Document from json

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aggregation.rs basic_search.rs custom_collector.rs custom_tokenizer.rs date_time_field.rs deleting_updating_documents.rs faceted_search.rs faceted_search_with_tweaked_score.rs fuzzy_search.rs index_from_multiple_threads.rs index_with_json.rs integer_range_search.rs ip_field.rs iterating_docs_and_positions.rs json_field.rs phrase_prefix_search.rs pre_tokenized_text.rs snippet.rs stop_words.rs warmer.rs

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```
use tantivy::schema::*;
```
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Document from json

For convenience, Document can be parsed directly from json.
```
fn main() -> tantivy::Result<()> {
```

Let’s first define a schema and an index. Check out the basic example if this is confusing to you.

first we need to define a schema …

    let mut schema_builder = Schema::builder();
    schema_builder.add_text_field("title", TEXT | STORED);
    schema_builder.add_text_field("body", TEXT);
    schema_builder.add_u64_field("year", INDEXED);
    let schema = schema_builder.build();

Let’s assume we have a json-serialized document.

    let mice_and_men_doc_json = r#"{
       "title": "Of Mice and Men",
       "year": 1937
    }"#;

We can parse our document

    let _mice_and_men_doc = TantivyDocument::parse_json(&schema, mice_and_men_doc_json)?;

Multi-valued field are allowed, they are expressed in JSON by an array. The following document has two titles.

    let frankenstein_json = r#"{
       "title": ["Frankenstein", "The Modern Prometheus"],
       "year": 1818
    }"#;
    let _frankenstein_doc = TantivyDocument::parse_json(&schema, frankenstein_json)?;

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Note that the schema is saved in your index directory.

As a result, Indexes are aware of their schema, and you can use this feature just by opening an existing Index, and calling index.schema()..parse_document(json).
```
    Ok(())
}
```