Releases the YouRetriever: The Simplest Interface to the Search API released the YouRetriever, the simplest interface to the Search API. The Search API was developed with Retrieval Augmented Generation (RAG) applications in mind by LLMs for LLMs. They achieve this by testing our API with various datasets to establish standards for LLM efficiency in the RAG-QA environment. They provided a detailed analysis of the differences and similarities between the Search API and the Google Search API. They provided a framework for evaluating LLMs in an RAG-QA environment. They used the RetrievalQA Chain to assess how well their retrievers did on Hotpot QA as well. A Hotpot dataset includes a query, an answer, and its context. When using the “distractor” mode, in which the LLM must avoid being fooled by intentionally false language, the context can shift about the question/answer. One of the tests involved replacing the dataset’s original context with the fragments of text returned by search APIs. Since APIs must search the entire internet for the desired information rather than relying solely on the list of snippets provided in the dataset, the internet serves as the distractor text in this case. When testing the efficacy of search APIs in conjunction with an LLM, they subject the systems to what they dub the “web distractor” scenario.

When possible, it returns more extensive snippets of information, and shortly, you’ll be able to choose how much text you’d like returned, from a single sample to the complete page. There are 27 results for “great Keith” when using the default parameters, and some documents have some content. For LLMs working in a RAG-QA environment, this makes our search API particularly useful.

They conducted their tests on the HotPotQA dataset. They use the datasets library to retrieve this information from the Huggingface dataset. Here, they use full wiki instead of distractor, but as was previously said, they will generate their context utilizing the search APIs.

Visit for further instructions on setting up. will soon release a broader search study, so stay tuned for more information. Anyone interested in being an early access partner should write to with some information about themselves, their use case, and the number of calls they anticipate making each day.

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Dhanshree Shenwai is a Computer Science Engineer and has a good experience in FinTech companies covering Financial, Cards & Payments and Banking domain with keen interest in applications of AI. She is enthusiastic about exploring new technologies and advancements in today’s evolving world making everyone's life easy.

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