misc: rename passage to text

Author: OlivierDehaene

README.md

@@ -286,7 +286,7 @@ docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/da
 
 `text-embeddings-inference` v0.4.0 added support for CamemBERT, RoBERTa and XLM-RoBERTa Sequence Classification models.
 Re-rankers models are Sequence Classification cross-encoders models with a single class that scores the similarity
-between a query and a passage.
+between a query and a text.
 
 See [this blogpost](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83) by
 the LlamaIndex team to understand how you can use re-rankers models in your RAG pipeline to improve
@@ -300,12 +300,12 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
 docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.5 --model-id $model --revision $revision
 ```
 
-And then you can rank the similarity between a query and a list of passages with:
+And then you can rank the similarity between a query and a list of texts with:
 
 ```bash
 curl 127.0.0.1:8080/rerank \
     -X POST \
-    -d '{"query":"What is Deep Learning?", "passages": ["Deep Learning is not...", "Deep learning is..."]}' \
+    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."]}' \
     -H 'Content-Type: application/json'
 ```
 

docs/openapi.json

@@ -898,7 +898,7 @@
             "example": "0",
             "minimum": 0
           },
-          "passage": {
+          "text": {
             "type": "string",
             "default": "null",
             "example": "Deep Learning is ...",
@@ -915,10 +915,10 @@
         "type": "object",
         "required": [
           "query",
-          "passages"
+          "texts"
         ],
         "properties": {
-          "passages": {
+          "texts": {
             "type": "array",
             "items": {
               "type": "string"
@@ -936,7 +936,7 @@
             "default": "false",
             "example": "false"
           },
-          "return_passages": {
+          "return_text": {
             "type": "boolean",
             "default": "false",
             "example": "false"

docs/source/en/quick_tour.md

@@ -56,7 +56,7 @@ curl 127.0.0.1:8080/embed \
 ## Re-rankers
 
 Re-rankers models are Sequence Classification cross-encoders models with a single class that scores the similarity 
-between a query and a passage.
+between a query and a text.
 
 See [this blogpost](https://blog.llamaindex.ai/boosting-rag-picking-the-best-embedding-reranker-models-42d079022e83) by
 the LlamaIndex team to understand how you can use re-rankers models in your RAG pipeline to improve
@@ -73,12 +73,12 @@ docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingf
 ```
 
 Once you have deployed a model you can use the `rerank` endpoint to rank the similarity between a query and a list
-of passages:
+of texts:
 
 ```bash
 curl 127.0.0.1:8080/rerank \
     -X POST \
-    -d '{"query":"What is Deep Learning?", "passages": ["Deep Learning is not...", "Deep learning is..."], "raw_scores": false}' \
+    -d '{"query":"What is Deep Learning?", "texts": ["Deep Learning is not...", "Deep learning is..."], "raw_scores": false}' \
     -H 'Content-Type: application/json'
 ```
 

router/src/lib.rs

@@ -280,7 +280,7 @@ pub(crate) struct RerankRequest {
     #[schema(example = "What is Deep Learning?")]
     pub query: String,
     #[schema(example = json!(["Deep Learning is ..."]))]
-    pub passages: Vec<String>,
+    pub texts: Vec<String>,
     #[serde(default)]
     #[schema(default = "false", example = "false")]
     pub truncate: bool,
@@ -289,7 +289,7 @@ pub(crate) struct RerankRequest {
     pub raw_scores: bool,
     #[serde(default)]
     #[schema(default = "false", example = "false")]
-    pub return_passages: bool,
+    pub return_text: bool,
 }
 
 #[derive(Serialize, ToSchema)]
@@ -298,7 +298,7 @@ pub(crate) struct Rank {
     pub index: usize,
     #[schema(nullable = true, example = "Deep Learning is ...", default = "null")]
     #[serde(skip_serializing_if = "Option::is_none")]
-    pub passage: Option<String>,
+    pub text: Option<String>,
     #[schema(example = "1.0")]
     pub score: f32,
 }

router/src/server.rs

@@ -330,14 +330,14 @@ async fn rerank(
 
     // Closure for rerank
     let rerank_inner = move |query: String,
-                             passage: String,
+                             text: String,
                              truncate: bool,
                              raw_scores: bool,
                              infer: Infer| async move {
         let permit = infer.try_acquire_permit().map_err(ErrorResponse::from)?;
 
         let response = infer
-            .predict((query, passage), truncate, raw_scores, permit)
+            .predict((query, text), truncate, raw_scores, permit)
             .await
             .map_err(ErrorResponse::from)?;
 
@@ -355,7 +355,7 @@ async fn rerank(
     let (compute_chars, compute_tokens, tokenization_time, queue_time, inference_time, response) = {
         metrics::increment_counter!("te_request_count", "method" => "batch");
 
-        let batch_size = req.passages.len();
+        let batch_size = req.texts.len();
         if batch_size > info.max_client_batch_size {
             let message = format!(
                 "batch size {batch_size} > maximum allowed batch size {}",
@@ -374,12 +374,12 @@ async fn rerank(
         let query_chars = req.query.chars().count();
         let mut compute_chars = query_chars * batch_size;
 
-        for passage in &req.passages {
-            compute_chars += passage.chars().count();
+        for text in &req.texts {
+            compute_chars += text.chars().count();
             let local_infer = infer.clone();
             futures.push(rerank_inner(
                 req.query.clone(),
-                passage.clone(),
+                text.clone(),
                 req.truncate,
                 req.raw_scores,
                 local_infer.0,
@@ -401,15 +401,15 @@ async fn rerank(
             total_tokenization_time += r.1.as_nanos() as u64;
             total_queue_time += r.2.as_nanos() as u64;
             total_inference_time += r.3.as_nanos() as u64;
-            let passage = if req.return_passages {
-                Some(req.passages[index].clone())
+            let text = if req.return_text {
+                Some(req.texts[index].clone())
             } else {
                 None
             };
 
             ranks.push(Rank {
                 index,
-                passage,
+                text,
                 score: r.4,
             })
         }