To integrate Sentence Transformer embeddings into an information retrieval system like Elasticsearch or OpenSearch, you need to store the embeddings as vectors in the index and use vector similarity search during queries. Here’s how to approach this:
1. Generating and Storing Embeddings
First, use a Sentence Transformer model (e.g., all-MiniLM-L6-v2) to convert text into dense vector embeddings. For each document in your dataset, generate an embedding by passing the text through the model, which outputs a fixed-size vector (e.g., 384 dimensions). In Elasticsearch/OpenSearch, create an index with a dense_vector field type to store these embeddings. For example, define a mapping like:
"mappings": {
"properties": {
"text_embedding": {
"type": "dense_vector",
"dims": 384,
"index": true,
"similarity": "cosine"
}
}
}
When indexing documents, include the precomputed embedding in the text_embedding field. For dynamic data, automate embedding generation using an ingest pipeline or external script before inserting documents.
2. Querying with Vector Similarity
During search, convert the user’s query text into an embedding using the same Sentence Transformer model. Use Elasticsearch/OpenSearch’s k-nearest neighbors (k-NN) search to find documents with embeddings closest to the query embedding. For example, a script_score query with cosine similarity:
"query": {
"script_score": {
"query": {"match_all": {}},
"script": {
"source": "cosineSimilarity(params.query_vector, 'text_embedding') + 1.0",
"params": {"query_vector": [0.12, -0.45, ..., 0.34]}
}
}
}
For large datasets, use approximate nearest neighbor (ANN) algorithms like HNSW, supported via the k-NN plugin, to improve speed. Configure parameters like ef_search to balance latency and accuracy.
3. Optimizations and Trade-offs
Precompute embeddings for static datasets to reduce latency. For real-time updates, use a hybrid approach: combine keyword search (BM25) with vector search for relevance. Monitor performance—high-dimensional vectors increase memory usage and query latency. Use hardware acceleration (GPUs for embedding generation, SSDs for vector storage) and limit returned results to reduce overhead. Test different similarity metrics (cosine, dot product) and model sizes (e.g., multi-qa-mpnet-base-dot-v1 for asymmetric retrieval) to align with your use case.