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concept#Data#AI#Analytics#Architecture

Semantic Search

Search that uses meaning instead of literal keyword matching, based on embeddings and semantic representations.

Semantic search augments keyword matching with semantic representations (embeddings) and retrieves content by meaning rather than literal terms.
Established
Medium

Classification

  • Medium
  • Technical
  • Architectural
  • Intermediate

Technical context

Document management systems (e.g., SharePoint, Confluence)Vector databases (e.g., Milvus, Weaviate)NLP models / inference services

Principles & goals

Prioritize data quality over model complexityUse hybrid retrieval: combine keyword and vector searchIntegrate transparent evaluation metrics and user feedback
Build
Domain, Team

Use cases & scenarios

Compromises

  • Bias or hallucinations from pretrained models
  • Privacy issues when handling sensitive content
  • Cost escalation from large vector indexes and model inference
  • Hybrid approach: use keyword filtering before vector retrieval.
  • Regular re-embedding pipelines for stale documents
  • Automated evaluation framework with user feedback

I/O & resources

  • Source corpus (documents, product data, logs)
  • Metadata and taxonomies
  • Embedding models or pipeline
  • Ranked hit lists with scores and sources
  • Explanations or highlighted passages
  • Monitoring metrics and user feedback

Description

Semantic search augments keyword matching with semantic representations (embeddings) and retrieves content by meaning rather than literal terms. It leverages vector similarity, knowledge graphs and ranking signals to improve relevance for documents, chatbots and product search. Successful adoption requires data preparation, model choice and evaluation metrics.

✔

  • Improved relevance via meaning-based matching
  • Better handling of synonyms and language variants
  • Flexible use across document types

✖

  • Requires explainable ranking signals for auditability
  • Higher storage and indexing overhead (vectors)
  • Dependency on embedding quality and domain adaptation

  • Mean Reciprocal Rank (MRR)

    Measures how high relevant hits appear in the ranking.

  • Recall@K

    Share of relevant documents in the top-K results.

  • P95 latency

    95th percentile of response time under production load.

Enterprise knowledge base with embeddings

Internal docs were embedded and exposed via a vector index; support cases are resolved faster.

E‑commerce semantic ranking

Product descriptions and user queries are semantically mapped, improving search results and conversion rates.

Chatbot with passage retrieval

Contextual passage retrieval yields more precise source citations in a knowledge chatbot's answers.

1

Requirements analysis: define relevance criteria and SLOs.

2

Data preparation: clean corpus and enrich metadata.

3

Model selection: evaluate and fine-tune embedding models.

4

Indexing: generate vectors and load into index.

5

Testing & rollout: A/B tests, monitoring and incremental rollout.

⚠️ Technical debt & bottlenecks

  • Unversioned embedding pipelines hinder reproducibility.
  • Missing monitoring for index drift leads to quality degradation.
  • Ad-hoc fallback rules increase long-term maintenance cost.
Embedding computationVector index I/ORanking and fusion layer
  • Using generic embeddings without domain adaptation leads to poor matches.
  • Ignoring privacy rules when indexing sensitive content.
  • Relying solely on vector scores without fallbacks for OOV queries.
  • Lack of evaluation data skews perception of relevance.
  • Over-optimizing for benchmarks instead of product-relevant metrics.
  • Underestimating inference and storage cost requirements.
Data engineering and ETLMachine learning fundamentals and embeddingsSearch and index architecture
Relevance and user satisfactionLatency and scalabilityCost and operational efficiency
  • • Compute and storage budget for indexes and models
  • • Privacy and compliance requirements
  • • Latency SLOs for interactive applications