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796acd1aee
5288: Improve AI logging r=dureuill a=Kerollmops This PR fixes #5285 and brings the changes from #5233 to simplify debugging indexation and search performance issues related to AI. The following texts can be found in the logs to debug and understand performance issues: - `embed_one: search` represents the time we spent waiting for the embedding generation, i.e., OpenAI, local HuggingFace, Ollama. - `filtered_universe: search::universe` the time spent filtering the documents. - ~`next_bucket: search::vector_sort` is the time spent finding the nearest neighbors (ANNs) in the vector store (arroy), locally~ was being triggered too many times. - `indexing::vectors` is the time arroy spends indexing the new vectors for a batch. - `documents::extract vectors` and `documents::merge vectors` to see the time spent generating and writing the embeddings. Co-authored-by: Kerollmops <clement@meilisearch.com> |
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a concurrent indexer combined with fast and relevant search algorithms
Introduction
This crate contains the internal engine used by Meilisearch.
It contains a library that can manage one and only one index. Meilisearch manages the multi-index itself. Milli is unable to store updates in a store: it is the job of something else above and this is why it is only able to process one update at a time.