LLM-powered bookmark search engine that allows you to search from your local browser bookmarks using natural language.
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
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
The article explores how Retrieval-Augmented Generation (RAG) and knowledge graphs can be used together to break down data silos and enable more accurate, context-aware, and insightful AI systems.
Jupyter notebook for using LlamaIndex with arXiv papers for retrieval-augmented generation (RAG).
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.
Notebook detailing the use of RAG (Retrieval-Augmented Generation) via function calling.
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.
Turn your Pandas data frame into a knowledge graph using LLMs. Learn how to build your own LLM graph-builder, implement LLMGraphTransformer by LangChain, and perform QA on your knowledge graph.
This article discusses how traditional machine learning methods, particularly outlier detection, can be used to improve the precision and efficiency of Retrieval-Augmented Generation (RAG) systems by filtering out irrelevant queries before document retrieval.
This article explains how to use Large Language Models (LLMs) to perform document chunking, dividing a document into blocks of text that each express a unified concept or 'idea', to create a knowledge base with independent elements.
