Embedding index (file-backed)

This backend builds an embedding index under a corpus and queries it using exact cosine similarity.

It is intended for larger corpora where you want a local, pip-installable workflow that does not depend on an external vector database.

Backend ID

embedding-index-file

This backend builds a retrieval snapshot that materializes snapshot artifacts under the corpus, for example:

Queries memory-map the embedding matrix and scan in batches so memory usage stays bounded, even when the index is larger than available RAM.

Embeddings are computed over chunks. Chunking is configured per configuration by selecting a chunker and its configuration.

Chunking is part of the index contract: evidence references chunk boundaries so you can trace retrieval outputs back to the original item text.