mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-07-06 19:09:28 +02:00
421 lines
18 KiB
Rust
421 lines
18 KiB
Rust
use super::interner::{FixedSizeInterner, Interned};
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use super::query_term::{
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self, number_of_typos_allowed, LocatedQueryTerm, LocatedQueryTermSubset, NTypoTermSubset,
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QueryTermSubset,
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};
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use super::small_bitmap::SmallBitmap;
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use super::SearchContext;
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use crate::search::new::interner::DedupInterner;
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use crate::Result;
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use std::cmp::Ordering;
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use std::collections::BTreeMap;
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/// A node of the [`QueryGraph`].
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///
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/// There are four types of nodes:
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/// 1. `Start` : unique, represents the start of the query
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/// 2. `End` : unique, represents the end of a query
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/// 3. `Deleted` : represents a node that was deleted.
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/// All deleted nodes are unreachable from the start node.
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/// 4. `Term` is a regular node representing a word or combination of words
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/// from the user query.
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#[derive(Clone)]
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pub struct QueryNode {
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pub data: QueryNodeData,
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pub predecessors: SmallBitmap<QueryNode>,
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pub successors: SmallBitmap<QueryNode>,
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}
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#[derive(Clone, PartialEq, Eq, Hash)]
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pub enum QueryNodeData {
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Term(LocatedQueryTermSubset),
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Deleted,
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Start,
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End,
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}
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/**
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A graph representing all the ways to interpret the user's search query.
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## Example 1
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For the search query `sunflower`, we need to register the following things:
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- we need to look for the exact word `sunflower`
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- but also any word which is 1 or 2 typos apart from `sunflower`
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- and every word that contains the prefix `sunflower`
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- and also the couple of adjacent words `sun flower`
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- as well as all the user-defined synonyms of `sunflower`
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All these derivations of a word will be stored in [`QueryTerm`].
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## Example 2:
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For the search query `summer house by`.
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We also look for all word derivations of each term. And we also need to consider
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the potential n-grams `summerhouse`, `summerhouseby`, and `houseby`.
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Furthermore, we need to know which words these ngrams replace. This is done by creating the
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following graph, where each node also contains a list of derivations:
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```txt
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┌───────┐
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┌─│houseby│─────────┐
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│ └───────┘ │
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┌───────┐ ┌───────┐ │ ┌───────┐ ┌────┐ │ ┌───────┐
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│ START │─┬─│summer │─┴─│ house │┌─│ by │─┼─│ END │
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└───────┘ │ └───────┘ └───────┘│ └────┘ │ └───────┘
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│ ┌────────────┐ │ │
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├─│summerhouse │───────┘ │
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│ └────────────┘ │
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│ ┌─────────────┐ │
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└─────────│summerhouseby│───────┘
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└─────────────┘
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```
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Note also that each node has a range of positions associated with it,
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such that `summer` is known to be a word at the positions `0..=0` and `houseby`
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is registered with the positions `1..=2`. When two nodes are connected by an edge,
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it means that they are potentially next to each other in the user's search query
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(depending on the [`TermsMatchingStrategy`](crate::search::TermsMatchingStrategy)
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and the transformations that were done on the query graph).
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*/
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#[derive(Clone)]
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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>,
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}
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impl QueryGraph {
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/// Build the query graph from the parsed user search query.
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pub fn from_query(
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ctx: &mut SearchContext,
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// NOTE: the terms here must be consecutive
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terms: &[LocatedQueryTerm],
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) -> Result<QueryGraph> {
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let nbr_typos = number_of_typos_allowed(ctx)?;
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let mut nodes_data: Vec<QueryNodeData> = vec![QueryNodeData::Start, QueryNodeData::End];
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let root_node = 0;
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let end_node = 1;
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// TODO: we could consider generalizing to 4,5,6,7,etc. ngrams
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let (mut prev2, mut prev1, mut prev0): (Vec<u16>, Vec<u16>, Vec<u16>) =
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(vec![], vec![], vec![root_node]);
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let original_terms_len = terms.len();
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for term_idx in 0..original_terms_len {
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let mut new_nodes = vec![];
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let new_node_idx = add_node(
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&mut nodes_data,
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QueryNodeData::Term(LocatedQueryTermSubset {
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term_subset: QueryTermSubset {
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original: Interned::from_raw(term_idx as u16),
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zero_typo_subset: NTypoTermSubset::All,
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one_typo_subset: NTypoTermSubset::All,
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two_typo_subset: NTypoTermSubset::All,
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},
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positions: terms[term_idx].positions.clone(),
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term_ids: term_idx as u8..=term_idx as u8,
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}),
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);
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new_nodes.push(new_node_idx);
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if !prev1.is_empty() {
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if let Some(ngram) =
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query_term::make_ngram(ctx, &terms[term_idx - 1..=term_idx], &nbr_typos)?
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{
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let ngram_idx = add_node(
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&mut nodes_data,
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QueryNodeData::Term(LocatedQueryTermSubset {
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term_subset: QueryTermSubset {
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original: ngram.value,
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zero_typo_subset: NTypoTermSubset::All,
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one_typo_subset: NTypoTermSubset::All,
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two_typo_subset: NTypoTermSubset::All,
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},
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positions: ngram.positions,
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term_ids: term_idx as u8 - 1..=term_idx as u8,
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}),
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);
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new_nodes.push(ngram_idx);
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}
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}
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if !prev2.is_empty() {
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if let Some(ngram) =
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query_term::make_ngram(ctx, &terms[term_idx - 2..=term_idx], &nbr_typos)?
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{
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let ngram_idx = add_node(
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&mut nodes_data,
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QueryNodeData::Term(LocatedQueryTermSubset {
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term_subset: QueryTermSubset {
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original: ngram.value,
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zero_typo_subset: NTypoTermSubset::All,
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one_typo_subset: NTypoTermSubset::All,
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two_typo_subset: NTypoTermSubset::All,
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},
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positions: ngram.positions,
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term_ids: term_idx as u8 - 2..=term_idx as u8,
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}),
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);
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new_nodes.push(ngram_idx);
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}
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}
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(prev0, prev1, prev2) = (new_nodes, prev0, prev1);
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}
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let root_node = Interned::from_raw(root_node);
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let end_node = Interned::from_raw(end_node);
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let mut nodes = FixedSizeInterner::new(
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nodes_data.len() as u16,
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QueryNode {
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data: QueryNodeData::Deleted,
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predecessors: SmallBitmap::new(nodes_data.len() as u16),
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successors: SmallBitmap::new(nodes_data.len() as u16),
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},
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);
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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;
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}
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let mut graph = QueryGraph { root_node, end_node, nodes };
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graph.rebuild_edges();
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Ok(graph)
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}
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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>]) {
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for &node_id in nodes {
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let node = &self.nodes.get(node_id);
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let old_node_pred = node.predecessors.clone();
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let old_node_succ = node.successors.clone();
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for pred in old_node_pred.iter() {
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self.nodes.get_mut(pred).successors.remove(node_id);
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}
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for succ in old_node_succ.iter() {
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self.nodes.get_mut(succ).predecessors.remove(node_id);
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}
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let node = self.nodes.get_mut(node_id);
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node.data = QueryNodeData::Deleted;
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node.predecessors.clear();
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node.successors.clear();
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}
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self.rebuild_edges();
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}
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fn rebuild_edges(&mut self) {
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for (_, node) in self.nodes.iter_mut() {
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node.successors.clear();
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node.predecessors.clear();
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}
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for node_id in self.nodes.indexes() {
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let node = self.nodes.get(node_id);
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let end_position = match &node.data {
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QueryNodeData::Term(term) => *term.positions.end(),
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QueryNodeData::Start => -1,
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QueryNodeData::Deleted => continue,
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QueryNodeData::End => continue,
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};
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let successors = {
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let mut successors = SmallBitmap::for_interned_values_in(&self.nodes);
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let mut min = i8::MAX;
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for (node_id, node) in self.nodes.iter() {
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let start_position = match &node.data {
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QueryNodeData::Term(term) => *term.positions.start(),
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QueryNodeData::End => i8::MAX,
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QueryNodeData::Start => continue,
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QueryNodeData::Deleted => continue,
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};
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if start_position <= end_position {
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continue;
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}
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match start_position.cmp(&min) {
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Ordering::Less => {
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min = start_position;
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successors.clear();
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successors.insert(node_id);
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}
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Ordering::Equal => {
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successors.insert(node_id);
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}
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Ordering::Greater => continue,
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}
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}
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successors
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};
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let node = self.nodes.get_mut(node_id);
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node.successors = successors.clone();
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for successor in successors.iter() {
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let successor = self.nodes.get_mut(successor);
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successor.predecessors.insert(node_id);
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}
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}
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}
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/// Remove all the nodes that correspond to a word starting at the given position and rebuild
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/// the edges of the graph appropriately.
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pub fn remove_words_starting_at_position(&mut self, position: i8) -> bool {
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let mut nodes_to_remove = vec![];
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for (node_idx, node) in self.nodes.iter() {
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let QueryNodeData::Term(LocatedQueryTermSubset { term_subset: _, positions, term_ids: _ }) = &node.data else { continue };
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if positions.start() == &position {
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nodes_to_remove.push(node_idx);
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}
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}
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self.remove_nodes(&nodes_to_remove);
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!nodes_to_remove.is_empty()
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}
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pub fn removal_order_for_terms_matching_strategy_last(&self) -> Vec<SmallBitmap<QueryNode>> {
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let (first_term_idx, last_term_idx) = {
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let mut first_term_idx = u8::MAX;
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let mut last_term_idx = 0u8;
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for (_, node) in self.nodes.iter() {
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match &node.data {
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QueryNodeData::Term(t) => {
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if *t.term_ids.end() > last_term_idx {
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last_term_idx = *t.term_ids.end();
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}
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if *t.term_ids.start() < first_term_idx {
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first_term_idx = *t.term_ids.start();
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}
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}
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QueryNodeData::Deleted | QueryNodeData::Start | QueryNodeData::End => continue,
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}
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}
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(first_term_idx, last_term_idx)
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};
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if first_term_idx >= last_term_idx {
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return vec![];
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}
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let cost_of_term_idx = |term_idx: u8| {
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if term_idx == first_term_idx {
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None
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} else {
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let rank = 1 + last_term_idx - term_idx;
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Some(rank as u16)
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}
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};
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let mut nodes_to_remove = BTreeMap::<u16, SmallBitmap<QueryNode>>::new();
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for (node_id, node) in self.nodes.iter() {
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let QueryNodeData::Term(t) = &node.data else { continue };
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let mut cost = 0;
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for id in t.term_ids.clone() {
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if let Some(t_cost) = cost_of_term_idx(id) {
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cost += t_cost;
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} else {
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continue;
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}
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}
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nodes_to_remove
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.entry(cost)
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.or_insert_with(|| SmallBitmap::for_interned_values_in(&self.nodes))
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.insert(node_id);
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}
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nodes_to_remove.into_values().collect()
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}
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}
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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;
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nodes_data.push(node_data);
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new_node_idx
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}
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impl QueryGraph {
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/*
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Build a query graph from a list of paths
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The paths are composed of source and dest terms.
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If the source term is `None`, then the last dest term is used
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as the predecessor of the dest term. If the source is Some(_),
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then an edge is built between the last dest term and the source,
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and between the source and new dest term.
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Note that the resulting graph will not correspond to a perfect
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representation of the set of paths.
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For example, consider the following paths:
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```txt
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PATH 1 : a -> b1 -> c1 -> d -> e1
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PATH 2 : a -> b2 -> c2 -> d -> e2
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```
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Then the resulting graph will be:
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```txt
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┌────┐ ┌────┐ ┌────┐
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┌──│ b1 │──│ c1 │─┐ ┌──│ e1 │
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┌────┐ │ └────┘ └────┘ │ ┌────┐ │ └────┘
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│ a │─┤ ├─│ d │─┤
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└────┘ │ ┌────┐ ┌────┐ │ └────┘ │ ┌────┐
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└──│ b2 │──│ c2 │─┘ └──│ e2 │
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└────┘ └────┘ └────┘
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```
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which is different from the fully correct representation:
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```txt
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┌────┐ ┌────┐ ┌────┐ ┌────┐
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┌──│ b1 │──│ c1 │───│ d │───│ e1 │
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┌────┐ │ └────┘ └────┘ └────┘ └────┘
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│ a │─┤
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└────┘ │ ┌────┐ ┌────┐ ┌────┐ ┌────┐
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└──│ b2 │──│ c2 │───│ d │───│ e2 │
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└────┘ └────┘ └────┘ └────┘
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```
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But we accept the first representation as it reduces the size
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of the graph and shouldn't cause much problems.
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*/
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pub fn build_from_paths(
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paths: Vec<Vec<(Option<LocatedQueryTermSubset>, LocatedQueryTermSubset)>>,
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) -> Self {
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let mut node_data = DedupInterner::default();
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let root_node = node_data.insert(QueryNodeData::Start);
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let end_node = node_data.insert(QueryNodeData::End);
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let mut paths_with_ids = vec![];
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for path in paths {
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let mut path_with_ids = vec![];
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for node in path {
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let (start_term, end_term) = node;
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let src_node_id = start_term.map(|x| node_data.insert(QueryNodeData::Term(x)));
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let dest_node_id = node_data.insert(QueryNodeData::Term(end_term));
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path_with_ids.push((src_node_id, dest_node_id));
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}
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paths_with_ids.push(path_with_ids);
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}
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let nodes_data = node_data.freeze();
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let nodes_data_len = nodes_data.len();
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let mut nodes = nodes_data.map_move(|n| QueryNode {
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data: n,
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predecessors: SmallBitmap::new(nodes_data_len),
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successors: SmallBitmap::new(nodes_data_len),
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});
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let root_node = Interned::from_raw(root_node.into_raw());
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let end_node = Interned::from_raw(end_node.into_raw());
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for path in paths_with_ids {
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let mut prev_node = root_node;
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for node in path {
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let (start_term, dest_term) = node;
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let end_term = Interned::from_raw(dest_term.into_raw());
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let src = if let Some(start_term) = start_term {
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let start_term = Interned::from_raw(start_term.into_raw());
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nodes.get_mut(prev_node).successors.insert(start_term);
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nodes.get_mut(start_term).predecessors.insert(prev_node);
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start_term
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} else {
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prev_node
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};
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nodes.get_mut(src).successors.insert(end_term);
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nodes.get_mut(end_term).predecessors.insert(src);
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prev_node = end_term;
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}
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nodes.get_mut(prev_node).successors.insert(end_node);
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nodes.get_mut(end_node).predecessors.insert(prev_node);
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}
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QueryGraph { root_node, end_node, nodes }
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}
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}
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