deepresearch-flow 0.10.14

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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Core Pain Points

• OCR Chaos: Raw markdown from OCR tools is often broken — tables drift, formulas break, 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.

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.
• Precision Translation — Translate OCR Markdown to Chinese/Japanese while freezing formulas, code, tables, and references.
• Local Knowledge DB — Web UI with Split View (Source/Translation/Summary), full-text search, and multi-dimensional filtering.
• Snapshot + API Serve — Production-ready SQLite snapshot with static assets and read-only JSON API.
• OCR Post-Processing — Fix broken references, merge split paragraphs, repair LaTeX and Mermaid diagrams.
• Semantic Search — LanceDB-backed vector search with hybrid recall and cloud reranking.
• MCP Integration — FastMCP server for AI agent access with bounded read tools, static-bearer Streamable HTTP/SSE, and GitHub OAuth at /oauth/mcp.

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Quick Start

1) Installation

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

2) Configuration

cp config.example.toml config.toml

Minimal config with weighted multi-provider routing:

main_model = [
  { model = "openai/gpt-4o-mini", weight = 4 },
  { model = "claude/claude-sonnet-4-5-20250929", weight = 1 }
]

[[providers]]
name = "openai"
type = "openai_compatible"
base = [
  { url = "https://api.openai.com/v1", weight = 1, key = [
    { value = "env:OPENAI_API_KEY", weight = 4 }
  ] }
]
models = [
  { model_name = "gpt-4o-mini", is_support_json_schema = true }
]

[[providers]]
name = "claude"
type = "claude"
base = [
  { url = "https://api.anthropic.com", weight = 1, key = [
    { value = "env:ANTHROPIC_API_KEY", weight = 1 }
  ] }
]
models = [
  { model_name = "claude-sonnet-4-5-20250929" }
]

Keys use env:VAR_NAME syntax to keep secrets out of config files. Multiple providers (Ollama, Gemini, DashScope, Azure OpenAI) are supported. For full configuration options (embedding, rerank, translator defaults, search), see config.example.toml.

3) The "Zero to Hero" Workflow

Step 1: Extract Structured Insights

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

Step 1.1: Verify & Retry Missing Fields

uv run deepresearch-flow paper db verify \
  --input-json ./paper_infos.json \
  --prompt-template deep_read \
  --output-json ./paper_verify.json

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

Step 2: Safe Translation

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

Step 2.5: OCR on PDFs/Images (Optional)

If your source documents are PDFs or scanned images:

cp ocr.example.toml ocr.toml
# Set: export PADDLE_OCR_TOKEN=xxx

uv run deepresearch-flow recognize ocr ./pdfs --config ocr.toml --output-dir ./ocr_output

Output follows the mineru layout (full.md + images/ per document).

Step 3: Repair OCR Outputs (Recommended)

Recommended order: fix → fix-math → fix-mermaid → fix.

# Fix OCR markdown structure
uv run deepresearch-flow recognize fix \
  --input ./docs --in-place

# Fix LaTeX formulas
uv run deepresearch-flow recognize fix-math \
  --input ./docs --model openai/gpt-4o-mini --in-place

# Fix Mermaid diagrams
uv run deepresearch-flow recognize fix-mermaid \
  --input ./paper_outputs --json \
  --model openai/gpt-4o-mini --in-place

# Retry only failed formulas/diagrams
uv run deepresearch-flow recognize fix-math \
  --input ./docs --model openai/gpt-4o-mini --retry-failed

# Final format normalization
uv run deepresearch-flow recognize fix \
  --input ./docs --in-place

Step 4: Serve Your Local Knowledge Base

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

Step 4.1: Add Semantic Search (Optional)

Build a LanceDB vector index from extracted JSON:

uv run deepresearch-flow paper embed \
  --config ./config.toml \
  --input ./paper_infos.json \
  --max-concurrency 4 \
  --document-window 8 \
  --output-embed-db ./paper_vectors

Serve with semantic search enabled:

uv run deepresearch-flow paper db serve \
  --input ./paper_infos.json \
  --md-root ./docs \
  --embed-db ./paper_vectors \
  --search-access-token "your-token"

Step 5: MCP Integration (Optional)

The project exposes bounded MCP tools for AI agent access via FastMCP. See the MCP documentation for endpoint, auth, and tool reference.

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Further Reading

• Advanced Workflows — Incremental builds, merging JSON/BibTeX, supplementing templates
• Deployment — CDN serving, Nginx/Caddy config, Docker, Compose
• API & MCP — Admin API, push/push-semantic, MCP endpoints, auth, and tools
• Reference — Translator, Extract, DB & Recognize in detail
• Snapshot Management — Snapshot migration, supplement, update

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Built with love for the Open Science community.