rag-paper

Local-first paper RAG for PDF research workflows.

rag-paper helps you build a private, local, searchable paper library from PDF files. It parses PDFs, chunks paper text, creates embeddings, stores vectors in local Chroma, and exposes retrieval through a CLI and MCP server for tools such as Codex CLI and Claude Code.

It is designed for researchers, students, engineers, and LLM power users who want to read papers efficiently while saving tokens and using affordable models such as DeepSeek, Qwen, local Ollama embeddings, or OpenAI-compatible embedding services.

中文 README

Keywords

paper RAG, local RAG, PDF RAG, academic search, Chroma, MCP server, Codex CLI, Claude Code, Zotero, Obsidian, citation graph, Mermaid, DOI enrichment, CrossRef, OpenAlex, semantic deduplication, local vector database, research assistant, low-cost LLM workflow

Related MCP server: MyDocsMCP

Why rag-paper

• Token-efficient paper reading: retrieve only relevant chunks instead of sending whole PDFs to an LLM.

• Local-first storage: vectors and metadata are stored locally in Chroma.

• MCP ready: expose paper search tools to Codex CLI, Claude Code, and other MCP clients.

• Low-cost model friendly: use local Ollama embeddings by default, or an OpenAI-compatible embedding endpoint.

• Zotero friendly: point root_path to one or more Zotero storage/export directories.

• Obsidian friendly: export citation graphs as Mermaid Markdown.

• Privacy controls: use skip_marker_file to prevent sensitive folders from being indexed.

Requirements

• Python 3.10+

• A local or remote embedding provider

• Default embedding setup: Ollama with qwen3-embedding:4b

ollama pull qwen3-embedding:4b

Installation

git clone https://github.com/your-name/rag-paper.git
cd rag-paper
python -m venv .venv
source .venv/bin/activate
pip install -e .

Windows PowerShell:

git clone https://github.com/your-name/rag-paper.git
Set-Location rag-paper
py -m venv .venv
.\.venv\Scripts\Activate.ps1
python -m pip install -e .

Quick Start

Create a config file:

rag-paper init-config --path ./config.json

Put PDFs under ./papers, or edit config.json and point papers[].root_path to your own directories.

Index PDFs:

rag-paper index

Search locally:

rag-paper search "retrieval augmented generation evaluation" --top-k 5

Inspect indexed papers:

rag-paper list-indexed-papers
rag-paper show-indexed-paper "attention" --limit 3

Delete a bad indexed record:

rag-paper delete-indexed-paper "10.55277/researchhub.x9vnpm0y.1"
rag-paper build-citation-graph

Start the MCP server:

rag-paper serve

Tech Stack

• PyMuPDF: PDF text extraction and PDF metadata reading.

• ChromaDB: local persistent vector database for paper chunks.

• Rank BM25: keyword retrieval path for hybrid search.

• MCP Python SDK: exposes paper search and inspection tools to Codex CLI, Claude Code, and other MCP clients.

Modules

Indexing

rag-paper index scans configured PDF roots, respects skip_marker_file, extracts text, chunks papers, creates embeddings, and writes chunks to Chroma. Before vectorization it shows the number of files and the root paths they came from unless --yes or indexing.assume_yes is enabled.

Indexing is incremental. rag-paper stores size + mtime_ns in the manifest and only computes SHA256 when the quick file signature changes. Failed files are recorded in a JSONL log and can be retried with rag-paper index --retry-failed.

Search and Retrieval

rag-paper search performs local hybrid retrieval over indexed chunks. It combines vector similarity from Chroma with BM25 keyword matching, then returns compact source-aware excerpts that are suitable for sending to an LLM instead of whole PDFs.

Search supports filters such as author, year, tag, and file name. The same retrieval engine powers the MCP tools.

Indexed Paper Inspection

rag-paper list-indexed-papers shows the current Chroma library as a terminal table, including total paper count, chunk count, title, DOI, year, and source path.

rag-paper show-indexed-paper supports fuzzy selectors over title, file name, source path, and DOI. It shows merged metadata and the first 5 chunk IDs by default; use --all-chunks to show every chunk ID.

rag-paper delete-indexed-paper deletes matching papers from Chroma and the index manifest. Without --yes, every matched paper must be confirmed one by one before deletion. After deleting indexed papers, run rag-paper build-citation-graph to refresh citation graph exports.

Metadata Enrichment

rag-paper enrich-metadata enriches indexed papers with DOI and bibliographic metadata. It supports CrossRef and OpenAlex, provider fallback, rate limiting, custom User-Agent, contact email, and HTTP/HTTPS/SOCKS5 proxies.

The enrichment module is decoupled from vectorization. It can run per indexed file, after indexing finishes, or manually. Results are written to paper_metadata.json and provider responses are cached in SQLite.

Title quality checks reject obvious spam, URLs, ad-like strings, and symbol-heavy PDF metadata titles. When a PDF title is not trusted, rag-paper prefers a title inferred from the first page, a provider title, or the file name.

Deduplication

rag-paper dedupe-papers reports duplicate candidates before indexing. It can compare DOI/title-year metadata and optional semantic signatures built from paper text or abstracts. Depending on configuration, duplicates can be reported or skipped.

Citation Graph

rag-paper build-citation-graph builds a graph from enriched DOI/OpenAlex metadata and exports JSON plus Mermaid Markdown. The Mermaid output is designed to work well in Obsidian for browsing paper relationships.

MCP Server

rag-paper serve starts an MCP server so external tools can query the local paper library. MCP clients can import papers, search chunks, list indexed papers, inspect metadata, delete indexed records, enrich metadata, deduplicate papers, fetch specific chunks, export context, and build citation graphs.

Typical Workflows

Use with Zotero

root_path is an array, so you can point rag-paper at multiple paper folders, including Zotero storage/export folders:

{
  "papers": [
    {
      "root_path": [
        "D:/Zotero/storage",
        "D:/Zotero/exports/LLM"
      ],
      "skip_marker_file": ".rag-paper-skip",
      "tags": ["zotero"]
    }
  ]
}

rag-paper recursively scans these roots and indexes only .pdf files.

Protect private folders with skip_marker_file

If a directory contains the configured marker file, rag-paper skips that directory and all of its children.

This is useful for privacy protection. For example, you can place .rag-paper-skip in folders containing unpublished papers, private notes, or papers that should not be exposed through MCP search.

{
  "papers": [
    {
      "root_path": ["./papers"],
      "skip_marker_file": ".rag-paper-skip"
    }
  ]
}

When a marker is detected, rag-paper highlights the warning and asks whether to continue unless --yes or indexing.assume_yes is enabled.

Back up and restore work

By default, core runtime data is stored under:

rag_paper_data/
  chroma_db/
  paper_metadata.json
  cache/
  citation_graph/
  logs/

Copying rag_paper_data/ is enough to back up the local Chroma vectors, metadata, cache, failure logs, retrieval stats, and citation graph exports when default paths are used.

To restore work on another device:

1. Install rag-paper.

2. Copy rag_paper_data/ into the project directory.

3. Copy your config.json if you customized paths.

4. Run rag-paper list-indexed-papers to verify the restored index.

Build a citation graph for Obsidian

After metadata enrichment, build a citation graph:

rag-paper build-citation-graph

rag-paper exports:

• JSON graph: rag_paper_data/citation_graph/citation_graph.json

• Mermaid Markdown: rag_paper_data/citation_graph/citation_graph.md

The Mermaid file can be opened directly in Obsidian or any Markdown tool with Mermaid support.

Metadata Enrichment

rag-paper can enrich indexed papers with DOI and bibliographic metadata.

Supported providers:

• CrossRef

• OpenAlex

Default order:

{
  "metadata_enrichment": {
    "providers": ["crossref", "openalex"]
  }
}

If the first provider fails or returns no match, rag-paper falls back to the next provider.

Run enrichment manually:

rag-paper enrich-metadata

Refresh existing metadata:

rag-paper enrich-metadata --force

Refresh one indexed PDF:

rag-paper enrich-metadata --file /path/to/paper.pdf --force

--file first checks whether the PDF has already been indexed in local Chroma. If not, rag-paper exits and asks you to index it first.

Metadata enrichment uses a SQLite cache by default:

rag_paper_data/cache/metadata_enrichment.sqlite3

This avoids repeatedly calling CrossRef/OpenAlex for the same DOI or title query.

MCP Usage

Start the MCP server:

rag-paper serve

Streamable HTTP configuration example:

{
  "mcpServers": {
    "rag-paper": {
      "url": "http://127.0.0.1:8765/mcp"
    }
  }
}

stdio configuration example:

{
  "mcpServers": {
    "rag-paper": {
      "command": "rag-paper",
      "args": ["serve"]
    }
  }
}

Available MCP tools include:

• service_info

• import_papers

• list_indexed_papers

• show_indexed_paper

• delete_indexed_paper

• search_papers

• search_by_metadata

• get_chunk

• export_context

• enrich_paper_metadata

• dedupe_papers

• build_paper_citation_graph

CLI Commands

rag-paper init-config
rag-paper index
rag-paper index --force
rag-paper index --file /path/to/paper.pdf
rag-paper index --only-new
rag-paper index --retry-failed
rag-paper enrich-metadata
rag-paper list-indexed-papers
rag-paper show-indexed-paper "selector"
rag-paper delete-indexed-paper "selector"
rag-paper search "query"
rag-paper dedupe-papers
rag-paper build-citation-graph
rag-paper serve

Notes on Indexing

rag-paper stores indexing state in:

rag_paper_data/chroma_db/index_manifest.json

It uses a two-stage change check:

1. Compare size + mtime_ns.

2. Compute SHA256 only when the quick check changed.

If indexing fails for a PDF, rag-paper records the failure in:

rag_paper_data/logs/index_failed.jsonl

Retry failed files:

rag-paper index --retry-failed

If you press Ctrl+C, completed files are already persisted in Chroma and the manifest, so the next run continues from the remaining files.

Development

pip install -e ".[dev]"
python -m pytest -q

License

This project is licensed under the MIT License. See LICENSE.

Configuration Reference

Common options:

• data_dir: core runtime data directory. Default: ./rag_paper_data

• papers[].root_path: array of PDF root directories. Useful with Zotero folders.

• papers[].skip_marker_file: marker filename used to skip private directories.

• papers[].tags: default tags for papers under the root paths.

• chroma.persist_dir: Chroma persistence directory.

• indexing.metadata_path: paper metadata JSON path.

• indexing.assume_yes: skip interactive confirmation.

• indexing.max_files: maximum PDFs to index.

• indexing.failed_path: index failure JSONL path.

• metadata_enrichment.enabled: enable DOI and metadata enrichment.

• metadata_enrichment.providers: provider order, e.g. ["crossref", "openalex"].

• metadata_enrichment.timing: per_file, after_index, or manual.

• metadata_enrichment.user_agent: User-Agent for CrossRef/OpenAlex requests.

• metadata_enrichment.mailto: contact email for provider etiquette.

• metadata_enrichment.openalex_email: OpenAlex email parameter.

• metadata_enrichment.requests_per_second: provider request rate limit.

• metadata_enrichment.http_proxy: HTTP proxy.

• metadata_enrichment.https_proxy: HTTPS proxy.

• metadata_enrichment.socks5_proxy: SOCKS5 proxy.

• metadata_enrichment.cache_path: SQLite enrichment cache path.

• dedup.enabled: enable duplicate report before indexing.

• dedup.action: report or skip.

• dedup.similarity_threshold: semantic duplicate threshold.

• citation_graph.path: citation graph JSON output.

• citation_graph.mermaid_path: Mermaid Markdown output for Obsidian.

• display.datetime_timezone: display timezone for metadata_enriched_at.

• display.datetime_format: strftime format for displayed datetimes.

• logging.level: log level.

• logging.stats_path: retrieval stats JSONL path.