tools-only LangChain4j Vector Stores - API References
Complete API reference for configuring and using vector stores with LangChain4j.
Vector Store Comparison
| Store | Setup | Performance | Scaling | Features |
|---|---|---|---|---|
| In-Memory | Easy | Fast | Single machine | Testing |
| Pinecone | SaaS | Fast | Automatic | Namespace, Metadata |
| Weaviate | Self-hosted | Medium | Manual | Hybrid search |
| Qdrant | Self-hosted | Fast | Manual | Filtering, GRPC |
| Chroma | Self-hosted | Medium | Manual | Simple API |
| PostgreSQL | Existing DB | Medium | Manual | SQL, pgvector |
| MongoDB | SaaS/Self | Medium | Automatic | Document store |
| Neo4j | Self-hosted | Medium | Manual | Graph + Vector |
| Milvus | Self-hosted | Very Fast | Manual | Large scale |
EmbeddingStore Interface
Core Methods
public interface EmbeddingStore<Embedded> {
// Add single embedding
String add(Embedding embedding);
String add(String id, Embedding embedding);
String add(Embedding embedding, Embedded embedded);
// Add multiple embeddings
List<String> addAll(List<Embedding> embeddings);
List<String> addAll(List<Embedding> embeddings, List<Embedded> embeddeds);
List<String> addAll(List<String> ids, List<Embedding> embeddings, List<Embedded> embeddeds);
// Search
EmbeddingSearchResult<Embedded> search(EmbeddingSearchRequest request);
// Remove
void remove(String id);
void removeAll(Collection<String> ids);
void removeAll(Filter filter);
void removeAll();
}
EmbeddingSearchRequest
Building Search Requests
EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
.queryEmbedding(embedding) // Required
.maxResults(5) // Default: 3
.minScore(0.7) // Threshold: 0-1
.filter(new IsEqualTo("status", "active")) // Optional
.build();
EmbeddingSearchResult
EmbeddingSearchResult<TextSegment> result = store.search(request);
List<EmbeddingMatch<TextSegment>> matches = result.matches();
for(
EmbeddingMatch<TextSegment> match :matches){
double score = match.score(); // 0-1 similarity
TextSegment segment = match.embedded(); // Retrieved content
String id = match.embeddingId(); // Unique ID
}
Vector Store Configurations
InMemoryEmbeddingStore
EmbeddingStore<TextSegment> store = new InMemoryEmbeddingStore<>();
// Merge multiple stores
InMemoryEmbeddingStore<TextSegment> merged =
InMemoryEmbeddingStore.merge(store1, store2);
PineconeEmbeddingStore
PineconeEmbeddingStore store = PineconeEmbeddingStore.builder()
.apiKey(apiKey) // Required
.indexName("index-name") // Required
.namespace("namespace") // Optional: organize data
.environment("gcp-starter") // or "aws-us-east-1"
.build();
WeaviateEmbeddingStore
WeaviateEmbeddingStore store = WeaviateEmbeddingStore.builder()
.host("localhost") // Required
.port(8080) // Default: 8080
.scheme("http") // "http" or "https"
.collectionName("Documents") // Required
.apiKey("optional-key")
.useGrpc(false) // Use REST or gRPC
.build();
QdrantEmbeddingStore
QdrantEmbeddingStore store = QdrantEmbeddingStore.builder()
.host("localhost") // Required
.port(6333) // Default: 6333
.collectionName("documents") // Required
.https(false) // SSL/TLS
.apiKey("optional-key") // For authentication
.preferGrpc(true) // gRPC or REST
.timeout(Duration.ofSeconds(30)) // Connection timeout
.build();
ChromaEmbeddingStore
ChromaEmbeddingStore store = ChromaEmbeddingStore.builder()
.baseUrl("http://localhost:8000") // Required
.collectionName("my-collection") // Required
.apiKey("optional") // For authentication
.logRequests(true) // Debug logging
.logResponses(true)
.build();
PgVectorEmbeddingStore
PgVectorEmbeddingStore store = PgVectorEmbeddingStore.builder()
.host("localhost") // Required
.port(5432) // Default: 5432
.database("embeddings") // Required
.user("postgres") // Required
.password("password") // Required
.table("embeddings") // Custom table name
.createTableIfNotExists(true) // Auto-create table
.dropTableIfExists(false) // Safety flag
.build();
MongoDbEmbeddingStore
MongoDbEmbeddingStore store = MongoDbEmbeddingStore.builder()
.databaseName("search") // Required
.collectionName("documents") // Required
.createIndex(true) // Auto-create index
.indexName("vector_index") // Index name
.indexMapping(indexMapping) // Index configuration
.fromClient(mongoClient) // Required
.build();
// Configure index mapping
IndexMapping mapping = IndexMapping.builder()
.dimension(1536) // Vector dimension
.metadataFieldNames(Set.of("userId", "source"))
.build();
Neo4jEmbeddingStore
Neo4jEmbeddingStore store = Neo4jEmbeddingStore.builder()
.withBasicAuth(uri, user, password) // Required
.dimension(1536) // Vector dimension
.label("Document") // Node label
.embeddingProperty("embedding") // Property name
.textProperty("text") // Text content property
.metadataPrefix("metadata_") // Metadata prefix
.build();
MilvusEmbeddingStore
MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
.host("localhost") // Required
.port(19530) // Default: 19530
.collectionName("documents") // Required
.dimension(1536) // Vector dimension
.indexType(IndexType.HNSW) // HNSW, IVF_FLAT, IVF_SQ8
.metricType(MetricType.COSINE) // COSINE, L2, IP
.username("root") // Optional
.password("Milvus") // Optional
.build();
Metadata and Filtering
Filter Operations
// Equality
new IsEqualTo("status","active")
new
IsNotEqualTo("archived","true")
// Comparison
new
IsGreaterThan("score",0.8)
new
IsLessThanOrEqualTo("days",30)
new
IsGreaterThanOrEqualTo("priority",5)
new
IsLessThan("errorRate",0.01)
// Membership
new
IsIn("category",Arrays.asList("tech", "guide"))
new
IsNotIn("status",Arrays.asList("deleted"))
// String operations
new
ContainsString("content","Spring")
// Logical
new
And(filter1, filter2)
new
Or(filter1, filter2)
new
Not(filter1)
Dynamic Filtering
.dynamicFilter(query ->{
String userId = extractUserIdFromQuery(query);
return new
IsEqualTo("userId",userId);
})
Integration with EmbeddingStoreIngestor
Basic Ingestor
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.embeddingModel(embeddingModel) // Required
.embeddingStore(store) // Required
.build();
IngestionResult result = ingestor.ingest(document);
Advanced Ingestor
EmbeddingStoreIngestor ingestor = EmbeddingStoreIngestor.builder()
.documentTransformer(doc -> {
doc.metadata().put("ingested_date", LocalDate.now());
return doc;
})
.documentSplitter(DocumentSplitters.recursive(500, 50))
.textSegmentTransformer(segment -> {
String enhanced = "File: " + segment.metadata().getString("filename") +
"\n" + segment.text();
return TextSegment.from(enhanced, segment.metadata());
})
.embeddingModel(embeddingModel)
.embeddingStore(store)
.build();
ingestor.
ingest(documents);
ContentRetriever Integration
Basic Retriever
ContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.maxResults(3)
.minScore(0.7)
.build();
Advanced Retriever
ContentRetriever retriever = EmbeddingStoreContentRetriever.builder()
.embeddingStore(embeddingStore)
.embeddingModel(embeddingModel)
.dynamicMaxResults(query -> 10)
.dynamicMinScore(query -> 0.75)
.dynamicFilter(query ->
new IsEqualTo("userId", getCurrentUserId())
)
.build();
Multi-Tenant Support
Namespace-based Isolation (Pinecone)
// User 1
var store1 = PineconeEmbeddingStore.builder()
.apiKey(key)
.indexName("docs")
.namespace("user-1")
.build();
// User 2
var store2 = PineconeEmbeddingStore.builder()
.apiKey(key)
.indexName("docs")
.namespace("user-2")
.build();
Metadata-based Isolation
.dynamicFilter(query ->
new
IsEqualTo("userId",getContextUserId())
)
Performance Optimization
Connection Configuration
// With timeout and pooling
store =QdrantEmbeddingStore.
builder()
.
host("localhost")
.
port(6333)
.
timeout(Duration.ofSeconds(30))
.
maxConnections(10)
.
build();
Batch Operations
// Batch add
List<Embedding> embeddings = embeddingModel.embedAll(segments).content();
List<String> ids = store.addAll(embeddings, segments);
Caching Strategy
// Cache results locally
Map<String, List<Content>> cache = new HashMap<>();
Monitoring and Debugging
Enable Logging
ChromaEmbeddingStore store = ChromaEmbeddingStore.builder()
.baseUrl("http://localhost:8000")
.collectionName("docs")
.logRequests(true)
.logResponses(true)
.build();
Best Practices
- Choose Right Store: In-memory for dev, Pinecone/Qdrant for production
- Configure Dimension: Match embedding model dimension (usually 1536)
- Set Thresholds: Adjust minScore based on precision needs (0.7-0.85 typical)
- Use Metadata: Add rich metadata for filtering and traceability
- Index Strategically: Create indexes on frequently filtered fields
- Monitor Performance: Track query latency and relevance metrics
- Plan Scaling: Consider multi-tenancy and sharding strategies
- Backup Data: Implement backup and recovery procedures
- Version Management: Track embedding model versions
- Test Thoroughly: Validate retrieval quality with sample queries