Kaarthik Andavar kaarthik108

This is the process I used to put redshift user defined functions into dbt.

Created a subdirectory: macros/udfs/
Created a file for each udf, e.g. macros/udfs/f_future_date.sql.

{% macro f_future_date() %}
CREATE OR REPLACE FUNCTION {{target.schema}}.f_future_date()
RETURNS TIMESTAMP
IMMUTABLE AS $$
SELECT '2100-01-01'::TIMESTAMP;

	You are Manus, an AI agent created by the Manus team.

	You excel at the following tasks:
	1. Information gathering, fact-checking, and documentation
	2. Data processing, analysis, and visualization
	3. Writing multi-chapter articles and in-depth research reports
	4. Creating websites, applications, and tools
	5. Using programming to solve various problems beyond development
	6. Various tasks that can be accomplished using computers and the internet

	import { useEffect, useState } from "react";

	type UseTextSelectionReturn = {
	text: string;
	rects: DOMRect[];
	ranges: Range[];
	selection: Selection \| null;
	};

	const getRangesFromSelection = (selection: Selection): Range[] => {

	import { useCallback, useEffect, useState } from "react";

	// Define custom types for SpeechRecognition and SpeechRecognitionEvent
	interface ISpeechRecognitionEvent extends Event {
	results: SpeechRecognitionResultList;
	resultIndex: number;
	}

	interface ISpeechRecognition extends EventTarget {
	lang: string;

	"""QA Chatbot streaming using FastAPI, LangChain Expression Language , OpenAI, and Chroma.

	Features
	--------
	- Persistent Chat Memory:
	Stores chat history in a local file.
	- Persistent Vector Store:
	Stores document embeddings in a local vector store.
	- Standalone Question Generation:
	Rephrases follow-up questions to standalone questions in their original language.

	# The goal of this file is to provide a FastAPI application for handling
	# chat requests amd generation AI-powered responses using conversation chains.
	# The application uses the LangChaing library, which includes a chatOpenAI model
	# for natural language processing.

	# The `StreamingConversationChain` class is responsible for creating and storing
	# conversation memories and generating responses. It utilizes the `ChatOpenAI` model
	# and a callback handler to stream responses as they're generated.

	# The application defines a `ChatRequest` model for handling chat requests,

	"""This is an example of how to use async langchain with fastapi and return a streaming response.
	The latest version of Langchain has improved its compatibility with asynchronous FastAPI,
	making it easier to implement streaming functionality in your applications.
	"""
	import asyncio
	import os
	from typing import AsyncIterable, Awaitable

	import uvicorn
	from dotenv import load_dotenv

	# This is a new feature, so make sure to update to the latest version of transformers!
	# You will also need to pip install tensorflow_text

	import tensorflow as tf
	from transformers import TFAutoModel, TFBertTokenizer


	class EndToEndModel(tf.keras.Model):
	def __init__(self, checkpoint):
	super().__init__()

	"""
	An alternative text clip for Moviepy, relying on Gizeh instead of ImageMagick

	Advantages:
	- Super fast (20x faster)
	- no need to install imagemagick
	- full-vector graphic, no aliasing problems
	- Easier font names

	Disadvantages:

	#!/bin/bash

	USER=${1:-sebble}

	STARS=$(curl -sI https://api.github.com/users/$USER/starred?per_page=1\|egrep '^Link'\|egrep -o 'page=[0-9]+'\|tail -1\|cut -c6-)
	PAGES=$((658/100+1))

	echo You have $STARS starred repositories.
	echo