> ## Documentation Index
> Fetch the complete documentation index at: https://docs.graphorlm.com/llms.txt
> Use this file to discover all available pages before exploring further.
# Quickstart
> Ingest your data, process documents, and start extracting insights in minutes
This quickstart gets you up and running with Graphor. You'll upload a document, apply advanced parsing, extract structured data, and chat with your content.
## Step 1: Create Your Project
1. Sign up for a [Graphor Cloud account](https://app.graphorlm.com/register)
2. Login at [app.graphorlm.com/login](https://app.graphorlm.com/login)
3. Click **New project** and give it a name
## Step 2: Upload a Document
The fastest way to add a document is to **drag and drop** it directly onto the Sources page.
Graphor supports PDFs, Word documents, images, spreadsheets, and [25+ other formats](/guides/data-ingestion#supported-document-types).
You can also ingest content from URLs, GitHub repositories, and YouTube videos. See the [Data Ingestion guide](/guides/data-ingestion) for all options.
After uploading, Graphor automatically processes your document using the **Fast** parsing method. Wait for the status to change to **Processed**.
**Navigation tip:** Single-click on a source to select it. Double-click to open Source details.
## Step 3: Apply Advanced Parsing
For complex documents, you can apply more powerful parsing methods:
| Method | Best For |
| ------------ | --------------------------------------------------------- |
| **Fast** | Simple text documents (default) |
| **Balanced** | Complex layouts with OCR |
| **Accurate** | Specialized documents requiring highest accuracy |
| **Agentic** | Multi-page tables, diagrams, images with rich annotations |
To apply a different parsing method:
1. Double-click on your document to open **Source details**
2. Go to the **Settings** tab
3. Select a parsing method
4. Click **Parse**
### Viewing Results
After parsing, check the **Results** tab to see:
* **Document view** — Visual preview of parsed content
* **Markdown view** — Raw text output
* **Page navigation** — Browse multi-page documents
Each parsing run is saved in the **Versions** panel on the left. You can compare different methods and switch the active version at any time.
For detailed information about parsing methods and options, see the [Data Ingestion guide](/guides/data-ingestion#document-parsing-methods).
## Step 4: Extract Structured Data
Transform your documents into structured data using **LLM-powered extraction**.
1. In Source details, go to the **Extraction** tab
2. Add **instructions** to guide the extraction (optional)
3. Define your schema with fields:
* **Field name**: e.g., `invoice_number`
* **Field type**: Text, Number, Boolean, or Array
* **Description**: What to extract
4. Click **Extract**
**Example for invoice extraction:**
*Instructions:*
> Extract all invoice information. Use YYYY-MM-DD for dates and USD for amounts.
*Schema:*
| Field Name | Type | Description |
| ---------------- | ------ | --------------------------------- |
| `invoice_number` | Text | The unique invoice identifier |
| `date` | Text | Invoice date in YYYY-MM-DD format |
| `total_amount` | Number | Total amount due |
The results show extracted data with **page references** indicating where each item was found.
For more schema examples and best practices, see the [Data Extraction guide](/guides/data-extraction).
## Step 5: Chat with Your Documents
Click the **Chat** button in the top-right corner to start asking questions.
**Two chat options:**
* **Sources page** → Chat with all documents
* **Source details page** → Chat with one specific document
Ask questions in natural language:
> **You:** What are the main findings in this document?
>
> **Chat:** The document presents three main findings...
>
> **You:** Tell me more about the first one.
The chat maintains **conversational memory**, so follow-up questions understand previous context.
For tips on getting better answers, see the [Document Chat guide](/guides/document-chat).
***
## What's Next?
Deep dive into parsing methods, multiple source types, and OCR options
Advanced schema design and extraction best practices
Tips for asking better questions and API integration