Choosing Between Full‑Text Search, Vector Databases, and Hybrid Retrieval

Decide the right retrieval approach to balance precision and semantic relevance, cut costs, and improve search quality — practical guidance and an implementation checklist.

Search systems must match user intent, data shape, and product constraints. This guide helps engineers, data scientists, and product managers choose between full‑text, vector retrieval, or a hybrid design and shows how to evaluate and implement each approach.

• When to use full‑text vs vectors and why hybrids are often best.
• How to analyze queries and datasets, measure accuracy, and avoid common mistakes.
• Architecture patterns, scaling tips, cost levers, and a clear implementation checklist.

🔎 ➜ 🧠 ➜ ⚖️ (lexical → semantic → hybrid)

Clarify search goals and dataset characteristics

Start by documenting what “good results” mean for your product: exact matches, ranked relevance, recall for broad queries, or semantic matches across paraphrases. Map goals to user journeys (e.g., support tickets, catalog browsing, knowledge base retrieval, recommendation).

Characterize the dataset: size (documents, tokens, vectors), structure (short snippets vs long documents), language(s), domain specificity, update frequency, and multimodality (text, images, audio). Those factors drive index choice, update strategy, and cost.

Quick answer — Use full‑text search when you need reliable lexical matching, structured filtering, Boolean queries, or low-cost mature tooling; use vector databases when you need semantic similarity (paraphrase, intent, embeddings, or multimodal retrieval) and relevance beyond keywords; prefer a hybrid (filter with full‑text, retrieve/rerank with vectors) for most production systems that require both precision and semantic relevance.

Quick answer: Full‑text gives reliable keyword/boolean/filtering and lower operational complexity; vectors enable semantic matching across paraphrase and multimodal inputs; a hybrid combines structured precision with semantic recall and is the pragmatic default for many apps.

Select by retrieval intent: semantic vs lexical

Ask: is the user trying to find documents containing specific tokens (lexical) or documents with similar meaning (semantic)?

• Lexical intent: exact product codes, legal clauses, log lines, command names, filtering by metadata. Full‑text search (BM25, inverted index) excels here.
• Semantic intent: paraphrased questions, intent matching, recommendations, cross‑modal retrieval. Vector embeddings and nearest‑neighbor search excel here.
• Mixed intent: many real queries mix both — e.g., “docs about OAuth 2.0 token revocation best practices.”

Decision rule: choose the method that directly addresses primary user intent; if both matter, design a hybrid pipeline.

Analyze data and query patterns

Collect representative logs or run a focused annotation study. Important signals:

• Query lengths and token types (IDs, stopwords, domain terms).
• Percentage of queries needing exact matches vs paraphrase understanding.
• Result universe size (few candidates vs many relevant docs) and expected recall/precision tradeoffs.
• Freshness requirements and update cadence.

Use simple experiments: run queries through both BM25 and embedding retrieval and inspect differences. Tag a sample of queries with desired retrieval type and measure disagreement to guide hybrid thresholds.

Measure accuracy: evaluation metrics and testing plan

Define success metrics before engineering. Common metrics:

Testing plan:

• Create a labeled test set (100–10k queries) with graded relevance labels (0–2 or 0–3).
• Evaluate baseline BM25, embedding nearest neighbor, and hybrid variants.
• Run A/B tests for UI changes or reranking models; measure downstream metrics like task completion or click‑through rate.

Design architecture: vector DB, full‑text, or hybrid

Architecture choices with tradeoffs:

• Full‑text only: inverted index + analyzers, supports filters, faceting, Boolean queries, cheap to operate. Use for catalogs, logs, rule‑based retrieval.
• Vector DB only: embeddings + ANN (HNSW, IVF+PQ). Good for semantic search and recommendations, but weaker at exact filtering and structured queries.
• Hybrid: common patterns:
  • Filter-then-retrieve: use full‑text to apply precise filters, then embed candidates for semantic ranking.
  • Retrieve-then-rerank: get top candidates from vectors, rerank with a cross‑encoder or BM25+features.
  • Union with deduplication: merge lexical and semantic hits, dedupe, then rank by hybrid score.

Example pipeline (hybrid filter → vector rerank):

1. Apply metadata filters (date, product id) in the full‑text layer.
2. Embed remaining docs and query; run ANN to get semantic candidates.
3. Rerank candidates with a cross‑encoder or weighted fusion of BM25 and cosine similarity.

Optimize performance, scaling, and costs

Key levers to optimize:

• Index size: shard logically, compress embeddings (quantization, PCA), prune low‑value docs.
• ANN tuning: adjust ef/search or ef/construction (HNSW), number of probes (IVF) for latency vs recall.
• Hybrid caching: cache embeddings for hot docs and results for frequent queries.
• Batch embedding and asynchronous updates to reduce online compute.
• Use cheap filters early to reduce ANN candidate set and lower cost.

Operational notes:

• Measure tail latency and set SLOs. Track 95–99th percentile latencies separately for full‑text and vector queries.
• Monitor cost per query (compute, storage egress) and model inference costs for embeddings.

Common pitfalls and how to avoid them

• Confusing token overlap with semantic relevance — remedy: baseline BM25 vs embedding comparisons and label disagreements.
• Relying on embedding similarity alone for exact matches (IDs, code) — remedy: add lexical filters or exact match checks before semantic steps.
• Poor evaluation sets that don’t reflect production traffic — remedy: sample real queries and label with business‑driven relevance criteria.
• Overindexing embeddings without compression — remedy: quantize or reduce embedding dimensionality and prune old data.
• Ignoring freshness or update patterns — remedy: design incremental update pipelines and measure staleness impact.

Implementation checklist

• Map user intents to lexical, semantic, or mixed retrieval.
• Create a representative, labeled test set and define metrics (Precision@k, nDCG, recall).
• Prototype BM25 and embedding retrieval on the same dataset; record disagreements.
• Choose architecture: full‑text, vector, or hybrid; specify filters and rerankers.
• Plan embedding generation cadence, storage format, and compression strategy.
• Tune ANN parameters and measure latency/recall tradeoffs.
• Instrument production: logs, SLOs, cost per query, and user feedback loop.
• Run A/B experiments for ranking, UI changes, and relevance improvements.

FAQ

When is BM25 enough?

When users expect keyword/ID matches, need precise filters, or the domain has limited paraphrase variation.

How many vectors per doc should I store?

Start with one dense embedding per document; add chunking for long docs or multi‑vector representations for high semantic diversity.

Should I use approximate nearest neighbor (ANN)?

Yes for scale — ANN balances latency and recall. Tune its parameters to meet your SLOs.

How to combine lexical and semantic scores?

Use weighted fusion, logistic models, or a learning‑to‑rank layer (features: BM25 score, cosine sim, metadata) and validate on held‑out labels.

How to handle fresh content?

Use incremental embedding pipelines and hybrid filters to serve recent documents from the full‑text index while background jobs catch embeddings up.