Elasticsearch를 임베딩 저장소로 활용한 LangChain4j

EmbeddingModel model = OllamaEmbeddingModel.builder()
  .baseUrl(ollama.getEndpoint())
  .modelName(MODEL_NAME)
  .build();

Logger.info("Embedding model has {} dimensions.", model.dimension());
// This gives: Embedding model has 4096 dimensions.

Response<Embedding> response = model.embed("A text here");

TextSegment game1 = TextSegment.from("""
    The game starts off with the main character Guybrush Threepwood stating "I want to be a pirate!"
    To do so, he must prove himself to three old pirate captains. During the perilous pirate trials, 
    he meets the beautiful governor Elaine Marley, with whom he falls in love, unaware that the ghost pirate 
    LeChuck also has his eyes on her. When Elaine is kidnapped, Guybrush procures crew and ship to track 
    LeChuck down, defeat him and rescue his love.
""", Metadata.from("gameName", "The Secret of Monkey Island"));
Response<Embedding> response1 = model.embed(game1);
TextSegment game2 = TextSegment.from("""
    Out Run is a pseudo-3D driving video game in which the player controls a Ferrari Testarossa 
    convertible from a third-person rear perspective. The camera is placed near the ground, simulating 
    a Ferrari driver's position and limiting the player's view into the distance. The road curves, 
    crests, and dips, which increases the challenge by obscuring upcoming obstacles such as traffic 
    that the player must avoid. The object of the game is to reach the finish line against a timer.
    The game world is divided into multiple stages that each end in a checkpoint, and reaching the end 
    of a stage provides more time. Near the end of each stage, the track forks to give the player a 
    choice of routes leading to five final destinations. The destinations represent different 
    difficulty levels and each conclude with their own ending scene, among them the Ferrari breaking 
    down or being presented a trophy.
""", Metadata.from("gameName", "Out Run"));
Response<Embedding> response2 = model.embed(game2);

EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();
embeddingStore.add(response1.content(), game1);
embeddingStore.add(response2.content(), game2);

<dependency>
  <groupId>dev.langchain4j</groupId>
  <artifactId>langchain4j-elasticsearch</artifactId>
  <version>${langchain4j.version}</version>
</dependency>

<dependency>
  <groupId>org.testcontainers</groupId>
  <artifactId>elasticsearch</artifactId>
  <version>1.20.1</version>
  <scope>test</scope>
</dependency>

// Create the elasticsearch container
ElasticsearchContainer container =
  new ElasticsearchContainer("docker.elastic.co/elasticsearch/elasticsearch:8.15.0")
    .withPassword("changeme");

// Start the container. This step might take some time...
container.start();

// As we don't want to make our TestContainers code more complex than
// needed, we will use login / password for authentication.
// But note that you can also use API keys which is preferred.
final CredentialsProvider credentialsProvider = new BasicCredentialsProvider();
credentialsProvider.setCredentials(AuthScope.ANY, new UsernamePasswordCredentials("elastic", "changeme"));

// Create a low level Rest client which connects to the elasticsearch container.
client = RestClient.builder(HttpHost.create("https://" + container.getHttpHostAddress()))
  .setHttpClientConfigCallback(httpClientBuilder -> {
    httpClientBuilder.setDefaultCredentialsProvider(credentialsProvider);
    httpClientBuilder.setSSLContext(container.createSslContextFromCa());
    return httpClientBuilder;
  })
  .build();

// Check the cluster is running
client.performRequest(new Request("GET", "/"));

EmbeddingStore<TextSegment> embeddingStore =
  ElasticsearchEmbeddingStore.builder()
    .restClient(client)
    .build();
embeddingStore.add(response1.content(), game1);
embeddingStore.add(response2.content(), game2);

EmbeddingStore<TextSegment> embeddingStore =
  ElasticsearchEmbeddingStore.builder()
    .indexName("games")
    .restClient(client)
    .build();
embeddingStore.add(response1.content(), game1);
embeddingStore.add(response2.content(), game2);

String question = "I want to pilot a car";
Embedding questionAsVector = model.embed(question).content();

EmbeddingSearchResult<TextSegment> result = embeddingStore.search(
  EmbeddingSearchRequest.builder()
    .queryEmbedding(questionAsVector)
    .build());

result.matches().forEach(m -> Logger.info("{} - score [{}]",
  m.embedded().metadata().getString("gameName"), m.score()));

Out Run - score [0.86672974]
The Secret of Monkey Island - score [0.85569763]

POST games/_search
{
  "query" : {
    "knn": {
      "field": "vector",
      "query_vector": [-0.019137882, /* ... */, -0.0148779955]
    }
  }
}

EmbeddingStore<TextSegment> embeddingStore =
  ElasticsearchEmbeddingStore.builder()
    .configuration(ElasticsearchConfigurationScript.builder().build())
    .indexName("games")
    .restClient(client)
    .build();

EmbeddingSearchResult<TextSegment> result = embeddingStore.search(
  EmbeddingSearchRequest.builder()
    .queryEmbedding(questionAsVector)
    .build());

POST games/_search
{
  "query": {
    "script_score": {
      "script": {
        "source": "(cosineSimilarity(params.query_vector, 'vector') + 1.0) / 2",
        "params": {
          "queryVector": [-0.019137882, /* ... */, -0.0148779955]
        }
      }
    }
  }
}

Out Run - score [0.871952]
The Secret of Monkey Island - score [0.86380446]