This is an early in-progress draft! It’s our first attempt at assembling this information, so is far from comprehensive still, and likely to have omissions or errors. Please help by joining the Slack ...

* 1. Take the user's query as-is. * 2. Embed it using an embedding model. * 3. Use the query's embedding to search an embedding store (containing small segments of your documents) * for the X most ...

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