Vector search vs Keyword search

Vector search and keyword search represent two fundamentally different approaches to information retrieval. Keyword search relies on exact or partial text matching, finding documents that contain specific words or phrases entered by the user. Vector search, in contrast, converts both queries and documents into numerical representations called embeddings, then finds results based on semantic similarity rather than literal word matches. This means vector search can understand context, meaning, and relationships between concepts, even when different terminology is used. While keyword search excels at finding precise terms and structured data, vector search shines when users need conceptually related information or express queries in natural language without knowing exact terminology.

The choice between vector search and keyword search significantly impacts how effectively users can find and analyze information within business intelligence and analytics platforms. Keyword search works well for structured queries with known terminology, but fails when users describe concepts differently than how they're documented. Vector search addresses this limitation by understanding intent and context, making it particularly valuable for exploring large datasets, customer feedback, or unstructured content where exact phrasing varies.

In modern analytics environments, combining both approaches often delivers the best results, allowing users to leverage precise matching when needed while benefiting from semantic understanding for exploratory analysis.

1. Keyword search scans documents for exact or fuzzy matches of the search terms, using techniques like tokenization, stemming, and Boolean operators to identify relevant results.

2. Vector search begins by converting text into numerical embeddings using machine learning models that capture semantic meaning and context.

3. The search query itself gets transformed into a vector using the same embedding model, creating a numerical representation of the user's intent.

4. The system calculates similarity scores between the query vector and document vectors, typically using distance metrics like cosine similarity.

5. Results are ranked by semantic relevance rather than keyword frequency, surfacing conceptually related content even without exact word matches.

1. A customer service team searches for "product defects" using keyword search and finds only tickets containing those exact words. When they switch to vector search, they also discover tickets mentioning "quality issues," "manufacturing problems," and "broken items" that describe the same concept using different language.

2. An analyst exploring sales data types "revenue decline" into a keyword search and gets limited results. Vector search understands this relates to "sales drop," "decreased income," and "lower earnings," providing a more comprehensive view of the business situation across different reports and dashboards.

3. A healthcare researcher using keyword search for "heart attack" misses critical studies that use medical terminology like "myocardial infarction" or "cardiac arrest." Vector search recognizes these as semantically related terms and surfaces all relevant research regardless of specific wording.

1. Vector search finds relevant information even when users don't know the exact terminology used in documents or datasets.

2. Keyword search provides fast, predictable results for precise queries where exact terms are known and important.

3. Vector search improves discovery of related concepts and insights that traditional matching would miss entirely.

4. Keyword search offers transparency in how results are matched, making it easier to understand why specific items appear.

5. Combining both approaches creates a flexible search experience that adapts to different user needs and query types.

6. Vector search reduces the learning curve for new users who may not yet know the specific vocabulary used within an organization.

ThoughtSpot recognizes that modern analytics requires both precision and flexibility in search capabilities. Spotter, your AI agent, leverages advanced semantic understanding to help users explore data naturally, while maintaining the option for precise keyword matching when needed. This hybrid approach reflects the reality that business users think in concepts and questions, not just exact field names or technical terms. By supporting both vector and keyword search paradigms, analytics platforms can meet users where they are, whether they're conducting targeted investigations or exploring data to discover unexpected insights.

1. Search-Based Analytics

2. Conversational Analytics

3. Self-Service Analytics

4. Business Intelligence

5. Data Democratization

6. Semantic Layer

7. AI-Powered Analytics

Understanding the differences between vector search and keyword search helps organizations choose the right approach for their specific analytics and information retrieval needs.