Merge #4371

4371: Fixes embedder issues r=irevoire a=dureuill

# Pull Request

## Related issue
Fixes #4361
Fixes #4370 

## What does this PR do?
- Truncate tokens to 512 for Hugging Face embedders
- Move the tokio runtime to OpenAI so that we no longer have a thread with rayon -> tokio -> rayon
- Spawn a new reqwest client after each new runtime to avoid spurious runtime error 

## Manual tests

- embedding failing document from `@CaroFG` with hugging face
- embedding movies with hugging face
- embedding and searching movies with openai

Co-authored-by: Louis Dureuil <louis@meilisearch.com>

milli/src/update/index_documents/extract/extract_vector_points.rs

@@ -339,9 +339,7 @@ pub fn extract_embeddings<R: io::Read + io::Seek>(
     indexer: GrenadParameters,
     embedder: Arc<Embedder>,
 ) -> Result<grenad::Reader<BufReader<File>>> {
-    let rt = tokio::runtime::Builder::new_current_thread().enable_io().enable_time().build()?;
-
-    let n_chunks = embedder.chunk_count_hint(); // chunk level parellelism
+    let n_chunks = embedder.chunk_count_hint(); // chunk level parallelism
     let n_vectors_per_chunk = embedder.prompt_count_in_chunk_hint(); // number of vectors in a single chunk
 
     // docid, state with embedding
@@ -375,11 +373,8 @@ pub fn extract_embeddings<R: io::Read + io::Seek>(
         current_chunk_ids.push(docid);
 
         if chunks.len() == chunks.capacity() {
-            let chunked_embeds = rt
-                .block_on(
-                    embedder
-                        .embed_chunks(std::mem::replace(&mut chunks, Vec::with_capacity(n_chunks))),
-                )
+            let chunked_embeds = embedder
+                .embed_chunks(std::mem::replace(&mut chunks, Vec::with_capacity(n_chunks)))
                 .map_err(crate::vector::Error::from)
                 .map_err(crate::Error::from)?;
 
@@ -396,8 +391,8 @@ pub fn extract_embeddings<R: io::Read + io::Seek>(
 
     // send last chunk
     if !chunks.is_empty() {
-        let chunked_embeds = rt
-            .block_on(embedder.embed_chunks(std::mem::take(&mut chunks)))
+        let chunked_embeds = embedder
+            .embed_chunks(std::mem::take(&mut chunks))
             .map_err(crate::vector::Error::from)
             .map_err(crate::Error::from)?;
         for (docid, embeddings) in chunks_ids
@@ -410,15 +405,17 @@ pub fn extract_embeddings<R: io::Read + io::Seek>(
     }
 
     if !current_chunk.is_empty() {
-        let embeds = rt
-            .block_on(embedder.embed(std::mem::take(&mut current_chunk)))
+        let embeds = embedder
+            .embed_chunks(vec![std::mem::take(&mut current_chunk)])
             .map_err(crate::vector::Error::from)
             .map_err(crate::Error::from)?;
 
+        if let Some(embeds) = embeds.first() {
             for (docid, embeddings) in current_chunk_ids.iter().zip(embeds.iter()) {
                 state_writer.insert(docid.to_be_bytes(), cast_slice(embeddings.as_inner()))?;
             }
         }
+    }
 
     writer_into_reader(state_writer)
 }

milli/src/vector/error.rs

@@ -67,6 +67,10 @@ pub enum EmbedErrorKind {
     OpenAiUnhandledStatusCode(u16),
     #[error("attempt to embed the following text in a configuration where embeddings must be user provided: {0:?}")]
     ManualEmbed(String),
+    #[error("could not initialize asynchronous runtime: {0}")]
+    OpenAiRuntimeInit(std::io::Error),
+    #[error("initializing web client for sending embedding requests failed: {0}")]
+    InitWebClient(reqwest::Error),
 }
 
 impl EmbedError {
@@ -117,6 +121,14 @@ impl EmbedError {
     pub(crate) fn embed_on_manual_embedder(texts: String) -> EmbedError {
         Self { kind: EmbedErrorKind::ManualEmbed(texts), fault: FaultSource::User }
     }
+
+    pub(crate) fn openai_runtime_init(inner: std::io::Error) -> EmbedError {
+        Self { kind: EmbedErrorKind::OpenAiRuntimeInit(inner), fault: FaultSource::Runtime }
+    }
+
+    pub fn openai_initialize_web_client(inner: reqwest::Error) -> Self {
+        Self { kind: EmbedErrorKind::InitWebClient(inner), fault: FaultSource::Runtime }
+    }
 }
 
 #[derive(Debug, thiserror::Error)]
@@ -183,10 +195,6 @@ impl NewEmbedderError {
         }
     }
 
-    pub fn openai_initialize_web_client(inner: reqwest::Error) -> Self {
-        Self { kind: NewEmbedderErrorKind::InitWebClient(inner), fault: FaultSource::Runtime }
-    }
-
     pub fn openai_invalid_api_key_format(inner: reqwest::header::InvalidHeaderValue) -> Self {
         Self { kind: NewEmbedderErrorKind::InvalidApiKeyFormat(inner), fault: FaultSource::User }
     }
@@ -237,8 +245,6 @@ pub enum NewEmbedderErrorKind {
     #[error("loading model failed: {0}")]
     LoadModel(candle_core::Error),
     // openai
-    #[error("initializing web client for sending embedding requests failed: {0}")]
-    InitWebClient(reqwest::Error),
     #[error("The API key passed to Authorization error was in an invalid format: {0}")]
     InvalidApiKeyFormat(reqwest::header::InvalidHeaderValue),
 }

milli/src/vector/hf.rs

@@ -145,7 +145,8 @@ impl Embedder {
         let token_ids = tokens
             .iter()
             .map(|tokens| {
-                let tokens = tokens.get_ids().to_vec();
+                let mut tokens = tokens.get_ids().to_vec();
+                tokens.truncate(512);
                 Tensor::new(tokens.as_slice(), &self.model.device).map_err(EmbedError::tensor_shape)
             })
             .collect::<Result<Vec<_>, EmbedError>>()?;

milli/src/vector/mod.rs

@@ -163,18 +163,24 @@ impl Embedder {
     ) -> std::result::Result<Vec<Embeddings<f32>>, EmbedError> {
         match self {
             Embedder::HuggingFace(embedder) => embedder.embed(texts),
-            Embedder::OpenAi(embedder) => embedder.embed(texts).await,
+            Embedder::OpenAi(embedder) => {
+                let client = embedder.new_client()?;
+                embedder.embed(texts, &client).await
+            }
             Embedder::UserProvided(embedder) => embedder.embed(texts),
         }
     }
 
-    pub async fn embed_chunks(
+    /// # Panics
+    ///
+    /// - if called from an asynchronous context
+    pub fn embed_chunks(
         &self,
         text_chunks: Vec<Vec<String>>,
     ) -> 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).await,
+            Embedder::OpenAi(embedder) => embedder.embed_chunks(text_chunks),
             Embedder::UserProvided(embedder) => embedder.embed_chunks(text_chunks),
         }
     }

milli/src/vector/openai.rs

@@ -8,7 +8,7 @@ use super::{DistributionShift, Embedding, Embeddings};
 
 #[derive(Debug)]
 pub struct Embedder {
-    client: reqwest::Client,
+    headers: reqwest::header::HeaderMap,
     tokenizer: tiktoken_rs::CoreBPE,
     options: EmbedderOptions,
 }
@@ -95,6 +95,13 @@ impl EmbedderOptions {
 }
 
 impl Embedder {
+    pub fn new_client(&self) -> Result<reqwest::Client, EmbedError> {
+        reqwest::ClientBuilder::new()
+            .default_headers(self.headers.clone())
+            .build()
+            .map_err(EmbedError::openai_initialize_web_client)
+    }
+
     pub fn new(options: EmbedderOptions) -> Result<Self, NewEmbedderError> {
         let mut headers = reqwest::header::HeaderMap::new();
         let mut inferred_api_key = Default::default();
@@ -111,25 +118,25 @@ impl Embedder {
             reqwest::header::CONTENT_TYPE,
             reqwest::header::HeaderValue::from_static("application/json"),
         );
-        let client = reqwest::ClientBuilder::new()
-            .default_headers(headers)
-            .build()
-            .map_err(NewEmbedderError::openai_initialize_web_client)?;
 
         // looking at the code it is very unclear that this can actually fail.
         let tokenizer = tiktoken_rs::cl100k_base().unwrap();
 
-        Ok(Self { options, client, tokenizer })
+        Ok(Self { options, headers, tokenizer })
     }
 
-    pub async fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+    pub async fn embed(
+        &self,
+        texts: Vec<String>,
+        client: &reqwest::Client,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
         let mut tokenized = false;
 
         for attempt in 0..7 {
             let result = if tokenized {
-                self.try_embed_tokenized(&texts).await
+                self.try_embed_tokenized(&texts, client).await
             } else {
-                self.try_embed(&texts).await
+                self.try_embed(&texts, client).await
             };
 
             let retry_duration = match result {
@@ -145,9 +152,9 @@ impl Embedder {
         }
 
         let result = if tokenized {
-            self.try_embed_tokenized(&texts).await
+            self.try_embed_tokenized(&texts, client).await
         } else {
-            self.try_embed(&texts).await
+            self.try_embed(&texts, client).await
         };
 
         result.map_err(Retry::into_error)
@@ -225,13 +232,13 @@ impl Embedder {
     async fn try_embed<S: AsRef<str> + serde::Serialize>(
         &self,
         texts: &[S],
+        client: &reqwest::Client,
     ) -> Result<Vec<Embeddings<f32>>, Retry> {
         for text in texts {
             log::trace!("Received prompt: {}", text.as_ref())
         }
         let request = OpenAiRequest { model: self.options.embedding_model.name(), input: texts };
-        let response = self
-            .client
+        let response = client
             .post(OPENAI_EMBEDDINGS_URL)
             .json(&request)
             .send()
@@ -256,7 +263,11 @@ impl Embedder {
             .collect())
     }
 
-    async fn try_embed_tokenized(&self, text: &[String]) -> Result<Vec<Embeddings<f32>>, Retry> {
+    async fn try_embed_tokenized(
+        &self,
+        text: &[String],
+        client: &reqwest::Client,
+    ) -> Result<Vec<Embeddings<f32>>, Retry> {
         pub const OVERLAP_SIZE: usize = 200;
         let mut all_embeddings = Vec::with_capacity(text.len());
         for text in text {
@@ -264,7 +275,7 @@ impl Embedder {
             let encoded = self.tokenizer.encode_ordinary(text.as_str());
             let len = encoded.len();
             if len < max_token_count {
-                all_embeddings.append(&mut self.try_embed(&[text]).await?);
+                all_embeddings.append(&mut self.try_embed(&[text], client).await?);
                 continue;
             }
 
@@ -273,22 +284,26 @@ impl Embedder {
                 Embeddings::new(self.options.embedding_model.dimensions());
             while tokens.len() > max_token_count {
                 let window = &tokens[..max_token_count];
-                embeddings_for_prompt.push(self.embed_tokens(window).await?).unwrap();
+                embeddings_for_prompt.push(self.embed_tokens(window, client).await?).unwrap();
 
                 tokens = &tokens[max_token_count - OVERLAP_SIZE..];
             }
 
             // end of text
-            embeddings_for_prompt.push(self.embed_tokens(tokens).await?).unwrap();
+            embeddings_for_prompt.push(self.embed_tokens(tokens, client).await?).unwrap();
 
             all_embeddings.push(embeddings_for_prompt);
         }
         Ok(all_embeddings)
     }
 
-    async fn embed_tokens(&self, tokens: &[usize]) -> Result<Embedding, Retry> {
+    async fn embed_tokens(
+        &self,
+        tokens: &[usize],
+        client: &reqwest::Client,
+    ) -> Result<Embedding, Retry> {
         for attempt in 0..9 {
-            let duration = match self.try_embed_tokens(tokens).await {
+            let duration = match self.try_embed_tokens(tokens, client).await {
                 Ok(embedding) => return Ok(embedding),
                 Err(retry) => retry.into_duration(attempt),
             }
@@ -297,14 +312,19 @@ impl Embedder {
             tokio::time::sleep(duration).await;
         }
 
-        self.try_embed_tokens(tokens).await.map_err(|retry| Retry::give_up(retry.into_error()))
+        self.try_embed_tokens(tokens, client)
+            .await
+            .map_err(|retry| Retry::give_up(retry.into_error()))
     }
 
-    async fn try_embed_tokens(&self, tokens: &[usize]) -> Result<Embedding, Retry> {
+    async fn try_embed_tokens(
+        &self,
+        tokens: &[usize],
+        client: &reqwest::Client,
+    ) -> Result<Embedding, Retry> {
         let request =
             OpenAiTokensRequest { model: self.options.embedding_model.name(), input: tokens };
-        let response = self
-            .client
+        let response = client
             .post(OPENAI_EMBEDDINGS_URL)
             .json(&request)
             .send()
@@ -322,12 +342,19 @@ impl Embedder {
         Ok(response.data.pop().map(|data| data.embedding).unwrap_or_default())
     }
 
-    pub async fn embed_chunks(
+    pub fn embed_chunks(
         &self,
         text_chunks: Vec<Vec<String>>,
     ) -> Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
-        futures::future::try_join_all(text_chunks.into_iter().map(|prompts| self.embed(prompts)))
-            .await
+        let rt = tokio::runtime::Builder::new_current_thread()
+            .enable_io()
+            .enable_time()
+            .build()
+            .map_err(EmbedError::openai_runtime_init)?;
+        let client = self.new_client()?;
+        rt.block_on(futures::future::try_join_all(
+            text_chunks.into_iter().map(|prompts| self.embed(prompts, &client)),
+        ))
     }
 
     pub fn chunk_count_hint(&self) -> usize {