Embeddings, Vector Storage, and Evaluation
Explore how to transform text into vector embeddings and store them in PostgreSQL with pgvector for efficient retrieval in large language model applications. Understand the importance of evaluation using golden datasets to measure retrieval accuracy, and learn how to implement operational quality gates to ensure your retrieval system remains reliable before moving on to prompt engineering.
In the previous lesson, we built an ingestion pipeline that transforms raw Markdown documentation into clean, structured DocumentChunk objects. We have successfully addressed the Garbage In problem; however, retrieval failures are usually silent and difficult to detect. The system does not surface an error; it simply retrieves the wrong context. This is why evaluation is a requirement, not an optimization.
A RAG system can appear to work perfectly, with the code running, the database accepting data, and the API returning a 200 OK response, while still failing to retrieve relevant information. If a user asks, “How do I rotate my API key?”, and the system retrieves a document about CSS Styling, the LLM will hallucinate an answer. We cannot fix this with better prompting. We must fix the retrieval.
In the distill phase, our goal is not to generate answers. It is to prove that our system can reliably retrieve the right information. In this lesson, we will convert our text chunks into embeddings, store them in PostgreSQL using pgvector, and use a golden dataset to mathematically measure our retrieval quality.
Creating the embeddings
Text is opaque to a machine. To perform a semantic search, we must convert our DocumentChunk objects into vectors. These vectors are essentially long arrays of floating-point numbers that ...