GPT-4o, EXA multi query web researcher with custom reranking algorithm

The video introduces the ultimate web researcher using the EXA web search API, which conducts multiple searches, chunks and reranks results, and employs AI models like GPT to answer queries. The process involves running queries, saving search results in JSON files, chunking them, reranking based on similarity searches, and utilizing local search functions for further exploration.

In the video, the presenter introduces the ultimate web researcher using the EXA web search API, which performs multiple web searches and then chunks the results to perform similarity searches across multiple queries. The process involves running a question, specifying the number of days to search within, generating alternative forms of the question, and keywords. These, along with the original question, are used to perform EXA searches with 10 results each. The search results are saved into appropriate JSON files and chunked into 800 tokens each for further analysis.

The chunked search results are then sorted and reranked based on similarity searches across all generated queries. The top chunks are identified, and the top five chunks for each query are counted to determine their relevance. These top chunks are then sent to the GPT model to provide an answer to the original question. Once this step is completed, the top chunks are saved into a JSON file for future reference and analysis. This reranking and chunking process aims to provide the most relevant information based on the initial search queries.

Following the initial web search and reranking process, the local search function can be utilized to delve deeper into the chunked search results. By using the chunk.JSON file generated from the web search, users can ask additional questions to further explore the content within the chunks. The local search function operates similarly to the ultimate web researcher but focuses on utilizing the pre-generated chunked data for subsequent queries.

The video highlights the modular and experimental nature of the ultimate web researcher, allowing for customization and improvement at each step of the process. The presenter emphasizes the importance of investigating the search results returned by EXA to ensure the relevance of the information used for answering queries. The process is designed to be adaptable, with the potential to incorporate other search APIs beyond EXA for more comprehensive research capabilities.

Overall, the ultimate web researcher with the EXA web search API showcases a sophisticated approach to web research by performing multiple searches, chunking and reranking search results, and utilizing advanced AI models like GPT for answering queries. The presenter offers insights into the code structure and functionality of the researcher, encouraging viewers to explore and enhance the methodology for their own research needs.