# Selective re-embed index

`com.tnsai.intelligence.rag.vector.SelectiveReembedIndex` is a local
vector index that re-embeds only chunks whose content hash changed.
It is a standalone index you use directly: Role RAG does not use it, it does
not implement `com.tnsai.memory.advanced.VectorIndex`, and nothing opens it
from an environment URL or `@VectorMemory.provider`. After a one-file edit,
only that file's new hashes are embedded again.

## Sync and search

```java
SelectiveReembedIndex index = new SelectiveReembedIndex(embeddings);
ReindexStats first = index.syncSource("a.md", List.of("alpha", "shared"));
ReindexStats again = index.syncSource("a.md", List.of("alpha changed", "shared"));
List<ScoredChunk> hits = index.search("alpha", 3);
```

`syncSource(sourceId, chunks)` replaces one source's ordered chunk
texts. Each chunk is keyed by SHA-256 of its UTF-8 bytes:

1. a hash that already has a vector is **reused**
2. a new hash calls `EmbeddingFunction.embed` once and stores that
   vector
3. a hash dropped from this source is **removed** only when no other
   source still owns it
4. consecutive chunks on the same source become neighbors, which
   `neighbors(contentHash)` returns

`ReindexStats` reports `considered`, `reused`, `embedded`, and
`removed`. Duplicate payloads across sources share one stored
embedding. `size()` is the unique-hash count. `uniqueEmbeddingRatio()`
is unique / naive, and `1.0` means no sharing.

`search(query, topK)` embeds the query and ranks unique stored
vectors by cosine similarity. Hits are `ScoredChunk` records
(`contentHash`, `sourceId`, `text`, `score`). Dimension is pinned on
the first embed, and a later mismatched length fails loud.

## Related

- [Remote vector stores](remote-vector-stores.md): the Qdrant and pgvector
  `VectorIndex` adapters.
- [KnowledgeBase API](knowledge-base.md): `InMemoryKnowledgeBase`, a different
  store.
- [Pipeline](pipeline.md): FILE ingest, which reuses a file's chunks while its
  ingestion fingerprint is unchanged.
- [Embeddings](embedding-prefixes.md): the `EmbeddingFunction` this index calls.