Knowledge bases and RAG.

Retrieval-augmented generation, or RAG, finds relevant document passages and gives them to a model alongside the question. It helps ground an answer in your sources; it does not guarantee the answer is correct.

#Trace the answer to its evidence

The fictional visitor guide says the garden opens at 9 am. A retrieved passage supports that answer and retains its source identifier. It says nothing about the designer; the answer should acknowledge missing evidence. Inspect extraction and retrieval before changing the answer prompt. Document and query embeddings need a compatible model and dimensions, plus a configured vector index. Matching dimensions alone is insufficient.

Passage 02 is highlighted in the fictional source document, carried into retrieved context and matched to the supported “9 am” phrase in the answer. The designer question has no matching evidence.

#Prepare a small collection

Use the document-to-RAG workflow offered in the node library for a convenient starting point, or build the individual stages: extract text, split it into chunks, generate embeddings, and store the vectors with useful source metadata.

Begin with a few documents you know well. Verify extracted text before indexing, especially for scanned, layout-heavy, or unsupported files. An embedding cannot repair text that was extracted incorrectly.

#Keep embeddings compatible

The stored documents and the search query need compatible embeddings. Preserve the chosen embedding model and dimensions for a collection. Changing those settings can require reindexing rather than mixing incompatible vectors.

Embedding and answer-generation calls can have separate provider requirements and costs. Check which service receives your document text before indexing private material.

#Retrieve, then answer

Search the collection with a test question and inspect the returned passages. Check whether the relevant source was found before changing the final model's prompt. Pass the retrieved context to the answer step and ask it to identify the supporting sources and uncertainty.

Try a question the documents do not answer. A useful workflow should say when its context is insufficient rather than inventing a source-backed claim.

#Maintain the collection

Keep source identifiers so changed documents can be traced. Original uploads and indexed embeddings are separate retained data; manage each deliberately. Use the supported browser and indexing workflow rather than assuming a per-document collection manager is available.

#Verify retrieval before changing the answer

Extract: inspect text from known documents before embedding it. Index: use compatible embedding models/dimensions and a configured vector database with indexes. Answer: inspect Technical Details and Sources JSON, including a question without supporting evidence. RAG Document Browser's Clear Database clears stored embeddings, not one selected upload.

#Checks and next steps

The built-in workflow requires an installation with the configured MongoDB vector database and compatible vector-search indexes. A JSON-only local storage setup does not by itself supply this service. Confirm availability with the installation administrator before diagnosing every missing result as an embedding-model problem.

Use RAG Query → Technical Details and Sources JSON to inspect retrieved evidence. RAG Document Browser offers document reports, statistics, and tag filtering. Its Clear Database action clears the caller's stored embeddings; it is not a selected-file delete, and it does not delete original uploads. Treat it as a broad destructive action and retain source documents and indexing settings if you need to rebuild.

Keep embedding model and dimensions consistent for the query and collection. Matching dimensions alone does not make two different embedding models compatible. Test both a known answer and an absent answer before relying on the final model response.