tessl/maven-io-quarkiverse-langchain4j--quarkus-langchain4j-ollama

Quarkus extension for integrating local Ollama language models with LangChain4j

Overview

Eval results

Files

Embedding Models

Name: tessl/maven-io-quarkiverse-langchain4j--quarkus-langchain4j-ollama
Author: tessl

Embedding models generate vector representations of text for semantic search, similarity comparison, clustering, and Retrieval-Augmented Generation (RAG) applications.

Capabilities

CDI Injection (Recommended)

Inject embedding models as CDI beans for automatic configuration and lifecycle management.

import jakarta.inject.Inject;
import dev.langchain4j.model.embedding.EmbeddingModel;

// Default configuration
@Inject
EmbeddingModel embeddingModel;

// Named configuration
@Inject
@Named("custom-embeddings")
EmbeddingModel customEmbeddingModel;

Usage:

import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.model.output.Response;

// Embed single text
Response<Embedding> response = embeddingModel.embed("Some text to embed");
Embedding embedding = response.content();
float[] vector = embedding.vector();

// Embed multiple texts
Response<List<Embedding>> batchResponse = embeddingModel.embedAll(List.of(
    "First document",
    "Second document",
    "Third document"
));
List<Embedding> embeddings = batchResponse.content();

Programmatic Embedding Model

Build embedding model instances programmatically for fine-grained control.

class OllamaEmbeddingModel implements EmbeddingModel {
    static Builder builder();
    Response<Embedding> embed(String text);
    Response<Embedding> embed(TextSegment textSegment);
    Response<List<Embedding>> embedAll(List<TextSegment> textSegments);
}

class OllamaEmbeddingModel.Builder {
    Builder baseUrl(String val);
    Builder tlsConfigurationName(String tlsConfigurationName);
    Builder timeout(Duration val);
    Builder model(String val);
    Builder logRequests(boolean logRequests);
    Builder logResponses(boolean logResponses);
    Builder configName(String configName);
    OllamaEmbeddingModel build();
}

Parameters:

baseUrl - Ollama server URL (default: "http://localhost:11434")
tlsConfigurationName - Named TLS configuration for HTTPS
timeout - Request timeout (default: 10 seconds)
model - Embedding model name (e.g., "nomic-embed-text", "mxbai-embed-large")
logRequests - Log request payloads for debugging
logResponses - Log response payloads for debugging
configName - Named configuration reference

Usage:

OllamaEmbeddingModel model = OllamaEmbeddingModel.builder()
    .baseUrl("http://localhost:11434")
    .model("nomic-embed-text")
    .timeout(Duration.ofSeconds(15))
    .logRequests(true)
    .build();

Response<Embedding> response = model.embed("The quick brown fox");
float[] vector = response.content().vector();
System.out.println("Embedding dimensions: " + vector.length);

Embedding Text Segments

Use TextSegment for metadata-rich embeddings, particularly useful in RAG applications.

import dev.langchain4j.data.segment.TextSegment;

// TextSegment with metadata
TextSegment segment = TextSegment.from("Document content",
    Metadata.from(Map.of(
        "source", "document.pdf",
        "page", 5,
        "title", "Introduction"
    )));

Response<Embedding> response = embeddingModel.embed(segment);

Batch embedding:

List<TextSegment> segments = List.of(
    TextSegment.from("First paragraph", Metadata.from("source", "doc1.txt")),
    TextSegment.from("Second paragraph", Metadata.from("source", "doc2.txt")),
    TextSegment.from("Third paragraph", Metadata.from("source", "doc3.txt"))
);

Response<List<Embedding>> response = embeddingModel.embedAll(segments);
List<Embedding> embeddings = response.content();

Embedding Store Integration

Combine with embedding stores for persistent vector search.

import dev.langchain4j.store.embedding.EmbeddingStore;
import dev.langchain4j.store.embedding.inmemory.InMemoryEmbeddingStore;

@Inject
EmbeddingModel embeddingModel;

// Create embedding store
EmbeddingStore<TextSegment> embeddingStore = new InMemoryEmbeddingStore<>();

// Add documents
List<TextSegment> documents = List.of(
    TextSegment.from("Quarkus is a Kubernetes-native Java framework."),
    TextSegment.from("Ollama runs large language models locally."),
    TextSegment.from("LangChain4j is a Java library for LLM integration.")
);

Response<List<Embedding>> embeddings = embeddingModel.embedAll(documents);
for (int i = 0; i < documents.size(); i++) {
    embeddingStore.add(embeddings.content().get(i), documents.get(i));
}

// Search
Embedding queryEmbedding = embeddingModel.embed("What is Quarkus?").content();
List<EmbeddingMatch<TextSegment>> matches = embeddingStore.findRelevant(queryEmbedding, 3);

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

RAG (Retrieval-Augmented Generation)

Use embeddings for context retrieval in RAG applications.

// 1. Embed and store knowledge base
List<TextSegment> knowledgeBase = loadDocuments();
Response<List<Embedding>> embeddings = embeddingModel.embedAll(knowledgeBase);
embeddingStore.addAll(embeddings.content(), knowledgeBase);

// 2. Retrieve relevant context for query
String userQuestion = "How do I configure Quarkus?";
Embedding questionEmbedding = embeddingModel.embed(userQuestion).content();
List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(questionEmbedding, 5);

// 3. Build context from retrieved documents
String context = relevant.stream()
    .map(match -> match.embedded().text())
    .collect(Collectors.joining("\n\n"));

// 4. Generate answer with context
String prompt = String.format("""
    Context: %s

    Question: %s

    Answer based on the context above:
    """, context, userQuestion);

String answer = chatModel.chat(prompt);

Similarity Computation

Compute semantic similarity between texts.

import static dev.langchain4j.model.embedding.EmbeddingUtils.cosineSimilarity;

// Embed two texts
Embedding embedding1 = embeddingModel.embed("I love programming").content();
Embedding embedding2 = embeddingModel.embed("I enjoy coding").content();
Embedding embedding3 = embeddingModel.embed("I like pizza").content();

// Compute cosine similarity (range: -1 to 1, higher = more similar)
double similarity12 = cosineSimilarity(embedding1, embedding2); // ~0.85
double similarity13 = cosineSimilarity(embedding1, embedding3); // ~0.45

System.out.println("'programming' vs 'coding': " + similarity12);
System.out.println("'programming' vs 'pizza': " + similarity13);

Clustering and Classification

Use embeddings for document clustering and classification.

// Embed document collection
List<String> documents = List.of(
    "The stock market rose today",
    "Sports team won championship",
    "New AI model released",
    "Economy shows growth",
    "Player scored winning goal",
    "Machine learning breakthrough"
);

List<Embedding> embeddings = documents.stream()
    .map(doc -> embeddingModel.embed(doc).content())
    .collect(Collectors.toList());

// Cluster by similarity (simplified example)
// In practice, use k-means or other clustering algorithms
for (int i = 0; i < embeddings.size(); i++) {
    for (int j = i + 1; j < embeddings.size(); j++) {
        double similarity = cosineSimilarity(embeddings.get(i), embeddings.get(j));
        if (similarity > 0.7) {
            System.out.println("Similar: " + documents.get(i) + " <-> " + documents.get(j));
        }
    }
}

Embedding Request and Response

Low-level embedding request/response objects for direct API access.

class EmbeddingRequest {
    static Builder builder();
    String getModel();
    String getInput();
}

class EmbeddingRequest.Builder {
    Builder model(String val);
    Builder input(String val);
    EmbeddingRequest build();
}

class EmbeddingResponse {
    float[][] getEmbeddings();
    void setEmbeddings(float[][] embeddings);
    static Builder builder();
}

class EmbeddingResponse.Builder {
    Builder embeddings(float[][] val);
    EmbeddingResponse build();
}

Usage with OllamaClient:

EmbeddingRequest request = EmbeddingRequest.builder()
    .model("nomic-embed-text")
    .input("Text to embed")
    .build();

EmbeddingResponse response = ollamaClient.embedding(request);
float[][] embeddings = response.getEmbeddings();
float[] vector = embeddings[0]; // First (and only) embedding

Popular Embedding Models

Common Ollama embedding models:

Model	Dimensions	Description	Use Case
nomic-embed-text	768	General purpose, efficient	General text embedding, RAG
mxbai-embed-large	1024	High quality, larger	Semantic search, classification
all-minilm	384	Fast, compact	Real-time search, large datasets
snowflake-arctic-embed	1024	High performance	Enterprise applications

Install with Ollama:

ollama pull nomic-embed-text
ollama pull mxbai-embed-large

Configuration

See Configuration for complete embedding model configuration options including:

Model selection (model-id)
Server connection (base-url, timeout)
TLS configuration
Logging options
Named configurations

Example configuration:

# Default embedding model
quarkus.langchain4j.ollama.embedding-model.model-id=nomic-embed-text

# Named configuration for different model
quarkus.langchain4j.ollama.large-embeddings.embedding-model.model-id=mxbai-embed-large
quarkus.langchain4j.ollama.large-embeddings.timeout=20s

Performance Considerations

Batch embedding: Use embedAll() for multiple texts to reduce network overhead
Model size: Smaller dimension models (384) are faster but less accurate
Caching: Cache embeddings for static content to avoid recomputation
Timeout: Increase timeout for large batches or slow models
Parallel processing: Embedding operations are CPU-intensive on the Ollama server

Batch size optimization:

// Process large document collections in batches
List<TextSegment> allDocuments = loadLargeDataset(); // e.g., 10,000 documents
int batchSize = 100;

for (int i = 0; i < allDocuments.size(); i += batchSize) {
    List<TextSegment> batch = allDocuments.subList(
        i,
        Math.min(i + batchSize, allDocuments.size())
    );
    Response<List<Embedding>> embeddings = embeddingModel.embedAll(batch);
    // Store embeddings
}

Error Handling

Embedding operations may throw exceptions:

RuntimeException - Connection failures, timeouts, model errors
Verify Ollama server is running and model is downloaded

try {
    Response<Embedding> response = embeddingModel.embed("text");
    float[] vector = response.content().vector();
} catch (Exception e) {
    logger.error("Embedding failed", e);
    // Fallback: use cached embedding or retry
}

Install with Tessl CLI

npx tessl i tessl/maven-io-quarkiverse-langchain4j--quarkus-langchain4j-ollama@1.7.0

docs

tessl/maven-io-quarkiverse-langchain4j--quarkus-langchain4j-ollama