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Short answer

Add a FAISS index on an embedding column, then call get_nearest_examples or search for vector similarity retrieval. [S1][S2]

Points

• FAISS finds examples by vector similarity. [S1]
• add_faiss_index attaches an index you query later. [S2]
• The index name is what get_nearest_examples / search use. [S2]

Claims

• id: C1 claim: FAISS enables searching for examples in a dataset. source: S1 evidence: "FAISS and Elasticsearch enables searching for examples in a dataset"
• id: C2 claim: FAISS retrieves by vector similarity. source: S1 evidence: "FAISS retrieves documents based on the similarity of their vector representations"
• id: C3 claim: add_faiss_index builds the index on a column. source: S2 evidence: "add_faiss_index"
• id: C4 claim: get_nearest_examples uses the named index. source: S2 evidence: "used to call get_nearest_examples() or search()"

Sources

• [S1] Search index · Hugging Face. Hugging Face (Datasets docs). https://huggingface.co/docs/datasets/faiss_es . License: Apache-2.0 (huggingface/datasets docs). Accessed 2026-11-05.
• [S2] Main classes. Hugging Face. https://huggingface.co/docs/datasets/package_reference/main_classes . License: Apache-2.0 (huggingface/datasets docs). Accessed 2026-11-05.

License: this note is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Summary by iinventedlight (AI-assisted).

Source notice: Hugging Face Datasets documentation, Apache License 2.0 (https://github.com/huggingface/datasets/blob/main/LICENSE).

@misc{iil_note_43ftbb3r,
  author = {{iinventedlight}},
  title = {{Add a FAISS search index to a Hugging Face dataset}},
  howpublished = {iinventedlight Library},
  year = {2026},
  date = {2026-11-04},
  url = {https://iinventedlight.com/library/notes/hf-datasets-faiss-search/},
  note = {Permanent ID iil:note:43ftbb3r. License: CC-BY-4.0.},
}

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