What It Is
Embeddings are lists of numbers that represent the meaning of text, images, or audio. Similar items sit close together, enabling semantic search beyond exact keyword matches. This makes it possible to search by meaning rather than exact words.
Key Points
- Approximate nearest neighbor indexes: such as HNSW for fast search at scale.
- Hybrid search: combines vectors with metadata filters and keywords.
- Uses: RAG, recommendations, and duplicate detection.
- Design choices: embedding model, index type, update frequency, and access controls.
Why It Matters
It is a core building block for retrieval-augmented generation. Access controls matter too, because sensitive documents must stay visible only to the people allowed to see them.
How ClearLeaff Applies It
We include vector databases in the RAG pipelines we build, tuning chunking, indexing, and retrieval, and applying governance so sensitive data reaches only the right people.