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fix broken link in docs #1454

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Oct 18, 2024
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fix broken link in docs #1454

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2 changes: 1 addition & 1 deletion docs/index.md
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Expand Up @@ -33,7 +33,7 @@ The re-ranking process is roughly the following:
* Vectorize each result snippet (or parts of it)
* Re-rank the results by aggregating the similarity, frequency and position, and adjusting for other factors like length variation, freshness, etc

The [Xethub study](https://about.xethub.com/blog/you-dont-need-a-vector-database) as [explained by Simson Garfinkel](https://www.linkedin.com/pulse/vector-databases-rag-simson-garfinkel-hzule/) showed that re-ranking so-called "naive" search engines like those that use the BM25 algorithm for retrieval, outperforms moving the data into a vector database for many common NLP tasks such as question answering.
The [Xethub study](https://swirlaiconnect.com/blog/using-vectors-without-a-vector-database) as [explained by Simson Garfinkel](https://www.linkedin.com/pulse/vector-databases-rag-simson-garfinkel-hzule/) showed that re-ranking so-called "naive" search engines like those that use the BM25 algorithm for retrieval, outperforms moving the data into a vector database for many common NLP tasks such as question answering.

SWIRL AI Connect also includes state-of-the-art cross-silo [Retrieval Augmented Generation (RAG)](https://en.wikipedia.org/wiki/Retrieval-augmented_generation) for generating AI insights like summarization, question answering and visualization of relevant result sets.

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