，可通过 `CHROME_PATH=/path/to/chrome` 指定具体可执行文件路径。每次渲染都在隔离的临时 Chrome 配置文件中运行，因此即使你正在使用 Chrome 也能正常工作。
-### Embed tools (standalone)
+### 嵌入工具（独立运行）
-Each stage runs independently, without the orchestrator:
+各阶段可独立运行，无需编排器（orchestrator）：
```bash
pip install 'pixelrag[embed]'
@@ -249,24 +234,17 @@ pixelrag embed --shard-dir ./tiles --output-dir ./embeddings --gpu-ids 0,1
pixelrag build-index --embeddings-dir ./embeddings --output-dir ./index
```
-### Training
+### 训练
-Fine-tuning lives in `train/` — a **separate uv project** (`wiki-screenshot-training`) with its own
-pinned env. It LoRA-fine-tunes `Qwen/Qwen3-VL-Embedding-2B` for webpage retrieval; run it from
-inside `train/` (`cd train && uv sync`). See [`train/README.md`](train/README.md) for the full recipe.
+微调代码位于 `train/` — 这是一个 **独立的 uv 项目**（`wiki-screenshot-training`），拥有自己固定版本的环境。它对 `Qwen/Qwen3-VL-Embedding-2B` 进行 LoRA 微调，用于网页检索；请在 `train/`（`cd train && uv sync`）内运行。完整流程请参阅 [`train/README.md`](train/README.md)。
-You don't need to retrain to use the model — the trained adapters are published at
-[`Chrisyichuan/wiki-screenshot-embedding-lora`](https://huggingface.co/Chrisyichuan/wiki-screenshot-embedding-lora/tree/main/lora_vit/ckpt200).
+使用模型无需自行重新训练 — 训练好的适配器已发布在 [`Chrisyichuan/wiki-screenshot-embedding-lora`](https://huggingface.co/Chrisyichuan/wiki-screenshot-embedding-lora/tree/main/lora_vit/ckpt200).
-We also release the full training set
-([`Chrisyichuan/screenshot-training-natural-filtered-v2`](https://huggingface.co/datasets/Chrisyichuan/screenshot-training-natural-filtered-v2)),
-so you can adapt other backbones yourself — a larger Qwen, or any other embedding model.
-The data curation pipeline (LLM-augmented query generation, filtering, hard-negative mining)
-is documented in [`train/docs/synthetic_data_pipeline.md`](train/docs/synthetic_data_pipeline.md).
+我们还发布了完整训练集（[`Chrisyichuan/screenshot-training-natural-filtered-v2`](https://huggingface.co/datasets/Chrisyichuan/screenshot-training-natural-filtered-v2)),），方便你自行适配其他骨干模型 — 例如更大的 Qwen，或任意其他嵌入模型。数据整理流水线（LLM 增强的查询生成、过滤、困难负样本挖掘）详见 [`train/docs/synthetic_data_pipeline.md`](train/docs/synthetic_data_pipeline.md)。
-## Citation
+## 引用
-If you find PixelRAG useful, please cite our paper:
+如果你觉得 PixelRAG 有用，请引用我们的论文：
```bibtex
@misc{wang2026pixelragwebscreenshotsbeat,
@@ -280,17 +258,17 @@ If you find PixelRAG useful, please cite our paper:
}
```
-## Acknowledgments
+## 致谢
-Thanks to [Rulin Shao](https://rulinshao.github.io/) for support.
+感谢 [Rulin Shao](https://rulinshao.github.io/) 的支持。
-Thanks also to [Claude Code](https://github.com/anthropics/claude-code) and
-[OpenAI Codex](https://github.com/openai/codex) for supporting open-source contributors with credits and plans,
-which we earned by working on [LEANN](https://github.com/StarTrail-org/LEANN).
+同时感谢 [Claude Code](https://github.com/anthropics/claude-code) 和
+[OpenAI Codex](https://github.com/openai/codex) 为开源贡献者提供积分与方案支持，
+这些支持是我们在参与 [LEANN](https://github.com/StarTrail-org/LEANN). 工作期间获得的。
-This work is done by the [Berkeley Sky Computing Lab](https://sky.cs.berkeley.edu/),
-[BAIR](https://bair.berkeley.edu/), and the [Berkeley NLP Group](https://nlp.cs.berkeley.edu/).
+本工作由 [Berkeley Sky Computing Lab](https://sky.cs.berkeley.edu/),
+[BAIR](https://bair.berkeley.edu/), 与 [Berkeley NLP Group](https://nlp.cs.berkeley.edu/). 完成。
-## License
+## 许可证
Apache-2.0