# Output Formats

URL: /docs/building/output-formats/
Section: building

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Output Formats Bengal can generate multiple output formats for your content, enabling search functionality, AI discovery, and programmatic access. Available Formats Per-Page Formats Generated for every page in your site: JSON (index.json): Structured data including metadata, HTML content, and plain text. LLM Text (index.txt): AI-friendly plain text format optimized for RAG (Retrieval-Augmented Generation) and LLM consumption. Site-Wide Formats Generated at the site root: Site Index (index.json): A searchable index of all pages (useful for client-side search). Full LLM Text (llm-full.txt): The complete content of your site in a single plain text file. Configuration Enable output formats in bengal.toml: [output_formats] enabled = true per_page = ["json"] site_wide = ["index_json"] [output_formats.options] excerpt_length = 200 # Excerpt length for site index json_indent = null # null for compact JSON, 2 for pretty-print llm_separator_width = 80 # Width of LLM text separators include_full_content_in_index = false # Include full content in site index exclude_sections = [] # Sections to exclude from output formats exclude_patterns = ["404.html", "search.html"] # Files to exclude Tip Tip Effective Defaults: The [features] section controls which formats are enabled. With default features (json = true, llm_txt = true), Bengal generates: per_page: ["json", "llm_txt"] (both JSON and LLM text) site_wide: ["index_json", "llm_full"] (search index and full LLM text) To disable LLM text generation, set features.llm_txt = false in your config. Note Note Visibility: Output formats respect page visibility settings. Hidden pages and drafts are excluded by default. Use exclude_sections or exclude_patterns for additional filtering. Use Cases Client-Side Search Fetch the site index to implement fast, client-side search without a backend. Note Note For larger sites, enable the Pre-built Lunr Index to improve performance. This requires the search optional dependency: pip install "bengal[search]" This generates search-index.json (a pre-serialized Lunr index) in addition to index.json, which loads faster in the browser. <!-- Simple search UI --> <input type="text" id="search-input" placeholder="Search..."> <ul id="search-results"></ul> <script> const searchInput = document.getElementById('search-input'); const resultsList = document.getElementById('search-results'); let searchIndex = []; // Fetch index once fetch('/index.json') .then(response => response.json()) .then(data => { searchIndex = data.pages; }); // Filter and display results searchInput.addEventListener('input', (e) => { const query = e.target.value.toLowerCase(); if (query.length < 2) { resultsList.innerHTML = ''; return; } const results = searchIndex.filter(page => (page.title && page.title.toLowerCase().includes(query)) || (page.excerpt && page.excerpt.toLowerCase().includes(query)) ).slice(0, 10); resultsList.innerHTML = results.map(page => ` <li> <a href="${page.url}"> <strong>${page.title}</strong> <p>${page.excerpt}</p> </a> </li> `).join(''); }); </script> AI & LLM Discovery Provide llm-full.txt to LLMs to allow them to ingest your entire documentation site efficiently. curl https://mysite.com/llm-full.txt Static API Use your static site as a read-only API for other applications. import requests # Get page data data = requests.get('https://mysite.com/docs/intro/index.json').json() print(data['title']) print(data['word_count'])

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Metadata:
- Author: lbliii
- Word Count: 432
- Reading Time: 2 minutes