return signedHeaders, ErrNone
}

// signValues data type represents structured form of AWS Signature V4 header.
type signValues struct {
	Credential    credentialHeader
	SignedHeaders []string
	Signature     string
}

// preSignValues data type represents structured form of AWS Signature V4 query string.
type preSignValues struct {
	signValues
	Date    time.Time

If something seems interesting and useful to your project, go ahead and check it, but otherwise, you might probably just skip them.

/// tip

If you want to **learn FastAPI** in a structured way (recommended), go and read the [Tutorial - User Guide](../tutorial/index.md){.internal-link target=_blank} chapter by chapter instead.

/**
 * Extracts text content and metadata from JSON files.
 * This extractor provides better structured data extraction compared to Tika's generic text extraction.
 *
 * <p>Features:
 * <ul>
 *   <li>Structured text extraction with key-value pairs</li>
 *   <li>Top-level field extraction as metadata</li>
 *   <li>Nested structure flattening with configurable depth</li>
 *   <li>Array element extraction</li>

import com.google.common.io.BaseEncoding;

/**
 * Response object for faceted search results containing query counts and field facets.
 * This class processes OpenSearch aggregations to provide structured facet information
 * for search result filtering and navigation.
 */
public class FacetResponse {
    /**
     * Map containing query facet counts, where keys are decoded query strings

# Tutorial - User Guide { #tutorial-user-guide }

This tutorial shows you how to use **FastAPI** with most of its features, step by step.

Each section gradually builds on the previous ones, but it's structured to separate topics, so that you can go directly to any specific one to solve your specific API needs.

It is also built to work as a future reference so you can come back and see exactly what you need.

## Run the code { #run-the-code }

/**
 * Extracts text content and metadata from Markdown files.
 * This extractor provides better structured data extraction compared to Tika's generic text extraction.
 *
 * <p>Features:
 * <ul>
 *   <li>YAML front matter metadata extraction</li>
 *   <li>Heading structure extraction</li>
 *   <li>Link URL extraction</li>
 *   <li>Code block content extraction</li>

import org.slf4j.Logger;
import org.slf4j.LoggerFactory;
import org.slf4j.MDC;

/**
 * Security audit logger for SMB operations.
 * Provides structured logging of security-relevant events with sensitive data masking.
 *
 * Features:
 * - Structured JSON logging
 * - Sensitive data masking
 * - Event categorization
 * - Performance metrics
 * - Compliance-ready audit trail

import org.codelibs.fess.crawler.entity.ExtractData;
import org.codelibs.fess.crawler.exception.ExtractException;

/**
 * Extracts text content and metadata from CSV files.
 * This extractor provides better structured data extraction compared to Tika's generic text extraction.
 *
 * <p>Features:
 * <ul>
 *   <li>Automatic delimiter detection (comma, tab, semicolon, pipe)</li>
 *   <li>Header row detection and extraction</li>

- Nested archive extraction

#### Web and Markup
- HTML, XHTML with XPath support
- XML documents
- JSON and structured data

#### Media Files
- Audio formats (MP3, WAV, FLAC)
- Video formats (MP4, AVI, MOV)
- Metadata extraction from media files

## Architecture

### Multi-Module Structure

```
fess-crawler-parent/
├── fess-crawler/              # Core crawler framework

Don't worry, with the way the code is structured, we'll make sure we use **a single SQLModel *session* per request** later, this is actually what the `check_same_thread` is trying to achieve.

### Create the Tables { #create-the-tables }