Understanding EmbeddingSearchResult and EmbeddingMatch in LangChain4j

The EmbeddingSearchResult class represents the result of a semantic search in an EmbeddingStore. It holds a list of the most relevant matches to your query, each represented by an EmbeddingMatch.

Example

EmbeddingSearchResult<TextSegment> result = embeddingStore.search(request);

What is EmbeddingMatch?

Each result inside EmbeddingSearchResult is an instance of EmbeddingMatch.

public class EmbeddingMatch<Embedded> {
    private final Double score;
    private final String embeddingId;
    private final Embedding embedding;
    private final Embedded embedded;
}

Here:

·      score: A relevance score (derived from cosine similarity). Higher means more relevant.

·      embeddingId: The unique identifier of the embedding in the store.

·      embedding: The vector representation that matched the query.

·      embedded: The original content that was embedded (like a paragraph, chunk, or TextSegment).

 

Example

EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
    .embedding(myQueryEmbedding)
    .maxResults(2)
    .minScore(0.5)
    .build();

EmbeddingSearchResult<TextSegment> result = embeddingStore.search(request);

for (EmbeddingMatch<TextSegment> match : result.matches()) {
    System.out.println("Score: " + match.score());
    System.out.println("Original content: " + match.embedded().text());
}

Find the below working Application.

 

EmbeddingSearchWithFilters.java

package com.sample.app.embeddings;

import java.util.HashMap;
import java.util.List;
import java.util.Map;

import dev.langchain4j.data.document.Metadata;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.model.embedding.EmbeddingModel;
import dev.langchain4j.model.embedding.onnx.bgesmallenv15q.BgeSmallEnV15QuantizedEmbeddingModel;
import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
import dev.langchain4j.store.embedding.EmbeddingSearchResult;
import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.filter.Filter;
import dev.langchain4j.store.embedding.filter.comparison.IsEqualTo;
import dev.langchain4j.store.embedding.filter.comparison.IsGreaterThanOrEqualTo;
import dev.langchain4j.store.embedding.filter.comparison.IsIn;
import dev.langchain4j.store.embedding.filter.logical.And;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;

public class EmbeddingSearchWithFilters {

    public static void main(String[] args) {

        // Step 1: Initialize embedding model
        EmbeddingModel model = new BgeSmallEnV15QuantizedEmbeddingModel();

        // Step 2: Prepare text data with metadata
        String sentence1 = "The stock market surged as tech companies reported strong earnings.";
        Map<String, Object> metaMap1 = new HashMap<>();
        metaMap1.put("category", "finance");
        metaMap1.put("source", "news");
        metaMap1.put("relevance", 9);
        Metadata meta1 = Metadata.from(metaMap1);

        String sentence2 = "Tech giants like Apple and Amazon saw record profits this quarter.";
        Map<String, Object> metaMap2 = new HashMap<>();
        metaMap2.put("category", "technology");
        metaMap2.put("source", "news");
        metaMap2.put("relevance", 10);
        Metadata meta2 = Metadata.from(metaMap2);

        String sentence3 = "Heavy rain caused flooding in coastal towns yesterday.";
        Map<String, Object> metaMap3 = new HashMap<>();
        metaMap3.put("category", "weather");
        metaMap3.put("source", "alert");
        metaMap3.put("relevance", 5);
        Metadata meta3 = Metadata.from(metaMap3);

        try {
            // Step 3: Generate embeddings
            Embedding embedding1 = model.embed(sentence1).content();
            Embedding embedding2 = model.embed(sentence2).content();
            Embedding embedding3 = model.embed(sentence3).content();

            // Step 4: Initialize in-memory embedding store
            EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();

            // Step 5: Add embeddings to the store with metadata
            embeddingStore.add(embedding1, TextSegment.from(sentence1, meta1));
            embeddingStore.add(embedding2, TextSegment.from(sentence2, meta2));
            embeddingStore.add(embedding3, TextSegment.from(sentence3, meta3));

            System.out.println("Embeddings with metadata added to the store successfully.");

            // Step 6: Prepare a query embedding
            Embedding queryEmbedding = model.embed("Which tech companies had profits?").content();

            Filter filter = new And(
                    new And(new IsEqualTo("source", "news"), new IsIn("category", List.of("technology", "finance"))),
                    new IsGreaterThanOrEqualTo("relevance", Integer.valueOf(9)));

            // Step 8: Build search request with filter
            EmbeddingSearchRequest request = EmbeddingSearchRequest.builder().queryEmbedding(queryEmbedding)
                    .maxResults(2).minScore(0.5).filter(filter).build();

            // Step 9: Perform search
            EmbeddingSearchResult<TextSegment> result = embeddingStore.search(request);

            // Step 10: Display results
            System.out.println("\nFiltered & Ranked Results:");
            result.matches().forEach(match -> {
                TextSegment segment = match.embedded();
                System.out.printf("Score: %.3f | Text: %s | Metadata: %s%n", match.score(), segment.text(),
                        segment.metadata());
            });

        } catch (Exception e) {
            System.err.println("An error occurred: " + e.getMessage());
            e.printStackTrace();
        }
    }
}

 

Output

Embeddings with metadata added to the store successfully.

Filtered & Ranked Results:
Score: 0.883 | Text: Tech giants like Apple and Amazon saw record profits this quarter. | Metadata: Metadata { metadata = {category=technology, relevance=10, source=news} }
Score: 0.882 | Text: The stock market surged as tech companies reported strong earnings. | Metadata: Metadata { metadata = {category=finance, relevance=9, source=news} }

 

  

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