Move crates under a sub folder to clean up the code

crates/milli/src/vector/mod.rs

@@ -0,0 +1,606 @@
+use std::collections::HashMap;
+use std::sync::Arc;
+
+use arroy::distances::{Angular, BinaryQuantizedAngular};
+use arroy::ItemId;
+use deserr::{DeserializeError, Deserr};
+use heed::{RoTxn, RwTxn, Unspecified};
+use ordered_float::OrderedFloat;
+use roaring::RoaringBitmap;
+use serde::{Deserialize, Serialize};
+
+use self::error::{EmbedError, NewEmbedderError};
+use crate::prompt::{Prompt, PromptData};
+use crate::ThreadPoolNoAbort;
+
+pub mod error;
+pub mod hf;
+pub mod json_template;
+pub mod manual;
+pub mod openai;
+pub mod parsed_vectors;
+pub mod settings;
+
+pub mod ollama;
+pub mod rest;
+
+pub use self::error::Error;
+
+pub type Embedding = Vec<f32>;
+
+pub const REQUEST_PARALLELISM: usize = 40;
+
+pub struct ArroyWrapper {
+    quantized: bool,
+    index: u16,
+    database: arroy::Database<Unspecified>,
+}
+
+impl ArroyWrapper {
+    pub fn new(database: arroy::Database<Unspecified>, index: u16, quantized: bool) -> Self {
+        Self { database, index, quantized }
+    }
+
+    pub fn index(&self) -> u16 {
+        self.index
+    }
+
+    pub fn dimensions(&self, rtxn: &RoTxn) -> Result<usize, arroy::Error> {
+        if self.quantized {
+            Ok(arroy::Reader::open(rtxn, self.index, self.quantized_db())?.dimensions())
+        } else {
+            Ok(arroy::Reader::open(rtxn, self.index, self.angular_db())?.dimensions())
+        }
+    }
+
+    pub fn quantize(
+        &mut self,
+        wtxn: &mut RwTxn,
+        index: u16,
+        dimension: usize,
+    ) -> Result<(), arroy::Error> {
+        if !self.quantized {
+            let writer = arroy::Writer::new(self.angular_db(), index, dimension);
+            writer.prepare_changing_distance::<BinaryQuantizedAngular>(wtxn)?;
+            self.quantized = true;
+        }
+        Ok(())
+    }
+
+    pub fn need_build(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).need_build(rtxn)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).need_build(rtxn)
+        }
+    }
+
+    pub fn build<R: rand::Rng + rand::SeedableRng>(
+        &self,
+        wtxn: &mut RwTxn,
+        rng: &mut R,
+        dimension: usize,
+    ) -> Result<(), arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).build(wtxn, rng, None)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).build(wtxn, rng, None)
+        }
+    }
+
+    pub fn add_item(
+        &self,
+        wtxn: &mut RwTxn,
+        dimension: usize,
+        item_id: arroy::ItemId,
+        vector: &[f32],
+    ) -> Result<(), arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension)
+                .add_item(wtxn, item_id, vector)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension)
+                .add_item(wtxn, item_id, vector)
+        }
+    }
+
+    pub fn del_item(
+        &self,
+        wtxn: &mut RwTxn,
+        dimension: usize,
+        item_id: arroy::ItemId,
+    ) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).del_item(wtxn, item_id)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).del_item(wtxn, item_id)
+        }
+    }
+
+    pub fn clear(&self, wtxn: &mut RwTxn, dimension: usize) -> Result<(), arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).clear(wtxn)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).clear(wtxn)
+        }
+    }
+
+    pub fn is_empty(&self, rtxn: &RoTxn, dimension: usize) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).is_empty(rtxn)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).is_empty(rtxn)
+        }
+    }
+
+    pub fn contains_item(
+        &self,
+        rtxn: &RoTxn,
+        dimension: usize,
+        item: arroy::ItemId,
+    ) -> Result<bool, arroy::Error> {
+        if self.quantized {
+            arroy::Writer::new(self.quantized_db(), self.index, dimension).contains_item(rtxn, item)
+        } else {
+            arroy::Writer::new(self.angular_db(), self.index, dimension).contains_item(rtxn, item)
+        }
+    }
+
+    pub fn nns_by_item(
+        &self,
+        rtxn: &RoTxn,
+        item: ItemId,
+        limit: usize,
+        filter: Option<&RoaringBitmap>,
+    ) -> Result<Option<Vec<(ItemId, f32)>>, arroy::Error> {
+        if self.quantized {
+            arroy::Reader::open(rtxn, self.index, self.quantized_db())?
+                .nns_by_item(rtxn, item, limit, None, None, filter)
+        } else {
+            arroy::Reader::open(rtxn, self.index, self.angular_db())?
+                .nns_by_item(rtxn, item, limit, None, None, filter)
+        }
+    }
+
+    pub fn nns_by_vector(
+        &self,
+        txn: &RoTxn,
+        item: &[f32],
+        limit: usize,
+        filter: Option<&RoaringBitmap>,
+    ) -> Result<Vec<(ItemId, f32)>, arroy::Error> {
+        if self.quantized {
+            arroy::Reader::open(txn, self.index, self.quantized_db())?
+                .nns_by_vector(txn, item, limit, None, None, filter)
+        } else {
+            arroy::Reader::open(txn, self.index, self.angular_db())?
+                .nns_by_vector(txn, item, limit, None, None, filter)
+        }
+    }
+
+    pub fn item_vector(&self, rtxn: &RoTxn, docid: u32) -> Result<Option<Vec<f32>>, arroy::Error> {
+        if self.quantized {
+            arroy::Reader::open(rtxn, self.index, self.quantized_db())?.item_vector(rtxn, docid)
+        } else {
+            arroy::Reader::open(rtxn, self.index, self.angular_db())?.item_vector(rtxn, docid)
+        }
+    }
+
+    fn angular_db(&self) -> arroy::Database<Angular> {
+        self.database.remap_data_type()
+    }
+
+    fn quantized_db(&self) -> arroy::Database<BinaryQuantizedAngular> {
+        self.database.remap_data_type()
+    }
+}
+
+/// One or multiple embeddings stored consecutively in a flat vector.
+pub struct Embeddings<F> {
+    data: Vec<F>,
+    dimension: usize,
+}
+
+impl<F> Embeddings<F> {
+    /// Declares an empty  vector of embeddings of the specified dimensions.
+    pub fn new(dimension: usize) -> Self {
+        Self { data: Default::default(), dimension }
+    }
+
+    /// Declares a vector of embeddings containing a single element.
+    ///
+    /// The dimension is inferred from the length of the passed embedding.
+    pub fn from_single_embedding(embedding: Vec<F>) -> Self {
+        Self { dimension: embedding.len(), data: embedding }
+    }
+
+    /// Declares a vector of embeddings from its components.
+    ///
+    /// `data.len()` must be a multiple of `dimension`, otherwise an error is returned.
+    pub fn from_inner(data: Vec<F>, dimension: usize) -> Result<Self, Vec<F>> {
+        let mut this = Self::new(dimension);
+        this.append(data)?;
+        Ok(this)
+    }
+
+    /// Returns the number of embeddings in this vector of embeddings.
+    pub fn embedding_count(&self) -> usize {
+        self.data.len() / self.dimension
+    }
+
+    /// Dimension of a single embedding.
+    pub fn dimension(&self) -> usize {
+        self.dimension
+    }
+
+    /// Deconstructs self into the inner flat vector.
+    pub fn into_inner(self) -> Vec<F> {
+        self.data
+    }
+
+    /// A reference to the inner flat vector.
+    pub fn as_inner(&self) -> &[F] {
+        &self.data
+    }
+
+    /// Iterates over the embeddings contained in the flat vector.
+    pub fn iter(&self) -> impl Iterator<Item = &'_ [F]> + '_ {
+        self.data.as_slice().chunks_exact(self.dimension)
+    }
+
+    /// Push an embedding at the end of the embeddings.
+    ///
+    /// If `embedding.len() != self.dimension`, then the push operation fails.
+    pub fn push(&mut self, mut embedding: Vec<F>) -> Result<(), Vec<F>> {
+        if embedding.len() != self.dimension {
+            return Err(embedding);
+        }
+        self.data.append(&mut embedding);
+        Ok(())
+    }
+
+    /// Append a flat vector of embeddings a the end of the embeddings.
+    ///
+    /// If `embeddings.len() % self.dimension != 0`, then the append operation fails.
+    pub fn append(&mut self, mut embeddings: Vec<F>) -> Result<(), Vec<F>> {
+        if embeddings.len() % self.dimension != 0 {
+            return Err(embeddings);
+        }
+        self.data.append(&mut embeddings);
+        Ok(())
+    }
+}
+
+/// An embedder can be used to transform text into embeddings.
+#[derive(Debug)]
+pub enum Embedder {
+    /// An embedder based on running local models, fetched from the Hugging Face Hub.
+    HuggingFace(hf::Embedder),
+    /// An embedder based on making embedding queries against the OpenAI API.
+    OpenAi(openai::Embedder),
+    /// An embedder based on the user providing the embeddings in the documents and queries.
+    UserProvided(manual::Embedder),
+    /// An embedder based on making embedding queries against an <https://ollama.com> embedding server.
+    Ollama(ollama::Embedder),
+    /// An embedder based on making embedding queries against a generic JSON/REST embedding server.
+    Rest(rest::Embedder),
+}
+
+/// Configuration for an embedder.
+#[derive(Debug, Clone, Default, serde::Deserialize, serde::Serialize)]
+pub struct EmbeddingConfig {
+    /// Options of the embedder, specific to each kind of embedder
+    pub embedder_options: EmbedderOptions,
+    /// Document template
+    pub prompt: PromptData,
+    /// If this embedder is binary quantized
+    pub quantized: Option<bool>,
+    // TODO: add metrics and anything needed
+}
+
+impl EmbeddingConfig {
+    pub fn quantized(&self) -> bool {
+        self.quantized.unwrap_or_default()
+    }
+}
+
+/// Map of embedder configurations.
+///
+/// Each configuration is mapped to a name.
+#[derive(Clone, Default)]
+pub struct EmbeddingConfigs(HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>);
+
+impl EmbeddingConfigs {
+    /// Create the map from its internal component.s
+    pub fn new(data: HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)>) -> Self {
+        Self(data)
+    }
+
+    /// Get an embedder configuration and template from its name.
+    pub fn get(&self, name: &str) -> Option<(Arc<Embedder>, Arc<Prompt>, bool)> {
+        self.0.get(name).cloned()
+    }
+
+    pub fn inner_as_ref(&self) -> &HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
+        &self.0
+    }
+
+    pub fn into_inner(self) -> HashMap<String, (Arc<Embedder>, Arc<Prompt>, bool)> {
+        self.0
+    }
+}
+
+impl IntoIterator for EmbeddingConfigs {
+    type Item = (String, (Arc<Embedder>, Arc<Prompt>, bool));
+
+    type IntoIter =
+        std::collections::hash_map::IntoIter<String, (Arc<Embedder>, Arc<Prompt>, bool)>;
+
+    fn into_iter(self) -> Self::IntoIter {
+        self.0.into_iter()
+    }
+}
+
+/// Options of an embedder, specific to each kind of embedder.
+#[derive(Debug, Clone, Hash, PartialEq, Eq, serde::Deserialize, serde::Serialize)]
+pub enum EmbedderOptions {
+    HuggingFace(hf::EmbedderOptions),
+    OpenAi(openai::EmbedderOptions),
+    Ollama(ollama::EmbedderOptions),
+    UserProvided(manual::EmbedderOptions),
+    Rest(rest::EmbedderOptions),
+}
+
+impl Default for EmbedderOptions {
+    fn default() -> Self {
+        Self::HuggingFace(Default::default())
+    }
+}
+
+impl Embedder {
+    /// Spawns a new embedder built from its options.
+    pub fn new(options: EmbedderOptions) -> std::result::Result<Self, NewEmbedderError> {
+        Ok(match options {
+            EmbedderOptions::HuggingFace(options) => Self::HuggingFace(hf::Embedder::new(options)?),
+            EmbedderOptions::OpenAi(options) => Self::OpenAi(openai::Embedder::new(options)?),
+            EmbedderOptions::Ollama(options) => Self::Ollama(ollama::Embedder::new(options)?),
+            EmbedderOptions::UserProvided(options) => {
+                Self::UserProvided(manual::Embedder::new(options))
+            }
+            EmbedderOptions::Rest(options) => {
+                Self::Rest(rest::Embedder::new(options, rest::ConfigurationSource::User)?)
+            }
+        })
+    }
+
+    /// Embed one or multiple texts.
+    ///
+    /// Each text can be embedded as one or multiple embeddings.
+    pub fn embed(
+        &self,
+        texts: Vec<String>,
+    ) -> std::result::Result<Vec<Embeddings<f32>>, EmbedError> {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.embed(texts),
+            Embedder::OpenAi(embedder) => embedder.embed(texts),
+            Embedder::Ollama(embedder) => embedder.embed(texts),
+            Embedder::UserProvided(embedder) => embedder.embed(texts),
+            Embedder::Rest(embedder) => embedder.embed(texts),
+        }
+    }
+
+    pub fn embed_one(&self, text: String) -> std::result::Result<Embedding, EmbedError> {
+        let mut embeddings = self.embed(vec![text])?;
+        let embeddings = embeddings.pop().ok_or_else(EmbedError::missing_embedding)?;
+        Ok(if embeddings.iter().nth(1).is_some() {
+            tracing::warn!("Ignoring embeddings past the first one in long search query");
+            embeddings.iter().next().unwrap().to_vec()
+        } else {
+            embeddings.into_inner()
+        })
+    }
+
+    /// Embed multiple chunks of texts.
+    ///
+    /// Each chunk is composed of one or multiple texts.
+    pub fn embed_chunks(
+        &self,
+        text_chunks: Vec<Vec<String>>,
+        threads: &ThreadPoolNoAbort,
+    ) -> std::result::Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.embed_chunks(text_chunks),
+            Embedder::OpenAi(embedder) => embedder.embed_chunks(text_chunks, threads),
+            Embedder::Ollama(embedder) => embedder.embed_chunks(text_chunks, threads),
+            Embedder::UserProvided(embedder) => embedder.embed_chunks(text_chunks),
+            Embedder::Rest(embedder) => embedder.embed_chunks(text_chunks, threads),
+        }
+    }
+
+    /// Indicates the preferred number of chunks to pass to [`Self::embed_chunks`]
+    pub fn chunk_count_hint(&self) -> usize {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.chunk_count_hint(),
+            Embedder::OpenAi(embedder) => embedder.chunk_count_hint(),
+            Embedder::Ollama(embedder) => embedder.chunk_count_hint(),
+            Embedder::UserProvided(_) => 1,
+            Embedder::Rest(embedder) => embedder.chunk_count_hint(),
+        }
+    }
+
+    /// Indicates the preferred number of texts in a single chunk passed to [`Self::embed`]
+    pub fn prompt_count_in_chunk_hint(&self) -> usize {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.prompt_count_in_chunk_hint(),
+            Embedder::OpenAi(embedder) => embedder.prompt_count_in_chunk_hint(),
+            Embedder::Ollama(embedder) => embedder.prompt_count_in_chunk_hint(),
+            Embedder::UserProvided(_) => 1,
+            Embedder::Rest(embedder) => embedder.prompt_count_in_chunk_hint(),
+        }
+    }
+
+    /// Indicates the dimensions of a single embedding produced by the embedder.
+    pub fn dimensions(&self) -> usize {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.dimensions(),
+            Embedder::OpenAi(embedder) => embedder.dimensions(),
+            Embedder::Ollama(embedder) => embedder.dimensions(),
+            Embedder::UserProvided(embedder) => embedder.dimensions(),
+            Embedder::Rest(embedder) => embedder.dimensions(),
+        }
+    }
+
+    /// An optional distribution used to apply an affine transformation to the similarity score of a document.
+    pub fn distribution(&self) -> Option<DistributionShift> {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.distribution(),
+            Embedder::OpenAi(embedder) => embedder.distribution(),
+            Embedder::Ollama(embedder) => embedder.distribution(),
+            Embedder::UserProvided(embedder) => embedder.distribution(),
+            Embedder::Rest(embedder) => embedder.distribution(),
+        }
+    }
+
+    pub fn uses_document_template(&self) -> bool {
+        match self {
+            Embedder::HuggingFace(_)
+            | Embedder::OpenAi(_)
+            | Embedder::Ollama(_)
+            | Embedder::Rest(_) => true,
+            Embedder::UserProvided(_) => false,
+        }
+    }
+}
+
+/// Describes the mean and sigma of distribution of embedding similarity in the embedding space.
+///
+/// The intended use is to make the similarity score more comparable to the regular ranking score.
+/// This allows to correct effects where results are too "packed" around a certain value.
+#[derive(Debug, Clone, Copy, PartialEq, Eq, Hash, Deserialize, Serialize)]
+#[serde(from = "DistributionShiftSerializable")]
+#[serde(into = "DistributionShiftSerializable")]
+pub struct DistributionShift {
+    /// Value where the results are "packed".
+    ///
+    /// Similarity scores are translated so that they are packed around 0.5 instead
+    pub current_mean: OrderedFloat<f32>,
+
+    /// standard deviation of a similarity score.
+    ///
+    /// Set below 0.4 to make the results less packed around the mean, and above 0.4 to make them more packed.
+    pub current_sigma: OrderedFloat<f32>,
+}
+
+impl<E> Deserr<E> for DistributionShift
+where
+    E: DeserializeError,
+{
+    fn deserialize_from_value<V: deserr::IntoValue>(
+        value: deserr::Value<V>,
+        location: deserr::ValuePointerRef<'_>,
+    ) -> Result<Self, E> {
+        let value = DistributionShiftSerializable::deserialize_from_value(value, location)?;
+        if value.mean < 0. || value.mean > 1. {
+            return Err(deserr::take_cf_content(E::error::<std::convert::Infallible>(
+                None,
+                deserr::ErrorKind::Unexpected {
+                    msg: format!(
+                        "the distribution mean must be in the range [0, 1], got {}",
+                        value.mean
+                    ),
+                },
+                location,
+            )));
+        }
+        if value.sigma <= 0. || value.sigma > 1. {
+            return Err(deserr::take_cf_content(E::error::<std::convert::Infallible>(
+                None,
+                deserr::ErrorKind::Unexpected {
+                    msg: format!(
+                        "the distribution sigma must be in the range ]0, 1], got {}",
+                        value.sigma
+                    ),
+                },
+                location,
+            )));
+        }
+
+        Ok(value.into())
+    }
+}
+
+#[derive(Serialize, Deserialize, Deserr)]
+#[serde(deny_unknown_fields)]
+#[deserr(deny_unknown_fields)]
+struct DistributionShiftSerializable {
+    mean: f32,
+    sigma: f32,
+}
+
+impl From<DistributionShift> for DistributionShiftSerializable {
+    fn from(
+        DistributionShift {
+            current_mean: OrderedFloat(current_mean),
+            current_sigma: OrderedFloat(current_sigma),
+        }: DistributionShift,
+    ) -> Self {
+        Self { mean: current_mean, sigma: current_sigma }
+    }
+}
+
+impl From<DistributionShiftSerializable> for DistributionShift {
+    fn from(DistributionShiftSerializable { mean, sigma }: DistributionShiftSerializable) -> Self {
+        Self { current_mean: OrderedFloat(mean), current_sigma: OrderedFloat(sigma) }
+    }
+}
+
+impl DistributionShift {
+    /// `None` if sigma <= 0.
+    pub fn new(mean: f32, sigma: f32) -> Option<Self> {
+        if sigma <= 0.0 {
+            None
+        } else {
+            Some(Self { current_mean: OrderedFloat(mean), current_sigma: OrderedFloat(sigma) })
+        }
+    }
+
+    pub fn shift(&self, score: f32) -> f32 {
+        let current_mean = self.current_mean.0;
+        let current_sigma = self.current_sigma.0;
+        // <https://math.stackexchange.com/a/2894689>
+        // We're somewhat abusively mapping the distribution of distances to a gaussian.
+        // The parameters we're given is the mean and sigma of the native result distribution.
+        // We're using them to retarget the distribution to a gaussian centered on 0.5 with a sigma of 0.4.
+
+        let target_mean = 0.5;
+        let target_sigma = 0.4;
+
+        // a^2 sig1^2 = sig2^2 => a^2 = sig2^2 / sig1^2 => a = sig2 / sig1, assuming a, sig1, and sig2 positive.
+        let factor = target_sigma / current_sigma;
+        // a*mu1 + b = mu2 => b = mu2 - a*mu1
+        let offset = target_mean - (factor * current_mean);
+
+        let mut score = factor * score + offset;
+
+        // clamp the final score in the ]0, 1] interval.
+        if score <= 0.0 {
+            score = f32::EPSILON;
+        }
+        if score > 1.0 {
+            score = 1.0;
+        }
+
+        score
+    }
+}
+
+/// Whether CUDA is supported in this version of Meilisearch.
+pub const fn is_cuda_enabled() -> bool {
+    cfg!(feature = "cuda")
+}
+
+pub fn arroy_db_range_for_embedder(embedder_id: u8) -> impl Iterator<Item = u16> {
+    let embedder_id = (embedder_id as u16) << 8;
+
+    (0..=u8::MAX).map(move |k| embedder_id | (k as u16))
+}