Evidence RAG Pilot

A page-native, traceable RAG design prototype for engineering datasheets. 面向工程数据手册的版面级、可追溯 RAG 技术设计原型。

Evidence RAG Pilot is a portfolio project for showing how I designed and implemented an inspectable document-RAG pipeline. Instead of reducing a PDF to detached token windows, the system keeps document identity, page number, PDF coordinates, native text, section context, and evidence images together.

这个项目聚焦展示我的文档 AI 与 RAG 技术设计。系统不把 PDF 简化为脱离版面的文本切片，而是让文档、页码、PDF 坐标、原文、章节上下文和证据图片始终保持关联。

Evaluation status / 测评状态： This project has not undergone a formal quantitative evaluation. The current release demonstrates the design, implementation, and qualitative experience; it does not claim benchmark accuracy, SOTA performance, or superiority over Pixel RAG or other systems. 本项目尚未进行正式的定量测评。当前版本只展示技术设计、工程实现和体验效果，不声明基准准确率、SOTA 表现或优于 Pixel RAG 等其他系统。

Demos / 在线演示

The public sites preserve the original demo structure: corpus overview, search-result presentation, chunk browser, document tree, and evidence-dialog page. They read versioned static snapshots and require no Python service, GPU, API key, or cloud-model call.

公开站保留原始 Demo 的页面结构：语料概览、检索结果展示、Chunk 浏览、文档目录树和证据对话页。站点读取版本化静态快照，不需要 Python 服务、GPU、API Key 或云端模型调用。

Related MCP server: PDFDashboardWithMCP

Design focus / 技术设计重点

• Page-native chunks / 版面级切块 — tables, figures, captions, drawings, and text regions remain tied to their original page geometry.

• Traceable evidence / 可追溯证据 — a result can return to its document, page, TOC path, PDF bounding boxes, native text, and merged evidence image.

• Hybrid retrieval / 混合检索 — the local pipeline combines multilingual dense retrieval with model-number-aware BM25 through reciprocal-rank fusion (RRF).

• Stable evidence contract / 稳定证据契约 — retrieval and generation models can change without changing the downstream evidence interface.

• Reusable MCP interface / 可复用 MCP 接口 — agents can request evidence packages without depending on the internal indexing implementation.

flowchart LR
    A["PDF pages"] --> B["Layout detection"]
    B --> C["Page-native grouping"]
    C --> D["Native text + visual description"]
    D --> E["Dense index"]
    D --> F["Model-aware BM25"]
    E --> G["RRF fusion"]
    F --> G
    G --> H["Evidence package: document + page + bbox + image"]
    H --> I["Optional answer model"]

The central product interface is the evidence package, not an opaque model response. Its fields remain inspectable even when layout, embedding, ranking, or answer models are replaced.

核心产品接口是证据包，而不是不可检查的模型回答。即使版面模型、Embedding、排序或生成模型被替换，证据字段仍保持可检查。

Local pipeline / 本地完整管线

Python 3.11–3.12 is supported.

git clone https://github.com/zzkws/evidence-rag-pilot.git
cd evidence-rag-pilot
python -m venv .venv
pip install -e ".[embedding,mcp]"

Download the full public corpus snapshot from v0.1.0, restore it under corpora/txc or corpora/tkd, then select a corpus with RAG_CORPUS.

RAG_CORPUS=tkd python -m pipeline.search "32.768 kHz load capacitance"

PowerShell:

$env:RAG_CORPUS = "tkd"
python -m pipeline.search "32.768 kHz load capacitance"

Processing stages / 处理阶段

1. pipeline.render — render PDF pages while preserving PDF-point coordinates.

2. pipeline.layout — detect and de-duplicate tables, figures, captions, and text regions.

3. pipeline.extract_text — recover the born-digital text layer per element.

4. pipeline.vlm_blocks — group elements and attach descriptions and TOC paths.

5. pipeline.merge_crops — create a readable evidence image for each chunk.

6. pipeline.index_build — build dense and BM25 indexes.

7. pipeline.search — retrieve and fuse ranked results with RRF.

run_full_corpus.py orchestrates resumable corpus processing. Offline VLM enrichment uses GEMINI_API_KEY; answer generation is optional and configured through generic OpenAI-compatible environment variables. Retrieval can run without an answer model.

Evidence MCP

The MCP server exposes a small evidence-oriented interface:

• build_evidence_package

• get_chunk

• get_evidence_image

pip install -e ".[embedding,mcp]"
python evidence_mcp_server.py

Repository map / 仓库结构

common/                 tokenization, drawing, and embedding adapters
pipeline/               PDF-to-evidence-index processing stages
webapp/                 original local demo interface and Python server
sites/txc-demo/         static public deployment of the TXC demo
sites/tkd-demo/         static public deployment of the TKD demo
tools/                  public export, safety checks, and release packaging
tests/                  core behavior and public-data contract tests

Current boundaries / 当前边界

• Scanned PDFs do not yet use a dedicated OCR engine.

• Tables are not converted into a cell-level structured representation.

• Continued tables are not automatically joined across pages.

• Public Sites display static snapshots; dense retrieval, RRF, ingestion, and optional answer generation remain local-pipeline capabilities.

• Datasheet redistribution rights remain separate from the Apache-2.0 code license.

License and data

Original source code is licensed under Apache-2.0. Dataset rights and third-party artifacts retain their own terms; see DATA_NOTICE.md and THIRD_PARTY_NOTICES.md. Security reports should follow SECURITY.md.