Tantivy

https://docs.rs/tantivy/0.25.0
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Tantivy is a search engine library written in Rust that provides a Lucene-like API for full-text...
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### Initialize Schema, Index Documents, and Perform Searches

Source: https://docs.rs/tantivy

Demonstrates the full lifecycle of a Tantivy index, including schema definition, document indexing, committing changes, and executing a search query.

```rust
// First we need to define a schema ...

// `TEXT` means the field should be tokenized and indexed,
// along with its term frequency and term positions.
//
// `STORED` means that the field will also be saved
// in a compressed, row-oriented key-value store.
// This store is useful to reconstruct the
// documents that were selected during the search phase.
let mut schema_builder = Schema::builder();
let title = schema_builder.add_text_field("title", TEXT | STORED);
let body = schema_builder.add_text_field("body", TEXT);
let schema = schema_builder.build();

// Indexing documents

let index = Index::create_in_dir(index_path, schema.clone())?;

// Here we use a buffer of 100MB that will be split
// between indexing threads.
let mut index_writer: IndexWriter = index.writer(100_000_000)?;

// Let's index a document!
index_writer.add_document(doc!(
    title => "The Old Man and the Sea",
    body => "He was an old man who fished alone in a skiff in \
            the Gulf Stream and he had gone eighty-four days \
            now without taking a fish."
))?;

// We need to call .commit() explicitly to force the
// index_writer to finish processing the documents in the queue,
// flush the current index to the disk, and advertise
// the existence of new documents.
index_writer.commit()?;

// # Searching

let reader = index.reader()?;

let searcher = reader.searcher();

let query_parser = QueryParser::for_index(&index, vec![title, body]);

// QueryParser may fail if the query is not in the right
// format. For user facing applications, this can be a problem.
// A ticket has been opened regarding this problem.
let query = query_parser.parse_query("sea whale")?;

// Perform search.
// `topdocs` contains the 10 most relevant doc ids, sorted by decreasing scores...
let top_docs: Vec<(Score, DocAddress)> =
    searcher.search(&query, &TopDocs::with_limit(10).order_by_score())?;

for (_score, doc_address) in top_docs {
    // Retrieve the actual content of documents given its `doc_address`.
    let retrieved_doc = searcher.doc::<TantivyDocument>(doc_address)?;
    println!("{}", retrieved_doc.to_json(&schema));
}
```
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