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tessl/maven-dev-langchain4j--langchain4j-milvus

Milvus embedding store integration for LangChain4j

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LangChain4j Milvus Embedding Store

Integration between LangChain4j and Milvus vector database for storing and searching embedding vectors with text content and metadata.

Installation

Maven:

<dependency>
    <groupId>dev.langchain4j</groupId>
    <artifactId>langchain4j-milvus</artifactId>
    <version>1.11.0-beta19</version>
</dependency>

Gradle:

implementation 'dev.langchain4j:langchain4j-milvus:1.11.0-beta19'

Requirements: Milvus 2.4.20 or newer

Quick Start

Local Milvus:

import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;

MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
    .host("localhost")
    .port(19530)
    .collectionName("my_embeddings")
    .dimension(384)
    .build();

// Add embedding
String id = store.add(embedding, textSegment);

// Search
EmbeddingSearchResult<TextSegment> results = store.search(searchRequest);

Zilliz Cloud:

MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
    .uri("https://xxx.api.gcp-us-west1.zillizcloud.com")
    .token("your-api-key")
    .collectionName("my_embeddings")
    .dimension(384)
    .build();

Core Imports

import dev.langchain4j.store.embedding.milvus.MilvusEmbeddingStore;
import dev.langchain4j.data.embedding.Embedding;
import dev.langchain4j.data.segment.TextSegment;
import dev.langchain4j.store.embedding.EmbeddingSearchRequest;
import dev.langchain4j.store.embedding.EmbeddingSearchResult;
import dev.langchain4j.store.embedding.EmbeddingMatch;
import io.milvus.param.IndexType;
import io.milvus.param.MetricType;
import io.milvus.common.clientenum.ConsistencyLevelEnum;

API Overview

MilvusEmbeddingStore

class MilvusEmbeddingStore implements EmbeddingStore<TextSegment> {
    static Builder builder();

    // Adding
    String add(Embedding embedding);
    void add(String id, Embedding embedding);
    String add(Embedding embedding, TextSegment textSegment);
    List<String> addAll(List<Embedding> embeddings);
    void addAll(List<String> ids, List<Embedding> embeddings, List<TextSegment> textSegments);

    // Searching
    EmbeddingSearchResult<TextSegment> search(EmbeddingSearchRequest request);

    // Removing
    void removeAll(Collection<String> ids);
    void removeAll(Filter filter);
    void removeAll();

    // Management
    void dropCollection(String collectionName);
}

Builder

class MilvusEmbeddingStore.Builder {
    // Connection - Local Milvus
    Builder host(String host);
    Builder port(Integer port);

    // Connection - Zilliz Cloud
    Builder uri(String uri);
    Builder token(String token);

    // Authentication
    Builder username(String username);
    Builder password(String password);

    // Database
    Builder databaseName(String databaseName);
    Builder milvusClient(MilvusServiceClient milvusClient);

    // Collection Configuration
    Builder collectionName(String collectionName);
    Builder dimension(Integer dimension);
    Builder indexType(IndexType indexType);
    Builder metricType(MetricType metricType);
    Builder consistencyLevel(ConsistencyLevelEnum consistencyLevel);
    Builder extraParameters(Map<String, Object> extraParameters);

    // Field Names
    Builder idFieldName(String idFieldName);
    Builder textFieldName(String textFieldName);
    Builder metadataFieldName(String metadataFieldName);
    Builder vectorFieldName(String vectorFieldName);

    // Behavior
    Builder retrieveEmbeddingsOnSearch(Boolean retrieveEmbeddingsOnSearch);
    Builder autoFlushOnInsert(Boolean autoFlushOnInsert);

    MilvusEmbeddingStore build();
}

Operations

Adding Embeddings

// Single with auto-generated ID
String id = store.add(embedding);

// Single with custom ID
store.add("custom-id", embedding);

// With text and metadata
TextSegment segment = TextSegment.from("text", metadata);
String id = store.add(embedding, segment);

// Batch with auto-generated IDs
List<String> ids = store.addAll(embeddings);

// Batch with custom IDs and segments
store.addAll(ids, embeddings, segments);

Details: operations/adding.md

Searching

import static dev.langchain4j.store.embedding.filter.MetadataFilterBuilder.metadataKey;

// Basic search
EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
    .queryEmbedding(queryEmbedding)
    .maxResults(10)
    .minScore(0.7)
    .build();

EmbeddingSearchResult<TextSegment> results = store.search(request);

// With metadata filter
Filter filter = metadataKey("category").isEqualTo("docs");
EmbeddingSearchRequest request = EmbeddingSearchRequest.builder()
    .queryEmbedding(queryEmbedding)
    .maxResults(10)
    .filter(filter)
    .build();

// Process results
for (EmbeddingMatch<TextSegment> match : results.matches()) {
    double score = match.score();
    String id = match.embeddingId();
    TextSegment segment = match.embedded();
}

Details: operations/searching.md

Removing

// By IDs
store.removeAll(Arrays.asList("id1", "id2", "id3"));

// By filter
Filter filter = metadataKey("status").isEqualTo("archived");
store.removeAll(filter);

// All
store.removeAll();

Details: operations/removing.md

Collection Management

// Drop collection (permanent deletion)
store.dropCollection("collection_name");

Details: operations/collection-management.md

Configuration

Connection Types

Local Milvus:

MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
    .host("localhost")          // default: "localhost"
    .port(19530)                // default: 19530
    .collectionName("my_coll")
    .dimension(384)
    .build();

Zilliz Cloud:

MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
    .uri("https://xxx.api.gcp-us-west1.zillizcloud.com")
    .token("your-api-key")
    .collectionName("my_coll")
    .dimension(384)
    .build();

With Authentication:

MilvusEmbeddingStore store = MilvusEmbeddingStore.builder()
    .host("localhost")
    .port(19530)
    .username("admin")
    .password("password")
    .collectionName("my_coll")
    .dimension(384)
    .build();

Index Types

import io.milvus.param.IndexType;

.indexType(IndexType.FLAT)      // Exact search (default)
.indexType(IndexType.IVF_FLAT)  // Balanced performance
.indexType(IndexType.IVF_PQ)    // Memory efficient
.indexType(IndexType.HNSW)      // High performance
.indexType(IndexType.DISKANN)   // Very large datasets

Metric Types

import io.milvus.param.MetricType;

.metricType(MetricType.COSINE)  // Cosine similarity (default)
.metricType(MetricType.L2)      // Euclidean distance
.metricType(MetricType.IP)      // Inner product

Consistency Levels

import io.milvus.common.clientenum.ConsistencyLevelEnum;

.consistencyLevel(ConsistencyLevelEnum.EVENTUALLY) // default, best performance
.consistencyLevel(ConsistencyLevelEnum.BOUNDED)    // bounded staleness
.consistencyLevel(ConsistencyLevelEnum.SESSION)    // per-session consistency
.consistencyLevel(ConsistencyLevelEnum.STRONG)     // immediate consistency

Full Configuration Reference: configuration.md

Key Concepts

Automatic Collection Creation

Collections are created automatically if they don't exist when building the store. Requires dimension parameter.

Dimension Matching

All embeddings must match the collection's configured dimension. Set dimension based on your embedding model.

ID Management

  • Auto-generated: Use add(embedding) → returns UUID
  • Custom: Use add(id, embedding) → specify your own ID

Metadata Filtering

Store metadata with TextSegment for filtering during search:

Metadata metadata = Metadata.from(Map.of(
    "category", "technical",
    "year", 2024
));
TextSegment segment = TextSegment.from("text", metadata);
store.add(embedding, segment);

// Later, filter in search
Filter filter = metadataKey("category").isEqualTo("technical");

Consistency and Visibility

  • EVENTUALLY: New data may not be immediately searchable (fastest)
  • STRONG: Immediate visibility, higher latency
  • Default is EVENTUALLY for performance

Performance Options

.autoFlushOnInsert(false)           // default, better batch performance
.retrieveEmbeddingsOnSearch(false)  // default, faster search

Common Patterns

See: patterns.md for:

  • Batch processing
  • Document ingestion
  • Duplicate detection
  • Incremental updates
  • Error handling

Advanced Topics

See: advanced.md for:

  • Index tuning with extraParameters
  • Consistency level trade-offs
  • Performance optimization
  • Connection pooling
  • Custom field names

Troubleshooting

See: troubleshooting.md for:

  • Deletion visibility issues
  • Dimension mismatch errors
  • Connection problems
  • Performance issues

Related Documentation

Operations Detail

Install with Tessl CLI

npx tessl i tessl/maven-dev-langchain4j--langchain4j-milvus@1.11.0
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