This content originally appeared on DEV Community and was authored by Offor Francis Chidoziem
In today’s data-rich world, finding the right information quickly is paramount. But what if your search engine isn’t up to the task?
Intro: Why Traditional Search Is Broken
You search for “how to master system design”, and your app returns results like: “how to master the design of a guitar system”
Why? Because tools like Elasticsearch or SQL LIKE queries are built on keyword matching, not understanding meaning.
That’s like judging people by how many buzzwords they say, not what they mean.
Problem with keyword search:
- Can’t handle synonyms (buy ≠ purchase)
- Can’t understand context (jaguar as a car vs animal)
- Falls short for long, natural language queries
That’s where semantic search powered by vector embeddings comes in. Let’s explore how it works.
What is Vector Search?
Vector search uses embeddings, numerical representations of text in high-dimensional vector space. These vectors encode the contextual meaning of text. For example:
- The phrase “apple fruit” and “red fruit” will have vectors closer in space than “apple computer” due to semantic similarity.
Visually, imagine plotting sentences as points in a space where distance reflects meaning. A query vector is compared with data vectors to find the closest semantic matches. This enables search engines to find results that align conceptually with queries, not just literally.
That’s what vector search does:
It converts your text into numbers (vectors), and then compares them in multi-dimensional space.
Example
- buy shoes: [0.12, -0.88, 0.45]
- Purchase sneakers: [0.13, -0.87, 0.47]
Even though the words are different, their meaning is similar, so the vectors are close together.
Text → Embedding Model → Vector Space → Search Similar Vectors
This is what powers tools like:
- Notion’s global search
- Google’s “Things you might mean”
- ChatGPT’s memory
System Architecture Overview
Detailed Flow:
User Query: A user types “meeting notes for sales team” into the search bar.
Embedding Generation: The backend service sends this query to the Embedding Model, which converts it into a high-dimensional vector (query embedding).
Vector Search: The query embedding is then sent to the Vector Database. The vector database performs a similarity search, typically using metrics like cosine similarity, to find the k most similar document embeddings. Cosine similarity measures the cosine of the angle between two vectors, with a value closer to 1 indicating higher similarity.
Metadata Retrieval: The vector database returns the IDs of the top k similar documents. The backend service then uses these IDs to fetch additional metadata (e.g., titles, snippets, creation dates) from the Metadata Database.
Reranking and Filtering:
Reranking: While vector similarity is powerful, a secondary reranking step can further refine results. This might involve using a more sophisticated cross-encoder model to re-score the top k results based on their semantic relevance to the original query, or applying business logic like recency or popularity.
Filters: Users might apply filters (e.g., “only documents from last month,” “only documents created by John Doe”). These filters are applied to the metadata retrieved from the metadata database, further narrowing down the results.Results to Frontend: The refined and ranked results, along with their metadata, are sent back to the frontend for display to the user.
Data Ingestion Pipeline
Before search works, you need to embed all your content.
Pipeline:
- Content is created (articles, notes, posts)
- Worker or cron job:
- Cleans the text
- Generates embedding (vector)
- Saves vector to Vector DB
- Saves metadata to relational DB
This process can be implemented through:
- Batch Jobs: Periodically processing large volumes of new or updated data.
- Cron Jobs: Scheduled tasks to update embeddings at regular intervals.
- Real-time Triggers: When a new document is created or an existing one is updated, a trigger can initiate the embedding process immediately.
Tech Stack Choices
The beauty of building such a system is the flexibility in choosing your tools:
Embedding Models: e.g..OpenAI text-embedding-3, Cohere, HuggingFace, in-house BERT
Vector Databases: e.g..Pinecone, FAISS(Facebook AI Similarity Search), Weaviate, Qdrant, Milvus, Chroma, Supabase
Backend Frameworks: choose your preferred frontend framework
eg Nest, Spring, Fastapi etc
Frontend Frameworks: choose your preferred backend framework
e.g.. nextjs, react etc
Metadata Store: Postgres, MongoDB etc
Message Queue: Redis, Kafka (optional for ingestion pipeline)
Performance & Scaling
Building a global search engine requires meticulous attention to performance and scalability:
Caching Embeddings: Cache frequently accessed document embeddings in memory or with Redis to reduce repeated calls to the embedding model or vector database.
Approximate Nearest Neighbor (ANN) Search: Vector databases use ANN algorithms (e.g., HNSW, IVFFlat) to quickly find approximate nearest neighbors in high-dimensional space, trading off a tiny bit of accuracy for massive speed improvements.
Sharding: Distribute your vector database across multiple servers or clusters to handle larger datasets and higher query loads.
Monitoring: Implement comprehensive monitoring (e.g., Prometheus, Grafana) to track query latency, throughput, error rates, and resource utilization, allowing you to identify and address bottlenecks proactively.
Search is no longer about matching words, it’s about understanding meaning.
If you’re building modern apps, especially in education, e-commerce, productivity, or AI semantic vector search is the way to go.
It’s powerful, flexible, and once you understand it, honestly… kinda fun to build.
Final Tips
- Start with OpenAI + Pinecone to prototype quickly
- Visualize your architecture before building
- Write tests for each pipeline step
Vector search engines are revolutionizing search technology embrace them to build the next generation of intelligent search.
This content originally appeared on DEV Community and was authored by Offor Francis Chidoziem