Merge #5039

5039: Add 3s timeout to embedding requests made during search r=irevoire a=dureuill

# Pull Request

## Related issue
Fixes #5032 

## What does this PR do?
- Add a 3-second timeout to embedding requests against a remote embedder made in the context of search. The timeout triggers when there are failing requests due to rate-limiting.
- Add a test of that timeout.

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

index-scheduler/src/lib.rs

@@ -5201,9 +5201,10 @@ mod tests {
 
             let configs = index_scheduler.embedders(configs).unwrap();
             let (hf_embedder, _, _) = configs.get(&simple_hf_name).unwrap();
-            let beagle_embed = hf_embedder.embed_one(S("Intel the beagle best doggo")).unwrap();
-            let lab_embed = hf_embedder.embed_one(S("Max the lab best doggo")).unwrap();
-            let patou_embed = hf_embedder.embed_one(S("kefir the patou best doggo")).unwrap();
+            let beagle_embed =
+                hf_embedder.embed_one(S("Intel the beagle best doggo"), None).unwrap();
+            let lab_embed = hf_embedder.embed_one(S("Max the lab best doggo"), None).unwrap();
+            let patou_embed = hf_embedder.embed_one(S("kefir the patou best doggo"), None).unwrap();
             (fakerest_name, simple_hf_name, beagle_embed, lab_embed, patou_embed)
         };
 

meilisearch/src/search/mod.rs

@@ -796,8 +796,10 @@ fn prepare_search<'t>(
                     let span = tracing::trace_span!(target: "search::vector", "embed_one");
                     let _entered = span.enter();
 
+                    let deadline = std::time::Instant::now() + std::time::Duration::from_secs(10);
+
                     embedder
-                        .embed_one(query.q.clone().unwrap())
+                        .embed_one(query.q.clone().unwrap(), Some(deadline))
                         .map_err(milli::vector::Error::from)
                         .map_err(milli::Error::from)?
                 }

meilisearch/tests/vector/openai.rs

@@ -137,13 +137,14 @@ fn long_text() -> &'static str {
 }
 
 async fn create_mock_tokenized() -> (MockServer, Value) {
-    create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false).await
+    create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false, false).await
 }
 
 async fn create_mock_with_template(
     document_template: &str,
     model_dimensions: ModelDimensions,
     fallible: bool,
+    slow: bool,
 ) -> (MockServer, Value) {
     let mock_server = MockServer::start().await;
     const API_KEY: &str = "my-api-key";
@@ -154,7 +155,11 @@ async fn create_mock_with_template(
     Mock::given(method("POST"))
         .and(path("/"))
         .respond_with(move |req: &Request| {
-            // 0. maybe return 500
+            // 0. wait for a long time
+            if slow {
+              std::thread::sleep(std::time::Duration::from_secs(1));
+            }
+            // 1. maybe return 500
             if fallible {
              let attempt = attempt.fetch_add(1, Ordering::Relaxed);
              let failed = matches!(attempt % 4, 0 | 1 | 3);
@@ -167,7 +172,7 @@ async fn create_mock_with_template(
                 }))
              }
             }
-            // 1. check API key
+            // 3. check API key
             match req.headers.get("Authorization") {
                 Some(api_key) if api_key == API_KEY_BEARER => {
                     {}
@@ -202,7 +207,7 @@ async fn create_mock_with_template(
                     )
                 }
             }
-            // 2. parse text inputs
+            // 3. parse text inputs
             let query: serde_json::Value = match req.body_json() {
                 Ok(query) => query,
                 Err(_error) => return ResponseTemplate::new(400).set_body_json(
@@ -223,7 +228,7 @@ async fn create_mock_with_template(
                 panic!("Expected {model_dimensions:?}, got {query_model_dimensions:?}")
             }
 
-            // 3. for each text, find embedding in responses
+            // 4. for each text, find embedding in responses
             let serde_json::Value::Array(inputs) = &query["input"] else {
                 panic!("Unexpected `input` value")
             };
@@ -283,7 +288,7 @@ async fn create_mock_with_template(
                 "embedding": embedding,
             })).collect();
 
-            // 4. produce output from embeddings
+            // 5. produce output from embeddings
             ResponseTemplate::new(200).set_body_json(json!({
                 "object": "list",
                 "data": data,
@@ -317,23 +322,27 @@ const DOGGO_TEMPLATE: &str = r#"{%- if doc.gender == "F" -%}Une chienne nommée
         {%- endif %}, de race {{doc.breed}}."#;
 
 async fn create_mock() -> (MockServer, Value) {
-    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false).await
+    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, false, false).await
 }
 
 async fn create_mock_dimensions() -> (MockServer, Value) {
-    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false).await
+    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large512, false, false).await
 }
 
 async fn create_mock_small_embedding_model() -> (MockServer, Value) {
-    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false).await
+    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Small, false, false).await
 }
 
 async fn create_mock_legacy_embedding_model() -> (MockServer, Value) {
-    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false).await
+    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Ada, false, false).await
 }
 
 async fn create_fallible_mock() -> (MockServer, Value) {
-    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true).await
+    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true, false).await
+}
+
+async fn create_slow_mock() -> (MockServer, Value) {
+    create_mock_with_template(DOGGO_TEMPLATE, ModelDimensions::Large, true, true).await
 }
 
 // basic test "it works"
@@ -1873,4 +1882,114 @@ async fn it_still_works() {
     ]
     "###);
 }
+
+// test with a server that responds 500 on 3 out of 4 calls
+#[actix_rt::test]
+async fn timeout() {
+    let (_mock, setting) = create_slow_mock().await;
+    let server = get_server_vector().await;
+    let index = server.index("doggo");
+
+    let (response, code) = index
+        .update_settings(json!({
+          "embedders": {
+              "default": setting,
+          },
+        }))
+        .await;
+    snapshot!(code, @"202 Accepted");
+    let task = server.wait_task(response.uid()).await;
+    snapshot!(task["status"], @r###""succeeded""###);
+    let documents = json!([
+      {"id": 0, "name": "kefir", "gender": "M", "birthyear": 2023, "breed": "Patou"},
+    ]);
+    let (value, code) = index.add_documents(documents, None).await;
+    snapshot!(code, @"202 Accepted");
+    let task = index.wait_task(value.uid()).await;
+    snapshot!(task, @r###"
+    {
+      "uid": "[uid]",
+      "indexUid": "doggo",
+      "status": "succeeded",
+      "type": "documentAdditionOrUpdate",
+      "canceledBy": null,
+      "details": {
+        "receivedDocuments": 1,
+        "indexedDocuments": 1
+      },
+      "error": null,
+      "duration": "[duration]",
+      "enqueuedAt": "[date]",
+      "startedAt": "[date]",
+      "finishedAt": "[date]"
+    }
+    "###);
+
+    let (documents, _code) = index
+        .get_all_documents(GetAllDocumentsOptions { retrieve_vectors: true, ..Default::default() })
+        .await;
+    snapshot!(json_string!(documents, {".results.*._vectors.default.embeddings" => "[vector]"}), @r###"
+    {
+      "results": [
+        {
+          "id": 0,
+          "name": "kefir",
+          "gender": "M",
+          "birthyear": 2023,
+          "breed": "Patou",
+          "_vectors": {
+            "default": {
+              "embeddings": "[vector]",
+              "regenerate": true
+            }
+          }
+        }
+      ],
+      "offset": 0,
+      "limit": 20,
+      "total": 1
+    }
+    "###);
+
+    let (response, code) = index
+        .search_post(json!({
+            "q": "grand chien de berger des montagnes",
+            "hybrid": {"semanticRatio": 0.99, "embedder": "default"}
+        }))
+        .await;
+    snapshot!(code, @"200 OK");
+    snapshot!(json_string!(response["semanticHitCount"]), @"0");
+    snapshot!(json_string!(response["hits"]), @"[]");
+
+    let (response, code) = index
+        .search_post(json!({
+            "q": "grand chien de berger des montagnes",
+            "hybrid": {"semanticRatio": 0.99, "embedder": "default"}
+        }))
+        .await;
+    snapshot!(code, @"200 OK");
+    snapshot!(json_string!(response["semanticHitCount"]), @"1");
+    snapshot!(json_string!(response["hits"]), @r###"
+    [
+      {
+        "id": 0,
+        "name": "kefir",
+        "gender": "M",
+        "birthyear": 2023,
+        "breed": "Patou"
+      }
+    ]
+    "###);
+
+    let (response, code) = index
+        .search_post(json!({
+            "q": "grand chien de berger des montagnes",
+            "hybrid": {"semanticRatio": 0.99, "embedder": "default"}
+        }))
+        .await;
+    snapshot!(code, @"200 OK");
+    snapshot!(json_string!(response["semanticHitCount"]), @"0");
+    snapshot!(json_string!(response["hits"]), @"[]");
+}
+
 // test with a server that wrongly responds 400

milli/src/search/hybrid.rs

@@ -201,7 +201,9 @@ impl<'a> Search<'a> {
                 let span = tracing::trace_span!(target: "search::hybrid", "embed_one");
                 let _entered = span.enter();
 
-                match embedder.embed_one(query) {
+                let deadline = std::time::Instant::now() + std::time::Duration::from_secs(3);
+
+                match embedder.embed_one(query, Some(deadline)) {
                     Ok(embedding) => embedding,
                     Err(error) => {
                         tracing::error!(error=%error, "Embedding failed");

milli/src/vector/mod.rs

@@ -1,5 +1,6 @@
 use std::collections::HashMap;
 use std::sync::Arc;
+use std::time::Instant;
 
 use arroy::distances::{BinaryQuantizedCosine, Cosine};
 use arroy::ItemId;
@@ -594,18 +595,23 @@ impl Embedder {
     pub fn embed(
         &self,
         texts: Vec<String>,
+        deadline: Option<Instant>,
     ) -> 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::OpenAi(embedder) => embedder.embed(texts, deadline),
+            Embedder::Ollama(embedder) => embedder.embed(texts, deadline),
             Embedder::UserProvided(embedder) => embedder.embed(texts),
-            Embedder::Rest(embedder) => embedder.embed(texts),
+            Embedder::Rest(embedder) => embedder.embed(texts, deadline),
         }
     }
 
-    pub fn embed_one(&self, text: String) -> std::result::Result<Embedding, EmbedError> {
-        let mut embeddings = self.embed(vec![text])?;
+    pub fn embed_one(
+        &self,
+        text: String,
+        deadline: Option<Instant>,
+    ) -> std::result::Result<Embedding, EmbedError> {
+        let mut embeddings = self.embed(vec![text], deadline)?;
         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");

milli/src/vector/ollama.rs

@@ -1,3 +1,5 @@
+use std::time::Instant;
+
 use rayon::iter::{IntoParallelIterator as _, ParallelIterator as _};
 
 use super::error::{EmbedError, EmbedErrorKind, NewEmbedderError, NewEmbedderErrorKind};
@@ -75,8 +77,12 @@ impl Embedder {
         Ok(Self { rest_embedder })
     }
 
-    pub fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
-        match self.rest_embedder.embed(texts) {
+    pub fn embed(
+        &self,
+        texts: Vec<String>,
+        deadline: Option<Instant>,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+        match self.rest_embedder.embed(texts, deadline) {
             Ok(embeddings) => Ok(embeddings),
             Err(EmbedError { kind: EmbedErrorKind::RestOtherStatusCode(404, error), fault: _ }) => {
                 Err(EmbedError::ollama_model_not_found(error))
@@ -92,7 +98,7 @@ impl Embedder {
     ) -> Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
         threads
             .install(move || {
-                text_chunks.into_par_iter().map(move |chunk| self.embed(chunk)).collect()
+                text_chunks.into_par_iter().map(move |chunk| self.embed(chunk, None)).collect()
             })
             .map_err(|error| EmbedError {
                 kind: EmbedErrorKind::PanicInThreadPool(error),

milli/src/vector/openai.rs

@@ -1,3 +1,5 @@
+use std::time::Instant;
+
 use ordered_float::OrderedFloat;
 use rayon::iter::{IntoParallelIterator, ParallelIterator as _};
 
@@ -206,32 +208,40 @@ impl Embedder {
         Ok(Self { options, rest_embedder, tokenizer })
     }
 
-    pub fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
-        match self.rest_embedder.embed_ref(&texts) {
+    pub fn embed(
+        &self,
+        texts: Vec<String>,
+        deadline: Option<Instant>,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+        match self.rest_embedder.embed_ref(&texts, deadline) {
             Ok(embeddings) => Ok(embeddings),
             Err(EmbedError { kind: EmbedErrorKind::RestBadRequest(error, _), fault: _ }) => {
                 tracing::warn!(error=?error, "OpenAI: received `BAD_REQUEST`. Input was maybe too long, retrying on tokenized version. For best performance, limit the size of your document template.");
-                self.try_embed_tokenized(&texts)
+                self.try_embed_tokenized(&texts, deadline)
             }
             Err(error) => Err(error),
         }
     }
 
-    fn try_embed_tokenized(&self, text: &[String]) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+    fn try_embed_tokenized(
+        &self,
+        text: &[String],
+        deadline: Option<Instant>,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
         let mut all_embeddings = Vec::with_capacity(text.len());
         for text in text {
             let max_token_count = self.options.embedding_model.max_token();
             let encoded = self.tokenizer.encode_ordinary(text.as_str());
             let len = encoded.len();
             if len < max_token_count {
-                all_embeddings.append(&mut self.rest_embedder.embed_ref(&[text])?);
+                all_embeddings.append(&mut self.rest_embedder.embed_ref(&[text], deadline)?);
                 continue;
             }
 
             let tokens = &encoded.as_slice()[0..max_token_count];
             let mut embeddings_for_prompt = Embeddings::new(self.dimensions());
 
-            let embedding = self.rest_embedder.embed_tokens(tokens)?;
+            let embedding = self.rest_embedder.embed_tokens(tokens, deadline)?;
             embeddings_for_prompt.append(embedding.into_inner()).map_err(|got| {
                 EmbedError::rest_unexpected_dimension(self.dimensions(), got.len())
             })?;
@@ -248,7 +258,7 @@ impl Embedder {
     ) -> Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
         threads
             .install(move || {
-                text_chunks.into_par_iter().map(move |chunk| self.embed(chunk)).collect()
+                text_chunks.into_par_iter().map(move |chunk| self.embed(chunk, None)).collect()
             })
             .map_err(|error| EmbedError {
                 kind: EmbedErrorKind::PanicInThreadPool(error),

milli/src/vector/rest.rs

@@ -1,4 +1,5 @@
 use std::collections::BTreeMap;
+use std::time::Instant;
 
 use deserr::Deserr;
 use rand::Rng;
@@ -154,19 +155,31 @@ impl Embedder {
         Ok(Self { data, dimensions, distribution: options.distribution })
     }
 
-    pub fn embed(&self, texts: Vec<String>) -> Result<Vec<Embeddings<f32>>, EmbedError> {
-        embed(&self.data, texts.as_slice(), texts.len(), Some(self.dimensions))
+    pub fn embed(
+        &self,
+        texts: Vec<String>,
+        deadline: Option<Instant>,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError> {
+        embed(&self.data, texts.as_slice(), texts.len(), Some(self.dimensions), deadline)
     }
 
-    pub fn embed_ref<S>(&self, texts: &[S]) -> Result<Vec<Embeddings<f32>>, EmbedError>
+    pub fn embed_ref<S>(
+        &self,
+        texts: &[S],
+        deadline: Option<Instant>,
+    ) -> Result<Vec<Embeddings<f32>>, EmbedError>
     where
         S: AsRef<str> + Serialize,
     {
-        embed(&self.data, texts, texts.len(), Some(self.dimensions))
+        embed(&self.data, texts, texts.len(), Some(self.dimensions), deadline)
     }
 
-    pub fn embed_tokens(&self, tokens: &[usize]) -> Result<Embeddings<f32>, EmbedError> {
-        let mut embeddings = embed(&self.data, tokens, 1, Some(self.dimensions))?;
+    pub fn embed_tokens(
+        &self,
+        tokens: &[usize],
+        deadline: Option<Instant>,
+    ) -> Result<Embeddings<f32>, EmbedError> {
+        let mut embeddings = embed(&self.data, tokens, 1, Some(self.dimensions), deadline)?;
         // unwrap: guaranteed that embeddings.len() == 1, otherwise the previous line terminated in error
         Ok(embeddings.pop().unwrap())
     }
@@ -178,7 +191,7 @@ impl Embedder {
     ) -> Result<Vec<Vec<Embeddings<f32>>>, EmbedError> {
         threads
             .install(move || {
-                text_chunks.into_par_iter().map(move |chunk| self.embed(chunk)).collect()
+                text_chunks.into_par_iter().map(move |chunk| self.embed(chunk, None)).collect()
             })
             .map_err(|error| EmbedError {
                 kind: EmbedErrorKind::PanicInThreadPool(error),
@@ -207,7 +220,7 @@ impl Embedder {
 }
 
 fn infer_dimensions(data: &EmbedderData) -> Result<usize, NewEmbedderError> {
-    let v = embed(data, ["test"].as_slice(), 1, None)
+    let v = embed(data, ["test"].as_slice(), 1, None, None)
         .map_err(NewEmbedderError::could_not_determine_dimension)?;
     // unwrap: guaranteed that v.len() == 1, otherwise the previous line terminated in error
     Ok(v.first().unwrap().dimension())
@@ -218,6 +231,7 @@ fn embed<S>(
     inputs: &[S],
     expected_count: usize,
     expected_dimension: Option<usize>,
+    deadline: Option<Instant>,
 ) -> Result<Vec<Embeddings<f32>>, EmbedError>
 where
     S: Serialize,
@@ -245,7 +259,18 @@ where
             }
             Err(retry) => {
                 tracing::warn!("Failed: {}", retry.error);
-                retry.into_duration(attempt)
+                if let Some(deadline) = deadline {
+                    let now = std::time::Instant::now();
+                    if now > deadline {
+                        tracing::warn!("Could not embed due to deadline");
+                        return Err(retry.into_error());
+                    }
+
+                    let duration_to_deadline = deadline - now;
+                    retry.into_duration(attempt).map(|duration| duration.min(duration_to_deadline))
+                } else {
+                    retry.into_duration(attempt)
+                }
             }
         }?;