This article discusses the development of multimodal Retrieval Augmented Generation (RAG) systems which allow for the processing of various file types using AI. The article provides a beginner-friendly guide with example Python code and explains the three levels of multimodal RAG systems.
This project provides an LLM Websearch Agent using a local SearXNG server for search functionality and includes Python scripts and a bash script for interacting with an LLM to summarize search results.
A tool to download, transcribe, summarize, and chat with media files like videos, audio, documents, web articles, and books, all locally and automated.
This article discusses the importance of determining user query intent to enhance search results. It covers how to identify search and answer intents, implement intent detection using language models, and adjust retrieval strategies accordingly.
This page provides documentation for the rerank API, including endpoints, request parameters, and response formats.
A web search extension for Oobabooga's text-generation-webui (now with nougat) that allows for web search integration with the AI.
A post discussing new techniques developed for parsing and searching PDFs, focusing on turning them into a hierarchical structure for RAG search. The approach involves dynamically generating chunks for searches, sending headers and sub-headers to the Language Model along with relevant chunks.
LangChain's ElasticsearchRetriever enables full flexibility in defining retrieval strategies, allowing users to experiment with different approaches.
This article discusses the integration of Large Language Models (LLMs) into Vespa, a full-featured search engine and vector database. It explores the benefits of using LLMs for Retrieval-augmented Generation (RAG), demonstrating how Vespa can efficiently retrieve the most relevant data and enrich responses with up-to-date information.
This article discusses the growing importance of search functionality LLM applications. The author highlights the potential of search engines to handle complex queries, understand context, and deliver relevant results. The use of AI and machine learning in search is also explored, with examples of current and potential applications.
