An analysis showing that structured outputs can sometimes perform worse than unstructured ones in certain tasks for different LLM models, emphasizing the importance of testing both approaches.
Discover the best Python libraries of 2024, categorized into general use and AI/ML/data tools, featuring innovative and practical solutions for developers and data scientists.
A script utilizing OpenAI's Llama models to interact within a terminal environment, allowing the models to execute Python code and communicate based on predefined prompts.
ServiceNow releases AgentLab, an open-source Python package for developing and evaluating web agents, addressing challenges in agent development such as poor scalability and difficulty in conducting reproducible experiments.
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 article explores automating the process of converting scientific code into LaTeX documents using GPT models and Python, aiming to streamline documentation workflows in scientific projects.
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.
Simple, unified interface to multiple Generative AI providers, supporting various providers including OpenAI, Anthropic, Azure, Google, AWS, Groq, Mistral, HuggingFace, and Ollama. It aims to facilitate the use of multiple LLMs with a standardized interface similar to OpenAI’s.
Apprise is a library that allows you to send notifications to almost all popular notification services, such as Telegram, Discord, Slack, and Amazon SNS. It supports multiple notification services, intuitive syntax, and can handle file attachments.
A Python hands-on guide to understand the principles of generating new knowledge by following logical processes in knowledge graphs. Discusses the limitations of LLMs in structured reasoning compared to the rigorous logical processes needed in certain fields.
