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.

bash
# 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.

bash
# 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.

bash
# 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

python
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

python
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)

python
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.

bash
# 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/:

bash
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

bash
# 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:

json
{
  "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 |