Unverified50% confidenceFactExact time
Chunks are the basic unit of retrieval; after a user asks a question, the retriever returns the top-K chunks closest to the question embedding.
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Long-term
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10/16/2026
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UnverifiedAt retrieval time, the user's question is likewise encoded into a vector, and the system uses measures such as cosine similarity to find the top-K chunks closest to the question vector.76% similarVerifiedIn the knowledge retrieval stage, the user's question is converted into a query vector via an embedding model, similarity is computed against the text chunk vectors in the knowledge base (typically using cosine similarity), and the top-K segments are returned by ranking.74% similarUnverifiedTop-K检索是从向量数据库中取相似度最高的前K个片段作为候选内容68% similarUnverifiedPre-storing or quickly computing Top-K results at each prefix node enables constant query cost — returning a fixed Top-K regardless of how many entries the prefix matches — and is the key to flattening P99 latency.65% similarUnverifiedEmbedding-based retrieval converts files or code blocks into vectors and stores them in an index, taking the most similar blocks at query time, which can turn stuffing the whole repo in into taking a small block by relevance, compressing the cost of a single call to a constant level.65% similar
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