klotz: agent* + llm*

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  1. Build Agentic AI with NVIDIA NIM and NeMo. Explore optimized AI models, connect AI agents to data, and deploy anywhere with NVIDIA NIM microservices.

    2025-02-14 Tags: , , , , , by klotz
  2. Hugging Face researchers developed an open-source AI research agent called 'Open Deep Research' in 24 hours, aiming to match OpenAI's Deep Research. The project demonstrates the potential of agent frameworks to enhance AI model capabilities, achieving 55.15% accuracy on the GAIA benchmark. The initiative highlights the rapid development and collaborative nature of open-source AI projects.

  3. The article discusses Browser Use, an open source AI agent system that offers a cost-free alternative to OpenAI's Operator. Browser Use provides flexibility by allowing users to choose their preferred AI model and comes with both a cloud and an open-source DIY version. This development is part of a broader trend in 2025 towards open source AI, challenging the dominance of expensive proprietary products.

    2025-01-30 Tags: , , , , by klotz
  4. A quickstart guide to installing, configuring, and using the Goose AI agent for software development tasks.

    2025-01-28 Tags: , , , , by klotz
  5. This blueprint demonstrates how to build an AI agent that automates blog post creation using LlamaIndex and NVIDIA's language and retrieval models, ensuring high-quality, well-researched content.

  6. An article detailing the capabilities and application of PydanticAI in building production-grade AI applications, particularly focusing on multi-agent systems.

    2025-01-08 Tags: , , , by klotz
  7. A tutorial on using Qwen2.5–7B-Instruct for creating a local, open-source, multi-agentic RAG system.

    The implementation described in the article focuses on creating a multi-agentic Retrieval-Augmented Generation (RAG) system using code agents and the Qwen2.5–7B-Instruct model. The system consists of three agents working together in a hierarchical structure:

    1. Manager Agent: This top-level agent breaks down user questions into sub-tasks, utilizes the Wikipedia search agent to find information, and combines the results to provide a final answer. Its system prompt is tailored to guide it through the process of decomposing tasks and coordinating with other agents.

    2. Wikipedia Search Agent: This agent interacts with the Wikipedia search tool to identify relevant pages and their summaries. It further delegates to the page search agent for detailed information retrieval from specific pages if needed. Its prompt is designed to help it navigate Wikipedia effectively and extract necessary information.

    3. Page Search Agent: This agent specializes in extracting precise information from a given Wikipedia page. It uses a semantic search tool to locate specific passages related to the query.

    To implement the multi-agent system efficiently, the article mentions several key decisions and modifications to the default Hugging Face implementation:

    • Prompting: Customized prompts for each agent, including specific examples that mirror the model’s chat template, to improve task-specific performance.
    • History Summarization: Limiting the history passed to each step to avoid excessive context length and improve execution speed.
    • Tool Wrapping: Wrapping managed agents as tools to allow better control over the prompts and streamline the architecture.
    • Error Handling: Implementing mechanisms to handle tool execution errors effectively.
    • Execution Limiting: Setting a maximum number of attempts for the page search agent to prevent infinite loops when searching for information that might not be present on the page.
    • Tool Response Modification: Adapting the tool response format to fit the Qwen2.5–7B-Instruct model’s chat template, which supports only system, user, and assistant roles.

    By structuring the implementation with these considerations, the system achieves the capability to perform complex, multi-hop question-answering tasks efficiently, despite being powered by a relatively small model running on consumer-grade hardware

    2025-01-01 Tags: , , , , by klotz
  8. An article on building an AI agent to interact with Apache Airflow using PydanticAI and Gemini 2.0, providing a structured and reliable method for managing DAGs through natural language queries.

    • Agent interacts with Apache Airflow via the Airflow REST API.
    • Agent can understand natural language queries about workflows, fetch real-time status updates, and return structured data.
    • Sample DAGs are implemented for demonstration purposes.
  9. This article discusses methods to measure and improve the accuracy of Large Language Model (LLM) applications, focusing on building an SQL Agent where precision is crucial. It covers setting up the environment, creating a prototype, evaluating accuracy, and using techniques like self-reflection and retrieval-augmented generation (RAG) to enhance performance.

    2024-12-20 Tags: , , , , , by klotz
  10. A collection of lightweight AI-powered tools built with LLaMA.cpp and small language models.

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