Google Cloud Vertex AI Ranking API

• Google Cloud Vertex AI Ranking documentation
• Google Cloud Vertex AI Ranking API description

Introduction

The Google Cloud Vertex AI Ranking API is a powerful tool that enhances search results by refining the relevance of retrieved documents to a given query. Unlike traditional search methods, it leverages advanced machine learning algorithms to understand the semantic context of both the query and the documents, delivering more precise and relevant results. By analyzing the semantic relationship between the query and each document, the API can reorder the candidate documents based on their calculated relevance scores, ensuring that the most relevant results appear at the top of the search results page.

Maven Dependency

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-vertex-ai</artifactId>
    <version>0.36.0</version>
</dependency>

Usage

To configure the model, you'll have to specify:

• the Google Cloud project ID,
• the project number,
• the location (ex. us-central1, europe-west1),
• and the model you want to use.

Note: You can find the project number in the Google Cloud console, or by running gcloud projects describe your-project-id.

You can score a single string or TextSegment against a query thanks to the score(text, query) and score(segment, query) methods.

It is also possible to score several strings or TextSegments against the query, with the scoreAll(segments, query) method:

VertexAiScoringModel scoringModel = VertexAiScoringModel.builder()
    .projectId(System.getenv("GCP_PROJECT_ID"))
    .projectNumber(System.getenv("GCP_PROJECT_NUM"))
    .projectLocation(System.getenv("GCP_LOCATION"))
    .model("semantic-ranker-512")
    .build();

Response<List<Double>> score = scoringModel.scoreAll(Stream.of(
        "The sky appears blue due to a phenomenon called Rayleigh scattering. " +
            "Sunlight is comprised of all the colors of the rainbow. Blue light has shorter " +
            "wavelengths than other colors, and is thus scattered more easily.",

        "A canvas stretched across the day,\n" +
            "Where sunlight learns to dance and play.\n" +
            "Blue, a hue of scattered light,\n" +
            "A gentle whisper, soft and bright."
        ).map(TextSegment::from).collect(Collectors.toList()),
    "Why is the sky blue?");

// [0.8199999928474426, 0.4300000071525574]

If you pass TextSegments which have a particular title key, the Ranker model can take this metadata into account in its calculation. To specify a custom title key, you can use the titleMetadataKey() builder method.`

You can use scoring models with AiServices and its contentAgregator() method, which takes a ContentAggregator class that can specify a scoring model:

VertexAiScoringModel scoringModel = VertexAiScoringModel.builder()
    .projectId(System.getenv("GCP_PROJECT_ID"))
    .projectNumber(System.getenv("GCP_PROJECT_NUM"))
    .projectLocation(System.getenv("GCP_LOCATION"))
    .model("semantic-ranker-512")
    .build();

ContentAggregator contentAggregator = ReRankingContentAggregator.builder()
    .scoringModel(scoringModel)
    ... 
    .build();

RetrievalAugmentor retrievalAugmentor = DefaultRetrievalAugmentor.builder()
    ...
    .contentAggregator(contentAggregator)
    .build();

return AiServices.builder(Assistant.class)
    .chatLanguageModel(...)
    .retrievalAugmentor(retrievalAugmentor)
    .build();