What is AI-powered semantic search?
Traditional keyword search looks for exact matches — search for "termination clause" and you get results containing those words. Semantic search understands meaning — the same search might also surface documents that discuss "ending the agreement" or "notice period for cancellation", because the system understands these are conceptually related.
This is achieved through vector embeddings: a numerical representation of a piece of text that captures its semantic meaning. Similar meanings produce similar vectors. By converting your content into embeddings and storing them in a vector database, you can find the most relevant content for any query — even when the exact words don't match.
Semantic search is also the foundation of RAG (retrieval-augmented generation), the technique behind "ask your knowledge base" interfaces where a user asks a natural language question and gets an answer drawn from your specific documents, not from the LLM's general training.
When does your app need it?
- Your users search across a large body of content (knowledge base, case library, product catalogue, document archive) and keyword search returns too many irrelevant results or misses relevant ones
- You want to let users ask natural language questions and get an answer sourced from your documents — not a generic AI response
- You need to find similar items — similar cases, similar products, similar past jobs — based on semantic similarity rather than shared tags or categories
- Your search query patterns are varied and unpredictable, making it impractical to define keyword rules for every possible phrasing
- You have a support or legal knowledge base where users need to find applicable precedents or procedures
- You want to power a recommendation system (similar articles, related products, "you might also need") based on content meaning
How much does it cost?
Adding AI-powered semantic search typically adds 8–16 hours of development — roughly $1,000–$4,000 AUD.
At the simpler end, this is an embedding pipeline for an existing content set, stored in a vector database, with a similarity search endpoint. At the more complex end, it includes hybrid search (combining vector similarity with keyword matching for precision), a RAG layer that generates natural language answers from retrieved chunks, re-ranking, and a feedback loop to improve result quality.
How it's typically built
Content is passed through an embedding model — OpenAI's text-embedding-3-small or Cohere's embedding models are common choices — to produce a vector for each piece of content. These vectors are stored in a vector database: Pinecone and Weaviate are managed cloud options; pgvector is a PostgreSQL extension that lets you use your existing database if you are already on Postgres and your dataset is not enormous.
When a user searches, their query is converted to a vector using the same embedding model, and the database returns the most similar content vectors. For a RAG setup, the top results are assembled into a context window and passed to an LLM with the user's question, producing a synthesised answer that cites the retrieved content. Chunking strategy — how you split long documents into indexable pieces — has a significant effect on quality and is part of the implementation work. Embeddings need to be regenerated when content is updated, so a re-indexing pipeline is also part of the build.
Questions to ask your developer
- What content needs to be searchable, and how large is the corpus? Dataset size influences the choice of vector database and ongoing infrastructure cost.
- Do you need a "chat with your documents" interface, or just improved search results? A RAG layer for natural language answers adds meaningful complexity.
- How often does your content change? Frequently updated content requires a re-indexing pipeline to keep embeddings current.
- Should semantic search replace or complement your existing keyword search? Hybrid approaches often outperform either alone.
- Which embedding model will you use? The embedding model must remain consistent between indexing and querying; changing it later requires re-indexing all content.
See also: AI text generation · Full-text search · App cost calculator