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

# Extract from documents

> Bootstrap a causal graph from your existing knowledge — proposals you review and accept.

Building a causal graph from scratch is a lot of typing. If you already have knowledge documents that describe the mechanisms in your domain, Nora can propose variables and edges automatically.

## The extraction workflow

1. Pick a knowledge collection or specific documents.
2. Run extraction.
3. Review the proposals in a staged view.
4. Accept, reject, or edit each.
5. Accepted items land in your Draft graph.

Nothing changes in your live graph until you accept and publish.

## Running extraction

1. Open the graph, click **Extract from documents**.
2. Choose the source:
   * A whole collection.
   * A tag-filtered slice.
   * Specific documents (multi-select).
3. Pick the extraction scope:
   * **Variables only** — find measurable quantities.
   * **Edges only** — assume variables exist, find relationships among them.
   * **Both** — end-to-end.
4. Run.

Extraction is asynchronous — you'll get a notification when the staged proposals are ready to review. Typical time: 1-10 minutes depending on source size.

## Reviewing proposals

The staging view groups proposals:

* **New variables** — with proposed name, type, unit, evidence quote.
* **New edges** — with proposed direction, sign, mechanism, evidence quotes.
* **Merges** — proposed unifications of similar variables (e.g., "customer\_lifetime\_value" and "clv" as the same variable).

Each proposal shows the source document, the quoted passage, and the confidence score. Click a proposal to see full context.

## Accepting

Three actions per proposal:

* **Accept** — merges into Draft as-is.
* **Edit & accept** — modify (rename, change type, adjust sign) then accept.
* **Reject** — dismiss. Add a reason to help future extractions do better.

Bulk accept/reject the whole batch, or subset by confidence.

## Confidence

Every proposal has a confidence score (0-1). High-confidence proposals often can be batch-accepted; low-confidence ones deserve individual review.

Filter the staging view: **> 0.8 confidence** for auto-approval-worthy, **\< 0.4** for likely-noise.

## Evidence trail

Every accepted proposal carries a link back to its source document + quote. In the graph, right-click a variable or edge → **View evidence** to see what supported it. Useful for auditability and for revising when the source changes.

## Re-running extraction

Re-running on the same source with the same settings is idempotent — Nora skips proposals it already made or that were previously rejected.

If a document was updated, extraction picks up on the new content and proposes changes to existing variables/edges.

## Combining extraction and hand-authoring

Most graphs converge on a mix: extraction seeds \~60% of the structure, then experts hand-tune the important edges. That's the intended workflow — don't hesitate to override.

## Extraction quality tips

* **Cleaner sources → cleaner extraction.** A well-structured doc extracts far better than a PDF scan.
* **Domain-specific glossary helps.** Attach a glossary document — extraction uses it to normalize variable names.
* **Iterate.** Extract, review, accept some, publish. Then extract again with a richer graph as context — later runs improve because they know your existing variables.