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Mention brute-force approach, link to vector indexing issue

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Refs #216. Closes #214
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Simon Willison 2023-09-03 19:10:42 -07:00
parent 94f0a1a337
commit f842fbea49
2 changed files with 5 additions and 1 deletions
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docs/embeddings/cli.md
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@ -285,6 +285,8 @@ llm-docs/plugins/index.md
The `llm similar` command searches a collection of embeddings for the items that are most similar to a given or item ID.
This currently uses a slow brute-force approach which does not scale well to large collections. See [issue 216](https://github.com/simonw/llm/issues/216) for plans to add a more scalable approach via vector indexes provided by plugins.
To search the `quotations` collection for items that are semantically similar to `'computer science'`:
```bash

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docs/embeddings/python-api.md
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@ -116,7 +116,9 @@ if Collection.exists(db, "entries"):
(embeddings-python-similar)=
## Retrieving similar items
Once you have populated a collection of embeddings you can retrieve the entries that are most similar to a given string using the `similar()` method:
Once you have populated a collection of embeddings you can retrieve the entries that are most similar to a given string using the `similar()` method.
This method uses a brute force approach, calculating distance scores against every document. This is fine for small collections, but will not scale to large collections. See [issue 216](https://github.com/simonw/llm/issues/216) for plans to add a more scalable approach via vector indexes provided by plugins.
```python
for entry in collection.similar("hound"):