Semantic Search
Semantic search goes beyond traditional keyword matching by understanding the intent and contextual meaning behind a search query. It uses vector embeddings to represent both the query and the searchable content as points in a high-dimensional space, then finds results that are conceptually similar — even when they use completely different words.
Semantic search vs keyword search
Keyword search matches exact terms: searching "reset password" only finds documents containing those words. Semantic search understands meaning: it would also find "change account credentials," "update login details," or "security settings" because they are semantically related. This is critical for customer support where customers describe problems in many different ways.
How it improves support quality
Customers rarely phrase their questions exactly as your documentation is written. Semantic search bridges this gap by understanding that "my app is crashing" and your troubleshooting guide titled "Application Stability Issues" are about the same topic. This dramatically improves the AI's ability to find and surface the right documentation.
Implementation in EchoSDK
EchoSDK's query pipeline uses semantic search via Firestore Vector Search. Customer queries are converted to embeddings and matched against your indexed documentation using cosine similarity. The top matching passages are then used as context for the RAG-powered answer generation.
Related terms
Vector Embeddings
Numerical representations of text that capture semantic meaning, enabling AI systems to find relevant content through similarity search.
Retrieval-Augmented Generation (RAG)
An AI technique that combines a language model with a retrieval system to generate answers grounded in specific documents or data sources.
Natural Language Processing (NLP)
A branch of AI focused on enabling computers to understand, interpret, and generate human language in a meaningful way.