deepresearch-flow 0.1.2

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

deepresearch-flow

DeepResearch Flow command-line tools for document extraction, OCR post-processing, and paper database operations.

Quick Start

pip install deepresearch-flow
# or
uv pip install deepresearch-flow

# Development install
pip install -e .

cp config.example.toml config.toml

# Extract from a docs folder
uv run deepresearch-flow paper extract \
  --input ./docs \
  --model openai/gpt-4o-mini

# Serve a local UI
uv run deepresearch-flow paper db serve \
  --input ./paper_infos_simple.json \
  --host 127.0.0.1 \
  --port 8000

Docker images:

docker run --rm -it nerdneils/deepresearch-flow --help
# or
docker run --rm -it ghcr.io/nerdneilsfield/deepresearch-flow --help

Commands

deepresearch-flow is the top-level CLI. Workflows live under paper and recognize. Use deepresearch-flow --help, deepresearch-flow paper --help, and deepresearch-flow recognize --help to explore flags.

Configuration details

Copy config.example.toml to config.toml and edit providers.

• Providers are configured under [[providers]].
• Use api_keys = ["env:OPENAI_API_KEY"] to read from environment variables.
• model_list is required for each provider and controls allowed provider/model values.
• Explicit model routing is required: --model provider/model.
• Supported provider types: ollama, openai_compatible, dashscope, gemini_ai_studio, gemini_vertex, azure_openai, claude.
• Provider-specific fields: azure_openai requires endpoint, api_version, deployment; gemini_vertex requires project_id, location; claude requires anthropic_version.
• Built-in prompt templates for extraction: simple, deep_read, eight_questions, three_pass.
• Template rename: seven_questions is now eight_questions.
• Render templates use paper db render-md --template-name with the same names.
• --language defaults to en; extraction stores it as output_language and render uses that field.
• When output_language is zh, render headings include both Chinese and English.
• Complex templates (deep_read, eight_questions, three_pass) run multi-stage extraction and persist per-document stage files under paper_stage_outputs/.
• Custom templates: use --prompt-system/--prompt-user with --schema-json, or --template-dir containing system.j2, user.j2, schema.json, render.j2.
• Custom templates run in single-stage extraction mode.
• Built-in schemas require publication_date and publication_venue.
• The simple template requires abstract, keywords, and a single-paragraph summary that covers the eight-question aspects.
• Extraction tolerates minor JSON formatting errors and ignores extra top-level fields when required keys validate.

paper extract — structured extraction from markdown

Extract structured JSON from markdown files using configured providers and prompt templates.

Key options:

• --input (repeatable): file or directory input.
• --glob: filter when scanning directories.
• --prompt-template / --language: select built-in prompts and output language.
• --prompt-system / --prompt-user / --schema-json: custom prompt + schema.
• --template-dir: use a directory containing system.j2, user.j2, schema.json, render.j2.
• --sleep-every / --sleep-time: throttle request initiation.
• --max-concurrency: override concurrency.
• --render-md: render markdown output as part of extraction.
• --dry-run: scan inputs and show summary metrics without calling providers.

Outputs:

• Aggregated JSON: paper_infos.json
• Errors: paper_errors.json
• Optional rendered Markdown: rendered_md/ by default

Incremental behavior:

• Reuses existing entries when source_path and source_hash match.
• Use --force to re-extract everything.
• Use --retry-failed to retry only failed documents listed in paper_errors.json.
• Use --verbose for detailed logs alongside progress bars.
• Extract-time rendering defaults to the same built-in template as --prompt-template.
• A summary table prints input/prompt/output character totals, token estimates, and throughput after each run.
• Progress bars include a live prompt/completion/total token ticker.

Examples:

# Scan a directory recursively (default: *.md)
deepresearch-flow paper extract \
  --input ./docs \
  --model openai/gpt-4o-mini

# Multiple inputs + custom output
deepresearch-flow paper extract \
  --input ./docs \
  --input ./more-docs \
  --output ./out/papers.json \
  --model openai/gpt-4o-mini

# Built-in template with output language
deepresearch-flow paper extract \
  --input ./docs \
  --prompt-template deep_read \
  --language zh \
  --model openai/gpt-4o-mini

# Custom template directory
deepresearch-flow paper extract \
  --input ./docs \
  --template-dir ./prompts \
  --model openai/gpt-4o-mini

# Extract + render in one run
deepresearch-flow paper extract \
  --input ./docs \
  --prompt-template eight_questions \
  --render-md \
  --model openai/gpt-4o-mini

# Throttle request initiation
deepresearch-flow paper extract \
  --input ./docs \
  --sleep-every 10 \
  --sleep-time 60 \
  --model openai/gpt-4o-mini

paper db — render, analyze, and serve extracted data

Render outputs, compute stats, and serve a local web UI over paper JSON.

JSON input formats:

• For db render-md, db statistics, db filter, and db generate-tags, the input is the aggregated JSON list.
• For db serve, each input JSON must be an object: {"template_tag": "simple", "papers": [...]}. When template_tag is missing, the server attempts to infer it as a fallback.

Web UI highlights:

• Summary/Source/PDF/PDF Viewer views with tab navigation.
• Split view: choose left/right panes independently (summary/source/pdf/pdf viewer) via URL params.
• Summary view includes a collapsible outline panel (top-left) and a back-to-top control (bottom-left).
• Summary template dropdown shows only available templates per paper.
• Source view renders Markdown and supports embedded HTML tables plus data:image/...;base64 <img> tags.
• PDF Viewer is served locally (PDF.js viewer assets) to avoid cross-origin issues with local PDFs.
• Merge behavior for multi-input serve: title similarity (>= 0.95), preferring bibtex.fields.title and falling back to paper_title.
• Cache merged inputs with --cache-dir; bypass with --no-cache.

Examples:

# Render Markdown from JSON
deepresearch-flow paper db render-md --input paper_infos.json

# Render with a built-in template and language fallback
deepresearch-flow paper db render-md \
  --input paper_infos.json \
  --template-name deep_read \
  --language zh

# Generate tags
deepresearch-flow paper db generate-tags \
  --input paper_infos.json \
  --output paper_infos_with_tags.json \
  --model openai/gpt-4o-mini

# Filter papers
deepresearch-flow paper db filter \
  --input paper_infos.json \
  --output filtered.json \
  --tags hardware_acceleration,fpga

# Statistics (rich tables)
deepresearch-flow paper db statistics \
  --input paper_infos.json \
  --top-n 20

# Serve a local read-only web UI (loads charts/libs via CDN)
deepresearch-flow paper db serve \
  --input paper_infos_simple.json \
  --input paper_infos_deep_read.json \
  --cache-dir .cache/db-serve \
  --host 127.0.0.1 \
  --port 8000

# Serve with optional BibTeX enrichment and source roots
deepresearch-flow paper db serve \
  --input paper_infos_simple.json \
  --input paper_infos_deep_read.json \
  --bibtex ./refs/library.bib \
  --md-root ./docs \
  --md-root ./more_docs \
  --pdf-root ./pdfs \
  --cache-dir .cache/db-serve \
  --host 127.0.0.1 \
  --port 8000

Web search syntax (Scholar-style):

• Default is AND: fpga kNN
• Quoted phrases: title:"nearest neighbor"
• OR: fpga OR asic
• Negation: -survey or -tag:survey
• Fields: title:, author:, tag:, venue:, year:, month:
• Year range: year:2020..2024

Other database helpers:

• append-bibtex
• sort-papers
• split-by-tag
• split-database
• statistics
• merge

recognize md — embed or unpack markdown images

recognize md embed replaces local image links in markdown with data:image/...;base64, URLs. recognize md unpack extracts embedded images into images/ and updates markdown links.

Key options:

• --input (repeatable): file or directory input.
• --recursive: recurse into directories.
• --output: output directory (flattened outputs).
• --enable-http: allow embedding HTTP(S) images (embed only).
• --workers: concurrent workers (default: 4).
• --dry-run: report planned outputs without writing files.
• --verbose: enable detailed logs for image resolution/HTTP fetches.

Notes:

• Progress bars report completion; a rich summary table lists counts, image totals, duration, and output locations.
• Summary paths are shown relative to the current working directory when possible.
• If the output directory is not empty, the command logs a warning before writing files.

Examples:

# Embed local images (flatten outputs)
deepresearch-flow recognize md embed \
  --input ./docs \
  --recursive \
  --output ./out_md

# Embed HTTP images (with browser User-Agent)
deepresearch-flow recognize md embed \
  --input ./docs \
  --enable-http \
  --output ./out_md

# Unpack embedded images into output/images/
deepresearch-flow recognize md unpack \
  --input ./docs \
  --recursive \
  --output ./out_md

recognize organize — flatten OCR outputs

Organize OCR outputs (layout: mineru) into flat markdown files, with optional image embedding.

Key options:

• --layout: OCR layout type (currently mineru).
• --input (repeatable): directories containing full.md + images/.
• --recursive: search for layout folders (required when inputs contain nested result directories).
• --output-simple: copy markdown + images to output (shared images/).
• --output-base64: embed images into markdown.
• --workers: concurrent workers (default: 4).
• --dry-run: report planned outputs without writing files.
• --verbose: enable detailed logs for layout discovery and file copying.

Notes:

• Use --recursive when the input directory contains nested layout folders (otherwise no layouts are discovered).
• If output directories are not empty, the command logs a warning before writing files.
• A summary table lists counts, image totals, duration, and output locations after completion.
• Summary paths are shown relative to the current working directory when possible.

Examples:

# Copy markdown + images into a flat output directory
deepresearch-flow recognize organize \
  --layout mineru \
  --input ./ocr_results \
  --recursive \
  --output-simple ./out_simple

# Embed images into markdown
deepresearch-flow recognize organize \
  --layout mineru \
  --input ./ocr_results \
  --output-base64 ./out_base64

Data formats (examples)

Aggregated extraction output is a JSON list:

[
  {
    "paper_title": "Example Paper",
    "paper_authors": ["Author A", "Author B"],
    "publication_date": "2024-01-01",
    "publication_venue": "ExampleConf",
    "source_path": "/abs/path/to/doc.md"
  }
]

db serve expects each input to be an object with a template_tag and a papers list:

{
  "template_tag": "simple",
  "papers": [
    {
      "paper_title": "Example Paper",
      "paper_authors": ["Author A"],
      "publication_date": "2024-01-01",
      "publication_venue": "ExampleConf"
    }
  ]
}