Understanding and Using Embeddings for Semantic Search

Learn how to choose, prepare, index, and evaluate embeddings to build fast, accurate semantic search—practical steps, pitfalls, and an implementation checklist.

Embeddings convert text into dense vectors that capture semantic meaning. They power semantic search, clustering, recommendations, and many NLP tasks. This guide walks through model selection, data preparation, indexing, search strategies, evaluation, and common mistakes to avoid.

• What embeddings are and why they matter for search and recommendations.
• How to choose models, prepare text, and index vectors for fast retrieval.
• Strategies to evaluate quality and practical implementation checklist to ship production systems.

Understand embeddings

Embeddings map text (words, sentences, documents) to fixed-size numeric vectors in a continuous space. Nearby vectors represent semantically similar items, enabling cosine or dot-product similarity searches.

Typical use cases include semantic search (query → nearest documents), clustering (group similar content), reranking (coarse candidate selection then refine), and recommendation systems.

Quick answer

Use sentence- or document-level embeddings from a model aligned to your domain, preprocess text consistently, index vectors with an approximate nearest neighbor (ANN) engine, tune distance metrics and thresholds, and evaluate with task-specific precision/recall or retrieval metrics.

Choose embedding models

Model selection depends on data domain, latency, cost, and desired dimensionality. Evaluate pre-trained general models first; fine-tune or adopt domain-adapted models if semantics differ substantially (e.g., legal, medical).

• General-purpose models: good for broad domains and quick starts.
• Domain-specific models: better captures terminology and nuance.
• Size vs cost: larger models yield richer vectors but increase inference cost and latency.
• Dimensionality: common sizes range 384–1536; higher dims can improve fidelity but increase index size and compute.

Example selection criteria: if you need sub-100ms latency and moderate cost, choose a medium-sized, CPU-efficient model. For best semantic accuracy in specialized text, consider a fine-tuned model or embeddings derived from an in-domain encoder.

Prepare text for embedding

Consistent preprocessing is crucial. Embedding models are sensitive to tokenization and context length, so normalize inputs before vectorizing.

• Normalize whitespace and Unicode, remove broken HTML, and optionally strip stopwords if domain-specific evaluation shows benefit.
• Preserve meaningful structure: titles, headings, code blocks, tables—either embed separately or mark them.
• Chunking: split long documents into semantically coherent chunks (200–1,000 words depending on model context window).
• Include metadata: store IDs, source, date, and any faceted tags alongside vectors for filtering and display.

Example chunking strategy: split by paragraph, limit to 500 tokens, and overlap 50 tokens between consecutive chunks to retain context continuity.

Index and store embeddings

Persist vectors and metadata in a vector store or database designed for ANN queries. Choose storage based on scale, latency, and functionality (filtering, hybrid searches).

• Vector stores: FAISS, Annoy, HNSWlib, Milvus, Pinecone, or cloud-managed vector DBs.
• Storage patterns: store raw vectors plus JSON metadata or reference IDs that link to a primary datastore.
• Compression: productize with quantization (e.g., IVF+PQ, OPQ) when memory is constrained; benchmark to measure accuracy loss.
• Sharding and replication: plan for horizontal scaling and fault tolerance for large corpora.

Search and similarity strategies

Design search pipelines that balance recall, speed, and precision. Common approach: coarse retrieval via ANN → rerank with more expensive model or exact measures.

• Similarity metric: cosine similarity is common for normalized vectors; dot product works with non-normalized outputs and some model families.
• Hybrid queries: combine lexical (BM25) and semantic signals for queries with rare terms or specific phrase matches.
• Reranking: use cross-encoders or small transformer models on top N candidates (N=10–100) to improve precision.
• Filtering and boosting: apply metadata filters (date, author) or boost exact title matches to improve UX.

Example flow: query → embed → ANN top-100 → metadata filter → rerank with cross-encoder → final top-10 results with snippets.

Evaluate embedding quality

Evaluation should match the downstream task. Use labeled relevance datasets, human judgments, or proxy tasks (clustering purity, retrieval MAP).

• Retrieval metrics: precision@k, recall@k, mean average precision (MAP), and normalized discounted cumulative gain (nDCG).
• Clustering: silhouette score, adjusted rand index when ground truth clusters exist.
• Human evaluation: collect judgments on a sampled set of query-result pairs for qualitative assessment.
• AB testing: measure downstream business metrics (click-through, task completion) to validate impact.

Common pitfalls and how to avoid them

• Inconsistent preprocessing — Remedy: standardize pipeline and version preprocessing code; store raw and processed text.
• Chunking that breaks semantics — Remedy: chunk by semantic boundaries and use overlap to preserve context.
• Using embeddings beyond model scope (domain mismatch) — Remedy: evaluate on in-domain samples and fine-tune if needed.
• Over-compressing vectors causing accuracy loss — Remedy: benchmark quantization settings and monitor retrieval metrics.
• Ignoring cold-start and stale data — Remedy: implement incremental indexing and periodic re-embedding for updated content.

Implementation checklist

• Select candidate embedding models and run small-scale comparisons on representative queries.
• Design preprocessing and chunking rules; implement deterministic pipeline.
• Decide storage: vector engine, metadata store, and backup strategy.
• Implement ANN indexing with chosen parameters; test latency and recall trade-offs.
• Add reranking and hybrid search if higher precision is required.
• Create evaluation suite: labeled queries, metrics, and human review process.
• Deploy monitoring for quality drift, latency, and index health; plan re-indexing cadence.

FAQ