Vector Search Fundamentals
Vector search is a foundational technique in retrieval systems, especially within Retrieval-Augmented Generation (RAG) pipelines. When you want to find relevant information from a large collection of documents, you first represent each document and query as a vectorβa list of numbers that captures semantic meaning. By comparing these vectors, you can identify which documents are most similar to your query, enabling efficient retrieval of useful knowledge for tasks like question answering or summarization.
Similarity metrics are essential for determining how close or relevant two vectors are to each other. The two most common metrics are cosine similarity and dot product. Cosine similarity measures the cosine of the angle between two vectors, focusing on their orientation rather than their magnitude. This means it is scale-invariantβit cares about the direction more than the length. Dot product, on the other hand, multiplies corresponding elements of the vectors and sums the result, which is sensitive to both direction and magnitude. While both metrics are used to rank similarity, cosine similarity is often preferred when you want to ignore differences in vector length, whereas dot product can be useful when magnitude carries important information.
The main trade-off in approximate nearest neighbor (ANN) search is between recall and latency. Higher recall means retrieving more of the truly relevant items, but achieving this often increases latencyβthe time it takes to return results. ANN algorithms speed up search by sacrificing some recall, returning results quickly but potentially missing some relevant matches. Choosing the right balance depends on your application's needs: interactive systems may favor lower latency, while research tasks may prioritize higher recall.
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Vector search is a foundational technique in retrieval systems, especially within Retrieval-Augmented Generation (RAG) pipelines. When you want to find relevant information from a large collection of documents, you first represent each document and query as a vectorβa list of numbers that captures semantic meaning. By comparing these vectors, you can identify which documents are most similar to your query, enabling efficient retrieval of useful knowledge for tasks like question answering or summarization.
Similarity metrics are essential for determining how close or relevant two vectors are to each other. The two most common metrics are cosine similarity and dot product. Cosine similarity measures the cosine of the angle between two vectors, focusing on their orientation rather than their magnitude. This means it is scale-invariantβit cares about the direction more than the length. Dot product, on the other hand, multiplies corresponding elements of the vectors and sums the result, which is sensitive to both direction and magnitude. While both metrics are used to rank similarity, cosine similarity is often preferred when you want to ignore differences in vector length, whereas dot product can be useful when magnitude carries important information.
The main trade-off in approximate nearest neighbor (ANN) search is between recall and latency. Higher recall means retrieving more of the truly relevant items, but achieving this often increases latencyβthe time it takes to return results. ANN algorithms speed up search by sacrificing some recall, returning results quickly but potentially missing some relevant matches. Choosing the right balance depends on your application's needs: interactive systems may favor lower latency, while research tasks may prioritize higher recall.
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