donbr

RAGAS: A Comprehensive Framework for RAG Evaluation and Synthetic Data Generation

Abstract

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

Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

appinteractive

import crypto from 'crypto'
import PouchDB from 'pouchdb'
import md5 from 'md5'
import { app } from 'electron'
import { join } from 'path'
import { existsSync, mkdirSync, rmdirSync } from 'fs'

class EncryptedPouchDB extends PouchDB {
  constructor(name, options = {}) {
    const userData = app.getPath('userData')

zklhp

# python -i chat_with_Raven.py rwkv.cpp-14B-Q4_1_O.bin

import os
import argparse
import pathlib
import sampling
import tokenizers
import rwkv_cpp_model
import rwkv_cpp_shared_library

vyorkin

• sp - Substrate Primitives

• sc - Substrate Client

• sp-api - Substrate runtime API

• sp-consensus - Common utilities for building and using consensus engines in Substrate

• sp-consensus-aura - Primitives for Aura PoA consensus engine

• sc-consensus-aura - Client primitives for Aura PoA consensus engine

• sp-core - Shareable Substrate types

• sp-finality-grandpa - Primitives for GRANDPA integration, suitable for WASM compilation

• sp-runtime - Runtime Modules shared primitive types

brenopolanski

import { ReactNode, useRef } from 'react'
import { Button, FormControl, FormErrorMessage, FormLabel, Icon, InputGroup } from '@chakra-ui/react'
import { useForm, UseFormRegisterReturn } from 'react-hook-form'
import { FiFile } from 'react-icons/fi'

type FileUploadProps = {
  register: UseFormRegisterReturn
  accept?: string
  multiple?: boolean
  children?: ReactNode

Jerryseazia

import 'package:flutter/material.dart';

void main() {
  runApp(MyApp());
}

class MyApp extends StatelessWidget {
  @override
  Widget build(BuildContext context) {
    return MaterialApp(

FilledStacks

{
	"Freezed model": {
	  "prefix": "frz",
	  "body": [
		"@freezed",
		"class ${1:${TM_FILENAME_BASE/(.*)/${1:/pascalcase}/g}} with _$${1} {",
		"  factory ${1}({",
		"    required ${2:String id},",
		"  }) = _${1};",
		"}"

maxkostinevich

#
# Github Actions for Serverless Framework
#
# Create AWS_KEY and AWS_SECRET secrets in Github repository settings
# If you're using env.yml file, store its content as ENV Github secret
#
# Master branch will be deployed as DEV and every new tag starting with "v**" (e.g. v1.0, v1.2, v2.0, etc) will be deployed as PROD
#
# Learn more: https://maxkostinevich.com/blog/how-to-deploy-serverless-applications-using-github-actions/
#

rosko

To put JSON to Gun

gun.putJSON(json);

To get JSON from Gun

gun.open(() => {