mirror of
https://github.com/meilisearch/MeiliSearch
synced 2024-06-13 16:19:50 +02:00
199 lines
6.0 KiB
Rust
199 lines
6.0 KiB
Rust
use std::sync::Arc;
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use ordered_float::OrderedFloat;
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use roaring::RoaringBitmap;
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use serde_json::Value;
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use crate::score_details::{self, ScoreDetails};
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use crate::vector::Embedder;
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use crate::{filtered_universe, DocumentId, Filter, Index, Result, SearchResult};
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enum RecommendKind<'a> {
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Id(DocumentId),
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Prompt { prompt: &'a str, context: Option<Value>, id: Option<DocumentId> },
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}
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pub struct Recommend<'a> {
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kind: RecommendKind<'a>,
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// this should be linked to the String in the query
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filter: Option<Filter<'a>>,
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offset: usize,
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limit: usize,
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rtxn: &'a heed::RoTxn<'a>,
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index: &'a Index,
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embedder_name: String,
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embedder: Arc<Embedder>,
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}
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impl<'a> Recommend<'a> {
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pub fn with_docid(
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id: DocumentId,
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offset: usize,
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limit: usize,
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index: &'a Index,
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rtxn: &'a heed::RoTxn<'a>,
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embedder_name: String,
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embedder: Arc<Embedder>,
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) -> Self {
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Self {
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kind: RecommendKind::Id(id),
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filter: None,
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offset,
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limit,
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rtxn,
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index,
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embedder_name,
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embedder,
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}
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}
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pub fn with_prompt(
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prompt: &'a str,
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id: Option<DocumentId>,
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context: Option<Value>,
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offset: usize,
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limit: usize,
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index: &'a Index,
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rtxn: &'a heed::RoTxn<'a>,
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embedder_name: String,
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embedder: Arc<Embedder>,
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) -> Self {
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Self {
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kind: RecommendKind::Prompt { prompt, context, id },
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filter: None,
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offset,
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limit,
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rtxn,
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index,
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embedder_name,
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embedder,
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}
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}
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pub fn filter(&mut self, filter: Filter<'a>) -> &mut Self {
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self.filter = Some(filter);
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self
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}
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pub fn execute(&self) -> Result<SearchResult> {
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let universe = filtered_universe(self.index, self.rtxn, &self.filter)?;
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let embedder_index =
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self.index
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.embedder_category_id
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.get(self.rtxn, &self.embedder_name)?
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.ok_or_else(|| crate::UserError::InvalidEmbedder(self.embedder_name.to_owned()))?;
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let writer_index = (embedder_index as u16) << 8;
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let readers: std::result::Result<Vec<_>, _> = (0..=u8::MAX)
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.map_while(|k| {
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arroy::Reader::open(self.rtxn, writer_index | (k as u16), self.index.vector_arroy)
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.map(Some)
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.or_else(|e| match e {
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arroy::Error::MissingMetadata => Ok(None),
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e => Err(e),
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})
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.transpose()
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})
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.collect();
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let readers = readers?;
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let mut results = Vec::new();
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/// FIXME: make id optional...
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let id = match &self.kind {
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RecommendKind::Id(id) => *id,
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RecommendKind::Prompt { prompt, context, id } => id.unwrap(),
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};
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let personalization_vector = if let RecommendKind::Prompt { prompt, context, id } =
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&self.kind
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{
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let fields_ids_map = self.index.fields_ids_map(self.rtxn)?;
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let document = if let Some(id) = id {
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Some(self.index.iter_documents(self.rtxn, std::iter::once(*id))?.next().unwrap()?.1)
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} else {
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None
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};
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let document = document
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.map(|document| crate::prompt::Document::from_doc_obkv(document, &fields_ids_map));
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let context =
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crate::prompt::recommend::Context::new(document.as_ref(), context.clone());
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/// FIXME: handle error bad template
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let template =
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liquid::ParserBuilder::new().stdlib().build().unwrap().parse(prompt).unwrap();
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/// FIXME: handle error bad context
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let rendered = template.render(&context).unwrap();
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/// FIXME: handle embedding error
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Some(self.embedder.embed_one(rendered).unwrap())
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} else {
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None
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};
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for reader in readers.iter() {
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let nns_by_item = reader.nns_by_item(
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self.rtxn,
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id,
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self.limit + self.offset + 1,
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None,
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Some(&universe),
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)?;
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if let Some(nns_by_item) = nns_by_item {
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let mut nns = match &personalization_vector {
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Some(vector) => {
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let candidates: RoaringBitmap =
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nns_by_item.iter().map(|(docid, _)| docid).collect();
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reader.nns_by_vector(
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self.rtxn,
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vector,
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self.limit + self.offset + 1,
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None,
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Some(&candidates),
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)?
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}
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None => nns_by_item,
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};
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results.append(&mut nns);
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}
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}
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results.sort_unstable_by_key(|(_, distance)| OrderedFloat(*distance));
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let mut documents_ids = Vec::with_capacity(self.limit);
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let mut document_scores = Vec::with_capacity(self.limit);
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// skip offset +1 to skip the target document that is normally returned
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for (docid, distance) in results.into_iter().skip(self.offset + 1) {
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documents_ids.push(docid);
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let score = 1.0 - distance;
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let score = self
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.embedder
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.distribution()
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.map(|distribution| distribution.shift(score))
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.unwrap_or(score);
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let score = ScoreDetails::Vector(score_details::Vector { similarity: Some(score) });
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document_scores.push(vec![score]);
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}
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Ok(SearchResult {
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matching_words: Default::default(),
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candidates: universe,
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documents_ids,
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document_scores,
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degraded: false,
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used_negative_operator: false,
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})
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}
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}
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