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· 8 min read
Yingfeng Zhang

Since the open-sourcing of Infinity, it has received a wide positive response from the community. Regarding the essential RAG technology we promote - multiple recall (vector recall, full-text search, and structured data query), some friends mentioned that simply using vectors can also meet the requirements. What we traditionally refer to as vector retrieval is a type of query based on dense vector data, known as Dense Embedding. There is another type of vector data, sparse vector, known as Sparse Embedding, which can provide the precise queries necessary for RAG. By combining these two types of vector data, multi-path recall can be achieved (2 paths of recall). With Sparse Embedding, there is no need for full-text search; BM25 can be completely replaced (BM25 is a common full-text indexing and sorting method, which can be seen as a variation of TF/IDF). Let's see if this is really possible. Dense Embedding refers to vectors where the dimensions may not be very high, but each dimension is numerically represented as a certain weight. Sparse Embedding refers to most dimensions of the vector being zero, with only a few dimensions having values; the overall vector dimension can be very high.