---
title: "Data Extraction"
id: "1736"
type: "page"
slug: "extract"
published_at: "2026-09-20T20:56:04+00:00"
modified_at: "2026-09-21T01:36:28+00:00"
url: "https://xedant.com/agents/research/docs/extract"
markdown_url: "https://xedant.com/agents/research/docs/extract.md"
excerpt: "Extraction solves the main problem of deep research: the volume of downloaded material is several…"
---

# Data Extraction

[https://xedant.com/agents/research/docs/extract.md](https://xedant.com/agents/research/docs/extract.md)

Extraction solves the main problem of deep research: the volume of downloaded material is several times larger than any language model’s context (the text window a model can read at once). Research Agent splits the text into manageable chunks and extracts structured data in batches — hundreds of pages turn into tables.

## Chunk API

The downloaded Markdown is split into chunks with a token budget: every piece is guaranteed to fit into the model’s context. A file that is itself larger than the budget is divided into consecutive parts — visibly pieces of one source. Chunks are saved as files with source headers, so you can see which fetched pages each piece is made of.

## Extract API

Extraction runs through an LLM (the AI model): each chunk is processed by its own call, which pulls structured records out of it by a given schema. The result formats are TSV (the default) and JSON. Processing is batch: a result table per chunk, with aggregation across all the chunks at once.

A long extraction can be stopped: the job has a stop button, and it asks for confirmation. The records collected by that moment are kept — the work does not have to start from scratch.

Extraction needs at least one enabled LLM service in the Services section — otherwise the operation ends with the status Failed and a services error. This is the pipeline’s only self-recoverable error: enable a service and retry.

## Tokens and cost

Every extraction is accounted for: tokens, calls and cost. The Completions section shows the full history of LLM calls, so the economics of a research — 9–27 cents on a cheap model — is transparent down to the cent.

## Additional APIs

- **Links** — extracting URLs by pattern from fetched pages (for example, collecting the Telegram channels of a niche);
- **Markdown Clean** — Markdown normalization: reports and documents are brought to a single look;
- **Telegram Verify** — checking the metadata of t.me channels: subscribers and activity by link.

[← Back to documentation](/agents/research/docs)