MeiliSearch/milli/src/search/new/query_graph.rs

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use super::interner::{FixedSizeInterner, Interned};
use super::query_term::{
self, number_of_typos_allowed, LocatedQueryTerm, LocatedQueryTermSubset, NTypoTermSubset,
QueryTermSubset,
};
use super::small_bitmap::SmallBitmap;
use super::SearchContext;
use crate::search::new::interner::DedupInterner;
use crate::Result;
use std::cmp::Ordering;
use std::collections::BTreeMap;
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/// A node of the [`QueryGraph`].
///
/// There are four types of nodes:
/// 1. `Start` : unique, represents the start of the query
/// 2. `End` : unique, represents the end of a query
/// 3. `Deleted` : represents a node that was deleted.
/// All deleted nodes are unreachable from the start node.
/// 4. `Term` is a regular node representing a word or combination of words
/// from the user query.
#[derive(Clone)]
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pub struct QueryNode {
pub data: QueryNodeData,
pub predecessors: SmallBitmap<QueryNode>,
pub successors: SmallBitmap<QueryNode>,
}
#[derive(Clone, PartialEq, Eq, Hash)]
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pub enum QueryNodeData {
Term(LocatedQueryTermSubset),
Deleted,
Start,
End,
}
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/**
A graph representing all the ways to interpret the user's search query.
## Example 1
For the search query `sunflower`, we need to register the following things:
- we need to look for the exact word `sunflower`
- but also any word which is 1 or 2 typos apart from `sunflower`
- and every word that contains the prefix `sunflower`
- and also the couple of adjacent words `sun flower`
- as well as all the user-defined synonyms of `sunflower`
All these derivations of a word will be stored in [`QueryTerm`].
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## Example 2:
For the search query `summer house by`.
We also look for all word derivations of each term. And we also need to consider
the potential n-grams `summerhouse`, `summerhouseby`, and `houseby`.
Furthermore, we need to know which words these ngrams replace. This is done by creating the
following graph, where each node also contains a list of derivations:
```txt
houseby
START summer house by END
summerhouse
summerhouseby
```
Note also that each node has a range of positions associated with it,
such that `summer` is known to be a word at the positions `0..=0` and `houseby`
is registered with the positions `1..=2`. When two nodes are connected by an edge,
it means that they are potentially next to each other in the user's search query
(depending on the [`TermsMatchingStrategy`](crate::search::TermsMatchingStrategy)
and the transformations that were done on the query graph).
*/
#[derive(Clone)]
pub struct QueryGraph {
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/// The index of the start node within `self.nodes`
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pub root_node: Interned<QueryNode>,
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/// The index of the end node within `self.nodes`
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pub end_node: Interned<QueryNode>,
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/// The list of all query nodes
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pub nodes: FixedSizeInterner<QueryNode>,
}
impl QueryGraph {
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/// Build the query graph from the parsed user search query.
pub fn from_query(
ctx: &mut SearchContext,
// NOTE: the terms here must be consecutive
terms: &[LocatedQueryTerm],
) -> Result<QueryGraph> {
let nbr_typos = number_of_typos_allowed(ctx)?;
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let mut nodes_data: Vec<QueryNodeData> = vec![QueryNodeData::Start, QueryNodeData::End];
let root_node = 0;
let end_node = 1;
// TODO: we could consider generalizing to 4,5,6,7,etc. ngrams
let (mut prev2, mut prev1, mut prev0): (Vec<u16>, Vec<u16>, Vec<u16>) =
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(vec![], vec![], vec![root_node]);
let original_terms_len = terms.len();
for term_idx in 0..original_terms_len {
let mut new_nodes = vec![];
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let new_node_idx = add_node(
&mut nodes_data,
QueryNodeData::Term(LocatedQueryTermSubset {
term_subset: QueryTermSubset {
original: Interned::from_raw(term_idx as u16),
zero_typo_subset: NTypoTermSubset::All,
one_typo_subset: NTypoTermSubset::All,
two_typo_subset: NTypoTermSubset::All,
},
positions: terms[term_idx].positions.clone(),
term_ids: term_idx as u8..=term_idx as u8,
}),
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);
new_nodes.push(new_node_idx);
if !prev1.is_empty() {
if let Some(ngram) =
query_term::make_ngram(ctx, &terms[term_idx - 1..=term_idx], &nbr_typos)?
{
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let ngram_idx = add_node(
&mut nodes_data,
QueryNodeData::Term(LocatedQueryTermSubset {
term_subset: QueryTermSubset {
original: ngram.value,
zero_typo_subset: NTypoTermSubset::All,
one_typo_subset: NTypoTermSubset::All,
two_typo_subset: NTypoTermSubset::All,
},
positions: ngram.positions,
term_ids: term_idx as u8 - 1..=term_idx as u8,
}),
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);
new_nodes.push(ngram_idx);
}
}
if !prev2.is_empty() {
if let Some(ngram) =
query_term::make_ngram(ctx, &terms[term_idx - 2..=term_idx], &nbr_typos)?
{
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let ngram_idx = add_node(
&mut nodes_data,
QueryNodeData::Term(LocatedQueryTermSubset {
term_subset: QueryTermSubset {
original: ngram.value,
zero_typo_subset: NTypoTermSubset::All,
one_typo_subset: NTypoTermSubset::All,
two_typo_subset: NTypoTermSubset::All,
},
positions: ngram.positions,
term_ids: term_idx as u8 - 2..=term_idx as u8,
}),
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);
new_nodes.push(ngram_idx);
}
}
(prev0, prev1, prev2) = (new_nodes, prev0, prev1);
}
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let root_node = Interned::from_raw(root_node);
let end_node = Interned::from_raw(end_node);
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let mut nodes = FixedSizeInterner::new(
nodes_data.len() as u16,
QueryNode {
data: QueryNodeData::Deleted,
predecessors: SmallBitmap::new(nodes_data.len() as u16),
successors: SmallBitmap::new(nodes_data.len() as u16),
},
);
for (node_idx, node_data) in nodes_data.into_iter().enumerate() {
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let node = nodes.get_mut(Interned::from_raw(node_idx as u16));
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node.data = node_data;
}
let mut graph = QueryGraph { root_node, end_node, nodes };
graph.build_initial_edges();
Ok(graph)
}
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/// Remove the given nodes, connecting all their predecessors to all their successors.
pub fn remove_nodes_keep_edges(&mut self, nodes: &[Interned<QueryNode>]) {
for &node_id in nodes {
let node = self.nodes.get(node_id);
let old_node_pred = node.predecessors.clone();
let old_node_succ = node.successors.clone();
for pred in old_node_pred.iter() {
let pred_successors = &mut self.nodes.get_mut(pred).successors;
pred_successors.remove(node_id);
pred_successors.union(&old_node_succ);
}
for succ in old_node_succ.iter() {
let succ_predecessors = &mut self.nodes.get_mut(succ).predecessors;
succ_predecessors.remove(node_id);
succ_predecessors.union(&old_node_pred);
}
let node = self.nodes.get_mut(node_id);
node.data = QueryNodeData::Deleted;
node.predecessors.clear();
node.successors.clear();
}
}
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/// Remove the given nodes and all their edges from the query graph.
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pub fn remove_nodes(&mut self, nodes: &[Interned<QueryNode>]) {
for &node_id in nodes {
let node = &self.nodes.get(node_id);
let old_node_pred = node.predecessors.clone();
let old_node_succ = node.successors.clone();
for pred in old_node_pred.iter() {
self.nodes.get_mut(pred).successors.remove(node_id);
}
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for succ in old_node_succ.iter() {
self.nodes.get_mut(succ).predecessors.remove(node_id);
}
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let node = self.nodes.get_mut(node_id);
node.data = QueryNodeData::Deleted;
node.predecessors.clear();
node.successors.clear();
}
}
/// Simplify the query graph by removing all nodes that are disconnected from
/// the start or end nodes.
pub fn simplify(&mut self) {
loop {
let mut nodes_to_remove = vec![];
for (node_idx, node) in self.nodes.iter() {
if (!matches!(node.data, QueryNodeData::End | QueryNodeData::Deleted)
&& node.successors.is_empty())
|| (!matches!(node.data, QueryNodeData::Start | QueryNodeData::Deleted)
&& node.predecessors.is_empty())
{
nodes_to_remove.push(node_idx);
}
}
if nodes_to_remove.is_empty() {
break;
} else {
self.remove_nodes(&nodes_to_remove);
}
}
}
fn build_initial_edges(&mut self) {
for (_, node) in self.nodes.iter_mut() {
node.successors.clear();
node.predecessors.clear();
}
for node_id in self.nodes.indexes() {
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let node = self.nodes.get(node_id);
let end_prev_term_id = match &node.data {
QueryNodeData::Term(term) => *term.term_ids.end() as i16,
QueryNodeData::Start => -1,
QueryNodeData::Deleted => continue,
QueryNodeData::End => continue,
};
let successors = {
let mut successors = SmallBitmap::for_interned_values_in(&self.nodes);
let mut min = i16::MAX;
for (node_id, node) in self.nodes.iter() {
let start_next_term_id = match &node.data {
QueryNodeData::Term(term) => *term.term_ids.start() as i16,
QueryNodeData::End => i16::MAX,
QueryNodeData::Start => continue,
QueryNodeData::Deleted => continue,
};
if start_next_term_id <= end_prev_term_id {
continue;
}
match start_next_term_id.cmp(&min) {
Ordering::Less => {
min = start_next_term_id;
successors.clear();
successors.insert(node_id);
}
Ordering::Equal => {
successors.insert(node_id);
}
Ordering::Greater => continue,
}
}
successors
};
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let node = self.nodes.get_mut(node_id);
node.successors = successors.clone();
for successor in successors.iter() {
let successor = self.nodes.get_mut(successor);
successor.predecessors.insert(node_id);
}
}
}
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pub fn removal_order_for_terms_matching_strategy_last(
&self,
ctx: &SearchContext,
) -> Vec<SmallBitmap<QueryNode>> {
let (first_term_idx, last_term_idx) = {
let mut first_term_idx = u8::MAX;
let mut last_term_idx = 0u8;
for (_, node) in self.nodes.iter() {
match &node.data {
QueryNodeData::Term(t) => {
if *t.term_ids.end() > last_term_idx {
last_term_idx = *t.term_ids.end();
}
if *t.term_ids.start() < first_term_idx {
first_term_idx = *t.term_ids.start();
}
}
QueryNodeData::Deleted | QueryNodeData::Start | QueryNodeData::End => continue,
}
}
(first_term_idx, last_term_idx)
};
if first_term_idx >= last_term_idx {
return vec![];
}
let cost_of_term_idx = |term_idx: u8| {
let rank = 1 + last_term_idx - term_idx;
rank as u16
};
let mut nodes_to_remove = BTreeMap::<u16, SmallBitmap<QueryNode>>::new();
let mut at_least_one_phrase = false;
for (node_id, node) in self.nodes.iter() {
let QueryNodeData::Term(t) = &node.data else { continue };
if ctx.term_interner.get(t.term_subset.original).zero_typo.phrase.is_some() {
at_least_one_phrase = true;
continue;
}
let mut cost = 0;
for id in t.term_ids.clone() {
cost = std::cmp::max(cost, cost_of_term_idx(id));
}
nodes_to_remove
.entry(cost)
.or_insert_with(|| SmallBitmap::for_interned_values_in(&self.nodes))
.insert(node_id);
}
let mut res: Vec<_> = nodes_to_remove.into_values().collect();
if !at_least_one_phrase {
res.pop();
}
res
}
}
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fn add_node(nodes_data: &mut Vec<QueryNodeData>, node_data: QueryNodeData) -> u16 {
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let new_node_idx = nodes_data.len() as u16;
nodes_data.push(node_data);
new_node_idx
}
impl QueryGraph {
/*
Build a query graph from a list of paths
The paths are composed of source and dest terms.
If the source term is `None`, then the last dest term is used
as the predecessor of the dest term. If the source is Some(_),
then an edge is built between the last dest term and the source,
and between the source and new dest term.
Note that the resulting graph will not correspond to a perfect
representation of the set of paths.
For example, consider the following paths:
```txt
PATH 1 : a -> b1 -> c1 -> d -> e1
PATH 2 : a -> b2 -> c2 -> d -> e2
```
Then the resulting graph will be:
```txt
b1 c1 e1
a d
b2 c2 e2
```
which is different from the fully correct representation:
```txt
b1 c1 d e1
a
b2 c2 d e2
```
But we accept the first representation as it reduces the size
of the graph and shouldn't cause much problems.
*/
pub fn build_from_paths(
paths: Vec<Vec<(Option<LocatedQueryTermSubset>, LocatedQueryTermSubset)>>,
) -> Self {
let mut node_data = DedupInterner::default();
let root_node = node_data.insert(QueryNodeData::Start);
let end_node = node_data.insert(QueryNodeData::End);
let mut paths_with_ids = vec![];
for path in paths {
let mut path_with_ids = vec![];
for node in path {
let (start_term, end_term) = node;
let src_node_id = start_term.map(|x| node_data.insert(QueryNodeData::Term(x)));
let dest_node_id = node_data.insert(QueryNodeData::Term(end_term));
path_with_ids.push((src_node_id, dest_node_id));
}
paths_with_ids.push(path_with_ids);
}
let nodes_data = node_data.freeze();
let nodes_data_len = nodes_data.len();
let mut nodes = nodes_data.map_move(|n| QueryNode {
data: n,
predecessors: SmallBitmap::new(nodes_data_len),
successors: SmallBitmap::new(nodes_data_len),
});
let root_node = Interned::from_raw(root_node.into_raw());
let end_node = Interned::from_raw(end_node.into_raw());
for path in paths_with_ids {
let mut prev_node = root_node;
for node in path {
let (start_term, dest_term) = node;
let end_term = Interned::from_raw(dest_term.into_raw());
let src = if let Some(start_term) = start_term {
// TODO: this is incorrect! should take the intersection
// between the prev node and the start term if they refer to the same
// original query term!
let start_term = Interned::from_raw(start_term.into_raw());
nodes.get_mut(prev_node).successors.insert(start_term);
nodes.get_mut(start_term).predecessors.insert(prev_node);
start_term
} else {
prev_node
};
nodes.get_mut(src).successors.insert(end_term);
nodes.get_mut(end_term).predecessors.insert(src);
prev_node = end_term;
}
nodes.get_mut(prev_node).successors.insert(end_node);
nodes.get_mut(end_node).predecessors.insert(prev_node);
}
QueryGraph { root_node, end_node, nodes }
}
}