Embedding Search

Semantic search on your documents is performed using simulated embeddings to understand conceptual similarity. It enables efficient searching through large collections by matching query meaning rather than exact keywords.

Hybrid search improves document retrieval by combining keyword matching with vector similarity. This approach ensures robust results by leveraging both exact term matching and semantic understanding.

Yes, you can add documents to a persistent knowledge base. The system supports indexing, searching, and continuously adding new documents to maintain an updatable vector search index.

The best way to find information without exact phrases is using vector-based semantic search. It locates documents based on conceptual similarity, finding relevant cloud deployment strategies even if the exact phrase is absent.

Document chunking splits text into manageable segments before applying vector similarity ranking. This process ensures precise relevance ranking by evaluating simulated embeddings for each specific chunk.

No external dependencies or APIs are required for semantic search. The system performs vector-based semantic search using simulated embeddings entirely within its own scripts.