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** Hardware:

ARM SoC is powered by Allwinner H618 quad-core Cortex-A53 processor with up to 1.5GHz,

** Cosine Similarity vs. Normalized Cross-Encoder

Cosine Similarity: A score of 1 signifies perfect vector alignment—the two embedding vectors point in exactly the same direction in high-dimensional space. This measures geometric similarity but doesn't guarantee deep semantic understanding.

Normalized Cross-Encoder: A score of 1 represents maximum semantic relevance—the model has determined through full cross-attention that the texts are semantically identical or perfectly matched in meaning. This captures nuanced, context-dependent relationships.

** Advantage:

Cosine Similarity: Fast and offline—embeddings can be pre-computed and stored, enabling rapid similarity search using vector databases (e.g., FAISS, Pinecone, Milvus). It's ideal for retrieval at scale but lacks contextual interaction between texts.

Normalized Cross-Encoder: Precise and bidirectional—by processing texts together with cross-attention, it captures fine-grained, bidirectional context and semantic nuances that dot-product methods miss.

Key Takeaway: Use cosine similarity for fast, scalable retrieval (e.g., finding candidate documents), and use normalized cross-encoders for precise re-ranking of those candidates where accuracy matters more than speed.

** Data Privacy:

Sometimes, apprehensions arise regarding cloud services due to data protection regulations. All processing occurs locally by running on the Edge, offering the opportunity to adopt Language Model-based solutions while adhering to stringent data protection protocols.

** Offline processing:

The production-ready system can also leverage an offline component. Since the knowledge base must be distributed to edge devices, this distribution typically involves precalculated embeddings to enable fast, on-device similarity search. However, incorporating cross-encoders—deployed server-side—significantly improves response quality by refining candidate results with more precise, context-aware relevance scoring, creating an efficient hybrid architecture.