This guide provides a streamlined approach to implementing RAGAS evaluation while managing OpenAI API rate limits effectively. It's designed to be straightforward, visual, and actionable.
RAGAS (Retrieval Augmented Generation Assessment) is a framework for evaluating RAG systems with:
Large Language Models (LLMs) have revolutionized AI applications by enabling natural language understanding and generation capabilities. However, as these applications grow more sophisticated, ensuring their quality, reliability, and accuracy becomes increasingly challenging. Two key architectures in the LLM ecosystem are Retrieval-Augmented Generation (RAG) systems and LLM-powered agents.
This guide introduces the concepts of RAG systems and agents, explains their relationship, and presents the Ragas framework for evaluating their performance. We'll explore examples from two practical implementations: evaluating a RAG system and evaluating an agent application.
Retrieval-Augmented Generation (RAG) has emerged as a powerful paradigm for enhancing Large Language Models (LLMs) with external knowledge [4]. At the heart of any effective RAG system lies well-crafted prompts that guide the retrieval and generation processes. As RAG systems move from development to production, managing these prompts becomes increasingly complex.
Prompt engineering for RAG systems presents unique challenges:
Retrieval-Augmented Generation (RAG) has emerged as a powerful approach for enhancing Large Language Models (LLMs) with external knowledge. However, evaluating RAG pipelines presents significant challenges due to the complexity of retrieval quality, generation accuracy, and the overall coherence of responses. This document provides a comprehensive analysis of using RAGAS (Retrieval Augmented Generation Assessment) for synthetic test data generation and LangSmith for RAG pipeline evaluation, based on the Jupyter notebook example provided.
Retrieval-Augmented Generation is a technique that enhances LLMs by providing them with relevant external knowledge. A typical RAG system consists of two main components[1]:
License Notice: This template is provided under the Creative Commons Attribution 4.0 International License. It incorporates elements from SixArm/consulting-agreement (CC-BY-SA-4.0) and shimon/consulting-contract-template (Public Domain).
DISCLAIMER: This template is for informational purposes only and does not constitute legal advice. Consult with a qualified legal professional before using this template for your business.
Retrieval-Augmented Generation (RAG) systems have emerged as a powerful approach for enhancing Large Language Models (LLMs) with domain-specific knowledge. However, evaluating these systems poses unique challenges due to their multi-component nature and the complexity of assessing both retrieval quality and generation faithfulness. This paper provides a comprehensive examination of RAGAS (Retrieval Augmented Generation Assessment), an open-source framework that addresses these challenges through reference-free evaluation metrics and sophisticated synthetic data generation. RAGAS distinguishes itself through its knowledge graph-based approach to test set generation and specialized query synthesizers that simulate diverse query types. We analyze its capabilities, implementation architecture, and comparative advantages against alternative frameworks, while also addressing current limitations and future research dire
Before configuring your MCP clients, it's important to understand the two components involved:
llms.txt: A website index format that provides background information, guidance, and links to detailed documentation for LLMs. As described in the LangChain documentation, llms.txt is "an index file containing links with brief descriptions of the content"[1]. It acts as a structured gateway to a project's documentation.
MCP (Model Context Protocol): A protocol enabling communication between AI agents and external tools, allowing LLMs to discover and use various capabilities. As stated by Anthropic, MCP is "an open protocol that standardizes how applications provide context to LLMs"[2].
In today's digital landscape, Large Language Models (LLMs) like ChatGPT, Claude, and Gemini constantly navigate the web to gather information and provide answers. But there's a fundamental problem: websites were designed for human consumption, not AI understanding. From complex HTML structures to JavaScript-heavy interfaces, LLMs often struggle to extract meaningful content from the modern web.
Enter llms.txt – a proposed web standard that could revolutionize how AI systems interact with online content.
Proposed in September 2024 by Jeremy Howard, co-founder of Answer.AI, llms.txt is a markdown-formatted file placed at a website's root directory (e.g., example.com/llms.txt)[^1]. This standardized file provides concise, structured information and links to detailed content, designed specifically to help language models better understand and navigate websites[^2].
NOTE: I created a sample solutions architecture document primarily for discussion purposes, covering different aspects of the overall solution.
In today's interconnected digital landscape, data exchanges between diverse systems necessitate effective transformation mechanisms. Organizations frequently need to convert data between different formats to ensure interoperability and seamless information flow. The Data Format Description Language (DFDL) has emerged as a powerful standard for modeling and describing text and binary data formats in a standardized way. This capability is crucial for legacy systems integration, data migration, and modern API interfaces.
JSON (JavaScript Object Notation) has become the de facto standard for data exchange in web applications, cloud services, and APIs due to its simplicity, human readability, and widespread support across programming languages. Converting various document formats to JSON is therefore a common requirement in many integration scenarios.
While DFDL provides a robust framework for describing and parsing diverse data fo
