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DBT Macros Guide: From Basics to Advanced Techniques

Table of Contents

1. Introduction
2. Basic Concepts
3. Macro Structure
4. Advanced Techniques
5. Best Practices
6. Real-World Examples

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LLM Model Utilization with Langchain

Langchain

At its core, LangChain is a framework built around LLMs. We can use it for chatbots, Generative Question-Answering (GQA), summarization, and much more.

The core idea of the library is that we can “chain” together different components to create more advanced use cases around LLMs.

Feature list: https://langchain.com/features.html

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# original post: https://rentry.org/sd-loopback-wave
# original author: https://rentry.org/AnimAnon
import os
import platform
import numpy as np
from tqdm import trange
import math
import subprocess as sp
import string
import random

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# To run you'll need some secrets:
# 1. SERPAPI_API_KEY secret in env var - get from https://serpapi.com/
# 2. OPENAI_API_KEY secret in env var - get from https://openai.com

import streamlit as st
import json, os
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI
from serpapi import GoogleSearch