Dhravya

const LegalHallucinationAnalysis = z.object({
  // Case identification
  case_id: z.string(),
  case_name: z.string(),
  date_occurred: z.string().regex(/^\d{4}-\d{2}-\d{2}$/),
  ai_model_used: z.string(),
  source_url: z.string().url().optional(),
  
  // Hallucination details
  hallucination_details: z.object({

Dhravya

// import { createAnthropic } from "@ai-sdk/anthropic"
// import { createGroq } from "@ai-sdk/groq"
// import { createGoogleGenerativeAI } from "@ai-sdk/google"
import { createOpenAI } from '@ai-sdk/openai';
import { createAiGateway } from '../../packages/ai-gateway-provider/src/index.js';
import { generateText } from 'ai';

export default {
	async fetch() {
		const originalFetch = fetch;

Dhravya

import { CloudClient } from 'chromadb';
import {createOpenAI} from "@ai-sdk/openai"

export default {
	async fetch(request, env, ctx): Promise<Response> {

		const openai = createOpenAI({
			apiKey: "sk-proj-"
		})
		const model = openai.textEmbeddingModel("text-embedding-3-small")

Dhravya

import {
  type AgentNamespace,
  type Connection,
  routeAgentRequest,
  type Agent,
  type Schedule,
} from "agents-sdk";
import { AIChatAgent } from "agents-sdk/ai-chat-agent";
import {
  createDataStreamResponse,

Dhravya

import torch
import re
from transformers import AutoModelForCausalLM, AutoTokenizer
from typing import List, Dict, Tuple
from tqdm import tqdm

class InfiniRetri:
    def __init__(self, model_name, chunk_size=512, top_k=50, phrase_window=5, device=None):
        self.chunk_size = chunk_size
        self.top_k = top_k

Dhravya

import { type NextApiRequest, type NextApiResponse } from 'next';
import Ffmpeg from 'fluent-ffmpeg';

export default function handler(req: NextApiRequest, res: NextApiResponse) {
  if (req.method === 'GET') {
    // Set response headers
    res.setHeader('Content-Type', 'video/mp4');
    res.setHeader('Connection', 'keep-alive');
    res.setHeader('Accept-Ranges', 'bytes');

Dhravya

import { createToolsFromOpenAPISpec, runWithTools } from "@cloudflare/ai-utils";

export default {
	async fetch(request, env, ctx) {
		const response = (await runWithTools(
			env.AI,
			"@hf/nousresearch/hermes-2-pro-mistral-7b",
			{
				messages: [
					{

Dhravya

		await runWithTools(env.AI, "@hf/nousresearch/hermes-2-pro-mistral-7b", {
			messages: [
				{ role: "system", content: "Put user given values in KV" },
				{ role: "user", content: prompt },
			],
			tools: [
				{
					name: "KV update",
					description: "Update a key-value pair in the database",
					parameters: {

Dhravya

let tweets = []; // Initialize an empty array to hold all tweet elements

const scrollInterval = 1000; 
const scrollStep = 5000; // Pixels to scroll on each step

let previousTweetCount = 0;
let unchangedCount = 0;

const scrollToEndIntervalID = setInterval(() => {
    window.scrollBy(0, scrollStep);

Dhravya

from rich import print
import time

FORBIDDEN_CHARS = ["⍼", "⳧"]

text= "really really really long text that has a lot of repeating stuff so that it can be coompressed"


def find_all_non_overlapping_repeating_substrings(text: str):
    text = (text + ' ') if text[-1] != ' ' else text