parallel-web-tools 0.0.6

Parallel Tools: CLI and data enrichment utilities for the Parallel API

Parallel-Web-Tools

CLI and data enrichment utilities for the Parallel API.

Note: This package provides the parallel-cli command-line tool and data enrichment utilities in the parallel-web-tools package. It depends on parallel-web, the official Parallel Python SDK, but does not contain it. Install parallel-web separately if you need direct SDK access.

Features

• CLI for Humans & AI Agents - Works interactively or fully via command-line arguments
• Web Search - AI-powered search with domain filtering and date ranges
• Content Extraction - Extract clean markdown from any URL
• Data Enrichment - Enrich CSV, DuckDB, and BigQuery data with AI
• AI-Assisted Planning - Use natural language to define what data you want
• Multiple Integrations - Polars, DuckDB, Snowflake, BigQuery, Spark

Installation

Standalone CLI (Recommended)

Install the standalone parallel-cli binary for search, extract, enrichment, and deep research (no Python required):

curl -fsSL https://raw.githubusercontent.com/parallel-web/parallel-web-tools/main/install-cli.sh | bash

This automatically detects your platform (macOS/Linux, x64/arm64) and installs to ~/.local/bin.

Note: The standalone binary includes core CLI features. For deployment commands (enrich deploy), use pip: pip install parallel-web-tools[snowflake] or [bigquery].

Python Package

For programmatic usage or data enrichment integrations:

# Full install with CLI and all connectors
pip install parallel-web-tools[all]

# Library only (minimal dependencies)
pip install parallel-web-tools

# With specific connectors
pip install parallel-web-tools[cli]          # CLI only
pip install parallel-web-tools[polars]       # Polars DataFrame
pip install parallel-web-tools[duckdb]       # DuckDB
pip install parallel-web-tools[bigquery]     # BigQuery
pip install parallel-web-tools[spark]        # Apache Spark

CLI Overview

parallel-cli
├── auth                    # Check authentication status
├── login                   # OAuth login (or use PARALLEL_API_KEY env var)
├── logout                  # Remove stored credentials
├── search                  # Web search
├── extract                 # Extract content from URLs
└── enrich                  # Data enrichment commands
    ├── run                 # Run enrichment
    ├── plan                # Create YAML config
    ├── suggest             # AI suggests output columns
    └── deploy              # Deploy to cloud systems (requires pip install)

Quick Start

1. Authenticate

# Interactive OAuth login
parallel-cli login

# Or set environment variable
export PARALLEL_API_KEY=your_api_key

2. Search the Web

# Natural language search
parallel-cli search "What is Anthropic's latest AI model?" --json

# Keyword search with filters
parallel-cli search -q "bitcoin price" --after-date 2024-01-01 --json

# Search specific domains
parallel-cli search "SEC filings for Apple" --include-domains sec.gov --json

3. Extract Content from URLs

# Extract content as markdown
parallel-cli extract https://example.com --json

# Extract with a specific focus
parallel-cli extract https://company.com --objective "Find pricing info" --json

# Get full page content
parallel-cli extract https://example.com --full-content --json

4. Enrich Data

# Let AI suggest what columns to add
parallel-cli enrich suggest "Find the CEO and annual revenue" --json

# Create a config file (interactive)
parallel-cli enrich plan -o config.yaml

# Create a config file (non-interactive, for AI agents)
parallel-cli enrich plan -o config.yaml \
    --source-type csv \
    --source companies.csv \
    --target enriched.csv \
    --source-columns '[{"name": "company", "description": "Company name"}]' \
    --intent "Find the CEO and annual revenue"

# Run enrichment from config
parallel-cli enrich run config.yaml

# Run enrichment directly (no config file needed)
parallel-cli enrich run \
    --source-type csv \
    --source companies.csv \
    --target enriched.csv \
    --source-columns '[{"name": "company", "description": "Company name"}]' \
    --intent "Find the CEO and annual revenue"

5. Deploy to Cloud Systems

# Deploy to BigQuery for SQL-native enrichment
parallel-cli enrich deploy --system bigquery --project my-gcp-project

Non-Interactive Mode (for AI Agents & Scripts)

All commands support --json output and can be fully controlled via CLI arguments:

# Search with JSON output
parallel-cli search "query" --json

# Extract with JSON output
parallel-cli extract https://url.com --json

# Suggest columns with JSON output
parallel-cli enrich suggest "Find CEO" --json

# Plan without prompts (provide all args)
parallel-cli enrich plan -o config.yaml \
    --source-type csv \
    --source input.csv \
    --target output.csv \
    --source-columns '[{"name": "company", "description": "Company name"}]' \
    --enriched-columns '[{"name": "ceo", "description": "CEO name"}]'

# Or use --intent to let AI determine the columns
parallel-cli enrich plan -o config.yaml \
    --source-type csv \
    --source input.csv \
    --target output.csv \
    --source-columns '[{"name": "company", "description": "Company name"}]' \
    --intent "Find CEO, revenue, and headquarters"

Integrations

Integration	Type	Install	Documentation
Polars	Python DataFrame	pip install parallel-web-tools[polars]	Setup Guide
DuckDB	SQL + Python	pip install parallel-web-tools[duckdb]	Setup Guide
Snowflake	SQL UDF	pip install parallel-web-tools[snowflake]	Setup Guide
BigQuery	Cloud Function	pip install parallel-web-tools[bigquery]	Setup Guide
Spark	SQL UDF	pip install parallel-web-tools[spark]	Demo Notebook

Quick Integration Examples

Polars:

import polars as pl
from parallel_web_tools.integrations.polars import parallel_enrich

df = pl.DataFrame({"company": ["Google", "Microsoft"]})
result = parallel_enrich(
    df,
    input_columns={"company_name": "company"},
    output_columns=["CEO name", "Founding year"],
)
print(result.result)

DuckDB:

import duckdb
from parallel_web_tools.integrations.duckdb import enrich_table

conn = duckdb.connect()
conn.execute("CREATE TABLE companies AS SELECT 'Google' as name")
result = enrich_table(
    conn,
    source_table="companies",
    input_columns={"company_name": "name"},
    output_columns=["CEO name", "Founding year"],
)
print(result.result.fetchdf())

Programmatic Usage

from parallel_web_tools import run_enrichment, run_enrichment_from_dict

# From YAML file
run_enrichment("config.yaml")

# From dictionary
run_enrichment_from_dict({
    "source": "data.csv",
    "target": "enriched.csv",
    "source_type": "csv",
    "source_columns": [{"name": "company", "description": "Company name"}],
    "enriched_columns": [{"name": "ceo", "description": "CEO name"}]
})

YAML Configuration Format

source: input.csv
target: output.csv
source_type: csv  # csv, duckdb, or bigquery
processor: core-fast  # lite, base, core, pro, ultra (add -fast for speed)

source_columns:
  - name: company_name
    description: The name of the company

enriched_columns:
  - name: ceo
    description: The CEO of the company
    type: str  # str, int, float, bool
  - name: revenue
    description: Annual revenue in USD
    type: float

Environment Variables

Variable	Description
PARALLEL_API_KEY	API key for authentication (alternative to parallel-cli login)
DUCKDB_FILE	Default DuckDB file path
BIGQUERY_PROJECT	Default BigQuery project ID

Related Packages

• parallel-web - Official Parallel Python SDK (this package depends on it)

Development

git clone https://github.com/parallel-web/parallel-web-tools.git
cd parallel-web-tools
uv sync --all-extras
uv run pytest tests/ -v

License

MIT