Qodo releases Qodo-Embed-1-1.5B, an open-source code embedding model that outperforms competitors from OpenAI and Salesforce, enhancing code search, retrieval, and understanding for enterprise development teams.
- "Deep Research" is a new trend in AI-driven research using large language models for multi-step investigations.
- The article compares Deep Research systems, highlighting capabilities and limitations like generating tangential content and handling nonsensical queries.
- Includes systems such as Gemini Advanced 1.5 Pro, OpenAI’s Deep Research, Perplexity’s Deep Research Mode, and You.com’s Research Feature.
A summary of personal experiences using generative models while programming, highlighting the benefits and practical applications of LLMs in productivity and programming tasks.
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.
