Retrieval settings

There are different ways orq.ai can retrieve information from sources loaded within the Knowledge Base.

You can configure these options on the Knowledge Settings page. Each option will yield different results, depending on your needs.

Vector Search

Vector search is the fastest method of searching through a database built with your Knowledge Sources. Here, our systems take the user query and look for the text segments most similar to their vector representations. The search will return the preprocessed chunks from the sources most similar and relevant to the user's query.

Keyword Search

Keyword Search is a different method for retrieving relevant results within a Knowledge Base. In this method, the entire content is indexed, and the system searches for segments containing the words from the user’s query.

Hybrid Search

Hybrid search uses both the previously mentioned Vector & Keyword searches, then combining results and returning the most relevant chunks to the model.

Search Parameters

All previous search types can be configured with the following parameters:

Chunk limit

This parameter sets the number of chunks most similar to the user's questions.

Threshold

This controls the relevance of the results on a scale from 0 to 1. Results scoring below threshold will be excluded from retrieval. The closer to 1, the more relevant and narrow the results will be.

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Setting a too high threshold can yield to no result to a search.

Reranking

Reranking invokes a model that analyzes your initial query and the result fetched by the Knowledge Base search. This model then scores the similarity of the chunks returned with the user query, then scores and ranks the chunks accordingly.

This ensures the results is the most relevant for your query.

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To use reranking within your Knowledge Base, you must enable at least one Reranking model within your Model Garden.