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https://github.com/meilisearch/MeiliSearch
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Small commit to add hybrid search and autoembedding
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42 changed files with 4045 additions and 246 deletions
142
milli/src/vector/mod.rs
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142
milli/src/vector/mod.rs
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use self::error::{EmbedError, NewEmbedderError};
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use crate::prompt::PromptData;
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pub mod error;
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pub mod hf;
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pub mod openai;
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pub mod settings;
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pub use self::error::Error;
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pub type Embedding = Vec<f32>;
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pub struct Embeddings<F> {
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data: Vec<F>,
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dimension: usize,
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}
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impl<F> Embeddings<F> {
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pub fn new(dimension: usize) -> Self {
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Self { data: Default::default(), dimension }
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}
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pub fn from_single_embedding(embedding: Vec<F>) -> Self {
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Self { dimension: embedding.len(), data: embedding }
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}
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pub fn from_inner(data: Vec<F>, dimension: usize) -> Result<Self, Vec<F>> {
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let mut this = Self::new(dimension);
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this.append(data)?;
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Ok(this)
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}
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pub fn dimension(&self) -> usize {
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self.dimension
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}
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pub fn into_inner(self) -> Vec<F> {
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self.data
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}
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pub fn as_inner(&self) -> &[F] {
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&self.data
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}
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pub fn iter(&self) -> impl Iterator<Item = &'_ [F]> + '_ {
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self.data.as_slice().chunks_exact(self.dimension)
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}
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pub fn push(&mut self, mut embedding: Vec<F>) -> Result<(), Vec<F>> {
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if embedding.len() != self.dimension {
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return Err(embedding);
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}
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self.data.append(&mut embedding);
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Ok(())
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}
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pub fn append(&mut self, mut embeddings: Vec<F>) -> Result<(), Vec<F>> {
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if embeddings.len() % self.dimension != 0 {
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return Err(embeddings);
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}
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self.data.append(&mut embeddings);
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Ok(())
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}
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}
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#[derive(Debug)]
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pub enum Embedder {
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HuggingFace(hf::Embedder),
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OpenAi(openai::Embedder),
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}
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#[derive(Debug, Clone, Default, serde::Deserialize, serde::Serialize)]
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pub struct EmbeddingConfig {
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pub embedder_options: EmbedderOptions,
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pub prompt: PromptData,
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// TODO: add metrics and anything needed
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}
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#[derive(Debug, Clone, Hash, PartialEq, Eq, serde::Deserialize, serde::Serialize)]
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pub enum EmbedderOptions {
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HuggingFace(hf::EmbedderOptions),
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OpenAi(openai::EmbedderOptions),
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}
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impl Default for EmbedderOptions {
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fn default() -> Self {
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Self::HuggingFace(Default::default())
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}
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}
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impl EmbedderOptions {
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pub fn huggingface() -> Self {
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Self::HuggingFace(hf::EmbedderOptions::new())
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}
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pub fn openai(api_key: String) -> Self {
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Self::OpenAi(openai::EmbedderOptions::with_default_model(api_key))
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}
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}
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impl Embedder {
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pub fn new(options: EmbedderOptions) -> std::result::Result<Self, NewEmbedderError> {
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Ok(match options {
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EmbedderOptions::HuggingFace(options) => Self::HuggingFace(hf::Embedder::new(options)?),
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EmbedderOptions::OpenAi(options) => Self::OpenAi(openai::Embedder::new(options)?),
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})
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}
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pub async fn embed(
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&self,
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texts: Vec<String>,
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) -> std::result::Result<Vec<Embeddings<f32>>, EmbedError> {
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match self {
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Embedder::HuggingFace(embedder) => embedder.embed(texts).await,
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Embedder::OpenAi(embedder) => embedder.embed(texts).await,
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}
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}
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pub async fn embed_chunks(
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&self,
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text_chunks: Vec<Vec<String>>,
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) -> std::result::Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
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match self {
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Embedder::HuggingFace(embedder) => embedder.embed_chunks(text_chunks).await,
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Embedder::OpenAi(embedder) => embedder.embed_chunks(text_chunks).await,
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}
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}
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pub fn chunk_count_hint(&self) -> usize {
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match self {
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Embedder::HuggingFace(embedder) => embedder.chunk_count_hint(),
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Embedder::OpenAi(embedder) => embedder.chunk_count_hint(),
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}
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}
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pub fn prompt_count_in_chunk_hint(&self) -> usize {
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match self {
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Embedder::HuggingFace(embedder) => embedder.prompt_count_in_chunk_hint(),
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Embedder::OpenAi(embedder) => embedder.prompt_count_in_chunk_hint(),
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}
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}
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}
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