Key takeaways
- Implement vector search by chunking, embedding, indexing with metadata and querying with the same model.
- RAG embeds the question, retrieves relevant chunks and grounds the model's answer in them.
- Accuracy improves with better embeddings, chunking, hybrid search, reranking and evaluation.
- Exact search is deterministic, while approximate indexes and embeddings can vary slightly.
- SQLite needs sqlite-vec or libSQL, while Elasticsearch and Neo4j support vector indexes natively.
Implementing vector search means turning content into embeddings, indexing them for fast similarity lookup, and querying with embedded user input, usually as the retrieval step of a RAG system. Most databases and search engines now support it. RAG.NextUpgrad combines dense vector search with BM25, reciprocal rank fusion and reranking, so this page covers implementing vector search, how engines and RAG retrieval work, improving accuracy, whether it is AI or deterministic, endpoints, and support in SQLite, Elasticsearch and Neo4j.
How do you implement vector search?
Choose an embedding model, split your content into chunks, embed each chunk, store vectors with metadata in an index such as pgvector, Faiss or a vector database, then embed each query and retrieve its nearest neighbours.
Also asked as: how to implement vector search
In code, the indexing job reads documents, cleans and chunks them, calls the embedding model in batches and writes vectors plus IDs, text and metadata to the store. The query path embeds the user's input with the same model, runs a nearest neighbour search with any metadata filters, and returns the top results, optionally reranked by a cross-encoder. Start with exact search for small datasets, then add an approximate index such as HNSW as data grows. Build a small labelled query set early to measure whether changes actually help.
How does a vector search engine work?
A vector search engine stores embeddings in an approximate nearest neighbour index, such as HNSW or IVF, and answers queries by navigating that index to find the closest vectors under a distance metric like cosine similarity.
Around the index, the engine manages storage, metadata filtering, inserts, updates and deletes, sharding and replication across machines, and APIs for queries. Many engines also compress vectors with quantization to save memory, and support hybrid search that combines vector similarity with keyword scores. Filtering is a hard problem, since restricting results by metadata can conflict with graph traversal, so engines use pre-filtering, post-filtering or filter-aware indexes. Performance depends on index parameters, vector dimensions, hardware and how often data changes.
How does RAG vector search work?
In retrieval-augmented generation, the user's question is embedded, a vector search retrieves the most relevant document chunks, and those chunks are inserted into the prompt so the language model answers from them, ideally with citations.
The approach was formalised by Lewis and colleagues in 2020 and is now the standard way to ground LLM answers in private or current data. Quality depends on every stage: chunking, embeddings, retrieval, reranking and the prompt that tells the model to stay within its sources. RAG.NextUpgrad runs dense and BM25 retrieval, fuses them with reciprocal rank fusion, reranks the candidates, streams cited answers, and uses a confidence gate to decline when evidence is weak. That last step prevents confident answers built on poor retrieval.
How do you improve vector search accuracy?
Use a stronger or domain-suited embedding model, improve chunking, add hybrid keyword search, rerank results with a cross-encoder, and tune index parameters for higher recall. Measure every change against a labelled query set.
Also asked as: how to improve vector search accuracy
Compare embedding models on public benchmarks such as MTEB and, more importantly, on your own queries. Chunk along natural boundaries such as headings and paragraphs, and add titles or section names to chunks for context. Rewrite vague queries, or generate a hypothetical answer and embed that, a technique known as HyDE. Apply metadata filters to remove irrelevant documents before ranking. Fine-tune embeddings on domain pairs when vocabulary is specialised. Raise search parameters such as efSearch if approximate search misses results that exact search finds.
Is vector search considered AI?
Partly. The search step itself is mathematics: finding the nearest vectors by distance. The intelligence comes from the embedding model, a machine learning model that converts text, images or audio into vectors capturing meaning.
So vector search is best described as an AI-powered retrieval technique, and it is widely grouped under AI and machine learning infrastructure. Without learned embeddings, nearest neighbour search is a classic algorithm used in statistics and recommendation for decades. With modern embeddings, it enables semantic search, recommendations, image similarity, deduplication and retrieval for LLMs. Some products also add learned components to the index itself, such as learned quantization. For practical purposes, if a system uses trained embeddings, calling it AI search is fair.
Is vector search deterministic?
Exact nearest neighbour search is deterministic for the same data, query and settings, apart from ties. Approximate indexes such as HNSW can return slightly different results depending on build order, random seeds, updates and parallel execution.
Embedding generation can also vary slightly between runs on GPUs or after a provider updates a model version, changing vectors and therefore rankings. Distributed engines that search shards in parallel may merge results differently under load. For reproducibility, pin embedding model versions, fix random seeds where the library allows, rebuild indexes consistently, and break ties by document ID. For evaluation, compare approximate results with exact search to understand how much variation exists. In most applications small differences are harmless, but audits may need exact search.
What is a vector search endpoint?
A vector search endpoint is the network-accessible service that hosts vector indexes and answers similarity queries. In Databricks Mosaic AI Vector Search, an endpoint is the compute resource you create before building and querying indexes.
Also asked as: what is vector search endpoint
Applications send a query vector or text, plus filters and the number of results, to the endpoint's API and receive matching items with scores. Google's Vertex AI Vector Search uses index endpoints to serve deployed indexes, and managed vector databases expose similar endpoints with authentication. Endpoints handle scaling, availability and access control, so applications do not manage index infrastructure directly. Monitor endpoint latency, query volume and errors, and keep credentials on the server side, never in front-end code.
Does SQLite support vector search?
Not natively in standard SQLite, but extensions add it. sqlite-vec, by Alex Garcia, provides vector storage and k-nearest neighbour queries through virtual tables, and libSQL, the SQLite fork behind Turso, includes built-in vector support.
sqlite-vec runs anywhere SQLite runs, including mobile apps, browsers through WebAssembly and edge devices, making it ideal for local-first and on-device AI features. It suits small to medium datasets, where fast brute-force or lightweight indexing is enough. You store embeddings alongside regular tables and join results with SQL. For millions of vectors with heavy concurrent traffic, a server database such as PostgreSQL with pgvector or a dedicated vector database fits better. sqlite-vec replaced the earlier sqlite-vss extension from the same author.
Does Elasticsearch support vector search?
Yes. Elasticsearch supports vector search through the dense_vector field type with approximate kNN search using HNSW, plus exact search through script scoring. It also supports sparse semantic retrieval with Elastic's ELSER model.
You define a dense_vector mapping with dimensions and a similarity metric, index embeddings generated by your own model or an inference endpoint, and run kNN queries with filters. Quantization options reduce memory for large indexes. Elasticsearch's main strength is hybrid search: combining BM25 keyword relevance with vector similarity, including reciprocal rank fusion, in one engine you may already run for logs or site search. OpenSearch, the open-source fork, offers similar vector capabilities through its k-NN plugin with several index engines.
Does Neo4j support vector search?
Yes. Neo4j added vector indexes in version 5, letting you store embeddings as node properties and query nearest neighbours with Cypher, using an HNSW-based index with cosine or euclidean similarity.
The strength of vector search in a graph database is combining similarity with relationships: find relevant chunks by vector search, then traverse connected entities, authors or documents for richer context. This pattern, often called GraphRAG, helps answer questions that span multiple linked facts. Create the index on a label and property, specifying dimensions and similarity function, then call the vector query procedure with an embedding. For pure vector workloads at large scale, a dedicated vector store may be faster, but Neo4j shines when relationships matter.
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.
