To use Sentence Transformers for semantic search, you can follow a three-step process: embedding generation, indexing, and similarity matching. First, you’ll encode documents into dense vector representations (embeddings) using a pre-trained model. These embeddings capture semantic meaning, allowing you to compare text similarity mathematically. Next, you’ll store these embeddings efficiently for fast retrieval. Finally, you’ll encode user queries and find the closest matches in your indexed data using similarity metrics like cosine distance. This approach scales well for applications like document search, recommendation systems, or FAQ retrieval.
Step 1: Embedding Generation and Indexing
Start by selecting a pre-trained Sentence Transformer model (e.g., all-MiniLM-L6-v2 for a balance of speed and accuracy). Use the model to convert your documents into embeddings. For example, if you have a list of support articles, process each article’s text through the model to generate a 384-dimensional vector (varies by model). Store these embeddings in a vector database like FAISS, Annoy, or Qdrant for efficient nearest-neighbor search. For smaller datasets, you can use numpy arrays and calculate distances directly. Ensure text preprocessing (lowercasing, removing special characters) is consistent between documents and queries to avoid mismatches.
Step 2: Query Processing and Search
When a user submits a query (e.g., “How to reset my password?”), encode it using the same model to get a query embedding. Use the vector database to find the closest document embeddings via similarity metrics. For example, with FAISS, you’d call index.search(query_embedding, k=5) to retrieve the top 5 matches. Cosine similarity is commonly used, but you might customize this based on your use case. To improve results, consider filtering by metadata (e.g., date or category) before ranking. For real-time applications, ensure the search completes within acceptable latency (e.g., <100ms) by testing different indexing configurations.
Step 3: Optimization and Scaling
Performance depends on model choice, indexing strategy, and hardware. Smaller models like all-MiniLM-L6-v2 work for latency-sensitive applications, while larger models (e.g., all-mpnet-base-v2) may improve accuracy. Use GPU acceleration during batch embedding generation. For large datasets (>1M documents), distributed vector databases like Elasticsearch with a plugin for dense vectors can help. Regularly update embeddings if your document corpus changes. Evaluate results using metrics like recall@k (e.g., how often the correct document appears in the top 3 results) and adjust parameters like embedding dimensions or search thresholds accordingly.