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🔥Vector Cosine Similarity (RAG & Embeddings)Easy
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EasyAI & Machine Learning•Acceptance: 64.8%
Vector Cosine Similarity (RAG & Embeddings)
Targeted in FAANG & Tech OA:GoogleAmazonMicrosoftSwiggyUber
Real-World Engineering Context
Used in Retrieval-Augmented Generation (RAG) and Pinecone/Qdrant vector stores to calculate semantic similarity between user queries and stored chunk embeddings.
Given two numeric vectors `vecA` and `vecB` of identical length, compute their Cosine Similarity rounded to 4 decimal places:
$$\text{similarity} = \frac{\vec{a} \cdot \vec{b}}{\|\vec{a}\| \|\vec{b}\|}$$
If either vector norm is 0, return `0.0`.
Sample Test Cases
Input: [[1,2,3],[1,2,3]]
Expected: 1
Input: [[1,0],[0,1]]
Expected: 0
Input: [[1,2,3],[2,4,6]]
Expected: 1
Constraints
- 1 <= vecA.length == vecB.length <= 10^4
- -1000 <= vecA[i], vecB[i] <= 1000
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