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Get Started Free →giuseppe-trisciuoglio/langchain4j-vector-stores-configuration
Provides configuration patterns for LangChain4J vector stores in RAG applications.
Passed 20 of 22 test cases with the skill, and 13 of 22 without it.
and 6 more in the full report
8 fixed12 passed either way1 still failing1 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 8 test cases the skill fixed. Same model, same prompt, with and without the skill.
We are building an evaluation pipeline where 100% exact recall accuracy is required, even if search queries execute slower. Which vector index type should be selected?
| Checked | Without skill | With skill |
|---|---|---|
| The index type is configured as IndexType.FLAT for exact nearest neighbor calculation. | Missed | Passed |
The instructions your model reads when the skill loads.
Configure vector stores for Retrieval-Augmented Generation applications with LangChain4J.
LangChain4J provides a unified abstraction for vector stores (PostgreSQL/pgvector, Pinecone, MongoDB Atlas, Milvus, Neo4j) with builder-based configuration, metadata filtering, and hybrid search support.
Configure an embedding store for vector operations:
@Bean
public EmbeddingStore<TextSegment> embeddingStore() {
return PgVectorEmbeddingStore.builder()
.host("localhost")
.port(5432)
.database("vectordb")
.user("username")
.password("password")
.table("embeddings")
.dimension(1536) // OpenAI embedding dimension
.createTable(true)
.useIndex(true)
.build();
}
Follow this workflow to ensure correct vector store setup:
test cases passed, vs 13 / 22 without
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