Nlweb Schema Org Grounding

NLWeb Schema.org Grounding

Before writing code

Fetch live references:

1. Fetch https://schema.org/ for the canonical Schema.org vocabulary.
2. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/config/site_types.xml in the live repo for the exact list of supported Schema.org types and the tool inheritance tree per type.
3. Fetch https://github.com/nlweb-ai/NLWeb/blob/main/docs/nlweb-prompts.md for how per-type prompts and <returnStruc> shapes work.
4. Web-search schema.org JSON-LD validator — Google's Rich Results Test is a quick way to validate before ingest.
5. Check AskAgent/python/methods/recipe_substitution.py, accompaniment.py, compare_items.py for examples of how type-specific tools consume the schema_object.

Conceptual Architecture

Why Schema.org Matters to NLWeb

NLWeb's defining design choice: results carry their full Schema.org object back to the agent. Unlike a generic RAG system that returns text chunks, NLWeb returns structured JSON-LD — so an agent receiving a Recipe result gets ingredients, cookTime, nutrition, recipeYield, not just a paragraph of text. This is what makes NLWeb results agent-actionable.

R.V. Guha (NLWeb's author) co-created Schema.org for exactly this reason — the data was already structured; NLWeb finally exposes it to agents.

Schema.org Types NLWeb Knows About

site_types.xml enumerates the types with per-type tool / prompt overrides. Common types (verify the live file):

NLWeb falls back to a default tool set for any Schema.org type not explicitly enumerated.

JSON-LD Embedding Patterns

Schema.org JSON-LD is typically embedded in HTML via a <script type="application/ld+json"> tag:

<script type="application/ld+json">
{
  "@context": "https://schema.org",
  "@type": "Recipe",
  "name": "Classic Tomato Soup",
  "url": "https://example.com/recipes/tomato-soup",
  "image": "https://example.com/images/tomato-soup.jpg",
  "author": { "@type": "Person", "name": "Jane Doe" },
  "datePublished": "2025-09-12",
  "description": "A simple weeknight tomato soup.",
  "recipeIngredient": ["6 ripe tomatoes", "1 onion", "..."],
  "recipeInstructions": [...],
  "nutrition": { "@type": "NutritionInformation", "calories": "200" },
  "cookTime": "PT30M",
  "recipeYield": "4 servings"
}
</script>

NLWeb's URL-list ingest path extracts this directly. The richer the JSON-LD, the more useful the result.

The schema_object Field in Responses

Every NLWeb result contains:

{
  "url": "...",
  "name": "...",
  "site": "...",
  "score": 0.87,
  "description": "...",
  "schema_object": { /* the full JSON-LD as ingested */ }
}

Agents can pattern-match on schema_object.@type to render appropriately, extract specific properties (e.g., offers.price for products), or chain to a follow-up tool call.

sites.xml and Per-Site Registration

In addition to config_nlweb.yaml's sites: allowlist, the demo data ships with a sites.xml-style registry tying site names to crawl sources and Schema.org type defaults. Check the live repo for the current registration convention — this is an area that's been evolving.

Schema.org Required Fields by Type (high-signal subset)

The fewer fields populated, the worse the result quality — especially for mode=generate answers.

Per-Type Prompt and Tool Inheritance

site_types.xml defines a tree:

• Root prompts apply to all types
• Per-type overrides specialize ranking, summarization, and tool selection

This is mixed-mode programming in action — small, type-aware LLM calls drive the response.

Implementation Guidance

Auditing an Existing Site

Before ingest:

1. Visit a representative page and view source — look for <script type="application/ld+json">.
2. Validate with Google's Rich Results Test or Schema.org validator.
3. Confirm the @type is one NLWeb's site_types.xml knows about — if not, results still work but use default prompts.

Authoring JSON-LD for NLWeb

• Always set @context: "https://schema.org" — NLWeb's parser keys off this.
• Always include url — it's the deduplication key across retrieval backends.
• Use specific subtypes (e.g., Recipe not CreativeWork) so type-specific tools activate.
• Embed images and dates — agents use them for rendering and freshness checks.
• Nest related objects with @type discriminators (e.g., author as Person, offers as Offer).

Validating Schema Quality Post-Ingest

After loading, hit a result and inspect schema_object:

curl 'http://localhost:8000/ask?query=quick+dinners&site=recipes&streaming=false&mode=list' | jq '.results[0].schema_object'

If schema_object is missing key fields, fix the source HTML — not NLWeb's config.

Adding a New Schema.org Type

If you want a custom domain (say, Podcast episodes) with type-specific tools:

1. Add a <site_type> entry in site_types.xml referencing your @type value.
2. Define type-specific prompts in prompts.xml (or inherit defaults).
3. Optionally write a handler in methods/ (see nlweb-tools-framework).
4. Reload and re-test.

Mapping Non-Schema.org Sources

If your source isn't JSON-LD (CSV, proprietary API), map fields to Schema.org at ingest time, not query time. Update rss2schema.py or write a small adapter that emits Schema.org JSON before calling db_load. The richer the mapping, the better the agent experience.

Common Pitfalls

• @type is missing or non-Schema.org — results work but type-specific tools never fire.
• url is relative — breaks deduplication; always emit absolute URLs.
• Date format is non-ISO — datePublished: "2025-09-12" works; "Sept 12, 2025" does not.
• offers is a bare string instead of an Offer object — agents lose the price field.
• Description is too short / too generic — ranking suffers because retrieval relies on description embeddings.

Always validate JSON-LD with an external tool before assuming ingest will work — silent parser failures are common.