4 posts tagged with "embeddings"

Your data already lives in Europe. Your databases run in European zones, your backups stay in European object storage, and your compliance story is clean right up until your application calls a model. Then a prompt full of customer data crosses the Atlantic on a key someone pasted into an environment variable, with no ceiling, no metering, and no answer to the question "where did that text actually go?"

Today we close that gap, and we do something bigger than close it. Three features ship together: vector search as a service over pgvector, embedding pipelines that run as real jobs with schedules and run history, and a managed inference proxy that puts one governed, OpenAI-compatible endpoint in front of OpenAI, Anthropic, Mistral, and Azure OpenAI. Together they make FoundryDB the European AI Data Platform: the one place where your data, your embeddings, and your model calls live under a single set of controls.

Retrieval-Augmented Generation (RAG) augments a language model's response by first retrieving relevant context from a database, then passing that context into the prompt. OpenSearch is a natural fit for the retrieval step: it runs the embedding model internally, stores the vectors, and returns ranked results in a single query. This post shows the retrieval step with real scores from a live OpenSearch 2.19.1 cluster managed by FoundryDB, and explains how to wire the retrieved chunks into a prompt and call an LLM.

This post uses a dedicated knowledge base index with 6 database documentation chunks, embedded using all-MiniLM-L6-v2 (384 dimensions). The retrieval, prompt assembly, and a complete prompt were all tested on a live FoundryDB cluster.

Keyword search breaks when users phrase queries differently from the words in your documents. Vector search fixes this by comparing meaning rather than tokens. This post walks through registering a sentence embedding model, building a k-NN index with an ingest pipeline, and running semantic queries against a FoundryDB-managed OpenSearch 2.19.1 cluster. All scores and outputs are from a real test run.

All commands use YOUR_OPENSEARCH_HOST and YOUR_PASSWORD as placeholders. Replace them with your cluster domain and app_user password from the FoundryDB dashboard.

Every RAG system needs the same boring middle layer: watch a table for changes, call an embedding API, write vectors back, handle retries, manage batches, build indexes, schedule cron jobs, and pray nothing drifts out of sync at 3 AM. FoundryDB's managed embedding pipelines eliminate that entire layer. You configure a pipeline, and your PostgreSQL data gets auto-vectorized with an HNSW index, ready for similarity search.

No ETL scripts. No cron jobs. No model orchestration code.