微信扫码
添加专属顾问
我要投稿
探索LangChain4j和Chroma向量数据库的集成示例,实现文本嵌入与存储。 核心内容: 1. 安装Chroma并启动Docker容器 2. 在pom.xml中添加LangChain4j-Chroma依赖 3. 示例代码演示文本嵌入与向量数据库存储过程
docker run -d \
--name chromadb \
-p 8000:8000 \
-v "$(pwd)/chroma_data:/chroma/chroma" \
-e IS_PERSISTENT=TRUE \
-e ANONYMIZED_TELEMETRY=TRUE \
docker.1ms.run/chromadb/chroma:latest
<dependency>
<groupId>dev.langchain4j</groupId>
<artifactId>langchain4j-chroma</artifactId>
<version>1.0.0-beta1</version>
</dependency>
public class JlamaChromaExample {
public static void main(String[] args) {
String chromaEndpoint = "http://localhost:8000";
EmbeddingStore<TextSegment> embeddingStore = ChromaEmbeddingStore
.builder()
.baseUrl(chromaEndpoint)
.collectionName("test1_collection")
.logRequests(true)
.logResponses(true)
.build();
EmbeddingModel embeddingModel = JlamaEmbeddingModel.builder()
.modelName("intfloat/e5-small-v2")
.build();
TextSegment segment1 = TextSegment.from("I like football.");
Embedding embedding1 = embeddingModel.embed(segment1).content();
embeddingStore.add(embedding1, segment1);
TextSegment segment2 = TextSegment.from("The weather is good today.");
Embedding embedding2 = embeddingModel.embed(segment2).content();
embeddingStore.add(embedding2, segment2);
Embedding queryEmbedding = embeddingModel.embed("What is your favourite sport?").content();
List<EmbeddingMatch<TextSegment>> relevant = embeddingStore.findRelevant(queryEmbedding, 1);
EmbeddingMatch<TextSegment> embeddingMatch = relevant.get(0);
System.out.println(embeddingMatch.score()); // 0.8144288493114709
System.out.println(embeddingMatch.embedded().text()); // I like football.
}
}
WARNING: Using incubator modules: jdk.incubator.vector
INFO c.g.tjake.jlama.model.AbstractModel - Model type = F32, Working memory type = F32, Quantized memory type = F32
WARN c.g.t.j.t.o.TensorOperationsProvider - Native operations not available. Consider adding 'com.github.tjake:jlama-native' to the classpath
INFO c.g.t.j.t.o.TensorOperationsProvider - Using Panama Vector Operations (OffHeap)
0.8279024262570531
I like football.
53AI,企业落地大模型首选服务商
产品:场景落地咨询+大模型应用平台+行业解决方案
承诺:免费场景POC验证,效果验证后签署服务协议。零风险落地应用大模型,已交付160+中大型企业
2025-07-05
一文看懂 LangChain:为什么火?核心模块都干啥?
2025-07-04
上下文工程:LangGraph四大高效调度策略,Agent告别“记忆过载”!
2025-07-04
LangChain教程——提示词模板
2025-07-03
继提示词工程、RAG技术浪潮后,LangChain领域中上下文工程正成为新的热门方向!
2025-07-01
LangChain教程——LangChain基本使用
2025-06-26
LangChain的OpenAI和ChatOpenAI,到底应该调用哪个?
2025-06-26
开发AI Agent到底用什么框架——LangGraph VS. LlamaIndex
2025-06-24
大模型开发基础:LangChain基础样例
2025-05-06
2025-05-08
2025-06-05
2025-04-18
2025-05-28
2025-04-13
2025-05-19
2025-04-18
2025-05-28
2025-04-25
2025-07-05
2025-06-26
2025-06-13
2025-05-21
2025-05-19
2025-05-08
2025-05-06
2025-04-22