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Theodorkl/LSP integration #3347

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Theodorkl/LSP integration #3347

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ba064d5
fix: Close unclosed HTML tags
theodorklauritzen Aug 15, 2024
090b975
feat: Change rank feature to comply with rules
theodorklauritzen Aug 22, 2024
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40 changes: 20 additions & 20 deletions en/reference/rank-features.html
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Original file line number Diff line number Diff line change
Expand Up @@ -847,7 +847,7 @@ <h3 id="attribute-match-features-normalized">Attribute match features - normaliz
</p><p>
<code>max( 1-age(name)/maxAge , 0 )</code>
</p><p>
Scales linearly with age, see <a href="#freshness">freshness plot</a>.
Scales linearly with age, see <a href="#freshness">freshness plot</a>.</p>
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This should be closed above, as <p> taggas cannot be nested.

The P element represents a paragraph. It cannot contain block-level elements (including P itself).

Source: https://www.w3.org/TR/html401/struct/text.html#h-9.3.1

</td></tr>

<tr><td>freshness(<em>name</em>).logscale</td>
Expand Down Expand Up @@ -983,7 +983,7 @@ <h3 id="attribute-match-features-not-normalized">Attribute match features - not
A number which is close to 0 when a point vector in the document is close
to a matching point vector in the query.
The document vectors and the query vector must be the same tensor
type, with one indexed dimension of size N, representing a point in an N-dimensional space.
type, with one indexed dimension of size N, representing a point in an N-dimensional space.</p>
<ul>
<li>
<p>
Expand Down Expand Up @@ -1183,7 +1183,7 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
<tr><td>nativeDotProduct</td>
<td>0</td>
<td><p id="nativeDotProduct">
Calculates the sparse dot product between query term weights and match weights as above, but for all term/field combinations.
Calculates the sparse dot product between query term weights and match weights as above, but for all term/field combinations.</p>
</td></tr>

<tr><td>firstPhase</td>
Expand Down Expand Up @@ -1456,7 +1456,7 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
<tr class="trx"><th class="thx">Feature name</th><th class="thx">Default</th><th class="thx">Description</th></tr>

<tr>
<td>foreach(<em>dimension</em>, <em>variable</em>, <em>feature</em>, <em>condition</em>, <em>operation</em>)</td>
<td>foreach(<em>dimension</em>,<em>variable</em>,<em>feature</em>,<em>condition</em>,<em>operation</em>)</td>
<td>n/a</td>
<td>
<p id="foreach(dimension,variable,feature,condition,operation)">
Expand All @@ -1470,7 +1470,7 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
<li> <em>terms</em>: All query term indices, from 0 and up to
<a href="rank-feature-configuration.html#foreach">maxTerms</a>.
</li> <li> <em>fields</em>: All index field names.
</li> <li> <em>attributes</em>: All attribute field names.
</li> <li> <em>attributes</em>: All attribute field names.</li>
</ul>
</li> <li> <em>variable</em>: The name of the variable 'storing' each of the items you are iterating over.
</li> <li> <em>feature</em>: The name of the feature you want to use the output value from. Use the <em>variable</em> as part of the feature name, and for each item you iterate over this <em>variable</em> is replaced with the actual item.
Expand All @@ -1480,7 +1480,7 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
<ul>
<li> <em>&gt;a</em>: Use feature output if greater than number a.
</li> <li> <em>&lt;a</em>: Use feature output if less than number a.
</li> <li> <em>true</em>: Use all feature output values.
</li> <li> <em>true</em>: Use all feature output values.</li>
</ul>
</li> <li> <em>operation</em>: The operation you want to perform on the feature output values. This can be:
<ul>
Expand All @@ -1489,8 +1489,8 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
</li> <li> <em>average</em>: Calculate the average of the values.
</li> <li> <em>max</em>: Find the max of the values.
</li> <li> <em>min</em>: Find the min of the values.
</li> <li> <em>count</em>: Count the number of values.
</ul>
</li> <li> <em>count</em>: Count the number of values.</li>
</ul></li>
</ul>
<p>
Lets say you want to calculate the average score of the <em>fieldMatch</em> feature for all index fields,
Expand Down Expand Up @@ -1527,10 +1527,10 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
instead of weighted sets. This will be a lot faster when you have full vectors in the document with
more than 5-10% non-zero values. You are also then not limited to integer weights. All the numeric
datatypes can be used with arrays, so you have full floating point support.
The 32 bit floating point type yields the fastest execution.
The 32 bit floating point type yields the fastest execution.</p>
<ul>
<li> <em>name</em>: The name of the weighted set string/integer or array of numeric attribute.
<li> <em>vector</em>: The name of the vector sent down with the query.
<li> <em>name</em>: The name of the weighted set string/integer or array of numeric attribute.</li>
<li> <em>vector</em>: The name of the vector sent down with the query.</li>
</ul>
<p>
Each unique string/integer in the weighted set corresponds to a dimension
Expand All @@ -1547,7 +1547,7 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
When using an array the dimensions is a positive integer starting at 0. If the query is sparse all non given dimensions
are zero. That also goes for dimensions that outside of the array size in each document.
</p><p>
Assume a weighted set string attribute X with:
Assume a weighted set string attribute X with:</p>
<pre>
"X": {
"x": 10,
Expand Down Expand Up @@ -1575,10 +1575,10 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
</td></tr>

<tr>
<td>tokenInputIds(<em>length</em>, <em>input_1</em>, <em>input_2</em>, <em>...</em>)</td>
<td>tokenInputIds(<em>length</em>,<em>input</em>,<em>...</em>)</td>
<td>n/a</td>
<td>
<p id="tokenInputIds(length, input_1, input_2, ...)">
<p id="tokenInputIds(length,input,...)">
Convenience function for generating token sequence input to Transformer models.
Creates a tensor with dimensions <code>d0[1], d1[length]</code>, where
<code>d0</code> is the batch dimension and <code>d1</code> is the maximum length
Expand Down Expand Up @@ -1616,10 +1616,10 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
</tr>

<tr>
<td>customTokenInputIds(<em>start_sequence_id</em>, <em>sep_sequence_id</em><em>length</em>, <em>input_1</em>, <em>input_2</em>, <em>...</em>)</td>
<td>customTokenInputIds(<em>startSequenceId</em>,<em>sepSequenceId</em><em>length</em>,<em>input</em>,<em>...</em>)</td>
<td>n/a</td>
<td>
<p id="customTokenInputIds(start_sequence_id, sep_sequence_id, length, input_1, input_2, ...)">
<p id="customTokenInputIds(startSequenceId,sepSequenceId,length,input,...)">
Convenience function for generating token sequence input to Transformer models.
Creates a tensor with dimensions <code>d0[1], d1[length]</code>, where
<code>d0</code> is the batch dimension and <code>d1</code> is the maximum length
Expand Down Expand Up @@ -1652,10 +1652,10 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>


<tr>
<td>tokenTypeIds(<em>length</em>, <em>input_1</em>, <em>input_2</em>, <em>...</em>)</td>
<td>tokenTypeIds(<em>length</em>,<em>input</em>,<em>...</em>)</td>
<td>n/a</td>
<td>
<p id="tokenTypeIds(length, input_1, input_2, ...)">
<p id="tokenTypeIds(length,input,...)">
Convenience function for generating token sequence input to Transformer models.
Similar to the <code>tokenInputIds</code>, creates a tensor of type
<code>d0[1],d1[length]</code> which represents a mask with zeros for the
Expand All @@ -1666,10 +1666,10 @@ <h3 id="rank-scores">Rank scores</h3></td></tr>
</tr>

<tr>
<td>tokenAttentionMask(<em>length</em>, <em>input_1</em>, <em>input_2</em>, <em>...</em>)</td>
<td>tokenAttentionMask(<em>length</em>,<em>input</em>,<em>...</em>)</td>
<td>n/a</td>
<td>
<p id="tokenAttentionMask(length, input_1, input_2, ...)">
<p id="tokenAttentionMask(length,input,...)">
Convenience function for generating token sequence input to Transformer models.
Similar to the <code>tokenInputIds</code>, creates a tensor of type
<code>d0[1],d1[length]</code> which represents a mask with ones for all
Expand Down
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