Workflow Recipes

Workflow Recipes

Common multi-step patterns combining Parallel's Search, Extract, and Deep Research APIs for scientific writing tasks.

Recipe Index

Recipe 1: Section Research Pipeline

Goal: Gather research and citations for writing a single section of a scientific paper.

APIs: Deep Research (pro-fast) + Search

# Step 1: Deep research for comprehensive background
python scripts/parallel_web.py research \
  "Recent advances in federated learning for healthcare AI, focusing on privacy-preserving training methods, real-world deployments, and regulatory considerations (2023-2025)" \
  --processor pro-fast -o sources/section_background.md

# Step 2: Targeted search for specific citations
python scripts/parallel_web.py search \
  "Find peer-reviewed papers on federated learning in hospitals" \
  --queries "federated learning clinical deployment" "privacy preserving ML healthcare" \
  --max-results 10 -o sources/section_citations.txt

Python version:

from parallel_web import ParallelDeepResearch, ParallelSearch

researcher = ParallelDeepResearch()
searcher = ParallelSearch()

# Step 1: Deep background research
background = researcher.research(
    query="Recent advances in federated learning for healthcare AI (2023-2025): "
          "privacy-preserving methods, real-world deployments, regulatory landscape",
    processor="pro-fast",
    description="Structure as: (1) Key approaches, (2) Clinical deployments, "
                "(3) Regulatory considerations, (4) Open challenges. Include statistics."
)

# Step 2: Find specific papers to cite
papers = searcher.search(
    objective="Find recent peer-reviewed papers on federated learning deployed in hospital settings",
    search_queries=[
        "federated learning hospital clinical study 2024",
        "privacy preserving machine learning healthcare deployment"
    ],
    source_policy={"allow_domains": ["nature.com", "thelancet.com", "arxiv.org", "pubmed.ncbi.nlm.nih.gov"]},
)

# Combine: use background for writing, papers for citations

When to use: Before writing each major section of a research paper, literature review, or grant proposal.

Recipe 2: Citation Verification

Goal: Verify that a citation is real and get complete metadata (DOI, volume, pages, year).

APIs: Search + Extract

# Option A: Search for the paper
python scripts/parallel_web.py search \
  "Vaswani et al 2017 Attention is All You Need paper NeurIPS" \
  --queries "Attention is All You Need DOI" --max-results 5

# Option B: Extract metadata from a DOI
python scripts/parallel_web.py extract \
  "https://doi.org/10.48550/arXiv.1706.03762" \
  --objective "Complete citation: authors, title, venue, year, pages, DOI"

Python version:

from parallel_web import ParallelSearch, ParallelExtract

searcher = ParallelSearch()
extractor = ParallelExtract()

# Step 1: Find the paper
result = searcher.search(
    objective="Find the exact citation details for the Attention Is All You Need paper by Vaswani et al.",
    search_queries=["Attention is All You Need Vaswani 2017 NeurIPS DOI"],
    max_results=5,
)

# Step 2: Extract full metadata from the paper's page
paper_url = result["results"][0]["url"]
metadata = extractor.extract(
    urls=[paper_url],
    objective="Complete BibTeX citation: all authors, title, conference/journal, year, pages, DOI, volume",
)

When to use: After writing a section, verify every citation in references.bib has correct and complete metadata.

Recipe 3: Literature Survey

Goal: Comprehensive survey of a research field, identifying key papers, themes, and gaps.

APIs: Deep Research + Search + Extract

from parallel_web import ParallelDeepResearch, ParallelSearch, ParallelExtract

researcher = ParallelDeepResearch()
searcher = ParallelSearch()
extractor = ParallelExtract()

topic = "CRISPR-based diagnostics for infectious diseases"

# Stage 1: Broad research overview
overview = researcher.research(
    query=f"Comprehensive review of {topic}: key developments, clinical applications, "
          f"regulatory status, commercial products, and future directions (2020-2025)",
    processor="ultra-fast",
    description="Structure as a literature review: (1) Historical development, "
                "(2) Current technologies, (3) Clinical applications, "
                "(4) Regulatory landscape, (5) Commercial products, "
                "(6) Limitations and future directions. Include key statistics and milestones."
)

# Stage 2: Find specific landmark papers
key_papers = searcher.search(
    objective=f"Find the most cited and influential papers on {topic} from Nature, Science, Cell, NEJM",
    search_queries=[
        "CRISPR diagnostics SHERLOCK DETECTR Nature",
        "CRISPR point-of-care testing clinical study",
        "nucleic acid detection CRISPR review"
    ],
    source_policy={
        "allow_domains": ["nature.com", "science.org", "cell.com", "nejm.org", "thelancet.com"],
    },
    max_results=15,
)

# Stage 3: Extract detailed content from top 5 papers
top_urls = [r["url"] for r in key_papers["results"][:5]]
detailed = extractor.extract(
    urls=top_urls,
    objective="Study design, key results, sensitivity/specificity data, and clinical implications",
)

When to use: Starting a literature review, systematic review, or comprehensive background section.

Recipe 4: Market Intelligence Report

Goal: Generate a comprehensive market research report on an industry or product category.

APIs: Deep Research (multi-stage)

researcher = ParallelDeepResearch()

industry = "AI-powered drug discovery"

# Stage 1: Market overview (ultra-fast for maximum depth)
market_overview = researcher.research(
    query=f"Comprehensive market analysis of {industry}: market size, growth rate, "
          f"key segments, geographic distribution, and forecast through 2030",
    processor="ultra-fast",
    description="Include specific dollar figures, CAGR percentages, and data sources. "
                "Break down by segment and geography."
)

# Stage 2: Competitive landscape
competitors = researcher.research_structured(
    query=f"Top 10 companies in {industry}: revenue, funding, key products, partnerships, and market position",
    processor="pro-fast",
)

# Stage 3: Technology and innovation trends
tech_trends = researcher.research(
    query=f"Technology trends and innovation landscape in {industry}: "
          f"emerging approaches, breakthrough technologies, patent landscape, and R&D investment",
    processor="pro-fast",
    description="Focus on specific technologies, quantify R&D spending, and identify emerging leaders."
)

# Stage 4: Regulatory and risk analysis
regulatory = researcher.research(
    query=f"Regulatory landscape and risk factors for {industry}: "
          f"FDA guidance, EMA requirements, compliance challenges, and market risks",
    processor="pro-fast",
)

When to use: Creating market research reports, investor presentations, or strategic analysis documents.

Recipe 5: Competitive Analysis

Goal: Compare multiple companies, products, or technologies side-by-side.

APIs: Search + Extract + Research

searcher = ParallelSearch()
extractor = ParallelExtract()
researcher = ParallelDeepResearch()

companies = ["OpenAI", "Anthropic", "Google DeepMind"]

# Step 1: Search for recent data on each company
for company in companies:
    result = searcher.search(
        objective=f"Latest product launches, funding, team size, and strategy for {company} in 2025",
        search_queries=[f"{company} product launch 2025", f"{company} funding valuation"],
        source_policy={"after_date": "2024-06-01"},
    )

# Step 2: Extract from company pages
company_pages = [
    "https://openai.com/about",
    "https://anthropic.com/company",
    "https://deepmind.google/about/",
]
company_data = extractor.extract(
    urls=company_pages,
    objective="Mission, key products, team size, founding date, and recent milestones",
)

# Step 3: Deep research for synthesis
comparison = researcher.research(
    query=f"Detailed comparison of {', '.join(companies)}: "
          f"products, pricing, technology approach, market position, strengths, weaknesses",
    processor="pro-fast",
    description="Create a structured comparison covering: "
                "(1) Product portfolio, (2) Technology approach, (3) Pricing, "
                "(4) Market position, (5) Strengths/weaknesses, (6) Future outlook. "
                "Include a summary comparison table."
)

Recipe 6: Fact-Check Pipeline

Goal: Verify specific claims or statistics before including in a document.

APIs: Search + Extract

searcher = ParallelSearch()
extractor = ParallelExtract()

claim = "The global AI market is expected to reach $1.8 trillion by 2030"

# Step 1: Search for corroborating sources
result = searcher.search(
    objective=f"Verify this claim: '{claim}'. Find authoritative sources that confirm or contradict this figure.",
    search_queries=["global AI market size 2030 forecast", "artificial intelligence market projection trillion"],
    max_results=8,
)

# Step 2: Extract specific figures from top sources
source_urls = [r["url"] for r in result["results"][:3]]
details = extractor.extract(
    urls=source_urls,
    objective="Specific market size figures, forecast years, CAGR, and methodology of the projection",
)

# Analyze: Do multiple authoritative sources agree?

When to use: Before including any specific statistic, market figure, or factual claim in a paper or report.

Recipe 7: Current Events Briefing

Goal: Get up-to-date synthesis of recent developments on a topic.

APIs: Search + Research

searcher = ParallelSearch()
researcher = ParallelDeepResearch()

topic = "EU AI Act implementation"

# Step 1: Find the latest news
latest = searcher.search(
    objective=f"Latest news and developments on {topic} from the past month",
    search_queries=[f"{topic} 2025", f"{topic} latest updates"],
    source_policy={"after_date": "2025-01-15"},
    max_results=15,
)

# Step 2: Synthesize into a briefing
briefing = researcher.research(
    query=f"Summarize the latest developments in {topic} as of February 2025: "
          f"key milestones, compliance deadlines, industry reactions, and implications",
    processor="pro-fast",
    description="Write a concise 500-word executive briefing with timeline of key events."
)

Recipe 8: Technical Documentation Gathering

Goal: Collect and synthesize technical documentation for a framework or API.

APIs: Search + Extract

searcher = ParallelSearch()
extractor = ParallelExtract()

# Step 1: Find documentation pages
docs = searcher.search(
    objective="Find official PyTorch documentation for implementing custom attention mechanisms",
    search_queries=["PyTorch attention mechanism tutorial", "PyTorch MultiheadAttention documentation"],
    source_policy={"allow_domains": ["pytorch.org", "github.com/pytorch"]},
)

# Step 2: Extract full content from documentation pages
doc_urls = [r["url"] for r in docs["results"][:3]]
full_docs = extractor.extract(
    urls=doc_urls,
    objective="Complete API reference, parameters, usage examples, and code snippets",
    full_content=True,
)

Recipe 9: Grant Background Research

Goal: Build a comprehensive background section for a grant proposal with verified statistics.

APIs: Deep Research + Search

researcher = ParallelDeepResearch()
searcher = ParallelSearch()

research_area = "AI-guided antibiotic discovery to combat antimicrobial resistance"

# Step 1: Significance and burden of disease
significance = researcher.research(
    query=f"Burden of antimicrobial resistance: mortality statistics, economic impact, "
          f"WHO priority pathogens, and projections. Include specific numbers.",
    processor="pro-fast",
    description="Focus on statistics suitable for NIH Significance section: "
                "deaths per year, economic cost, resistance trends, and urgency."
)

# Step 2: Innovation landscape
innovation = researcher.research(
    query=f"Current approaches to {research_area}: successes (halicin, etc.), "
          f"limitations of current methods, and what makes our approach novel",
    processor="pro-fast",
    description="Focus on Innovation section: what has been tried, what gaps remain, "
                "and what new approaches are emerging."
)

# Step 3: Find specific papers for preliminary data context
papers = searcher.search(
    objective="Find landmark papers on AI-discovered antibiotics and ML approaches to drug discovery",
    search_queries=[
        "halicin AI antibiotic discovery Nature",
        "machine learning antibiotic resistance prediction",
        "deep learning drug discovery antibiotics"
    ],
    source_policy={"allow_domains": ["nature.com", "science.org", "cell.com", "pnas.org"]},
)

When to use: Writing Significance, Innovation, or Background sections for NIH, NSF, or other grant proposals.

Combining with Other Skills

With research-lookup (Academic Papers)

# Use parallel-web for general research
researcher.research("Current state of quantum computing applications")

# Use research-lookup for academic paper search (auto-routes to Perplexity)
# python research_lookup.py "find papers on quantum error correction in Nature and Science"

With citation-management (BibTeX)

# Step 1: Find paper with parallel search
result = searcher.search(objective="Vaswani et al Attention Is All You Need paper")

# Step 2: Get DOI from results
doi = "10.48550/arXiv.1706.03762"

# Step 3: Convert to BibTeX with citation-management skill
# python scripts/doi_to_bibtex.py 10.48550/arXiv.1706.03762

With scientific-schematics (Diagrams)

# Step 1: Research a process
result = researcher.research("How does the CRISPR-Cas9 gene editing mechanism work step by step")

# Step 2: Use the research to inform a schematic
# python scripts/generate_schematic.py "CRISPR-Cas9 gene editing workflow: guide RNA design -> Cas9 binding -> DNA cleavage -> repair pathway" -o figures/crispr_mechanism.png

Performance Cheat Sheet

See Also

• API Reference - Complete API parameter reference
• Search Best Practices - Effective search queries
• Deep Research Guide - Processor selection and output formats
• Extraction Patterns - URL content extraction