add scoring onnx (#1769)

docs/docs/integrations/scoring-reranking-models/in-process.md

@@ -4,8 +4,8 @@ sidebar_position: 1
 
 # In-process (ONNX)
 
-LangChain4j provides local Scoring (Reranking) models, powered by [ONNX runtime](https://onnxruntime.ai/docs/get-started/with-java.html), 
-running in the same Java process.
+LangChain4j provides local scoring (reranking) models,
+powered by [ONNX runtime](https://onnxruntime.ai/docs/get-started/with-java.html), running in the same Java process.
 
 Many models (e.g., from [Hugging Face](https://huggingface.co/)) can be used,
 as long as they are in the ONNX format.
@@ -16,7 +16,7 @@ Many models already converted to ONNX format are available [here](https://huggin
 
 ### Usage
 
-By default, Scoring (Reranking) use the CPU. 
+By default, scoring (reranking) model uses the CPU. 
 ```xml
 <dependency>
     <groupId>dev.langchain4j</groupId>
@@ -33,7 +33,8 @@ Response<Double> response = scoringModel.score("query", "passage");
 Double score = response.content();
 ```
 
-If you want to use the GPU, onnxruntime_gpu version can be found  [here](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html).
+If you want to use the GPU, `onnxruntime_gpu` version can be found
+[here](https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html).
 ```xml
 <dependency>
     <groupId>dev.langchain4j</groupId>
@@ -65,4 +66,4 @@ OnnxScoringModel scoringModel = new OnnxScoringModel(pathToModel, options, pathT
 
 Response<Double> response = scoringModel.score("query", "passage");
 Double score = response.content();
-```
+```

langchain4j-onnx-scoring/src/main/java/dev/langchain4j/model/scoring/onnx/AbstractInProcessScoringModel.java

@@ -9,7 +9,7 @@ import dev.langchain4j.model.scoring.ScoringModel;
 import java.util.List;
 import java.util.stream.Collectors;
 
-public abstract class AbstractInProcessScoringModel implements ScoringModel {
+abstract class AbstractInProcessScoringModel implements ScoringModel {
 
     public AbstractInProcessScoringModel() {
     }

langchain4j-onnx-scoring/src/main/java/dev/langchain4j/model/scoring/onnx/OnnxScoringBertCrossEncoder.java

@@ -14,7 +14,7 @@ import java.util.*;
 
 import static ai.onnxruntime.OnnxTensor.createTensor;
 
-public class OnnxScoringBertCrossEncoder {
+class OnnxScoringBertCrossEncoder {
 
     private final OrtEnvironment environment;
     private final OrtSession session;

langchain4j-onnx-scoring/src/main/test/java/dev/langchain4j/model/scoring/onnx/OnnxScoringModelIT.java

@@ -42,7 +42,7 @@ class OnnxScoringModelIT {
         Files.copy(tokenizerUrl.openStream(), tokenizerPath, REPLACE_EXISTING);
 
         // To check the modelMaxLength parameter, refer to the model configuration file at  https://huggingface.co/Xenova/ms-marco-MiniLM-L-6-v2/resolve/main/tokenizer_config.json
-        model = new OnnxScoringModel(modelPath.toString(), new OrtSession.SessionOptions(), tokenizerPath.toString(), 512,  false);
+        model = new OnnxScoringModel(modelPath.toString(), new OrtSession.SessionOptions(), tokenizerPath.toString(), 512, false);
     }
 
     @Test
@@ -60,13 +60,12 @@ class OnnxScoringModelIT {
         List<Double> scores = response.content();
         assertThat(scores).hasSize(2);
 
-        // python output results: [ 8.845855712890625, -11.245561599731445 ]
-        assertThat(scores.get(0)).isCloseTo(8.84, withPercentage(1));
-        assertThat(scores.get(1)).isCloseTo(-11.24, withPercentage(1));
+        // python output results on https://huggingface.co/Xenova/ms-marco-MiniLM-L-6-v2: [ 8.663132667541504, -11.245542526245117 ]
+        assertThat(scores.get(0)).isCloseTo(8.663132667541504, withPercentage(0.1));
+        assertThat(scores.get(1)).isCloseTo(-11.245542526245117, withPercentage(0.1));
 
         assertThat(response.tokenUsage().totalTokenCount()).isGreaterThan(0);
 
         assertThat(response.finishReason()).isNull();
-
     }
 }