To use a Sentence Transformer for semantic search, you need to convert text into embeddings (dense vector representations) and compare their similarity. Here’s a step-by-step breakdown:
1. Model Selection and Document Embedding
First, choose a pre-trained Sentence Transformer model (e.g., all-MiniLM-L6-v2 for general use or multi-qa-mpnet for question-answering). These models map sentences or paragraphs to fixed-size vectors. For indexing, process your documents by splitting them into chunks (if they’re long) and generate embeddings for each chunk using the model. For example, a 10,000-document corpus would produce a 10,000x384 matrix if using all-MiniLM-L6-v2 (which outputs 384-dimensional vectors). Tools like sentence-transformers simplify this process with methods like model.encode(texts).
2. Efficient Indexing with Vector Databases
Storing raw embeddings in a traditional database is inefficient for similarity searches. Instead, use a vector database like FAISS, Annoy, or HNSWLib to index embeddings. These libraries organize vectors for fast approximate nearest neighbor (ANN) searches. For instance, FAISS allows you to create an index with faiss.IndexFlatIP (inner product for cosine similarity) and add embeddings via index.add(embeddings). This step ensures queries return results in milliseconds, even for millions of documents.
3. Query Execution and Results
For search, embed the user’s query using the same model, then use the index to find the closest document embeddings. Cosine similarity is typically used to rank results. For example, a query like “climate change effects” would be embedded into a vector, and FAISS would return documents with the highest similarity scores. You can further filter results (e.g., by metadata) or rerank them with cross-encoders for improved precision. Tools like sentence-transformers provide built-in utilities for this workflow.
Practical Considerations
- Scalability: Batch-process documents to avoid memory issues.
- Preprocessing: Clean text (remove HTML, normalize whitespace) and truncate to the model’s maximum token limit (e.g., 512 tokens for most models).
- Evaluation: Measure recall@k (e.g., how often the true match is in the top 10 results) to validate performance.
This approach balances speed and accuracy, leveraging modern NLP models and vector search techniques to enable semantic search in applications.