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Embedding-based Routing(EBR)
A semantic routing system that converts queries and route definitions into high-dimensional vector embeddings, using cosine similarity or other distance metrics to match incoming requests to the most semantically similar handler, enabling fuzzy matching, multi-lingual support, and context-aware routing beyond simple keyword matching
In 30 seconds
- What
- Converts queries and route definitions into vector embeddings, matching incoming requests to handlers by semantic similarity rather than keywords.
- When to use
- Routing needs to handle ambiguous queries, multiple languages, or fuzzy boundaries where exact keyword matching fails.
- Watch out
- Embedding quality and similarity thresholds drift over time; stale embeddings or miscalibrated thresholds cause silent misrouting.
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Embedding-based Routing: Overview
A semantic routing system that converts queries and route definitions into high-dimensional vector embeddings, using cosine similarity or other distance metrics to match incoming requests to the most semantically similar handler, enabling fuzzy matching, multi-lingual support, and context-aware routing beyond simple keyword matching
- Semantic similarity matching using vector embeddings
- Language-agnostic routing through meaning preservation
- Fuzzy boundary handling for ambiguous queries
- Pre-computed route embeddings for fast matching
- Configurable similarity thresholds and metrics
- Embedding caching for performance optimization
- Support for multiple embedding models
- Drift monitoring and embedding updates
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References
The papers, specifications, and repositories this pattern is based on.
- Is Cosine-Similarity of Embeddings Really About Similarity? (2024)arXiv:2403.05440
- Semantic Routing for Enhanced Performance of LLM-Assisted Networks (2024)arXiv:2404.15869
- Sentence-BERT: Sentence Embeddings using Siamese BERT-NetworksarXiv:1908.10084
- Efficient Natural Language Response Suggestion for Smart ReplyarXiv:1705.00652
- Semantic Router Documentation - Aurelio Labs
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