A passage of a document, typically a few paragraphs. Documents are split into chunks, and retrieval returns chunks. See Chunking.
The method used to split documents into chunks (Recursive, Character, Token, Markdown, Code or JSON), with its settings such as size and overlap.
All the documents (and their chunks) in a project.
The measure of how close two embeddings are. Higher means more similar. Scores are only comparable within one model.
The length of the vectors a model produces. More dimensions means more storage per chunk.
A retrieved chunk that isn’t one of the expected chunks for the question.
A list of numbers (a vector) that represents the meaning of a text. Texts with similar meanings have close embeddings.
The model that turns text into embeddings. It’s the main thing Truvec helps you choose.
The chunks that contain the answer to a test question. The answer key used to grade a model.
A set of test questions with their expected chunks. See Golden datasets.
The share of questions for which at least one expected chunk is in the top k.
Large language model, such as GPT or Claude. In RAG, it writes the answer from the retrieved chunks.
The average of 1 ÷ the rank of the first expected chunk. 1.0 means the right chunk is always first.
A 0-to-1 score of how well all expected chunks are ranked, giving more credit to higher positions.
Text repeated between consecutive chunks, so ideas aren’t cut in half at chunk boundaries.
The share of the top k retrieved chunks that are expected chunks.
A technique where a system retrieves relevant passages from your documents and gives them to an LLM to answer a question. See How RAG retrieval works.
The share of a question’s expected chunks that appear in the top k.
A model that reorders retrieved chunks by relevance after the initial search. Useful when the right chunk is retrieved but not ranked first.
Finding the chunks most relevant to a question. It’s the step Truvec evaluates.
The similarity of the best expected chunk minus the similarity of the best distractor. A larger gap means the model separates right from wrong chunks more clearly.
The unit models read and providers bill: roughly three quarters of an English word.
The number of chunks retrieved for each question, and the cut-off at which metrics are computed.
A database that stores embeddings and finds the ones closest to a query. Truvec uses Qdrant.