Key takeaways
- Semantic search matches the meaning of a query with the meaning of documents, usually by comparing embeddings.
- Similarity search is the vector operation underneath; semantic search is the user-facing capability built on it.
- It is an NLP technique; LLMs are optional, used to embed text, rewrite queries or generate answers.
- RAG uses semantic search to find passages, then generates an answer from them.
- Test with real queries and relevance labels, and improve with hybrid keyword search, better chunks and reranking.
Semantic search means searching by meaning rather than exact words: a query like "laptop gets hot when gaming" finds an article titled "managing thermal throttling on notebooks" even though they share no keywords. It works by converting queries and documents into embeddings and finding the closest matches. It is the retrieval step in RAG systems such as RAG.NextUpgrad, where I pair it with keyword search. This page covers the meaning, semantic search engines, similarity search, whether it is NLP or uses LLMs, how it relates to RAG, building it in Python, testing and improving it, and the Semantic Scholar academic search engine.
What is semantic search?
Semantic search is a search technique that tries to understand the intent and meaning of a query and return results that are relevant in meaning, not just results that contain the same words.
Also asked as: what is semantic search meaning · what is semantic search and similarity search
Traditional keyword search matches terms, so synonyms, paraphrases and natural-language questions often fail. Semantic search uses language models to represent text as vectors in which similar meanings are close together, so it handles "how do I fix a slow laptop" and "speed up my computer" as related. Its weakness is exact details: specific codes, names and numbers are better handled by keyword search, which is why production systems combine both.
What is a semantic search engine?
A semantic search engine is a search system that ranks results by meaning, typically using embedding models to encode documents and queries and a vector index to find nearest matches, often combined with keyword search, filters and reranking.
Also asked as: what is semantic search engine
Examples range from web search engines, which use semantic understanding heavily, to site search, e-commerce product search, documentation search and enterprise knowledge search. Building blocks include an embedding model, a vector database or library, a keyword index for hybrid search, a reranker for precision, and an interface that may show snippets or generated answers. Many search platforms now offer semantic features built in.
What is the difference between semantic search and similarity search?
Similarity search is the technical operation: finding vectors nearest to a query vector by cosine similarity or distance. Semantic search is the application: using similarity search over meaning-based embeddings to return relevant results to people.
Similarity search can be used for many things beyond text meaning, such as finding similar images, songs, products or fraud patterns. Semantic search specifically aims at relevance to a user's intent, which often requires more than raw similarity: query understanding, hybrid keyword matching, filters such as language or date, and reranking. In short, similarity search is a tool; semantic search is a product built with it.
Is semantic search NLP?
Yes. Semantic search is an application of natural language processing: it uses NLP models, especially transformer-based embedding models, to understand the meaning of queries and documents.
It sits at the intersection of NLP and information retrieval. NLP provides the models that represent meaning; information retrieval provides indexing, ranking and evaluation methods. Related NLP techniques often support it, such as query expansion, named entity recognition for filters, and language detection. Semantic search also extends beyond text: multimodal models can match text queries to images, which uses the same principles.
Does semantic search use LLMs?
Not necessarily. Semantic search usually uses embedding models, which are smaller transformer encoders, not large generative LLMs. LLMs can be added to rewrite queries, generate hypothetical answers to search with, rerank results or produce a summarised answer.
A basic semantic search system needs only an embedding model and a vector index, which is fast and cheap. Adding an LLM increases cost and latency, so use it where it helps: rephrasing vague questions, generating answers with citations in RAG, or reranking a small set of candidates. Many embedding models are themselves derived from language models, which is why the line can seem blurry.
Is semantic search the same as RAG?
No. Semantic search finds and returns relevant documents. RAG, retrieval-augmented generation, uses retrieval, often semantic search, to find passages and then has an LLM generate an answer from them.
Also asked as: is semantic search same as rag
So semantic search is a component of most RAG systems. Search results alone let users read sources directly; RAG adds a synthesised answer, ideally with citations. RAG quality depends heavily on search quality: if the right passage is not retrieved, the answer cannot be right. That is why improving semantic search, through hybrid retrieval and reranking, is the most effective way to improve a RAG system.
How do I do semantic search in Python?
Use the sentence-transformers library to embed documents and queries, then compute cosine similarity and return the top matches. For larger collections, store embeddings in a vector index such as FAISS, Chroma or pgvector.
Also asked as: how to do semantic search in python
# pip install sentence-transformers
from sentence_transformers import SentenceTransformer, util
model = SentenceTransformer("all-MiniLM-L6-v2")
docs = ["Paging splits memory into fixed-size pages.",
"A deadlock occurs when processes wait on each other forever.",
"Thermal throttling slows a laptop when it gets too hot."]
doc_emb = model.encode(docs, convert_to_tensor=True)
q = model.encode("why does my laptop slow down when gaming", convert_to_tensor=True)
best = util.semantic_search(q, doc_emb, top_k=1)[0][0]
print(docs[best["corpus_id"]], round(best["score"], 2))
Swap in a stronger or multilingual embedding model for production.
How do I test semantic search?
Create a set of real queries with known relevant documents, run your search, and measure metrics such as recall at k, precision at k and mean reciprocal rank. Also review results by hand for a sample of queries.
Also asked as: how to test semantic search
A simple process:
- Collect 50 to 100 real queries from users or logs.
- Label the relevant documents for each.
- Compute recall at 5 and 10: how often a relevant document appears in the top results.
- Include hard cases: exact codes, synonyms, misspellings and multi-part questions.
- Re-run after every change to embeddings, chunking or ranking.
Track failures by type, such as exact-term misses or wrong-topic matches, to choose the right fix.
How do I improve semantic search?
The biggest improvements usually come from hybrid search, adding keyword ranking such as BM25 to vector search, better chunking of documents, a stronger or domain-suited embedding model, metadata filters, and a reranker on the top results.
Also asked as: how to improve semantic search
Checklist:
- Hybrid retrieval merged with reciprocal-rank fusion.
- Chunking by structure, with titles and headings included in each chunk.
- Embedding model chosen by tests on your own queries.
- Query handling: rewrite vague queries, expand acronyms.
- Reranking with a cross-encoder on the top 20 to 50.
- Feedback: log clicks or ratings to find weak queries.
Measure each change; some ideas that sound good do not help on a given dataset.
How do I search on Semantic Scholar?
Semantic Scholar is a free academic search engine from the Allen Institute for AI. Enter keywords, a paper title or an author's name in the search box, then filter by year, field of study, publication type and venue.
Also asked as: how to search semantic scholar
Useful features: paper pages show abstracts, citations, references and AI-generated short summaries; "highly influential citations" highlight important follow-up work; you can create a library and set alerts for new papers; and an API supports programmatic access. For literature reviews, start with a few key papers, follow their citations and references, and save relevant ones to your library. Always open and read the actual paper before citing it.
Sources
GenAI engineer and AI product builder with 2+ years shipping production-grade AI systems: RAG pipelines, fine-tuned LLMs, hybrid retrieval and multi-modal apps across vision, speech and OCR, architected end to end from ingestion to deployment. Leads engineering for SCULT INDIA's 14-member team, founded the 500+ member TechVerse Enclave community and has mentored 200+ students. Three hackathon first prizes: Changethon 2025 (IIT Roorkee), Product Genesis at Vividhotsava 2025 (CSJMU) and BYTEBATTLE (MeetKats).
Open to GenAI roles, hackathon judging, mentorship sessions and guest talks at colleges. For campus sessions the inviting institution covers the visit end to end: travel (a First AC train ticket for journeys up to five hours, a flight for anything longer), accommodation, meals, and pickup and drop; the honorarium and workshop fees are negotiable on WhatsApp or on the call, and requests are welcome from institutions that can cover the visit this way. Email pranjulrathour41@gmail.com.
