alterlab-parallel-web

Parallel Web Systems API

Overview

This skill provides access to Parallel Web Systems APIs for web search, deep research, and content extraction. It is the primary tool for all web-related operations in the scientific writer workflow.

Primary interface: Parallel Chat API (OpenAI-compatible) for search and research. Secondary interface: Extract API for URL verification and special cases only.

API Documentation: https://docs.parallel.ai API Key: https://platform.parallel.ai Environment Variable: PARALLEL_API_KEY

When to Use This Skill

Use this skill for ALL of the following:

• Web Search: Any query that requires searching the internet for information
• Deep Research: Comprehensive research reports on any topic
• Market Research: Industry analysis, competitive intelligence, market data
• Current Events: News, recent developments, announcements
• Technical Information: Documentation, specifications, product details
• Statistical Data: Market sizes, growth rates, industry figures
• General Information: Company profiles, facts, comparisons

Use Extract API only for:

• Citation verification (confirming a specific URL's content)
• Special cases where you need raw content from a known URL

Do NOT use this skill for:

• Academic-specific paper searches (use research-lookup which routes to Perplexity for purely academic queries)
• Google Scholar / PubMed database searches (use citation-management skill)

---

Two Capabilities

1. Web Search (search command)

Search the web via the Parallel Chat API (base model) and get a synthesized summary with cited sources.

Best for: General web searches, current events, fact-finding, technical lookups, news, market data.

# Basic search
python scripts/parallel_web.py search "latest advances in quantum computing 2025"

# Use core model for more complex queries
python scripts/parallel_web.py search "compare EV battery chemistries NMC vs LFP" --model core

# Save results to file
python scripts/parallel_web.py search "renewable energy policy updates" -o results.txt

# JSON output for programmatic use
python scripts/parallel_web.py search "AI regulation landscape" --json -o results.json

Key Parameters:

• objective: Natural language description of what you want to find
• --model: Chat model to use (base default, or core for deeper research)
• -o: Output file path
• --json: Output as JSON

Response includes: Synthesized summary organized by themes, with inline citations and a sources list.

2. Deep Research (research command)

Run comprehensive multi-source research via the Parallel Chat API (core model) that produces detailed intelligence reports with citations.

Best for: Market research, comprehensive analysis, competitive intelligence, technology surveys, industry reports, any research question requiring synthesis of multiple sources.

# Default deep research (core model)
python scripts/parallel_web.py research "comprehensive analysis of the global EV battery market"

# Save research report to file
python scripts/parallel_web.py research "AI adoption in healthcare 2025" -o report.md

# Use base model for faster, lighter research
python scripts/parallel_web.py research "latest funding rounds in AI startups" --model base

# JSON output
python scripts/parallel_web.py research "renewable energy storage market in Europe" --json -o data.json

Key Parameters:

• query: Research question or topic
• --model: Chat model to use (core default for deep research, or base for faster results)
• -o: Output file path
• --json: Output as JSON

3. URL Extraction (extract command) — Verification Only

Extract content from specific URLs. Use only for citation verification and special cases.

For general research, use search or research instead.

# Verify a citation's content
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings"

# Get full page content for verification
python scripts/parallel_web.py extract "https://docs.example.com/api" --full-content

# Save extraction to file
python scripts/parallel_web.py extract "https://paper-url.com" --objective "methodology" -o extracted.md

---

Model Selection Guide

The Chat API supports two research models. Use base for most searches and core for deep research.

Model	Latency	Strengths	Use When
base	15s-100s	Standard research, factual queries	Web searches, quick lookups
core	60s-5min	Complex research, multi-source synthesis	Deep research, comprehensive reports

Recommendations:

• search command defaults to base — fast, good for most queries
• research command defaults to core — thorough, good for comprehensive reports
• Override with --model when you need different depth/speed tradeoffs

---

Python API Usage

Search

from parallel_web import ParallelSearch

searcher = ParallelSearch()
result = searcher.search(
    objective="Find latest information about transformer architectures in NLP",
    model="base",
)

if result["success"]:
    print(result["response"])  # Synthesized summary
    for src in result["sources"]:
        print(f"  {src['title']}: {src['url']}")

Deep Research

from parallel_web import ParallelDeepResearch

researcher = ParallelDeepResearch()
result = researcher.research(
    query="Comprehensive analysis of AI regulation in the EU and US",
    model="core",
)

if result["success"]:
    print(result["response"])  # Full research report
    print(f"Citations: {result['citation_count']}")

Extract (Verification Only)

from parallel_web import ParallelExtract

extractor = ParallelExtract()
result = extractor.extract(
    urls=["https://docs.example.com/api-reference"],
    objective="API authentication methods and rate limits",
)

if result["success"]:
    for r in result["results"]:
        print(r["excerpts"])

---

MANDATORY: Save All Results to Sources Folder

Every web search and deep research result MUST be saved to the project's sources/ folder.

This ensures all research is preserved for reproducibility, auditability, and context window recovery.

Saving Rules

Operation	-o Flag Target	Filename Pattern
Web Search	sources/search_<topic>.md	search_YYYYMMDD_HHMMSS_<brief_topic>.md
Deep Research	sources/research_<topic>.md	research_YYYYMMDD_HHMMSS_<brief_topic>.md
URL Extract	sources/extract_<source>.md	extract_YYYYMMDD_HHMMSS_<brief_source>.md

How to Save (Always Use -o Flag)

CRITICAL: Every call to parallel_web.py MUST include the -o flag pointing to the sources/ folder.

# Web search — ALWAYS save to sources/
python scripts/parallel_web.py search "latest advances in quantum computing 2025" \
  -o sources/search_20250217_143000_quantum_computing.md

# Deep research — ALWAYS save to sources/
python scripts/parallel_web.py research "comprehensive analysis of the global EV battery market" \
  -o sources/research_20250217_144000_ev_battery_market.md

# URL extraction (verification only) — save to sources/
python scripts/parallel_web.py extract "https://example.com/article" --objective "key findings" \
  -o sources/extract_20250217_143500_example_article.md

Why Save Everything

1. Reproducibility: Every claim in the final document can be traced back to its raw source material
2. Context Window Recovery: If context is compacted mid-task, saved results can be re-read from sources/
3. Audit Trail: The sources/ folder provides complete transparency into how information was gathered
4. Reuse Across Sections: Saved research can be referenced by multiple sections without duplicate API calls
5. Cost Efficiency: Avoid redundant API calls by checking sources/ for existing results
6. Peer Review Support: Reviewers can verify the research backing every claim

Logging

When saving research results, always log:

[HH:MM:SS] SAVED: Search results to sources/search_20250217_143000_quantum_computing.md
[HH:MM:SS] SAVED: Deep research report to sources/research_20250217_144000_ev_battery_market.md

Before Making a New Query, Check Sources First

Before calling parallel_web.py, check if a relevant result already exists in sources/:

ls sources/  # Check existing saved results

---

Integration with Scientific Writer

Routing Table

Task	Tool	Command
Web search (any)	parallel_web.py search	python scripts/parallel_web.py search "query" -o sources/search_<topic>.md
Deep research	parallel_web.py research	python scripts/parallel_web.py research "query" -o sources/research_<topic>.md
Citation verification	parallel_web.py extract	python scripts/parallel_web.py extract "url" -o sources/extract_<source>.md
Academic paper search	research_lookup.py	Routes to Perplexity sonar-pro-search
DOI/metadata lookup	parallel_web.py extract	Extract from DOI URLs (verification)

When Writing Scientific Documents

1. Before writing any section, use search or research to gather background information — save results to sources/
2. For academic citations, use research-lookup (which routes academic queries to Perplexity) — save results to sources/
3. For citation verification (confirming a specific URL), use parallel_web.py extract — save results to sources/
4. For current market/industry data, use parallel_web.py research --model core — save results to sources/
5. Before any new query, check sources/ for existing results to avoid duplicate API calls

---

Environment Setup

# Required: Set your Parallel API key
export PARALLEL_API_KEY="your_api_key_here"

# Required Python packages
pip install openai        # For Chat API (search/research)
pip install parallel-web  # For Extract API (verification only)

Get your API key at https://platform.parallel.ai

---

Error Handling

The script handles errors gracefully and returns structured error responses:

{
  "success": false,
  "error": "Error description",
  "timestamp": "2025-02-14 12:00:00"
}

Common issues:

• PARALLEL_API_KEY not set: Set the environment variable
• openai not installed: Run pip install openai
• parallel-web not installed: Run pip install parallel-web (only needed for extract)
• Rate limit exceeded: Wait and retry (default: 300 req/min for Chat API)

---

Complementary Skills

Skill	Use For
research-lookup	Academic paper searches (routes to Perplexity for scholarly queries)
citation-management	Google Scholar, PubMed, CrossRef database searches
literature-review	Systematic literature reviews across academic databases
scientific-schematics	Generate diagrams from research findings