> ## Documentation Index
> Fetch the complete documentation index at: https://docs.cloudsquid.io/llms.txt
> Use this file to discover all available pages before exploring further.

# Table Types

> Understand Extraction, Reconcile, and Storage tables — and when to use each.

Every Cloudsquid project contains tables. Tables are typed — the type determines what operations are available and how data flows through them.

## Extraction Tables

An Extraction Table stores uploaded documents alongside the structured data extracted from them. Each row corresponds to one file.

**When to use:** You have PDFs, images, spreadsheets, audio, or video files and want specific fields pulled out into structured columns.

**Key API operations:**

* Upload a file → `POST /projects/{name}/tables/{id}/files` → returns `row_id`
* Start an AI run → `POST /projects/{name}/tables/{id}/run` → returns `run_id`
* Poll for results → `GET /projects/{name}/tables/{id}/run/{run_id}`
* Extract synchronously → `POST /projects/{name}/tables/{id}/extract`

**Settings:** `active_pipeline` (flash vs pro), `bounding_boxes` (source-location highlighting), `review_mode` (human approval gate).

***

## Reconcile Tables

A Reconcile Table validates or matches rows of data using an AI agent. Each row is one reconciliation task — the agent compares your input against reference data and returns a structured result.

**When to use:** You've extracted invoice line items and want to match them against purchase orders, or you want to validate extracted fields against a known reference dataset.

**Key API operations:**

* Create a task (without running it) → `POST /projects/{name}/tables/{id}/tasks` → returns `task_id`
* Run reconciliation synchronously → `POST /projects/{name}/tables/{id}/reconcile`
* Use the async run pattern with `row_id` from a task

**Input format:** `AgentJobInput` — pass `files` (references to extraction table rows by UUID) and/or `data` (arbitrary JSON payload).

***

## Storage Tables

A Storage Table holds reference data — CSVs or row-by-row JSON inserts. It acts as the lookup source for reconciliation agents.

**When to use:** You need to maintain a table of vendors, product codes, exchange rates, or any reference dataset that reconciliation agents query against.

**Key API operations:**

* Upload or overwrite a CSV → `PUT /projects/{name}/tables/{id}` (mode: `overwrite` or `append`)
* Insert rows as JSON → `POST /projects/{name}/tables/{id}/data`
* Read rows → `GET /projects/{name}/tables/{id}/data`

***

## How they work together

The canonical pipeline uses all three table types in sequence:

1. **Storage Table** — load your reference data (vendor list, product catalog, etc.)
2. **Extraction Table** — upload documents and extract structured fields
3. **Reconcile Table** — pass extracted rows into reconciliation, matched against the storage table

This pattern covers the full lifecycle: ingest → extract → validate.

***

<CardGroup cols={2}>
  <Card title="Pipelines" icon="microchip" href="/concepts/pipelines">
    Choose the right AI model for your extraction use case.
  </Card>

  <Card title="Async Run Pattern" icon="repeat" href="/concepts/async-run-pattern">
    The three-step upload → start → poll flow for extraction at scale.
  </Card>
</CardGroup>