A minimum viable semantic search implementation requires three core components: text embeddings, a vector database, and a similarity metric. First, convert text into numerical vectors (embeddings) using a pre-trained model like Sentence-BERT or a lightweight alternative. These embeddings capture semantic meaning, allowing comparisons between phrases. Next, store these vectors in a database optimized for fast similarity searches, such as FAISS or Annoy. Finally, calculate similarity between a query’s embedding and stored vectors using metrics like cosine similarity. This setup balances simplicity with effectiveness, avoiding complex infrastructure while delivering meaningful search results.
To implement this, start by generating embeddings for your documents. For example, using Python’s sentence-transformers library, you can embed text with two lines of code: load a pre-trained model (all-MiniLM-L6-v2 is a good default) and call model.encode(text). Store these vectors in a FAISS index, which can be built in-memory with faiss.IndexFlatIP for inner-product similarity (equivalent to cosine similarity if vectors are normalized). When a user submits a query, embed it using the same model, then search the FAISS index for the nearest neighbors. A basic version might return the top 5 matches, displaying titles or snippets. For small datasets (under 100k entries), this runs efficiently on a single machine without GPUs.
Key considerations include embedding quality, scalability, and preprocessing. Choose an embedding model that aligns with your domain—for general-purpose text, MiniLM or MPNet variants work well. If your data includes technical terms, consider fine-tuning the model or using a domain-specific alternative. For scalability, FAISS supports approximate nearest neighbor search (IndexIVFFlat) to handle millions of vectors, but this adds complexity. Preprocessing steps like lowercasing, removing stopwords, or truncating text to the model’s max token length (e.g., 512 tokens for BERT) can improve consistency. Avoid over-engineering: skip query expansion or reranking layers in an MVP. A simple Flask/FastAPI endpoint wrapping the embedding model and FAISS lookup is sufficient for testing viability before investing in distributed systems or cloud services.