Langchain4j has officially adopted bge-small-en-v1.5 as the default embedding model for Easy RAG applications. This blog post introduces the model, explains why it was chosen, how it’s integrated into Langchain4j, and why it is ideal for developers who want lightweight, high-performance RAG applications.
Retrieval-Augmented Generation (RAG) workflows rely heavily on powerful embedding models to transform text into vector representations. The better the embeddings, the more relevant the retrieved context and the better the final response quality from the LLM.
Langchain4j has made an exciting default choice for this purpose: bge-small-en-v1.5.
Why bge-small-en-v1.5?
· Top Performance: This model ranks impressively high on the MTEB (Massive Text Embedding Benchmark, https://huggingface.co/spaces/mteb/leaderboard), outperforming many larger alternatives while staying compact and efficient.
· Tiny Footprint: Its quantized version weighs just 24MB, making it ideal for edge deployments, local development, and low-latency use cases. Quantization is a technique used in machine learning to reduce the size of a model and make it faster, without significantly sacrificing performance.
How to Use It in Langchain4j
Langchain4j offers a plug-and-play experience. You can load the quantized version of bge-small-en-v1.5 with a single line of Java code.
EmbeddingModel embeddingModel = new BgeSmallEnV15QuantizedEmbeddingModel();
No need to spin up separate services or deal with heavy models. This model runs directly in-process using ONNX Runtime under the hood.
Where It Fits: Easy RAG with Langchain4j?
Langchain4j's Easy RAG setup aims to minimize configuration and maximize developer productivity. By making bge-small-en-v1.5 the default, Langchain4j ensures:
· No extra downloads or model selection hassle
· Instant availability for embedding documents and queries
· High-quality retrieval performance with minimal compute requirements
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