RAG with Inspectable Retrieval
The implementation is fairly standard. The more interesting question is how much of the retrieval pipeline should be visible in the interface.
I added RAG to the LLM prototype. The core implementation is the familiar version: chunk documents, generate embeddings, store vectors, retrieve close matches, and inject those chunks into the LLM context before generating a response.
For this pass, I used gpt-oss, embeddinggemma, and PostgreSQL.
The retrieval itself is not the most interesting part to me. What I care about is making the pipeline visible: generating the prompt embedding, searching memories and reference sources, scoring chunks, and injecting the final context into the LLM.
The UI also shows which chunks matched and how strong each match was. Seeing that context makes the system feel less opaque, because the answer has a visible trail back to the material that shaped it.
