When a document is added to a knowledge‑base collection with `ADD_KB` or `ADD_WEBSITE`, the system performs several steps to make it searchable:
1.**Content Extraction**– Files are read and plain‑text is extracted (PDF, DOCX, HTML, etc.).
2.**Chunking**– The text is split into 500‑token chunks to keep embeddings manageable.
3.**Embedding Generation**– Each chunk is sent to the configured LLM embedding model (default **BGE‑small‑en‑v1.5**) to produce a dense vector.
4.**Storage**– Vectors, along with metadata (source file, chunk offset), are stored in Qdrant under the collection’s namespace.
5.**Indexing**– Qdrant builds an IVF‑PQ index for fast approximate nearest‑neighbor search.
## Index Refresh
If a document is updated, the system re‑processes the file and replaces the old vectors. The index is automatically refreshed; no manual action is required.
## Example
```basic
ADD_KB "company-policies"
ADD_WEBSITE "https://example.com/policies"
```
After execution, the `company-policies` collection contains indexed vectors ready for semantic search via the `FIND` keyword.
