Add tokenized test

meilisearch/tests/vector/openai.rs

@@ -1,6 +1,7 @@
 use std::collections::BTreeMap;
 use std::io::Write;
 use std::sync::atomic::{AtomicU32, Ordering};
+use std::sync::OnceLock;
 
 use meili_snap::{json_string, snapshot};
 use wiremock::matchers::{method, path};
@@ -21,6 +22,12 @@ struct OpenAiResponse {
     large_512: Option<Vec<f32>>,
 }
 
+#[derive(serde::Deserialize)]
+struct OpenAiTokenizedResponses {
+    tokens: Vec<u64>,
+    embedding: Vec<f32>,
+}
+
 impl OpenAiResponses {
     fn get(&self, text: &str, model_dimensions: ModelDimensions) -> Option<&[f32]> {
         let entry = self.0.get(text)?;
@@ -81,7 +88,7 @@ impl ModelDimensions {
 }
 
 fn openai_responses() -> &'static OpenAiResponses {
-    static OPENAI_RESPONSES: std::sync::OnceLock<OpenAiResponses> = std::sync::OnceLock::new();
+    static OPENAI_RESPONSES: OnceLock<OpenAiResponses> = OnceLock::new();
     OPENAI_RESPONSES.get_or_init(|| {
         // json file that was compressed with gzip
         // decompress with `gzip --keep -d openai_responses.json.gz`
@@ -96,6 +103,43 @@ fn openai_responses() -> &'static OpenAiResponses {
     })
 }
 
+fn openai_tokenized_responses() -> &'static OpenAiTokenizedResponses {
+    static OPENAI_TOKENIZED_RESPONSES: OnceLock<OpenAiTokenizedResponses> = OnceLock::new();
+    OPENAI_TOKENIZED_RESPONSES.get_or_init(|| {
+        // json file that was compressed with gzip
+        // decompress with `gzip --keep -d openai_tokenized_responses.json.gz`
+        // recompress with `gzip --keep -c openai_tokenized_responses.json > openai_tokenized_responses.json.gz`
+        let compressed_responses = include_bytes!("openai_tokenized_responses.json.gz");
+        let mut responses = Vec::new();
+        let mut decoder = flate2::write::GzDecoder::new(&mut responses);
+
+        decoder.write_all(compressed_responses).unwrap();
+        drop(decoder);
+        serde_json::from_slice(&responses).unwrap()
+    })
+}
+
+fn long_text() -> &'static str {
+    static LONG_TEXT: OnceLock<String> = OnceLock::new();
+    LONG_TEXT.get_or_init(|| {
+        // decompress with `gzip --keep -d intel_gen.txt.gz`
+        // recompress with `gzip --keep -c intel_gen.txt > intel_gen.txt.gz`
+        let compressed_long_text = include_bytes!("intel_gen.txt.gz");
+        let mut long_text = Vec::new();
+        let mut decoder = flate2::write::GzDecoder::new(&mut long_text);
+
+        decoder.write_all(compressed_long_text).unwrap();
+        drop(decoder);
+        let long_text = std::str::from_utf8(&long_text).unwrap();
+
+        long_text.repeat(3)
+    })
+}
+
+async fn create_mock_tokenized() -> (MockServer, Value) {
+    create_mock_with_template("{{doc.text}}", ModelDimensions::Large, false).await
+}
+
 async fn create_mock_with_template(
     document_template: &str,
     model_dimensions: ModelDimensions,
@@ -176,28 +220,19 @@ async fn create_mock_with_template(
             };
             let query_model_dimensions = ModelDimensions::from_request(&query);
             if query_model_dimensions != model_dimensions {
-                return ResponseTemplate::new(400).set_body_json(json!({
+                panic!("Expected {model_dimensions:?}, got {query_model_dimensions:?}")
-                    "error": {
-                        "message": format!("Expected {model_dimensions:?}, got {query_model_dimensions:?}"),
-                        "type": "invalid_model_dimensions",
-                        "query": query,
-                    }
-                }))
             }
 
             // 3. for each text, find embedding in responses
             let serde_json::Value::Array(inputs) = &query["input"] else {
-                return ResponseTemplate::new(400).set_body_json(json!({
+                panic!("Unexpected `input` value")
-                    "error": {
-                        "message": "Unexpected `input` value",
-                        "type": "test_response",
-                        "query": query
-                    }
-                }))
             };
 
+            let openai_tokenized_responses = openai_tokenized_responses();
+            let embeddings = if inputs == openai_tokenized_responses.tokens.as_slice() {
+                vec![openai_tokenized_responses.embedding.clone()]
+            } else {
                 let mut embeddings = Vec::new();
-
                 for input in inputs {
                     let serde_json::Value::String(input) = input else {
                         return ResponseTemplate::new(400).set_body_json(json!({
@@ -209,8 +244,21 @@ async fn create_mock_with_template(
                         }))
                     };
 
-                let Some(embedding) = openai_responses().get(input, model_dimensions) else {
+                    if input == long_text() {
                         return ResponseTemplate::new(400).set_body_json(json!(
+                            {
+                                "error": {
+                                  "message": "This model's maximum context length is 8192 tokens, however you requested 10554 tokens (10554 in your prompt; 0 for the completion). Please reduce your prompt; or completion length.",
+                                  "type": "invalid_request_error",
+                                  "param": null,
+                                  "code": null,
+                                }
+                            }
+                        ));
+                    }
+
+                    let Some(embedding) = openai_responses().get(input, model_dimensions) else {
+                        return ResponseTemplate::new(404).set_body_json(json!(
                             {
                                 "error": {
                                     "message": "Could not find embedding for text",
@@ -225,6 +273,9 @@ async fn create_mock_with_template(
 
                     embeddings.push(embedding.to_vec());
                 }
+                embeddings
+            };
+
 
             let data : Vec<_> = embeddings.into_iter().enumerate().map(|(index, embedding)| json!({
                 "object": "embedding",
@@ -517,6 +568,67 @@ async fn it_works() {
 
 // tokenize long text
 
+// basic test "it works"
+#[actix_rt::test]
+async fn tokenize_long_text() {
+    let (_mock, setting) = create_mock_tokenized().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, "text": long_text()}
+    ]);
+    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": 1,
+      "indexUid": "doggo",
+      "status": "succeeded",
+      "type": "documentAdditionOrUpdate",
+      "canceledBy": null,
+      "details": {
+        "receivedDocuments": 1,
+        "indexedDocuments": 1
+      },
+      "error": null,
+      "duration": "[duration]",
+      "enqueuedAt": "[date]",
+      "startedAt": "[date]",
+      "finishedAt": "[date]"
+    }
+    "###);
+
+    let (response, code) = index
+        .search_post(json!({
+            "q": "grand chien de berger des montagnes",
+            "showRankingScore": true,
+            "attributesToRetrieve": ["id"],
+            "hybrid": {"semanticRatio": 1.0}
+        }))
+        .await;
+    snapshot!(code, @"200 OK");
+    snapshot!(json_string!(response["hits"]), @r###"
+    [
+      {
+        "id": 0,
+        "_rankingScore": 0.07944583892822266
+      }
+    ]
+    "###);
+}
+
 // "wrong parameters"
 
 #[actix_rt::test]