bge-base-en-v1.5

Converts English text sequences into 768-dimensional dense vector embeddings using a BERT-based architecture optimized for semantic similarity tasks. Implements the BGE (BAAI General Embedding) approach which fine-tunes masked language modeling with contrastive learning objectives to produce embeddings where semantically similar texts cluster in vector space. Runs inference via ONNX quantization for reduced model size (~90MB) and faster CPU/browser execution without sacrificing embedding quality.

Processes multiple text sequences in parallel, applying mean pooling over token-level representations to produce document-level embeddings. The architecture extracts the [CLS] token or applies mean pooling across all token embeddings depending on configuration, enabling efficient vectorization of document collections. Supports batching to amortize model loading overhead and leverage ONNX's batch inference optimizations.

Computes cosine similarity between pairs of embeddings to quantify semantic relatedness on a scale of -1 to 1. Given two 768-dimensional vectors, calculates the dot product normalized by L2 norms, enabling fast similarity comparisons without recomputing embeddings. This is the standard metric for evaluating retrieval quality in RAG and semantic search systems.

Provides a pre-trained checkpoint that can be further fine-tuned on domain-specific or task-specific corpora using standard transformer fine-tuning approaches (contrastive loss, triplet loss, or supervised learning). The base BGE model learns general semantic representations that transfer well to specialized domains like legal documents, medical texts, or code when adapted with domain data. Supports both supervised fine-tuning (with labeled pairs) and unsupervised contrastive learning on unlabeled corpora.

Executes the ONNX-quantized BGE model directly in the browser using transformers.js, which wraps ONNX.js for client-side inference. No server calls are required — embeddings are computed locally in JavaScript, enabling privacy-preserving semantic search and RAG without sending text to external APIs. The ONNX quantization reduces model size to ~90MB, making it practical for browser download and caching.

Embeddings generated by BGE are compatible with standard vector database APIs (Pinecone, Weaviate, Milvus, Qdrant, Chroma) via their 768-dimensional format and cosine similarity metric. The model outputs are directly indexable in these systems, enabling approximate nearest neighbor (ANN) search over millions of documents with sub-millisecond latency. Integration is straightforward: embed documents offline, upsert vectors to the database, then query with embedded user input.