deepresearch-flow 0.3.0

Workflow tools for paper extraction, review, and research automation.

ai-deepresearch-flow

From documents to deep research insight — automatically.

English | 中文

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The Core Pain Points

• OCR Chaos: Raw markdown from OCR tools is often broken -- tables drift, formulas break, and references are non-clickable.
• Translation Nightmares: Translating technical papers often destroys code blocks, LaTeX formulas, and table structures.
• Information Overload: Extracting structured insights (authors, venues, summaries) from hundreds of PDFs manually is impossible.
• Context Switching: Managing PDFs, summaries, and translations in different windows kills focus.

The Solution

DeepResearch Flow provides a unified pipeline to Repair, Translate, Extract, and Serve your research library.

Key Features

• Smart Extraction: Turn unstructured Markdown into schema-enforced JSON (summaries, metadata, Q&A) using LLMs (OpenAI, Claude, Gemini, etc.).
• Precision Translation: Translate OCR Markdown to Chinese/Japanese (.zh.md, .ja.md) while freezing formulas, code, tables, and references. No more broken layout.
• Local Knowledge DB: A high-performance local Web UI to browse papers with Split View (Source vs. Translated vs. Summary), full-text search, and multi-dimensional filtering.
• OCR Post-Processing: Automatically fix broken references ([1] -> [^1]), merge split paragraphs, and standardize layouts.

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Quick Start

1) Installation

# Recommended: using uv for speed
uv pip install deepresearch-flow

# Or standard pip
pip install deepresearch-flow

2) Configuration

Set up your LLM providers. We support OpenAI, Claude, Gemini, Ollama, and more.

cp config.example.toml config.toml
# Edit config.toml to add your API keys (e.g., env:OPENAI_API_KEY)

3) The "Zero to Hero" Workflow

Step 1: Extract Insights

Scan a folder of markdown files and extract structured summaries.

uv run deepresearch-flow paper extract \
  --input ./docs \
  --model openai/gpt-4o-mini \
  --prompt-template deep_read

Step 2: Translate Safely

Translate papers to Chinese, protecting LaTeX and tables.

uv run deepresearch-flow translator translate \
  --input ./docs \
  --target-lang zh \
  --model openai/gpt-4o-mini \
  --fix-level moderate

Step 3: Serve Your Database

Launch a local UI to read and manage your papers.

uv run deepresearch-flow paper db serve \
  --input paper_infos.json \
  --md-root ./docs \
  --md-translated-root ./docs \
  --host 127.0.0.1

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Comprehensive Guide

1. Translator: OCR-Safe Translation

The translator module is built for scientific documents. It uses a node-based architecture to ensure stability.

• Structure Protection: automatically detects and "freezes" code blocks, LaTeX ($$...$$), HTML tables, and images before sending text to the LLM.
• OCR Repair: use --fix-level to merge broken paragraphs and convert text references ([1]) to clickable Markdown footnotes ([^1]).
• Context-Aware: supports retries for failed chunks and falls back gracefully.

# Translate with structure protection and OCR repairs
uv run deepresearch-flow translator translate \
  --input ./paper.md \
  --target-lang ja \
  --fix-level aggressive \
  --model claude/claude-3-5-sonnet-20240620

2. Paper Extract: Structured Knowledge

Turn loose markdown files into a queryable database.

• Templates: built-in prompts like simple, eight_questions, and deep_read guide the LLM to extract specific insights.
• Async and throttled: precise control over concurrency (--max-concurrency) and rate limits (--sleep-every).
• Incremental: skips already processed files; resumes from where you left off.

uv run deepresearch-flow paper extract \
  --input ./library \
  --output paper_data.json \
  --template-dir ./my-custom-prompts \
  --max-concurrency 10

3. Database and UI: Your Personal ArXiv

The db serve command creates a local research station.

• Split View: read the original PDF/Markdown on the left and the Summary/Translation on the right.
• Full Text Search: search by title, author, year, or content tags (tag:fpga year:2023..2024).
• Stats: visualize publication trends and keyword frequencies.
• PDF Viewer: built-in PDF.js viewer prevents cross-origin issues with local files.

uv run deepresearch-flow paper db serve \
  --input paper_infos.json \
  --pdf-root ./pdfs \
  --cache-dir .cache/db

4. Recognize: OCR Post-Processing

Tools to clean up raw outputs from OCR engines like MinerU.

• Embed Images: convert local image links to Base64 for a portable single-file Markdown.
• Unpack Images: extract Base64 images back to files.
• Organize: flatten nested OCR output directories.
• Fix: apply OCR fixes and rumdl formatting during organize, or as a standalone step.

uv run deepresearch-flow recognize md embed --input ./raw_ocr --output ./clean_md

# Organize MinerU output and apply OCR fixes
uv run deepresearch-flow recognize organize \
  --input ./mineru_outputs \
  --output-simple ./ocr_md \
  --fix

# Fix and format existing markdown outputs
uv run deepresearch-flow recognize fix \
  --input ./ocr_md \
  --output ./ocr_md_fixed

# Fix in place
uv run deepresearch-flow recognize fix \
  --input ./ocr_md \
  --in-place

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Docker Support

Don't want to manage Python environments?

docker run --rm -v $(pwd):/app -it ghcr.io/nerdneilsfield/deepresearch-flow --help

Configuration

The config.toml is your control center. It supports:

• Multiple Providers: mix and match OpenAI, DeepSeek (DashScope), Gemini, Claude, and Ollama.
• Model Routing: explicit routing to specific models (--model provider/model_name).
• Environment Variables: keep secrets safe using env:VAR_NAME syntax.

See config.example.toml for a full reference.

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Built with love for the Open Science community.