When choosing an AI database, benchmarks only go so far — real value comes from building with it. This post features hands-on guides demonstrating how SingleStore handles vector search, hybrid analytics and the LLM lifecycle in production-like workflows. Each section outlines what you build, why the database layer matters and what to watch for when you move from a notebook to a service.
Semantic search and vector search in an AI database
Build semantic search with production-grade retrieval.
Start with the end-to-end notebook that generates embeddings, stores them in vector columns and retrieves results with vector similarity in SQL. See the full flow in semantic search with Hugging Face models and datasets. You will stand up embeddings, load them efficiently and execute fast nearest-neighbor queries directly in the database.
Compare multiple embedding models side by side.
Real systems often need more than one representation. This pattern shows how to persist multiple embeddings per row, keep dimensions straight and call out to external providers for on-the-fly vector generation. Work through inserting embeddings from multiple models into SingleStore using external functions. Deep learning models can uncover hidden patterns in data through pattern recognition, making it useful for A/B testing models, enriching context with domain-specific vectors and evolving your retrieval stack without schema churn.
Real-time recommendations and personalization with vector search
Operational recommendation engine with live signals.
This guide assembles a real-time recommender that blends vector similarity with transactional and behavioral features, then serves low-latency queries. Machine learning models analyze user preferences and classify data to generate recommendations tailored to individual tastes. It is a practical reference for building personalization where freshness and throughput both matter. Read the walkthrough: real-time recommendation engine.
Domain example on a well-known dataset.
If you prefer a concrete domain, the movie tutorial ingests ratings and metadata, creates text embeddings for titles and tags and combines similarity with quality signals to rank candidates. The system uses training data and historical data to improve the accuracy of its recommendations. Try the notebook: movie recommendation with vector search in SingleStore. It shows how to fuse vector search and analytics in a single SQL plan.
Multimodal large language models: Text, audio and images
LLM apps that see, hear and speak.
This example layers retrieval over structured and unstructured data, adds speech and vision (images, audio, and text data) and routes queries through an LLM agent that can issue SQL and vector searches as tools. These apps can process natural language, perform tasks like translating languages and act as virtual assistants. The database stores embeddings and metadata, and the app uses similarity search where it improves grounding. See this post on designing LLMs that interact through text, images and voice
The LLM lifecycle on an AI database: Evaluate and fine-tune
Evaluate models with metrics that reflect your task.
Before scaling, measure. This guide separates model and system evaluation, discusses relevance and safety metrics and shows a notebook-driven workflow for running repeatable tests on your data. Evaluation metrics often involve checking the completeness of the LLM response, cosine similarity to reference material, hallucination indexes and more. Read the complete guide to evaluating large language models.
Fine-tune for domain accuracy, keep retrieval for breadth.
When a base model is close but not precise enough, fine-tuning on your data narrows drift and improves response style. This article outlines when to fine-tune, how to structure datasets and how to validate gains against held-out tasks. See fine-tuning large language models. Use retrieval for coverage and freshness, and fine-tuning for sharper decision boundaries.
Why developers choose SingleStore as their AI database
These examples highlight a consistent pattern. You can ingest and transform data quickly, persist vectors in a native type, query with vector similarity inside SQL and co-locate real-time features and historical context. The result is simpler architecture and fewer hops between embedding generation, retrieval and ranking.
When you move from a notebook to a service, you keep the same data model and the same query surface. This enables use cases including fraud detection, identifying fraudulent transactions, supporting autonomous vehicles, enabling self learning and learning systems and integrating with machine learning systems for real-time and batch processing.
Quick scan
- LLM lifecycle. Choose models with the right metrics, then adapt them safely. See: Complete guide to evaluating large language models and fine-tuning large language models.
- Semantic search. End-to-end retrieval with embeddings in native vector columns. See: Semantic search with Hugging Face models and datasets.
Multiple models. Store and compare several embeddings per record, generate vectors via external functions. See: Inserting embeddings from multiple models.
Recommendations. Combine vector similarity with features to serve low-latency personalization. See: Real-time recommendation engine.
Movie domain. Apply the approach to a known dataset and blend similarity with quality signals. See: Movie recommendation with vector search in SingleStore.
Multimodal apps: Text, audio and image handling with retrieval and tool-calling. See: How to build LLM apps that can see, hear and speak.
