Overview
Dataset pipelines support 5 source types, each designed for a different data collection pattern. Sources can be combined to build complex multi-step workflows.Independent Source
A single API call with static parameters. Use this for searches, listings, or any one-off data extraction.From File Source
Batch API calls driven by inputs from an external file. Each line/row in the file becomes a separate API request.- TXT — one value per line
- CSV — uses the column matching
input_key - JSON/JSONL — uses the field matching
input_key
Dependent Source
Batch API calls that use output from a parent source. The dependency chain is resolved automatically — the parent source runs first, and its results feed into the dependent source.Dependency Configuration
Multi-level chains are supported — a dependent source can itself be the parent of another dependent source:
Union Source
Combines records from multiple parent sources into a single dataset. Optionally deduplicates records by a specified field.Union Configuration
When to use: Merging results from multiple searches, combining data from different platforms.
LLM Source
Processes data from a parent source through LLM operations — without making any API calls. Use this for classification, summarization, enrichment, and more.LLM Operations
LLM sources require the
llm extra: pip install "anysite-cli[llm]". See LLM Analysis for detailed configuration.Per-Source Transform & Export
Sources can include post-collection transforms and exports:Transform Options
Export Options
Database Load Options (per-source)
Input Templates
For endpoints that require complex input structures, useinput_template:
{value} placeholder is replaced with each input value from the dependency.
Common Source Options
These options apply to all API-based source types (independent, from_file, dependent):Next Steps
Scheduling
Set up incremental collection, cron scheduling, and webhooks
Database Loading
Load pipeline results into SQLite, PostgreSQL, or ClickHouse