Semantic Search
Finding information by meaning rather than exact keyword match.
/ quick answer
Semantic Search embeds queries and documents into vectors so the system can retrieve conceptually similar material even when wording differs. Finding information by meaning rather than exact keyword match.
What is Semantic Search?
Semantic Search embeds queries and documents into vectors so the system can retrieve conceptually similar material even when wording differs.
What is an example of Semantic Search?
A query for 'refund after trial' returns policy pages titled 'cancellation window' and 'billing exceptions'.
Why does Semantic Search matter for AI and automation?
Finding information by meaning rather than exact keyword match. It connects to the workflows, prompts and tool stacks linked on this page, so you can move from definition to execution without leaving Onexial.
/ continue exploring
Related concepts
The vocabulary this page depends on.
- →BM25
The keyword-ranking algorithm behind classic search engines.
- →Retrieval
Selecting the most relevant chunks for a query before generation.
- →Hybrid Search
Combining keyword (BM25) and vector search for better recall.
- →RAG (Retrieval-Augmented Generation)
Inject external knowledge into an LLM at query time.
Related workflows
Turn this into a repeatable process.
- →RAG Content Ingestion Pipeline
Convert messy docs into searchable, cited knowledge chunks for AI systems.
- →Build an Internal Knowledge Bot
Ship a Slack bot that answers questions from your company docs.
- →Customer Feedback Intelligence System
Turn support tickets, calls, reviews, and surveys into product signals.
- →AI SEO Cluster Builder
Create a structured topical map from keywords, SERPs, and internal knowledge nodes.
Related tool stacks
The tools that run it in production.
- →RAG Starter Stack
Minimum viable stack to ship a production RAG chatbot.
- →Knowledge Graph Stack
Relationship layer that maps concepts, workflows, prompts, tools, and cases.
- →RAG Context Enrichment Stack
A technical stack designed to enrich the contextual data provided to a Retrieval Augmented Generation (RAG) system, improving the quality and depth of LLM responses.
Related prompts
Reusable prompts for this job.
- →Grounded Answer Prompt
Force the model to answer only from provided sources, with citations.
Comparisons & alternatives
Pick between the options.
- →Perplexity vs ChatGPT Search
Two answer engines, two philosophies: citation-first research vs conversational assistant.
- →Perplexity vs You.com vs SearchGPT
Three AI-first search engines compared.