mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-11-24 05:44:25 +01:00
397 lines
18 KiB
Rust
397 lines
18 KiB
Rust
/*! Implementation of a generic graph-based ranking rule.
|
|
|
|
A graph-based ranking rule is a ranking rule that works by representing
|
|
its possible operations and their relevancy cost as a directed acyclic multi-graph
|
|
built on top of the query graph. It then computes its buckets by finding the
|
|
cheapest paths from the start node to the end node and computing the document ids
|
|
that satisfy those paths.
|
|
|
|
For example, the proximity ranking rule builds a graph where the edges between two
|
|
nodes represent a condition that the term of the source node is in a certain proximity
|
|
to the term of the destination node. With the query "pretty house by" where the term
|
|
"pretty" has three possible proximities to the term "house" and "house" has two
|
|
proximities to "by", the graph will look like this:
|
|
|
|
```txt
|
|
┌───────┐ ┌───────┐─────1────▶┌───────┐──1──▶┌─────┐ ┌───────┐
|
|
│ START │──0─▶│pretty │─────2────▶│ house │ │ by │─0─▶│ END │
|
|
└───────┘ └───────┘─────3────▶└───────┘──2-─▶└─────┘ └───────┘
|
|
```
|
|
The proximity ranking rule's first bucket will be determined by the union of all
|
|
the shortest paths from START to END, which in this case is:
|
|
```txt
|
|
START --0-> pretty --1--> house --1--> by --0--> end
|
|
```
|
|
The path's corresponding document ids are found by taking the intersection of the
|
|
document ids of each edge. That is, we find the documents where both `pretty` is
|
|
1-close to `house` AND `house` is 1-close to `by`.
|
|
|
|
For the second bucket, we get the union of the second-cheapest paths, which are:
|
|
```txt
|
|
START --0-> pretty --1--> house --2--> by --0--> end
|
|
START --0-> pretty --2--> house --1--> by --0--> end
|
|
```
|
|
That is we find the documents where either:
|
|
- `pretty` is 1-close to `house` AND `house` is 2-close to `by`
|
|
- OR: `pretty` is 2-close to `house` AND `house` is 1-close to `by`
|
|
*/
|
|
|
|
use std::collections::HashSet;
|
|
use std::ops::ControlFlow;
|
|
|
|
use roaring::RoaringBitmap;
|
|
|
|
use super::interner::MappedInterner;
|
|
use super::logger::SearchLogger;
|
|
use super::query_graph::QueryNode;
|
|
use super::ranking_rule_graph::{
|
|
ConditionDocIdsCache, DeadEndsCache, ProximityGraph, RankingRuleGraph, RankingRuleGraphTrait,
|
|
TypoGraph,
|
|
};
|
|
use super::small_bitmap::SmallBitmap;
|
|
use super::{QueryGraph, RankingRule, RankingRuleOutput, SearchContext};
|
|
use crate::search::new::query_graph::QueryNodeData;
|
|
use crate::Result;
|
|
|
|
pub type Proximity = GraphBasedRankingRule<ProximityGraph>;
|
|
impl Default for GraphBasedRankingRule<ProximityGraph> {
|
|
fn default() -> Self {
|
|
Self::new("proximity".to_owned())
|
|
}
|
|
}
|
|
pub type Typo = GraphBasedRankingRule<TypoGraph>;
|
|
impl Default for GraphBasedRankingRule<TypoGraph> {
|
|
fn default() -> Self {
|
|
Self::new("typo".to_owned())
|
|
}
|
|
}
|
|
|
|
/// A generic graph-based ranking rule
|
|
pub struct GraphBasedRankingRule<G: RankingRuleGraphTrait> {
|
|
id: String,
|
|
// When the ranking rule is not iterating over its buckets,
|
|
// its state is `None`.
|
|
state: Option<GraphBasedRankingRuleState<G>>,
|
|
}
|
|
impl<G: RankingRuleGraphTrait> GraphBasedRankingRule<G> {
|
|
/// Creates the ranking rule with the given identifier
|
|
pub fn new(id: String) -> Self {
|
|
Self { id, state: None }
|
|
}
|
|
}
|
|
|
|
/// The internal state of a graph-based ranking rule during iteration
|
|
pub struct GraphBasedRankingRuleState<G: RankingRuleGraphTrait> {
|
|
/// The current graph
|
|
graph: RankingRuleGraph<G>,
|
|
/// Cache to retrieve the docids associated with each edge
|
|
conditions_cache: ConditionDocIdsCache<G>,
|
|
/// Cache used to optimistically discard paths that resolve to no documents.
|
|
dead_ends_cache: DeadEndsCache<G::Condition>,
|
|
/// A structure giving the list of possible costs from each node to the end node
|
|
all_distances: MappedInterner<QueryNode, Vec<u16>>,
|
|
/// An index in the first element of `all_distances`, giving the cost of the next bucket
|
|
cur_distance_idx: usize,
|
|
}
|
|
|
|
impl<'ctx, G: RankingRuleGraphTrait> RankingRule<'ctx, QueryGraph> for GraphBasedRankingRule<G> {
|
|
fn id(&self) -> String {
|
|
self.id.clone()
|
|
}
|
|
fn start_iteration(
|
|
&mut self,
|
|
ctx: &mut SearchContext<'ctx>,
|
|
_logger: &mut dyn SearchLogger<QueryGraph>,
|
|
_universe: &RoaringBitmap,
|
|
query_graph: &QueryGraph,
|
|
) -> Result<()> {
|
|
let graph = RankingRuleGraph::build(ctx, query_graph.clone())?;
|
|
let condition_docids_cache = ConditionDocIdsCache::default();
|
|
let dead_ends_cache = DeadEndsCache::new(&graph.conditions_interner);
|
|
|
|
// Then pre-compute the cost of all paths from each node to the end node
|
|
let all_distances = graph.initialize_distances_with_necessary_edges();
|
|
|
|
let state = GraphBasedRankingRuleState {
|
|
graph,
|
|
conditions_cache: condition_docids_cache,
|
|
dead_ends_cache,
|
|
all_distances,
|
|
cur_distance_idx: 0,
|
|
};
|
|
|
|
self.state = Some(state);
|
|
|
|
Ok(())
|
|
}
|
|
|
|
fn next_bucket(
|
|
&mut self,
|
|
ctx: &mut SearchContext<'ctx>,
|
|
logger: &mut dyn SearchLogger<QueryGraph>,
|
|
universe: &RoaringBitmap,
|
|
) -> Result<Option<RankingRuleOutput<QueryGraph>>> {
|
|
// If universe.len() <= 1, the bucket sort algorithm
|
|
// should not have called this function.
|
|
assert!(universe.len() > 1);
|
|
// Will crash if `next_bucket` is called before `start_iteration` or after `end_iteration`,
|
|
// should never happen
|
|
let mut state = self.state.take().unwrap();
|
|
|
|
// If the cur_distance_idx does not point to a valid cost in the `all_distances`
|
|
// structure, then we have computed all the buckets and can return.
|
|
if state.cur_distance_idx
|
|
>= state.all_distances.get(state.graph.query_graph.root_node).len()
|
|
{
|
|
self.state = None;
|
|
return Ok(None);
|
|
}
|
|
|
|
// Retrieve the cost of the paths to compute
|
|
let cost =
|
|
state.all_distances.get(state.graph.query_graph.root_node)[state.cur_distance_idx];
|
|
state.cur_distance_idx += 1;
|
|
|
|
let mut bucket = RoaringBitmap::new();
|
|
|
|
let GraphBasedRankingRuleState {
|
|
graph,
|
|
conditions_cache: condition_docids_cache,
|
|
dead_ends_cache,
|
|
all_distances,
|
|
cur_distance_idx: _,
|
|
} = &mut state;
|
|
|
|
let original_universe = universe;
|
|
let mut universe = universe.clone();
|
|
|
|
let original_graph = graph.clone();
|
|
let mut used_conditions = SmallBitmap::for_interned_values_in(&graph.conditions_interner);
|
|
let mut considered_paths = vec![];
|
|
let mut good_paths = vec![];
|
|
|
|
// For each path of the given cost, we will compute its associated
|
|
// document ids.
|
|
// In case the path does not resolve to any document id, we try to figure out why
|
|
// and update the `dead_ends_cache` accordingly.
|
|
// Updating the dead_ends_cache helps speed up the execution of `visit_paths_of_cost` and reduces
|
|
// the number of future candidate paths given by that same function.
|
|
graph.visit_paths_of_cost(
|
|
graph.query_graph.root_node,
|
|
cost,
|
|
all_distances,
|
|
dead_ends_cache,
|
|
|path, graph, dead_ends_cache| {
|
|
if universe.is_empty() {
|
|
return Ok(ControlFlow::Break(()));
|
|
}
|
|
|
|
/* TODO: there are a couple ways to improve the speed of path computation.
|
|
|
|
1. Since the `visit_paths_of_cost` method uses a depth-first-search, we know that
|
|
consecutive calls to this closure have a high chance of giving paths sharing
|
|
some prefix. It would be good to reuse `subpath_docids` and `visited_conditions`
|
|
to find out what this common prefix is, to avoid recomputing it. In a way, doing
|
|
this serves as the dual of the DeadEndsCache: it takes advantage of our knowledge that
|
|
some paths *aren't* deadends. There is however a subtlety in that the universe might
|
|
have changed between the two consecutive calls. This is why we should subtract the docids
|
|
of the previous path (if successful) to the `subpath_docids`, at the same time as we do
|
|
it for the universe.
|
|
|
|
2. We perform way too many intersections with the universe. For the first visited path,
|
|
the operation we do is essentially:
|
|
universe & (c1 & universe) & (c2 & universe) & (c3 & universe) & etc.
|
|
This is a good idea *only if the universe is very small*. But if the universe is (almost)
|
|
a superset of each condition, then these intersections serve no purpose and slow down the search.
|
|
Maybe in the future we have a `deserialize_within_universe` method, which would speed up
|
|
these intersections. But for now, we have to be careful.
|
|
|
|
3. We could know in advance how many paths of a certain cost exist, and only update the
|
|
DeadEndsCache if (m)any remaining paths exist. There is a subtlety here because
|
|
on the next call of `next_bucket`, we will want an updated and correct DeadEndsCache.
|
|
We need to think about that. We could also avoid putting forbidden edges in this cache
|
|
if we know, somehow, that we won't visit this edge again.
|
|
|
|
4. Finally, but that will be a long term difficult project. We should compute the path *lazily*.
|
|
That is, when we do `path_docids &= condition`. We shouldn't *actually* perform the intersection,
|
|
but simply register that operation. It's only when we ask if the path_docids is empty that
|
|
**the minimum amount of work to determine whether the path is empty** is carried out. In practice,
|
|
that means performing a MultiOps on each container, in order or not, until any resulting container
|
|
is found to be non-empty. (In fact, when we ask `is_empty`, we should probably find the container
|
|
that has the highest chance of being non-empty and compute that one first).
|
|
|
|
*/
|
|
|
|
// Accumulate the path for logging purposes only
|
|
considered_paths.push(path.to_vec());
|
|
|
|
let mut path_docids = universe.clone();
|
|
|
|
// We store the edges and their docids in vectors in case the path turns out to be
|
|
// empty and we need to figure out why it was empty.
|
|
let mut visited_conditions = vec![];
|
|
// let mut cached_condition_docids = vec![];
|
|
let mut subpath_docids = vec![];
|
|
|
|
for (latest_condition_path_idx, &latest_condition) in path.iter().enumerate() {
|
|
visited_conditions.push(latest_condition);
|
|
|
|
let condition_docids = condition_docids_cache.get_condition_docids(
|
|
ctx,
|
|
latest_condition,
|
|
graph,
|
|
&universe,
|
|
)?;
|
|
|
|
// If the edge is empty, then the path will be empty as well, we update the graph
|
|
// and caches accordingly and skip to the next candidate path.
|
|
if condition_docids.is_empty() {
|
|
// 1. Store in the cache that this edge is empty for this universe
|
|
dead_ends_cache.forbid_condition(latest_condition);
|
|
// 2. remove all the edges with this condition from the ranking rule graph
|
|
graph.remove_edges_with_condition(latest_condition);
|
|
return Ok(ControlFlow::Continue(()));
|
|
}
|
|
path_docids &= condition_docids;
|
|
subpath_docids.push(path_docids.clone());
|
|
|
|
// If the (sub)path is empty, we try to figure out why and update the caches accordingly.
|
|
if path_docids.is_empty() {
|
|
let len_prefix = subpath_docids.len() - 1;
|
|
// First, we know that this path is empty, and thus any path
|
|
// that is a superset of it will also be empty.
|
|
dead_ends_cache.forbid_condition_after_prefix(
|
|
visited_conditions[..len_prefix].iter().copied(),
|
|
latest_condition,
|
|
);
|
|
|
|
if visited_conditions.len() > 1 {
|
|
let mut subprefix = vec![];
|
|
// Deadend if the intersection between this edge and any
|
|
// previous prefix is disjoint with the universe
|
|
for (past_condition, subpath_docids) in visited_conditions[..len_prefix]
|
|
.iter()
|
|
.zip(subpath_docids[..len_prefix].iter())
|
|
{
|
|
if *past_condition == latest_condition {
|
|
todo!();
|
|
};
|
|
subprefix.push(*past_condition);
|
|
if condition_docids.is_disjoint(subpath_docids) {
|
|
dead_ends_cache.forbid_condition_after_prefix(
|
|
subprefix.iter().copied(),
|
|
latest_condition,
|
|
);
|
|
}
|
|
}
|
|
|
|
// keep the same prefix and check the intersection with
|
|
// all the remaining conditions
|
|
let mut forbidden = dead_ends_cache.forbidden.clone();
|
|
let mut cursor = dead_ends_cache;
|
|
for &c in visited_conditions[..len_prefix].iter() {
|
|
cursor = cursor.advance(c).unwrap();
|
|
forbidden.union(&cursor.forbidden);
|
|
}
|
|
|
|
let past_path_docids = &subpath_docids[subpath_docids.len() - 2];
|
|
|
|
let remaining_conditions =
|
|
path[latest_condition_path_idx..].iter().skip(1);
|
|
for next_condition in remaining_conditions {
|
|
if forbidden.contains(*next_condition) {
|
|
continue;
|
|
}
|
|
let next_condition_docids = condition_docids_cache
|
|
.get_condition_docids(ctx, *next_condition, graph, &universe)?;
|
|
|
|
if past_path_docids.is_disjoint(next_condition_docids) {
|
|
cursor.forbid_condition(*next_condition);
|
|
}
|
|
}
|
|
}
|
|
|
|
return Ok(ControlFlow::Continue(()));
|
|
}
|
|
}
|
|
assert!(!path_docids.is_empty());
|
|
// Accumulate the path for logging purposes only
|
|
good_paths.push(path.to_vec());
|
|
for condition in path {
|
|
used_conditions.insert(*condition);
|
|
}
|
|
bucket |= &path_docids;
|
|
// Reduce the size of the universe so that we can more optimistically discard candidate paths
|
|
universe -= path_docids;
|
|
|
|
if universe.is_empty() {
|
|
Ok(ControlFlow::Break(()))
|
|
} else {
|
|
Ok(ControlFlow::Continue(()))
|
|
}
|
|
},
|
|
)?;
|
|
// println!(" {} paths of cost {} in {}", paths.len(), cost, self.id);
|
|
G::log_state(
|
|
&original_graph,
|
|
&good_paths,
|
|
dead_ends_cache,
|
|
original_universe,
|
|
all_distances,
|
|
cost,
|
|
logger,
|
|
);
|
|
|
|
// We modify the next query graph so that it only contains the subgraph
|
|
// that was used to compute this bucket
|
|
// But we only do it in case the bucket length is >1, because otherwise
|
|
// we know the child ranking rule won't be called anyway
|
|
let mut next_query_graph = original_graph.query_graph;
|
|
if bucket.len() > 1 {
|
|
next_query_graph.simplify();
|
|
// 1. Gather all the words and phrases used in the computation of this bucket
|
|
let mut used_words = HashSet::new();
|
|
let mut used_phrases = HashSet::new();
|
|
for condition in used_conditions.iter() {
|
|
let (ws, ps) =
|
|
condition_docids_cache.get_condition_used_words_and_phrases(condition);
|
|
used_words.extend(ws);
|
|
used_phrases.extend(ps);
|
|
}
|
|
// 2. Remove the unused words and phrases from all the nodes in the graph
|
|
let mut nodes_to_remove = vec![];
|
|
for (node_id, node) in next_query_graph.nodes.iter_mut() {
|
|
let term = match &mut node.data {
|
|
QueryNodeData::Term(term) => term,
|
|
QueryNodeData::Deleted | QueryNodeData::Start | QueryNodeData::End => continue,
|
|
};
|
|
if let Some(new_term) = ctx
|
|
.term_interner
|
|
.get(term.value)
|
|
.removing_forbidden_terms(&used_words, &used_phrases)
|
|
{
|
|
if new_term.is_empty() {
|
|
nodes_to_remove.push(node_id);
|
|
} else {
|
|
term.value = ctx.term_interner.insert(new_term);
|
|
}
|
|
}
|
|
}
|
|
// 3. Remove the empty nodes from the graph
|
|
next_query_graph.remove_nodes(&nodes_to_remove);
|
|
}
|
|
|
|
self.state = Some(state);
|
|
|
|
Ok(Some(RankingRuleOutput { query: next_query_graph, candidates: bucket }))
|
|
}
|
|
|
|
fn end_iteration(
|
|
&mut self,
|
|
_ctx: &mut SearchContext<'ctx>,
|
|
_logger: &mut dyn SearchLogger<QueryGraph>,
|
|
) {
|
|
self.state = None;
|
|
}
|
|
}
|