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</pre><pre class="rust"><code><span class="kw">use </span>std::cmp::Reverse;
<span class="kw">use </span>std::collections::{BinaryHeap, HashMap};
<span class="kw">use </span><span class="kw">crate</span>::query::bm25::idf;
<span class="kw">use </span><span class="kw">crate</span>::query::{BooleanQuery, BoostQuery, Occur, Query, TermQuery};
<span class="kw">use </span><span class="kw">crate</span>::schema::{Field, FieldType, IndexRecordOption, Term, Value};
<span class="kw">use </span><span class="kw">crate</span>::tokenizer::{BoxTokenStream, FacetTokenizer, PreTokenizedStream, Tokenizer};
<span class="kw">use crate</span>::{DocAddress, <span class="prelude-ty">Result</span>, Searcher, TantivyError};
<span class="attribute">#[derive(Debug, PartialEq)]
</span><span class="kw">struct </span>ScoreTerm {
<span class="kw">pub </span>term: Term,
<span class="kw">pub </span>score: f32,
}
<span class="kw">impl </span>ScoreTerm {
<span class="kw">fn </span>new(term: Term, score: f32) -&gt; <span class="self">Self </span>{
<span class="self">Self </span>{ term, score }
}
}
<span class="kw">impl </span>Eq <span class="kw">for </span>ScoreTerm {}
<span class="kw">impl </span>PartialOrd <span class="kw">for </span>ScoreTerm {
<span class="kw">fn </span>partial_cmp(<span class="kw-2">&amp;</span><span class="self">self</span>, other: <span class="kw-2">&amp;</span><span class="self">Self</span>) -&gt; <span class="prelude-ty">Option</span>&lt;std::cmp::Ordering&gt; {
<span class="self">self</span>.score.partial_cmp(<span class="kw-2">&amp;</span>other.score)
}
}
<span class="kw">impl </span>Ord <span class="kw">for </span>ScoreTerm {
<span class="kw">fn </span>cmp(<span class="kw-2">&amp;</span><span class="self">self</span>, other: <span class="kw-2">&amp;</span><span class="self">Self</span>) -&gt; std::cmp::Ordering {
<span class="self">self</span>.partial_cmp(other).unwrap_or(std::cmp::Ordering::Equal)
}
}
<span class="doccomment">/// A struct used as helper to build [`MoreLikeThisQuery`](crate::query::MoreLikeThisQuery)
/// This more-like-this implementation is inspired by the Apache Lucene
/// and closely follows the same implementation with adaptation to Tantivy vocabulary and API.
///
/// [MoreLikeThis](https://github.com/apache/lucene/blob/main/lucene/queries/src/java/org/apache/lucene/queries/mlt/MoreLikeThis.java#L147)
/// [MoreLikeThisQuery](https://github.com/apache/lucene/blob/main/lucene/queries/src/java/org/apache/lucene/queries/mlt/MoreLikeThisQuery.java#L36)
</span><span class="attribute">#[derive(Debug, Clone)]
</span><span class="kw">pub struct </span>MoreLikeThis {
<span class="doccomment">/// Ignore words which do not occur in at least this many docs.
</span><span class="kw">pub </span>min_doc_frequency: <span class="prelude-ty">Option</span>&lt;u64&gt;,
<span class="doccomment">/// Ignore words which occur in more than this many docs.
</span><span class="kw">pub </span>max_doc_frequency: <span class="prelude-ty">Option</span>&lt;u64&gt;,
<span class="doccomment">/// Ignore words less frequent than this.
</span><span class="kw">pub </span>min_term_frequency: <span class="prelude-ty">Option</span>&lt;usize&gt;,
<span class="doccomment">/// Don&#39;t return a query longer than this.
</span><span class="kw">pub </span>max_query_terms: <span class="prelude-ty">Option</span>&lt;usize&gt;,
<span class="doccomment">/// Ignore words if less than this length.
</span><span class="kw">pub </span>min_word_length: <span class="prelude-ty">Option</span>&lt;usize&gt;,
<span class="doccomment">/// Ignore words if greater than this length.
</span><span class="kw">pub </span>max_word_length: <span class="prelude-ty">Option</span>&lt;usize&gt;,
<span class="doccomment">/// Boost factor to use when boosting the terms
</span><span class="kw">pub </span>boost_factor: <span class="prelude-ty">Option</span>&lt;f32&gt;,
<span class="doccomment">/// Current set of stop words.
</span><span class="kw">pub </span>stop_words: Vec&lt;String&gt;,
}
<span class="kw">impl </span>Default <span class="kw">for </span>MoreLikeThis {
<span class="kw">fn </span>default() -&gt; <span class="self">Self </span>{
<span class="self">Self </span>{
min_doc_frequency: <span class="prelude-val">Some</span>(<span class="number">5</span>),
max_doc_frequency: <span class="prelude-val">None</span>,
min_term_frequency: <span class="prelude-val">Some</span>(<span class="number">2</span>),
max_query_terms: <span class="prelude-val">Some</span>(<span class="number">25</span>),
min_word_length: <span class="prelude-val">None</span>,
max_word_length: <span class="prelude-val">None</span>,
boost_factor: <span class="prelude-val">Some</span>(<span class="number">1.0</span>),
stop_words: <span class="macro">vec!</span>[],
}
}
}
<span class="kw">impl </span>MoreLikeThis {
<span class="doccomment">/// Creates a [`BooleanQuery`] using a document address to collect
/// the top stored field values.
</span><span class="kw">pub fn </span>query_with_document(
<span class="kw-2">&amp;</span><span class="self">self</span>,
searcher: <span class="kw-2">&amp;</span>Searcher,
doc_address: DocAddress,
) -&gt; <span class="prelude-ty">Result</span>&lt;BooleanQuery&gt; {
<span class="kw">let </span>score_terms = <span class="self">self</span>.retrieve_terms_from_doc_address(searcher, doc_address)<span class="question-mark">?</span>;
<span class="kw">let </span>query = <span class="self">self</span>.create_query(score_terms);
<span class="prelude-val">Ok</span>(query)
}
<span class="doccomment">/// Creates a [`BooleanQuery`] using a set of field values.
</span><span class="kw">pub fn </span>query_with_document_fields(
<span class="kw-2">&amp;</span><span class="self">self</span>,
searcher: <span class="kw-2">&amp;</span>Searcher,
doc_fields: <span class="kw-2">&amp;</span>[(Field, Vec&lt;Value&gt;)],
) -&gt; <span class="prelude-ty">Result</span>&lt;BooleanQuery&gt; {
<span class="kw">let </span>score_terms = <span class="self">self</span>.retrieve_terms_from_doc_fields(searcher, doc_fields)<span class="question-mark">?</span>;
<span class="kw">let </span>query = <span class="self">self</span>.create_query(score_terms);
<span class="prelude-val">Ok</span>(query)
}
<span class="doccomment">/// Creates a [`BooleanQuery`] from an ascendingly sorted list of ScoreTerm
/// This will map the list of ScoreTerm to a list of [`TermQuery`] and compose a
/// BooleanQuery using that list as sub queries.
</span><span class="kw">fn </span>create_query(<span class="kw-2">&amp;</span><span class="self">self</span>, <span class="kw-2">mut </span>score_terms: Vec&lt;ScoreTerm&gt;) -&gt; BooleanQuery {
score_terms.sort_by(|left_ts, right_ts| right_ts.cmp(left_ts));
<span class="kw">let </span>best_score = score_terms.first().map_or(<span class="number">1f32</span>, |x| x.score);
<span class="kw">let </span><span class="kw-2">mut </span>queries = Vec::new();
<span class="kw">for </span>ScoreTerm { term, score } <span class="kw">in </span>score_terms {
<span class="kw">let </span><span class="kw-2">mut </span>query: Box&lt;<span class="kw">dyn </span>Query&gt; =
Box::new(TermQuery::new(term, IndexRecordOption::Basic));
<span class="kw">if let </span><span class="prelude-val">Some</span>(factor) = <span class="self">self</span>.boost_factor {
query = Box::new(BoostQuery::new(query, score * factor / best_score));
}
queries.push((Occur::Should, query));
}
BooleanQuery::from(queries)
}
<span class="doccomment">/// Finds terms for a more-like-this query.
/// doc_address is the address of document from which to find terms.
</span><span class="kw">fn </span>retrieve_terms_from_doc_address(
<span class="kw-2">&amp;</span><span class="self">self</span>,
searcher: <span class="kw-2">&amp;</span>Searcher,
doc_address: DocAddress,
) -&gt; <span class="prelude-ty">Result</span>&lt;Vec&lt;ScoreTerm&gt;&gt; {
<span class="kw">let </span>doc = searcher.doc(doc_address)<span class="question-mark">?</span>;
<span class="kw">let </span>field_to_values = doc
.get_sorted_field_values()
.iter()
.map(|(field, values)| {
(
<span class="kw-2">*</span>field,
values.iter().map(|v| (<span class="kw-2">**</span>v).clone()).collect::&lt;Vec&lt;Value&gt;&gt;(),
)
})
.collect::&lt;Vec&lt;<span class="kw">_</span>&gt;&gt;();
<span class="self">self</span>.retrieve_terms_from_doc_fields(searcher, <span class="kw-2">&amp;</span>field_to_values)
}
<span class="doccomment">/// Finds terms for a more-like-this query.
/// field_to_field_values is a mapping from field to possible values of that field.
</span><span class="kw">fn </span>retrieve_terms_from_doc_fields(
<span class="kw-2">&amp;</span><span class="self">self</span>,
searcher: <span class="kw-2">&amp;</span>Searcher,
field_to_values: <span class="kw-2">&amp;</span>[(Field, Vec&lt;Value&gt;)],
) -&gt; <span class="prelude-ty">Result</span>&lt;Vec&lt;ScoreTerm&gt;&gt; {
<span class="kw">if </span>field_to_values.is_empty() {
<span class="kw">return </span><span class="prelude-val">Err</span>(TantivyError::InvalidArgument(
<span class="string">&quot;Cannot create more like this query on empty field values. The document may not \
have stored fields&quot;
</span>.to_string(),
));
}
<span class="kw">let </span><span class="kw-2">mut </span>field_to_term_freq_map = HashMap::new();
<span class="kw">for </span>(field, values) <span class="kw">in </span>field_to_values {
<span class="self">self</span>.add_term_frequencies(searcher, <span class="kw-2">*</span>field, values, <span class="kw-2">&amp;mut </span>field_to_term_freq_map)<span class="question-mark">?</span>;
}
<span class="self">self</span>.create_score_term(searcher, field_to_term_freq_map)
}
<span class="doccomment">/// Computes the frequency of values for a field while updating the term frequencies
/// Note: A FieldValue can be made up of multiple terms.
/// We are interested in extracting terms within FieldValue
</span><span class="kw">fn </span>add_term_frequencies(
<span class="kw-2">&amp;</span><span class="self">self</span>,
searcher: <span class="kw-2">&amp;</span>Searcher,
field: Field,
values: <span class="kw-2">&amp;</span>[Value],
term_frequencies: <span class="kw-2">&amp;mut </span>HashMap&lt;Term, usize&gt;,
) -&gt; <span class="prelude-ty">Result</span>&lt;()&gt; {
<span class="kw">let </span>schema = searcher.schema();
<span class="kw">let </span>tokenizer_manager = searcher.index().tokenizers();
<span class="kw">let </span>field_entry = schema.get_field_entry(field);
<span class="kw">if </span>!field_entry.is_indexed() {
<span class="kw">return </span><span class="prelude-val">Ok</span>(());
}
<span class="comment">// extract the raw value, possibly tokenizing &amp; filtering to update the term frequency map
</span><span class="kw">match </span>field_entry.field_type() {
FieldType::Facet(<span class="kw">_</span>) =&gt; {
<span class="kw">let </span>facets: Vec&lt;<span class="kw-2">&amp;</span>str&gt; = values
.iter()
.map(|value| <span class="kw">match </span>value {
Value::Facet(<span class="kw-2">ref </span>facet) =&gt; <span class="prelude-val">Ok</span>(facet.encoded_str()),
<span class="kw">_ </span>=&gt; <span class="prelude-val">Err</span>(TantivyError::InvalidArgument(
<span class="string">&quot;invalid field value&quot;</span>.to_string(),
)),
})
.collect::&lt;<span class="prelude-ty">Result</span>&lt;Vec&lt;<span class="kw">_</span>&gt;&gt;&gt;()<span class="question-mark">?</span>;
<span class="kw">for </span>fake_str <span class="kw">in </span>facets {
FacetTokenizer.token_stream(fake_str).process(<span class="kw-2">&amp;mut </span>|token| {
<span class="kw">if </span><span class="self">self</span>.is_noise_word(token.text.clone()) {
<span class="kw">let </span>term = Term::from_field_text(field, <span class="kw-2">&amp;</span>token.text);
<span class="kw-2">*</span>term_frequencies.entry(term).or_insert(<span class="number">0</span>) += <span class="number">1</span>;
}
});
}
}
FieldType::Str(text_options) =&gt; {
<span class="kw">let </span><span class="kw-2">mut </span>token_streams: Vec&lt;BoxTokenStream&gt; = <span class="macro">vec!</span>[];
<span class="kw">for </span>value <span class="kw">in </span>values {
<span class="kw">match </span>value {
Value::PreTokStr(tok_str) =&gt; {
token_streams.push(PreTokenizedStream::from(tok_str.clone()).into());
}
Value::Str(<span class="kw-2">ref </span>text) =&gt; {
<span class="kw">if let </span><span class="prelude-val">Some</span>(tokenizer) = text_options
.get_indexing_options()
.map(|text_indexing_options| {
text_indexing_options.tokenizer().to_string()
})
.and_then(|tokenizer_name| tokenizer_manager.get(<span class="kw-2">&amp;</span>tokenizer_name))
{
token_streams.push(tokenizer.token_stream(text));
}
}
<span class="kw">_ </span>=&gt; (),
}
}
<span class="kw">for </span><span class="kw-2">mut </span>token_stream <span class="kw">in </span>token_streams {
token_stream.process(<span class="kw-2">&amp;mut </span>|token| {
<span class="kw">if </span>!<span class="self">self</span>.is_noise_word(token.text.clone()) {
<span class="kw">let </span>term = Term::from_field_text(field, <span class="kw-2">&amp;</span>token.text);
<span class="kw-2">*</span>term_frequencies.entry(term).or_insert(<span class="number">0</span>) += <span class="number">1</span>;
}
});
}
}
FieldType::U64(<span class="kw">_</span>) =&gt; {
<span class="kw">for </span>value <span class="kw">in </span>values {
<span class="kw">let </span>val = value.as_u64().ok_or_else(|| {
TantivyError::InvalidArgument(<span class="string">&quot;invalid value&quot;</span>.to_string())
})<span class="question-mark">?</span>;
<span class="kw">if </span>!<span class="self">self</span>.is_noise_word(val.to_string()) {
<span class="kw">let </span>term = Term::from_field_u64(field, val);
<span class="kw-2">*</span>term_frequencies.entry(term).or_insert(<span class="number">0</span>) += <span class="number">1</span>;
}
}
}
FieldType::Date(<span class="kw">_</span>) =&gt; {
<span class="kw">for </span>value <span class="kw">in </span>values {
<span class="kw">let </span>timestamp_micros = value
.as_date()
.ok_or_else(|| TantivyError::InvalidArgument(<span class="string">&quot;invalid value&quot;</span>.to_string()))<span class="question-mark">?
</span>.into_timestamp_micros();
<span class="kw">if </span>!<span class="self">self</span>.is_noise_word(timestamp_micros.to_string()) {
<span class="kw">let </span>term = Term::from_field_i64(field, timestamp_micros);
<span class="kw-2">*</span>term_frequencies.entry(term).or_insert(<span class="number">0</span>) += <span class="number">1</span>;
}
}
}
FieldType::I64(<span class="kw">_</span>) =&gt; {
<span class="kw">for </span>value <span class="kw">in </span>values {
<span class="kw">let </span>val = value.as_i64().ok_or_else(|| {
TantivyError::InvalidArgument(<span class="string">&quot;invalid value&quot;</span>.to_string())
})<span class="question-mark">?</span>;
<span class="kw">if </span>!<span class="self">self</span>.is_noise_word(val.to_string()) {
<span class="kw">let </span>term = Term::from_field_i64(field, val);
<span class="kw-2">*</span>term_frequencies.entry(term).or_insert(<span class="number">0</span>) += <span class="number">1</span>;
}
}
}
FieldType::F64(<span class="kw">_</span>) =&gt; {
<span class="kw">for </span>value <span class="kw">in </span>values {
<span class="kw">let </span>val = value.as_f64().ok_or_else(|| {
TantivyError::InvalidArgument(<span class="string">&quot;invalid value&quot;</span>.to_string())
})<span class="question-mark">?</span>;
<span class="kw">if </span>!<span class="self">self</span>.is_noise_word(val.to_string()) {
<span class="kw">let </span>term = Term::from_field_f64(field, val);
<span class="kw-2">*</span>term_frequencies.entry(term).or_insert(<span class="number">0</span>) += <span class="number">1</span>;
}
}
}
<span class="kw">_ </span>=&gt; {}
}
<span class="prelude-val">Ok</span>(())
}
<span class="doccomment">/// Determines if the term is likely to be of interest based on &quot;more-like-this&quot; settings
</span><span class="kw">fn </span>is_noise_word(<span class="kw-2">&amp;</span><span class="self">self</span>, word: String) -&gt; bool {
<span class="kw">let </span>word_length = word.len();
<span class="kw">if </span>word_length == <span class="number">0 </span>{
<span class="kw">return </span><span class="bool-val">true</span>;
}
<span class="kw">if </span><span class="self">self
</span>.min_word_length
.map(|min| word_length &lt; min)
.unwrap_or(<span class="bool-val">false</span>)
{
<span class="kw">return </span><span class="bool-val">true</span>;
}
<span class="kw">if </span><span class="self">self
</span>.max_word_length
.map(|max| word_length &gt; max)
.unwrap_or(<span class="bool-val">false</span>)
{
<span class="kw">return </span><span class="bool-val">true</span>;
}
<span class="self">self</span>.stop_words.contains(<span class="kw-2">&amp;</span>word)
}
<span class="doccomment">/// Couputes the score for each term while ignoring not useful terms
</span><span class="kw">fn </span>create_score_term(
<span class="kw-2">&amp;</span><span class="self">self</span>,
searcher: <span class="kw-2">&amp;</span>Searcher,
per_field_term_frequencies: HashMap&lt;Term, usize&gt;,
) -&gt; <span class="prelude-ty">Result</span>&lt;Vec&lt;ScoreTerm&gt;&gt; {
<span class="kw">let </span><span class="kw-2">mut </span>score_terms: BinaryHeap&lt;Reverse&lt;ScoreTerm&gt;&gt; = BinaryHeap::new();
<span class="kw">let </span>num_docs = searcher
.segment_readers()
.iter()
.map(|segment_reader| segment_reader.num_docs() <span class="kw">as </span>u64)
.sum::&lt;u64&gt;();
<span class="kw">for </span>(term, term_frequency) <span class="kw">in </span>per_field_term_frequencies.iter() {
<span class="comment">// ignore terms with less than min_term_frequency
</span><span class="kw">if </span><span class="self">self
</span>.min_term_frequency
.map(|min_term_frequency| <span class="kw-2">*</span>term_frequency &lt; min_term_frequency)
.unwrap_or(<span class="bool-val">false</span>)
{
<span class="kw">continue</span>;
}
<span class="kw">let </span>doc_freq = searcher.doc_freq(term)<span class="question-mark">?</span>;
<span class="comment">// ignore terms with less than min_doc_frequency
</span><span class="kw">if </span><span class="self">self
</span>.min_doc_frequency
.map(|min_doc_frequency| doc_freq &lt; min_doc_frequency)
.unwrap_or(<span class="bool-val">false</span>)
{
<span class="kw">continue</span>;
}
<span class="comment">// ignore terms with more than max_doc_frequency
</span><span class="kw">if </span><span class="self">self
</span>.max_doc_frequency
.map(|max_doc_frequency| doc_freq &gt; max_doc_frequency)
.unwrap_or(<span class="bool-val">false</span>)
{
<span class="kw">continue</span>;
}
<span class="comment">// ignore terms with zero frequency
</span><span class="kw">if </span>doc_freq == <span class="number">0 </span>{
<span class="kw">continue</span>;
}
<span class="comment">// compute similarity &amp; score
</span><span class="kw">let </span>idf = idf(doc_freq, num_docs);
<span class="kw">let </span>score = (<span class="kw-2">*</span>term_frequency <span class="kw">as </span>f32) * idf;
<span class="kw">if let </span><span class="prelude-val">Some</span>(limit) = <span class="self">self</span>.max_query_terms {
<span class="kw">if </span>score_terms.len() &gt; limit {
<span class="comment">// update the least significant term
</span><span class="kw">let </span>least_significant_term_score = score_terms.peek().unwrap().<span class="number">0</span>.score;
<span class="kw">if </span>least_significant_term_score &lt; score {
score_terms.peek_mut().unwrap().<span class="number">0 </span>= ScoreTerm::new(term.clone(), score);
}
} <span class="kw">else </span>{
score_terms.push(Reverse(ScoreTerm::new(term.clone(), score)));
}
} <span class="kw">else </span>{
score_terms.push(Reverse(ScoreTerm::new(term.clone(), score)));
}
}
<span class="kw">let </span>score_terms_vec: Vec&lt;ScoreTerm&gt; = score_terms
.into_iter()
.map(|reverse_score_term| reverse_score_term.<span class="number">0</span>)
.collect();
<span class="prelude-val">Ok</span>(score_terms_vec)
}
}
</code></pre></div>
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