What are Embeddings?
Embeddings turn text into numbers that capture meaning, enabling search by intent, not keywords.
Semantic search
Find by meaning, not exact keywords.
RAG retrieval
Bring the most relevant chunks into the context.
Clustering & dedupe
Group similar items, remove near‑duplicates.
Recommendations
Suggest similar docs, tickets, or products.
Chunking and overlap
Use 300–800 token chunks with 10–20% overlap to retain context.
Hybrid search
Combine BM25 and vector search for robustness on short queries.
Metadata filters
Filter by product, version, region, or permissions at query time.
Model choice
Pick domain‑suitable and multilingual models if your content requires it.