├── README.md ├── components ├── .DS_Store ├── Answer │ ├── Answer.tsx │ └── answer.module.css ├── Footer.tsx └── Navbar.tsx ├── license ├── next-env.d.ts ├── next.config.js ├── package-lock.json ├── package.json ├── pages ├── .DS_Store ├── _app.tsx ├── _document.tsx ├── api │ ├── .DS_Store │ ├── search.ts │ └── vectordbqa.ts └── index.tsx ├── postcss.config.js ├── public ├── .DS_Store ├── 0001.jpg ├── 0002.jpg ├── 0003.jpg ├── 0004.jpg ├── 0005.jpg ├── 0006.jpg ├── 0007.jpg ├── 0008.jpg ├── 0009.jpg ├── 0010.jpg ├── 0011.jpg ├── 0012.jpg ├── 0013.jpg ├── 0014.jpg ├── 0015.jpg ├── 0016.jpg ├── 0017.jpg ├── 0018.jpg ├── 0019.jpg ├── 0020.jpg ├── 0021.jpg ├── 0022.jpg ├── 0023.jpg ├── 0024.jpg ├── 0025.jpg ├── 0026.jpg ├── 0027.jpg ├── 0028.jpg ├── 0029.jpg ├── 0030.jpg ├── 0031.jpg ├── 0032.jpg ├── 0033.jpg ├── 0034.jpg ├── 0035.jpg ├── 0036.jpg ├── 0037.jpg ├── 0038.jpg ├── 0039.jpg ├── 0040.jpg ├── 0041.jpg ├── 0042.jpg ├── 0043.jpg ├── 0044.jpg ├── 0045.jpg ├── 0046.jpg ├── 0047.jpg 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├── 0203.jpg ├── 0204.jpg ├── 0205.jpg ├── 0206.jpg ├── 0207.jpg ├── 0208.jpg ├── 0209.jpg ├── 0210.jpg ├── 0211.jpg ├── 0212.jpg ├── 0213.jpg ├── 0214.jpg ├── 0215.jpg ├── 0216.jpg ├── 0217.jpg ├── 0218.jpg ├── 0219.jpg ├── 0220.jpg ├── 0221.jpg ├── 0222.jpg ├── 0223.jpg ├── 0224.jpg ├── 0225.jpg ├── 0226.jpg ├── 0227.jpg ├── 0228.jpg ├── 0229.jpg ├── 0230.jpg ├── 0231.jpg ├── 0232.jpg ├── 0233.jpg ├── 0234.jpg ├── 0235.jpg ├── 0236.jpg ├── 0237.jpg ├── 0238.jpg ├── 0239.jpg ├── 0240.jpg ├── 0241.jpg ├── 0242.jpg ├── 0243.jpg ├── 0244.jpg ├── 0245.jpg ├── 0246.jpg ├── 0247.jpg ├── 0248.jpg ├── 0249.jpg ├── 0250.jpg ├── 0251.jpg ├── 0252.jpg ├── 0253.jpg ├── 0254.jpg ├── 0255.jpg ├── 0256.jpg ├── 0257.jpg ├── 0258.jpg ├── 0259.jpg ├── 0260.jpg ├── 0261.jpg ├── 0262.jpg ├── 0263.jpg ├── 0264.jpg ├── 0265.jpg ├── 0266.jpg ├── 0267.jpg ├── 0269.jpg ├── 0270.jpg ├── 0271.jpg ├── 0272.jpg ├── 0273.jpg ├── 0274.jpg ├── 0275.jpg ├── 0276.jpg ├── 0277.jpg ├── 0278.jpg ├── 0279.jpg ├── 0280.jpg ├── 0281.jpg ├── 0284.jpg ├── 0285.jpg ├── 0286.jpg ├── 0288.jpg ├── 0289.jpg ├── 0290.jpg ├── 0292.jpg ├── 0293.jpg ├── 0294.jpg ├── 0295.jpg ├── 0296.jpg ├── 0297.jpg ├── 0298.jpg ├── 0299.jpg ├── 0300.jpg ├── 0301.jpg ├── 0302.jpg ├── 0303.jpg ├── 0304.jpg ├── 0305.jpg ├── 0306.jpg ├── 0307.jpg ├── 0308.jpg ├── 0309.jpg ├── 0310.jpg ├── 0311.jpg ├── 0312.jpg ├── 0313.jpg ├── 0314.jpg ├── 0315.jpg ├── 0316.jpg ├── 0317.jpg ├── 0318.jpg ├── 0319.jpg ├── 0320.jpg ├── 0321.jpg ├── 0322.jpg ├── 0323.jpg ├── 0324.jpg ├── 0325.jpg ├── 0326.jpg ├── 0327.jpg ├── 0328.jpg ├── 0329.jpg ├── 0330.jpg ├── 0331.jpg ├── 0332.jpg ├── 0333.jpg ├── 0334.jpg ├── 0335.jpg ├── 0336.jpg ├── 0337.jpg ├── 0338.jpg ├── 0339.jpg ├── 0340.jpg ├── 0341.jpg ├── 0342.jpg ├── 0343.jpg ├── 0344.jpg ├── 0345.jpg ├── 0346.jpg ├── 0347.jpg ├── 0348.jpg ├── 0349.jpg ├── 0350.jpg ├── 0351.jpg ├── 0352.jpg ├── 0353.jpg ├── 0354.jpg ├── 0355.jpg ├── 0356.jpg ├── 0357.jpg ├── 0358.jpg ├── 0359.jpg ├── 0360.jpg ├── 0361.jpg ├── 0362.jpg ├── 0363.jpg ├── 0364.jpg ├── 0365.jpg ├── 0366.jpg ├── 0367.jpg ├── 0368.jpg ├── 0369.jpg ├── favicon.jpeg └── lex.jpeg ├── scripts ├── audio_transcription │ ├── 0366.txt │ ├── 0367.txt │ ├── 0368.txt │ ├── 0369.txt │ └── episodes.csv ├── dump_audio.py ├── eval.ipynb ├── get_data.ipynb └── run_whisper.py ├── styles └── globals.css ├── tailwind.config.js ├── tsconfig.json └── types └── index.ts /README.md: -------------------------------------------------------------------------------- 1 | # Lex GPT 2 | 3 | This app enables AI-powered search for Lex Fridman podcast. 4 | 5 | This is also a testbed for exploring Langchain functionality. 6 | 7 | ## Dataset 8 | 9 | Scrape ep 1-325 Whisper transcriptions via @karpathy for first 325 episodes: 10 | 11 | https://karpathy.ai/lexicap/index.html 12 | 13 | Trascribe remaining episodes (through episode 365) with Whisper. 14 | 15 | Transcribed data is split / embedded (Pinecone) with Langchain. 16 | 17 | All steps outlined in: `scripts/get_data.ipynb` 18 | 19 | ## Search 20 | 21 | Use Langchain `VectorDBQAChain` to: 22 | * Embed the user query 23 | * Perform similarity search on Pinecone embeddings 24 | * Synthesize the answer from relevant chunks with `GPT 3.5` 25 | 26 | ## Search 27 | 28 | Relevant chunks with metadata (links) are displayed as source documents. 29 | 30 | This builds on the excellent UI from https://github.com/mckaywrigley/wait-but-why-gpt. 31 | 32 | ## Deploy 33 | 34 | Note: the app that supports streaming is deployed to fly.io: https://lex-gpt.fly.dev/ 35 | 36 | This is because Vercel requires edge functions for streaming. 37 | 38 | We are working on getting edge functions working with Langchain. 39 | 40 | In the meantime, use https://lex-gpt.fly.dev/ for the more performant app. 41 | 42 | ## Credits 43 | 44 | Thanks to [Mckay Wrigley](https://twitter.com/mckaywrigley) for open-sourcing his UI. 45 | 46 | Thanks to Lex Fridman for the excellent podcast. 47 | 48 | Thanks to Karapthy for the Whisper transcriptions. 49 | 50 | ## Contact 51 | 52 | If you have any questions, feel free to reach out to me on [Twitter](https://twitter.com/RLanceMartin)! 53 | -------------------------------------------------------------------------------- /components/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rlancemartin/lex-gpt/07b05e8c36f5087a4614e0e716415825d25d81a1/components/.DS_Store -------------------------------------------------------------------------------- /components/Answer/Answer.tsx: -------------------------------------------------------------------------------- 1 | import React, { useEffect, useState } from "react"; 2 | import styles from "./answer.module.css"; 3 | 4 | interface AnswerProps { 5 | text: string; 6 | } 7 | 8 | export const Answer: React.FC = ({ text }) => { 9 | const [words, setWords] = useState([]); 10 | 11 | useEffect(() => { 12 | setWords(text.split(" ")); 13 | }, [text]); 14 | 15 | return ( 16 |
17 | {words.map((word, index) => ( 18 | 23 | {word}{" "} 24 | 25 | ))} 26 |
27 | ); 28 | }; 29 | -------------------------------------------------------------------------------- /components/Answer/answer.module.css: -------------------------------------------------------------------------------- 1 | .fadeIn { 2 | animation: fadeIn 0.5s ease-in-out forwards; 3 | opacity: 0; 4 | } 5 | 6 | @keyframes fadeIn { 7 | from { 8 | opacity: 0; 9 | } 10 | to { 11 | opacity: 1; 12 | } 13 | } 14 | -------------------------------------------------------------------------------- /components/Footer.tsx: -------------------------------------------------------------------------------- 1 | import { IconBrandGithub, IconBrandTwitter } from "@tabler/icons-react"; 2 | import { FC } from "react"; 3 | 4 | export const Footer: FC = () => { 5 | return ( 6 |
7 |
8 | 9 |
10 | Created by 11 | 17 | Lance Martin 18 | 19 | based on 20 | 26 | Lex Fridman 27 | 28 | {`'s blog`} 29 | 35 | Podcast 36 | 37 | . 38 |
39 | 40 |
41 | 47 | 48 | 49 | 50 | 56 | 57 | 58 |
59 |
60 | ); 61 | }; 62 | -------------------------------------------------------------------------------- /components/Navbar.tsx: -------------------------------------------------------------------------------- 1 | import { IconExternalLink } from "@tabler/icons-react"; 2 | import Image from "next/image"; 3 | import { FC } from "react"; 4 | import king from "../public/lex.jpeg"; 5 | 6 | export const Navbar: FC = () => { 7 | return ( 8 |
9 |
10 | 14 | The Network State GPT 20 |
Lex GPT
21 | 22 |
23 |
24 | 30 |
lexfridman.com
31 | 32 | 36 | 37 |
38 |
39 | ); 40 | }; 41 | -------------------------------------------------------------------------------- /license: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 Lance Martin 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /next-env.d.ts: -------------------------------------------------------------------------------- 1 | /// 2 | /// 3 | 4 | // NOTE: This file should not be edited 5 | // see https://nextjs.org/docs/basic-features/typescript for more information. 6 | -------------------------------------------------------------------------------- /next.config.js: -------------------------------------------------------------------------------- 1 | /** @type {import('next').NextConfig} */ 2 | const nextConfig = { 3 | reactStrictMode: true, 4 | images: { 5 | remotePatterns: [ 6 | { 7 | protocol: "https", 8 | hostname: "**" 9 | } 10 | ] 11 | }, 12 | webpack(config) { 13 | config.experiments = { 14 | asyncWebAssembly: true, 15 | layers: true, 16 | }; 17 | 18 | return config; 19 | }, 20 | }; 21 | 22 | module.exports = nextConfig; 23 | -------------------------------------------------------------------------------- /package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "app", 3 | "version": "0.1.0", 4 | "private": true, 5 | "scripts": { 6 | "dev": "next dev", 7 | "build": "next build", 8 | "start": "next start", 9 | "lint": "next lint", 10 | "scrape": "tsx scripts/scrape.ts", 11 | "embed": "tsx scripts/embed.ts", 12 | "images": "tsx scripts/images.ts" 13 | }, 14 | "dependencies": { 15 | "@dqbd/tiktoken": "^1.0.2", 16 | "@huggingface/inference": "^1.5.2", 17 | "@microsoft/fetch-event-source": "^2.0.1", 18 | "@next/font": "^13.2.3", 19 | "@pinecone-database/pinecone": "^0.0.10", 20 | "@tabler/icons-react": "^2.7.0", 21 | "@types/node": "18.14.2", 22 | "@types/react": "18.0.28", 23 | "@types/react-dom": "18.0.11", 24 | "chromadb": "^1.3.1", 25 | "cohere-ai": "^5.1.0", 26 | "endent": "^2.1.0", 27 | "eslint": "8.35.0", 28 | "eslint-config-next": "13.2.1", 29 | "eventsource-parser": "^0.1.0", 30 | "hnswlib-node": "^1.4.1", 31 | "langchain": "^0.0.52-0", 32 | "memory-cache": "^0.2.0", 33 | "next": "13.2.1", 34 | "react": "18.2.0", 35 | "react-dom": "18.2.0", 36 | "typescript": "4.9.5" 37 | }, 38 | "devDependencies": { 39 | "@next/env": "^13.2.3", 40 | "@supabase/supabase-js": "^2.10.0", 41 | "autoprefixer": "^10.4.13", 42 | "axios": "^1.3.4", 43 | "cheerio": "^1.0.0-rc.12", 44 | "gpt-3-encoder": "^1.1.4", 45 | "openai": "^3.2.1", 46 | "postcss": "^8.4.21", 47 | "tailwindcss": "^3.2.7", 48 | "tsx": "^3.12.3" 49 | } 50 | } 51 | -------------------------------------------------------------------------------- /pages/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rlancemartin/lex-gpt/07b05e8c36f5087a4614e0e716415825d25d81a1/pages/.DS_Store -------------------------------------------------------------------------------- /pages/_app.tsx: -------------------------------------------------------------------------------- 1 | import "@/styles/globals.css"; 2 | import { Inter } from "@next/font/google"; 3 | import type { AppProps } from "next/app"; 4 | 5 | const inter = Inter({ subsets: ["latin"] }); 6 | 7 | export default function App({ Component, pageProps }: AppProps<{}>) { 8 | return ( 9 |
10 | 11 |
12 | ); 13 | } 14 | -------------------------------------------------------------------------------- /pages/_document.tsx: -------------------------------------------------------------------------------- 1 | import { Html, Head, Main, NextScript } from 'next/document' 2 | 3 | export default function Document() { 4 | return ( 5 | 6 | 7 | 8 |
9 | 10 | 11 | 12 | ) 13 | } 14 | -------------------------------------------------------------------------------- /pages/api/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rlancemartin/lex-gpt/07b05e8c36f5087a4614e0e716415825d25d81a1/pages/api/.DS_Store -------------------------------------------------------------------------------- /pages/api/search.ts: -------------------------------------------------------------------------------- 1 | import { PineconeStore } from "langchain/vectorstores/pinecone" 2 | import { OpenAIEmbeddings } from "langchain/embeddings/openai"; 3 | import { PineconeClient } from "@pinecone-database/pinecone"; 4 | import { NextApiRequest, NextApiResponse } from "next"; 5 | 6 | type Data = {}; 7 | const handler = async (req: NextApiRequest, res: NextApiResponse) => { 8 | 9 | // Query 10 | const query = req.body.query; 11 | 12 | // Vector DB 13 | const pinecone = new PineconeClient(); 14 | await pinecone.init({ 15 | environment: "us-east1-gcp", 16 | apiKey: process.env.PINECONE_API_KEY ?? "", 17 | }); 18 | const index = pinecone.Index("lex-gpt"); 19 | const vectorStore = await PineconeStore.fromExistingIndex( 20 | new OpenAIEmbeddings(), {pineconeIndex: index}, 21 | ); 22 | // Return chunks to display as references 23 | const results = await vectorStore.similaritySearch(query, 7); 24 | res.status(200).send(results); 25 | } 26 | 27 | export default handler; -------------------------------------------------------------------------------- /pages/api/vectordbqa.ts: -------------------------------------------------------------------------------- 1 | import { PineconeStore } from "langchain/vectorstores/pinecone" 2 | import { OpenAIEmbeddings } from "langchain/embeddings/openai"; 3 | import { PineconeClient } from "@pinecone-database/pinecone"; 4 | import { VectorDBQAChain } from "langchain/chains"; 5 | import { ChatOpenAI } from "langchain/chat_models/openai"; 6 | import { CallbackManager } from "langchain/callbacks"; 7 | import type { NextApiRequest, NextApiResponse } from "next"; 8 | import { NextRequest, NextResponse } from "next/server"; 9 | 10 | export const config = { 11 | runtime: "edge" 12 | }; 13 | export default async function handler(req: NextRequest) 14 | { 15 | 16 | // Inputs 17 | const { prompt } = (await req.json()) as { prompt: string; }; 18 | 19 | // Vector DB 20 | const pinecone = new PineconeClient(); 21 | await pinecone.init({ 22 | environment: "us-east1-gcp", 23 | apiKey: process.env.PINECONE_API_KEY ?? "", 24 | }); 25 | 26 | const index = pinecone.Index("lex-gpt"); 27 | const vectorStore = await PineconeStore.fromExistingIndex( 28 | new OpenAIEmbeddings(), {pineconeIndex: index}, 29 | ); 30 | 31 | // Call LLM and stream output 32 | const encoder = new TextEncoder(); 33 | const stream = new TransformStream(); 34 | const writer = stream.writable.getWriter(); 35 | const llm = new ChatOpenAI({ 36 | temperature: 0.0, 37 | streaming: true, 38 | callbackManager: CallbackManager.fromHandlers({ 39 | handleLLMNewToken: async (token) => { 40 | await writer.ready; 41 | await writer.write(encoder.encode(`data: ${token.replace(/["'\n\r]/g,'')}\n\n`)); 42 | }, 43 | handleLLMEnd: async () => { 44 | await writer.ready; 45 | await writer.close(); 46 | }, 47 | handleLLMError: async (e) => { 48 | await writer.ready; 49 | await writer.abort(e); 50 | }, 51 | }), 52 | }); 53 | 54 | const chain = VectorDBQAChain.fromLLM(llm, vectorStore); 55 | chain.returnSourceDocuments=false; 56 | chain.k=4; 57 | 58 | chain.call({ 59 | query: prompt, 60 | }).catch(console.error); 61 | 62 | return new NextResponse(stream.readable, { 63 | headers: { 64 | "Content-Type": "text/event-stream", 65 | "Cache-Control": "no-cache", 66 | }, 67 | }); 68 | } -------------------------------------------------------------------------------- /pages/index.tsx: -------------------------------------------------------------------------------- 1 | import { Answer } from "@/components/Answer/Answer"; 2 | import { Footer } from "@/components/Footer"; 3 | import { Navbar } from "@/components/Navbar"; 4 | import { LEXChunk } from "@/types"; 5 | import { IconArrowRight, IconExternalLink, IconSearch } from "@tabler/icons-react"; 6 | import Head from "next/head"; 7 | import Image from "next/image"; 8 | import { KeyboardEvent, useEffect, useRef, useState } from "react"; 9 | import { fetchEventSource } from '@microsoft/fetch-event-source'; 10 | 11 | export default function Home() { 12 | 13 | const inputRef = useRef(null); 14 | const [query, setQuery] = useState(""); 15 | const [chunks, setChunks] = useState([]); 16 | const [answer, setAnswer] = useState(""); 17 | const [loading, setLoading] = useState(false); 18 | const [showSettings, setShowSettings] = useState(false); 19 | 20 | // Handle answer 21 | const handleAnswer = async () => { 22 | 23 | if (!query) { 24 | alert("Please enter a query."); 25 | return; 26 | } 27 | 28 | setAnswer(""); 29 | setChunks([]); 30 | setLoading(true); 31 | 32 | // Similarity search for relevant chunks 33 | const search_results = await fetch("/api/search", { 34 | method: "POST", 35 | headers: { 36 | "Content-Type": "application/json" 37 | }, 38 | body: JSON.stringify({ query }) 39 | }); 40 | 41 | if (!search_results.ok) { 42 | setLoading(false); 43 | throw new Error(search_results.statusText); 44 | } 45 | const results: LEXChunk[] = await search_results.json(); 46 | setChunks(results); 47 | 48 | // Prompt for LLM summarization 49 | const prompt = `You are a helpful assistant that accurately answers queries using Lex Fridman podcast episodes. Use the text provided to form your answer, but avoid copying word-for-word from the posts. Try to use your own words when possible. Keep your answer under 5 sentences. Be accurate, helpful, concise, and clear. Use the following passages to provide an answer to the query: "${query}"` 50 | const ctrl = new AbortController(); 51 | 52 | fetchEventSource("/api/vectordbqa", { 53 | method: "POST", 54 | headers: { 55 | "Content-Type": "application/json" 56 | }, 57 | body: JSON.stringify({ prompt }), 58 | onmessage: (event) => { 59 | setLoading(false); 60 | if (event.data === "DONE") { 61 | // Complete 62 | } else { 63 | // Stream text 64 | setAnswer((prev) => prev + event.data); 65 | } 66 | }}); 67 | 68 | }; 69 | 70 | const handleKeyDown = (e: KeyboardEvent) => { 71 | if (e.key === "Enter") { 72 | handleAnswer(); 73 | } 74 | }; 75 | 76 | // Render page 77 | return ( 78 | <> 79 | 80 | Lex GPT 81 | 85 | 89 | 93 | 94 | 95 |
96 | 97 |
98 |
99 | { ( 100 |
101 | 102 | setQuery(e.target.value)} 109 | onKeyDown={handleKeyDown} 110 | /> 111 |
112 | ) } 113 | {loading ? ( 114 |
115 | 116 |
Passages
117 |
118 |
119 |
120 |
121 |
122 |
123 |
124 |
125 | ) : answer ? ( 126 |
127 |
Answer
128 | 129 | 130 |
131 |
Passages
132 | 133 | {chunks.map((chunk, index) => ( 134 |
135 |
136 |
137 |
138 | {chunk.metadata.title} 145 |
146 |
{chunk.metadata.title}
147 |
148 |
149 | 155 | 156 | 157 |
158 |
{chunk.pageContent}
159 |
160 |
161 | ))} 162 |
163 |
164 | ) : chunks.length > 0 ? ( 165 |
166 |
Passages
167 | {chunks.map((chunk, index) => ( 168 |
169 |
170 |
171 |
172 | {chunk.metadata.title} 179 |
180 |
{chunk.metadata.title}
181 |
182 |
183 | 189 | 190 | 191 |
192 |
{chunk.pageContent}
193 |
194 |
195 | ))} 196 |
197 | ) : ( 198 |
{`AI-powered search and chat for the Lex Fridman podcast.`}
199 | )} 200 |
201 |
202 |
203 |
204 | 205 | ); 206 | } 207 | -------------------------------------------------------------------------------- /postcss.config.js: -------------------------------------------------------------------------------- 1 | module.exports = { 2 | plugins: { 3 | tailwindcss: {}, 4 | autoprefixer: {}, 5 | }, 6 | } 7 | -------------------------------------------------------------------------------- /public/.DS_Store: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rlancemartin/lex-gpt/07b05e8c36f5087a4614e0e716415825d25d81a1/public/.DS_Store -------------------------------------------------------------------------------- /public/0001.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/rlancemartin/lex-gpt/07b05e8c36f5087a4614e0e716415825d25d81a1/public/0001.jpg -------------------------------------------------------------------------------- /public/0002.jpg: 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https://raw.githubusercontent.com/rlancemartin/lex-gpt/07b05e8c36f5087a4614e0e716415825d25d81a1/public/lex.jpeg -------------------------------------------------------------------------------- /scripts/audio_transcription/episodes.csv: -------------------------------------------------------------------------------- 1 | ,index,number,link,title,img 2 | 0,"Paul Rosolie: Amazon Jungle, Uncontacted Tribes, Anacondas, and Ayahuasca | Lex Fridman Podcast #369",369.0,https://www.youtube.com/watch?v=gPfriiHBBek&list=UUSHZKyawb77ixDdsGog4iWA&index=1&pp=iAQB,"Paul Rosolie: Amazon Jungle, Uncontacted Tribes, Anacondas, and Ayahuasca | Lex Fridman Podcast #369",https://i.ytimg.com/vi/gPfriiHBBek/hqdefault.jpg?sqp=-oaymwEcCNACELwBSFXyq4qpAw4IARUAAIhCGAFwAcABBg==&rs=AOn4CLAgoUYvKbwt5oSKCoM2YGR__0bmVQ 3 | -------------------------------------------------------------------------------- /scripts/dump_audio.py: -------------------------------------------------------------------------------- 1 | import yt_dlp 2 | import logging 3 | import argparse 4 | import requests 5 | import pandas as pd 6 | from youtubesearchpython import * 7 | 8 | def get_episodes(channel_id,last_episode,logging): 9 | 10 | # Get playlist 11 | playlist = Playlist(playlist_from_channel_id(channel_id)) 12 | while playlist.hasMoreVideos: 13 | print('Getting more videos...') 14 | playlist.getNextVideos() 15 | print(f'Videos Retrieved: {len(playlist.videos)}') 16 | 17 | # Store 18 | stor_metadata=pd.DataFrame() 19 | for v in playlist.videos: 20 | try: 21 | ep_number = int(v['title'].split("|")[-1].split("#")[-1]) 22 | stor_metadata.loc[v['title'],'number']=ep_number 23 | stor_metadata.loc[v['title'],'link']=v['link'] 24 | stor_metadata.loc[v['title'],'title']=v['title'] 25 | stor_metadata.loc[v['title'],'img']=v['thumbnails'][3]['url'] 26 | except: 27 | logging.info("Metadata extraction failed on %s", v['title']) 28 | 29 | stor_metadata = stor_metadata[stor_metadata.number > last_episode] 30 | return stor_metadata 31 | 32 | def dump_audio(stor_metadata,logging): 33 | # Iterate through episodes 34 | for ix in stor_metadata.index: 35 | ep_number=int(stor_metadata.loc[ix,'number']) 36 | logging.info("Dumping audio for: %s"%ep_number) 37 | img_url=stor_metadata.loc[ix,'img'] 38 | ep_link=stor_metadata.loc[ix,'link'] 39 | # Write img 40 | with open("../public/0%s.jpg"%str(ep_number), 'wb') as f: 41 | response = requests.get(img_url) 42 | f.write(response.content) 43 | # Write audio 44 | ydl_opts = { 45 | 'format': 'm4a/bestaudio/best', 46 | 'outtmpl': 'audio/%s.m4a'%str(ep_number), 47 | 'noplaylist': True, 48 | 'postprocessors': [{ 49 | 'key': 'FFmpegExtractAudio', 50 | 'preferredcodec': 'm4a', 51 | }]} 52 | with yt_dlp.YoutubeDL(ydl_opts) as ydl: 53 | error_code = ydl.download(ep_link) 54 | # Save 55 | stor_metadata.reset_index().to_csv("audio_transcription/episodes.csv") 56 | 57 | def get_last_episode(): 58 | parser = argparse.ArgumentParser(description='Process last_episode') 59 | parser.add_argument('last_episode', type=int, help='the last episode number') 60 | args = parser.parse_args() 61 | return args.last_episode 62 | 63 | def main(): 64 | # Set up logger 65 | logging.basicConfig(filename='audio_dump.log', 66 | filemode='w', 67 | level=logging.DEBUG) 68 | 69 | # Lex Fridman 70 | last_episode = get_last_episode() 71 | channel_id = "UCSHZKyawb77ixDdsGog4iWA" 72 | stor_metadata = get_episodes(channel_id,last_episode,logging) 73 | dump_audio(stor_metadata,logging) 74 | 75 | if __name__ == '__main__': 76 | main() 77 | -------------------------------------------------------------------------------- /scripts/eval.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": 1, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import re\n", 10 | "import json\n", 11 | "import numpy as np\n", 12 | "import pandas as pd\n", 13 | "from langchain.vectorstores import Chroma\n", 14 | "from langchain.chat_models import ChatOpenAI\n", 15 | "from langchain.chains import QAGenerationChain\n", 16 | "from langchain.document_loaders import TextLoader\n", 17 | "from langchain.embeddings.openai import OpenAIEmbeddings\n", 18 | "from langchain.text_splitter import RecursiveCharacterTextSplitter" 19 | ] 20 | }, 21 | { 22 | "cell_type": "code", 23 | "execution_count": 2, 24 | "metadata": {}, 25 | "outputs": [], 26 | "source": [ 27 | "# Read\n", 28 | "episode_id = \"0333\"\n", 29 | "doc = pd.read_csv(\"audio_transcription/%s.txt\"%episode_id,sep='\\t')\n", 30 | "doc.columns=['links','time','chunks']\n", 31 | "\n", 32 | "# Clean text chunks \n", 33 | "doc['clean_chunks']=doc['chunks'].astype(str).apply(lambda x: x.strip())\n", 34 | "\n", 35 | "# Write updated file (b/c we isolate sections for question-answer formulation below)\n", 36 | "with open('audio_transcription/%s_text.txt'%episode_id, \"w\") as f:\n", 37 | " for i in doc.index:\n", 38 | " string = doc.loc[i,'clean_chunks']\n", 39 | " f.write(string + \"\\n\") " 40 | ] 41 | }, 42 | { 43 | "cell_type": "code", 44 | "execution_count": 6, 45 | "metadata": {}, 46 | "outputs": [ 47 | { 48 | "data": { 49 | "text/plain": [ 50 | "[{'question': 'What is the transformer architecture in deep learning?',\n", 51 | " 'answer': 'The transformer architecture is a neural network architecture that is general purpose and can process different sensory modalities like vision, audio, text, and video. It is simultaneously expressive in the forward pass, optimizable via backpropagation, gradient descent, and efficient high parallelism compute graph.'},\n", 52 | " {'question': 'What is a transformer and how is it designed?',\n", 53 | " 'answer': 'A transformer is a series of blocks with attention and a multilayer perceptron. It is designed to be very expressive in a forward pass, optimizable in a backward pass, and efficient in hardware. The residual connections support the ability to learn short algorithms fast and first, and then gradually extend them longer during training.'},\n", 54 | " {'question': 'What is a transformer architecture and why is it important?',\n", 55 | " 'answer': 'A transformer architecture is a type of neural network that is remarkably resilient and can simultaneously optimize for lots of properties of a desirable neural network architecture. It is important because it is a big step in AI and has proven to be extremely powerful and can solve arbitrary problems.'},\n", 56 | " {'question': 'What is a language model and how does it work?',\n", 57 | " 'answer': 'A language model is a system that predicts the next word in a sequence of words. It can be done using count-based models like Ngram models or neural networks like transformers. The objective is simple, but to predict the next word accurately, the model needs to understand the context of the text, which requires a lot of knowledge about the world.'},\n", 58 | " {'question': \"What is the author's opinion on the transition to software 2.0?\",\n", 59 | " 'answer': \"The author believes that neural nets are taking over software and that the way we program computers is going to change. The author thinks that at some point, there's going to be a compilation process from the data sets and the objective and the architecture specification into the binary, which is really just the neural net weights and the forward pass of the neural net.\"},\n", 60 | " {'question': 'What is the task of programming in software 2.0?',\n", 61 | " 'answer': 'The task of programming in software 2.0 is changing the data set and loss functions of the neural net, as well as specifying the architecture of the neural net.'},\n", 62 | " {'question': 'What are the three properties of a good dataset for supervised learning?',\n", 63 | " 'answer': 'The three properties of a good dataset for supervised learning are that it needs to be large, accurate, and diverse.'}]" 64 | ] 65 | }, 66 | "execution_count": 6, 67 | "metadata": {}, 68 | "output_type": "execute_result" 69 | } 70 | ], 71 | "source": [ 72 | "# Note: feeding complete file failed for non-obvious reasons (JSON error; searched at length for invalid chars but could not find)\n", 73 | "# In any case, I fed small chunks of the dataset into the QA generation and it worked (and maybe improved the QA since inputs were selected \n", 74 | "filename = 'audio_transcription/0333_text_transformer.txt'\n", 75 | "loader = TextLoader(filename)\n", 76 | "doc_karpathy = loader.load()[0]\n", 77 | "txt_transformer = doc_karpathy.page_content\n", 78 | "\n", 79 | "filename = 'audio_transcription/0333_text_sw2dot0.txt'\n", 80 | "loader = TextLoader(filename)\n", 81 | "doc_karpathy = loader.load()[0]\n", 82 | "txt_sw20 = doc_karpathy.page_content\n", 83 | "\n", 84 | "filename = 'audio_transcription/0333_text_sd.txt'\n", 85 | "loader = TextLoader(filename)\n", 86 | "doc_karpathy = loader.load()[0]\n", 87 | "txt_sd= doc_karpathy.page_content\n", 88 | "\n", 89 | "chain = QAGenerationChain.from_llm(ChatOpenAI(temperature = 0))\n", 90 | "qa_karpathy_transformer = chain.run(txt_transformer)\n", 91 | "\n", 92 | "chain = QAGenerationChain.from_llm(ChatOpenAI(temperature = 0))\n", 93 | "qa_karpathy_sw20 = chain.run(txt_sw20)\n", 94 | "\n", 95 | "chain = QAGenerationChain.from_llm(ChatOpenAI(temperature = 0))\n", 96 | "qa_karpathy_sd= chain.run(txt_sd)\n", 97 | "\n", 98 | "qa_all = qa_karpathy_transformer + qa_karpathy_sw20 + qa_karpathy_sd\n", 99 | "qa_all" 100 | ] 101 | }, 102 | { 103 | "attachments": {}, 104 | "cell_type": "markdown", 105 | "metadata": {}, 106 | "source": [ 107 | "`Notes -` \n", 108 | "\n", 109 | "https://langchain.readthedocs.io/en/latest/use_cases/evaluation/qa_generation.html" 110 | ] 111 | }, 112 | { 113 | "cell_type": "code", 114 | "execution_count": 22, 115 | "metadata": {}, 116 | "outputs": [], 117 | "source": [ 118 | "from langchain.llms import OpenAI\n", 119 | "from langchain.vectorstores import Chroma\n", 120 | "from langchain.vectorstores import FAISS\n", 121 | "from langchain.chains import VectorDBQA\n", 122 | "from langchain.evaluation.qa import QAEvalChain\n", 123 | "\n", 124 | "def make_splits(chunk_size,overlap,texts,links,episode_id):\n", 125 | "\n", 126 | " text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunk_size, chunk_overlap=overlap) \n", 127 | " splits = text_splitter.split_text(texts)\n", 128 | " bins = np.linspace(0,len(links)-1,len(splits),dtype=int)\n", 129 | " sampled_links = [links[i] for i in bins]\n", 130 | " metadatas=[{\"source\":link,\"id\":episode_id,\"link\":link} for link in sampled_links]\n", 131 | " db = FAISS.from_texts(splits, OpenAIEmbeddings(), metadatas=metadatas) \n", 132 | " chain = VectorDBQA.from_chain_type(llm=ChatOpenAI(temperature=0), \n", 133 | " chain_type=\"stuff\", \n", 134 | " vectorstore=db, \n", 135 | " input_key=\"question\")\n", 136 | " \n", 137 | " return chain \n", 138 | "\n", 139 | "episode_id = \"0333\"\n", 140 | "doc = pd.read_csv(\"audio_transcription/%s.txt\"%episode_id,sep='\\t')\n", 141 | "doc.columns=['links','time','chunks']\n", 142 | "doc['clean_chunks']=doc['chunks'].astype(str).apply(lambda x: x.strip())\n", 143 | "\n", 144 | "chains = {}\n", 145 | "overlap = 50\n", 146 | "for chunk_size in [250,500,1000,2000,3000]:\n", 147 | " chain = make_splits(chunk_size,overlap,list(doc['clean_chunks']),list(doc['links']),episode_id)\n", 148 | " chains[chunk_size] = chain\n" 149 | ] 150 | }, 151 | { 152 | "cell_type": "code", 153 | "execution_count": 36, 154 | "metadata": {}, 155 | "outputs": [], 156 | "source": [ 157 | "def run_eval(chain):\n", 158 | " predictions = []\n", 159 | " predicted_dataset = []\n", 160 | " for data in qa_all:\n", 161 | " new_data = {\"question\": data[\"question\"],\"answer\": data[\"answer\"]}\n", 162 | " predictions.append(chain(new_data))\n", 163 | " predicted_dataset.append(new_data)\n", 164 | " return predictions,predicted_dataset\n", 165 | "\n", 166 | "predictions_list = []\n", 167 | "scores_list = []\n", 168 | "eval_chain = QAEvalChain.from_llm(llm=ChatOpenAI(temperature=0))\n", 169 | "for chunk_size in [250,500,1000,2000,3000]:\n", 170 | " predictions,predicted_dataset=run_eval(chains[chunk_size])\n", 171 | " predictions_list.append(predictions)\n", 172 | " graded_outputs = eval_chain.evaluate(predicted_dataset, predictions, question_key=\"question\", prediction_key=\"result\")\n", 173 | " scores_list.append(graded_outputs)" 174 | ] 175 | }, 176 | { 177 | "cell_type": "code", 178 | "execution_count": 35, 179 | "metadata": {}, 180 | "outputs": [ 181 | { 182 | "data": { 183 | "text/plain": [ 184 | "[{'text': 'INCORRECT'},\n", 185 | " {'text': 'INCORRECT'},\n", 186 | " {'text': 'CORRECT'},\n", 187 | " {'text': 'CORRECT'},\n", 188 | " {'text': 'CORRECT'},\n", 189 | " {'text': 'INCORRECT'},\n", 190 | " {'text': 'INCORRECT'}]" 191 | ] 192 | }, 193 | "execution_count": 35, 194 | "metadata": {}, 195 | "output_type": "execute_result" 196 | } 197 | ], 198 | "source": [ 199 | "# 250 -\n", 200 | "graded_outputs" 201 | ] 202 | }, 203 | { 204 | "cell_type": "code", 205 | "execution_count": 38, 206 | "metadata": {}, 207 | "outputs": [], 208 | "source": [ 209 | "stor=pd.DataFrame()\n", 210 | "for i,chunk_size in enumerate([250,500,1000,2000,3000]):\n", 211 | " d=scores_list[i]\n", 212 | " incorrect_counts = []\n", 213 | " for dictionary in d:\n", 214 | " if dictionary['text'] == 'INCORRECT':\n", 215 | " incorrect_counts.append(1)\n", 216 | " else:\n", 217 | " incorrect_counts.append(0)\n", 218 | " stor.loc[chunk_size,'num_incorrect']=sum(incorrect_counts)" 219 | ] 220 | }, 221 | { 222 | "cell_type": "code", 223 | "execution_count": 42, 224 | "metadata": {}, 225 | "outputs": [], 226 | "source": [ 227 | "stor['pct_incorrect'] = stor['num_incorrect'] / 7" 228 | ] 229 | }, 230 | { 231 | "cell_type": "code", 232 | "execution_count": 51, 233 | "metadata": {}, 234 | "outputs": [ 235 | { 236 | "data": { 237 | "image/png": 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", 238 | "text/plain": [ 239 | "
" 240 | ] 241 | }, 242 | "metadata": {}, 243 | "output_type": "display_data" 244 | } 245 | ], 246 | "source": [ 247 | "import matplotlib.pyplot as plt\n", 248 | "%matplotlib inline\n", 249 | "stor['pct_correct'] = 1 - stor['pct_incorrect']\n", 250 | "stor['pct_correct'].plot(kind='bar')\n", 251 | "plt.title('Percentage of Correct Answers')\n", 252 | "plt.xlabel('Chunk Size')\n", 253 | "plt.ylabel('Percentage')\n", 254 | "plt.show()" 255 | ] 256 | }, 257 | { 258 | "cell_type": "code", 259 | "execution_count": 46, 260 | "metadata": {}, 261 | "outputs": [ 262 | { 263 | "data": { 264 | "text/html": [ 265 | "
\n", 266 | "\n", 279 | "\n", 280 | " \n", 281 | " \n", 282 | " \n", 283 | " \n", 284 | " \n", 285 | " \n", 286 | " \n", 287 | " \n", 288 | " \n", 289 | " \n", 290 | " \n", 291 | " \n", 292 | " \n", 293 | " \n", 294 | " \n", 295 | " \n", 296 | " \n", 297 | " \n", 298 | " \n", 299 | " \n", 300 | " \n", 301 | " \n", 302 | " \n", 303 | " \n", 304 | " \n", 305 | " \n", 306 | " \n", 307 | " \n", 308 | " \n", 309 | " \n", 310 | " \n", 311 | " \n", 312 | " \n", 313 | " \n", 314 | "
num_incorrectpct_incorrect
2504.00.571429
5002.00.285714
10001.00.142857
20001.00.142857
30002.00.285714
\n", 315 | "
" 316 | ], 317 | "text/plain": [ 318 | " num_incorrect pct_incorrect\n", 319 | "250 4.0 0.571429\n", 320 | "500 2.0 0.285714\n", 321 | "1000 1.0 0.142857\n", 322 | "2000 1.0 0.142857\n", 323 | "3000 2.0 0.285714" 324 | ] 325 | }, 326 | "execution_count": 46, 327 | "metadata": {}, 328 | "output_type": "execute_result" 329 | } 330 | ], 331 | "source": [ 332 | "stor" 333 | ] 334 | } 335 | ], 336 | "metadata": { 337 | "kernelspec": { 338 | "display_name": "Python 3 (ipykernel)", 339 | "language": "python", 340 | "name": "python3" 341 | }, 342 | "language_info": { 343 | "codemirror_mode": { 344 | "name": "ipython", 345 | "version": 3 346 | }, 347 | "file_extension": ".py", 348 | "mimetype": "text/x-python", 349 | "name": "python", 350 | "nbconvert_exporter": "python", 351 | "pygments_lexer": "ipython3", 352 | "version": "3.9.16" 353 | }, 354 | "orig_nbformat": 4 355 | }, 356 | "nbformat": 4, 357 | "nbformat_minor": 2 358 | } 359 | -------------------------------------------------------------------------------- /scripts/get_data.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "code", 5 | "execution_count": null, 6 | "metadata": {}, 7 | "outputs": [], 8 | "source": [ 9 | "import os,re\n", 10 | "import yt_dlp\n", 11 | "import json\n", 12 | "import time\n", 13 | "import math \n", 14 | "import httplib2\n", 15 | "import requests\n", 16 | "import pinecone \n", 17 | "import pandas as pd\n", 18 | "import numpy as np\n", 19 | "import urllib.request\n", 20 | "from bs4 import BeautifulSoup\n", 21 | "from bs4.element import Comment\n", 22 | "from youtubesearchpython import *\n", 23 | "from langchain.llms import OpenAIChat\n", 24 | "from bs4 import BeautifulSoup, SoupStrainer\n", 25 | "from langchain.vectorstores import Pinecone\n", 26 | "from langchain.chains import VectorDBQAWithSourcesChain\n", 27 | "from langchain.embeddings.openai import OpenAIEmbeddings\n", 28 | "from langchain.text_splitter import RecursiveCharacterTextSplitter" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "## Lext GPT\n", 36 | "\n", 37 | "`Here, we will prepare the VectorDB index for Lex Fridman podcast:`\n", 38 | "\n", 39 | "* Scrape source data from: https://karpathy.ai/lexicap/\n", 40 | "* Use Whisper to transcribe episodes that Karpathy has not already done\n", 41 | "* Chunk data\n", 42 | "* Embed it to Pinecone\n", 43 | "* Test VectorDBQA chain on it \n", 44 | "* App (https://lex-gpt.vercel.app/) will read from same Pinecone DB\n", 45 | " \n", 46 | "`1. Get video urls -` " 47 | ] 48 | }, 49 | { 50 | "cell_type": "code", 51 | "execution_count": null, 52 | "metadata": {}, 53 | "outputs": [], 54 | "source": [ 55 | "# Videos\n", 56 | "channel_id = \"UCSHZKyawb77ixDdsGog4iWA\" # Get ID from ChannelsSearch\n", 57 | "playlist = Playlist(playlist_from_channel_id(channel_id))\n", 58 | "\n", 59 | "# Episode data\n", 60 | "stor_metadata=pd.DataFrame()\n", 61 | "for v in playlist.videos:\n", 62 | " try:\n", 63 | " ep_number = int(v['title'].split(\"|\")[-1].split(\"#\")[-1])\n", 64 | " stor_metadata.loc[v['title'],'number']=ep_number\n", 65 | " stor_metadata.loc[v['title'],'link']=v['link']\n", 66 | " stor_metadata.loc[v['title'],'title']=v['title']\n", 67 | " stor_metadata.loc[v['title'],'img']=v['thumbnails'][3]['url']\n", 68 | " except:\n", 69 | " print(\"Failed on %s\", v['title'])\n", 70 | "\n", 71 | "last_complete_video = 365\n", 72 | "new_ep = stor_metadata[stor_metadata.number > last_complete_video]" 73 | ] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "metadata": {}, 78 | "source": [ 79 | "`2. Get audio -` " 80 | ] 81 | }, 82 | { 83 | "cell_type": "code", 84 | "execution_count": null, 85 | "metadata": {}, 86 | "outputs": [], 87 | "source": [ 88 | "# Iterate through episodes \n", 89 | "for ix in new_ep.index:\n", 90 | " \n", 91 | " ep_number=int(new_ep.loc[ix,'number'])\n", 92 | " print(\"EPISODE: %s\"%ep_number)\n", 93 | " img_url=new_ep.loc[ix,'img']\n", 94 | " ep_link=new_ep.loc[ix,'link']\n", 95 | " # Write img \n", 96 | " with open(\"../public/0%s.jpg\"%str(ep_number), 'wb') as f:\n", 97 | " response = requests.get(img_url)\n", 98 | " f.write(response.content)\n", 99 | " # Write audio\n", 100 | " ydl_opts = {\n", 101 | " 'format': 'm4a/bestaudio/best',\n", 102 | " 'outtmpl': 'audio/%s.m4a'%str(ep_number),\n", 103 | " 'noplaylist': True,\n", 104 | " 'postprocessors': [{ \n", 105 | " 'key': 'FFmpegExtractAudio',\n", 106 | " 'preferredcodec': 'm4a',\n", 107 | " }]}\n", 108 | " with yt_dlp.YoutubeDL(ydl_opts) as ydl:\n", 109 | " error_code = ydl.download(ep_link)\n", 110 | " \n", 111 | "new_ep.reset_index().to_csv(\"audio_transcription/episodes.csv\")" 112 | ] 113 | }, 114 | { 115 | "cell_type": "markdown", 116 | "metadata": {}, 117 | "source": [ 118 | "`3. Run Whisper -`\n", 119 | " \n", 120 | "* On GPU, ideally: 10-20 min / video on 2080Ti with `medium` model\n", 121 | "* Run `python run_whisper.py`\n", 122 | "\n", 123 | "If running this step on a remote machine:\n", 124 | "* scp the transcription: `audio_transcription/episodes.csv`\n", 125 | "* scp the audio files: `audio/*`\n", 126 | "* Run `python run_whisper.py`\n", 127 | "* Then, scp the `audio_transcription/` back to local " 128 | ] 129 | }, 130 | { 131 | "cell_type": "code", 132 | "execution_count": null, 133 | "metadata": {}, 134 | "outputs": [], 135 | "source": [ 136 | "! python run_whisper.py" 137 | ] 138 | }, 139 | { 140 | "cell_type": "markdown", 141 | "metadata": {}, 142 | "source": [ 143 | "`4. Scrape Karpathy transcriptions -`" 144 | ] 145 | }, 146 | { 147 | "cell_type": "code", 148 | "execution_count": null, 149 | "metadata": {}, 150 | "outputs": [], 151 | "source": [ 152 | "# Get text -\n", 153 | "def tag_visible(element):\n", 154 | " if element.parent.name in ['style', 'script', 'head', 'title', 'meta', '[document]']:\n", 155 | " return False\n", 156 | " if isinstance(element, Comment):\n", 157 | " return False\n", 158 | " return True\n", 159 | "\n", 160 | "def text_from_html(body):\n", 161 | " soup = BeautifulSoup(body, 'html.parser')\n", 162 | " texts = soup.findAll(string=True)\n", 163 | " visible_texts = filter(tag_visible, texts) \n", 164 | " return u\" \".join(t.strip() for t in visible_texts)\n", 165 | "\n", 166 | "def get_text_and_title(url):\n", 167 | " html = urllib.request.urlopen(url).read()\n", 168 | " t=(text_from_html(html))\n", 169 | " title=t.split(\"|\")[0].split(\"back to index\")[1].strip()\n", 170 | " return t, title\n", 171 | "\n", 172 | "# Get links -\n", 173 | "def get_links(URL):\n", 174 | " http = httplib2.Http()\n", 175 | " status, response = http.request(URL)\n", 176 | " links = []\n", 177 | " for link in BeautifulSoup(response, 'html.parser', parse_only=SoupStrainer('a')):\n", 178 | " if link.has_attr('href'):\n", 179 | " links.append(link['href'])\n", 180 | " links_clean = [l for l in links if \"https\" in l]\n", 181 | " return links_clean\n", 182 | "\n", 183 | "# Get image -\n", 184 | "def get_img(URL,title,episode_id):\n", 185 | " response = requests.get(URL)\n", 186 | " soup = BeautifulSoup(response.text, 'html.parser')\n", 187 | " img_tags = soup.find_all('img')\n", 188 | " urls = [img['src'] for img in img_tags]\n", 189 | " for url in urls:\n", 190 | " response = requests.get(url)\n", 191 | " imgpath=\"../public/0%s.jpg\"%episode_id\n", 192 | " with open(imgpath, 'wb') as f:\n", 193 | " if 'http' not in url:\n", 194 | " url = '{}{}'.format(site, url)\n", 195 | " response = requests.get(url)\n", 196 | " f.write(response.content)\n", 197 | " return imgpath\n", 198 | "\n", 199 | "# Full pipeline - \n", 200 | "def pre_process(URL,episode_id):\n", 201 | "\n", 202 | " t,title=get_text_and_title(URL)\n", 203 | " links=get_links(URL)\n", 204 | " img=get_img(URL,title,episode_id)\n", 205 | " stor_chunk = pd.DataFrame()\n", 206 | " stor_chunk['chunks']= t.split(\"link |\")\n", 207 | " stor_chunk['clean_chunks']=stor_chunk['chunks'].apply(lambda x: re.sub(r\"[^a-zA-Z ]+\", '', x)).apply(lambda x: x.strip())\n", 208 | " stor_chunk['links']=links\n", 209 | " all_text = stor_chunk['clean_chunks'].str.cat(sep=' ')\n", 210 | " return all_text, links, title\n", 211 | "\n", 212 | "# Make splits - \n", 213 | "def make_splits(chunks,URL):\n", 214 | "\n", 215 | " # ID\n", 216 | " episode_id=URL.split(\"/\")[-1].split(\"-\")[0]\n", 217 | "\n", 218 | " # Pre-processing\n", 219 | " texts,links,title=pre_process(URL,episode_id)\n", 220 | " \n", 221 | " # Splits \n", 222 | " text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunks, \n", 223 | " chunk_overlap=50) \n", 224 | " texts_recusive = text_splitter.split_text(texts)\n", 225 | " print(len(texts_recusive)) \n", 226 | "\n", 227 | " # Metadata \n", 228 | " N = len(texts_recusive) \n", 229 | " bins = np.linspace(0, len(links)-1, N, dtype=int)\n", 230 | " sampled_links = [links[i] for i in bins]\n", 231 | " # Here we can add \"link\", \"title\", etc that can be fetched in the app \n", 232 | " metadatas=[{\"source\":title + \" \" +link,\"id\":episode_id,\"link\":link,\"title\":title} for link in sampled_links]\n", 233 | " print(len(metadatas))\n", 234 | " return texts_recusive,metadatas,title,episode_id" 235 | ] 236 | }, 237 | { 238 | "cell_type": "code", 239 | "execution_count": null, 240 | "metadata": {}, 241 | "outputs": [], 242 | "source": [ 243 | "# Get all pages \n", 244 | "http = httplib2.Http()\n", 245 | "status, response = http.request(\"https://karpathy.ai/lexicap/\")\n", 246 | "links = []\n", 247 | "for link in BeautifulSoup(response, 'html.parser', parse_only=SoupStrainer('a')):\n", 248 | " if link.has_attr('href'):\n", 249 | " links.append(link['href'])\n", 250 | "links_tx = [\"https://karpathy.ai/lexicap/\"+l for l in links if \"0\" in l]" 251 | ] 252 | }, 253 | { 254 | "cell_type": "code", 255 | "execution_count": null, 256 | "metadata": {}, 257 | "outputs": [], 258 | "source": [ 259 | "# *** Chunk size: key parameter *** \n", 260 | "chunks = 1500\n", 261 | "splits_scrape = [ ]\n", 262 | "metadatas_scrape = [ ]\n", 263 | " \n", 264 | "# Iterate \n", 265 | "stor=pd.DataFrame()\n", 266 | "for page in links_tx:\n", 267 | " try:\n", 268 | " print(\"Writing: %s\"%page)\n", 269 | " # Make splits\n", 270 | " splits,metadatas,title,episode_id=make_splits(chunks,page)\n", 271 | " stor.loc[episode_id,'title']=title \n", 272 | " with open('docs/%s.txt'%episode_id, \"w\") as f:\n", 273 | " for string in splits:\n", 274 | " f.write(string + \"\\n\") \n", 275 | " f.close()\n", 276 | " with open('metadatas/%s.json'%episode_id, \"w\") as f:\n", 277 | " json.dump(metadatas, f)\n", 278 | " f.close()\n", 279 | " splits_scrape.append(splits)\n", 280 | " metadatas_scrape.append(metadatas)\n", 281 | " except:\n", 282 | " print(\"Error on page: %s\"%page)" 283 | ] 284 | }, 285 | { 286 | "cell_type": "markdown", 287 | "metadata": {}, 288 | "source": [ 289 | "`5. Get newer transcripts -`" 290 | ] 291 | }, 292 | { 293 | "cell_type": "code", 294 | "execution_count": null, 295 | "metadata": {}, 296 | "outputs": [], 297 | "source": [ 298 | "# *** Chunk size: key parameter *** \n", 299 | "chunks = 1500\n", 300 | "splits_new = [ ]\n", 301 | "metadatas_new = [ ]\n", 302 | "\n", 303 | "# Read the csv file\n", 304 | "new_ep=pd.read_csv(\"audio_transcription/episodes.csv\",index_col=None)\n", 305 | "\n", 306 | "for ix in new_ep.index:\n", 307 | "\n", 308 | " # Get data\n", 309 | " title=new_ep.loc[ix,'title']\n", 310 | " ep_number=int(new_ep.loc[ix,'number'])\n", 311 | " \n", 312 | " # Consistency w/ convention used in Karpathy transcription\n", 313 | " episode_id=\"0\"+str(ep_number) \n", 314 | " file_path='audio_transcription/%s.txt'%str(episode_id)\n", 315 | " transcript=pd.read_csv(file_path,sep='\\t',header=None)\n", 316 | " transcript.columns=['links','time','chunks']\n", 317 | " \n", 318 | " # Clean text chunks \n", 319 | " transcript['clean_chunks']=transcript['chunks'].astype(str).apply(lambda x: x.strip())\n", 320 | " links = list(transcript['links'])\n", 321 | " texts = transcript['clean_chunks'].str.cat(sep=' ')\n", 322 | "\n", 323 | " # Splits \n", 324 | " text_splitter = RecursiveCharacterTextSplitter(chunk_size=chunks, \n", 325 | " chunk_overlap=50) \n", 326 | " splits = text_splitter.split_text(texts)\n", 327 | " print(len(splits)) \n", 328 | "\n", 329 | " # Metadata \n", 330 | " N = len(splits) \n", 331 | " bins = np.linspace(0, len(links)-1, N, dtype=int)\n", 332 | " sampled_links = [links[i] for i in bins]\n", 333 | " \n", 334 | " # Here we can add \"link\", \"title\", etc that can be fetched in the app \n", 335 | " metadatas=[{\"source\":title + \" \" +link,\"id\":episode_id,\"link\":link,\"title\":title} for link in sampled_links]\n", 336 | " print(len(metadatas)) \n", 337 | "\n", 338 | " # Append to output \n", 339 | " splits_new.append(splits)\n", 340 | " metadatas_new.append(metadatas)" 341 | ] 342 | }, 343 | { 344 | "cell_type": "markdown", 345 | "metadata": {}, 346 | "source": [ 347 | "`6. Assemble final list -`" 348 | ] 349 | }, 350 | { 351 | "cell_type": "code", 352 | "execution_count": null, 353 | "metadata": {}, 354 | "outputs": [], 355 | "source": [ 356 | "# Join the list of lists \n", 357 | "splits_all = []\n", 358 | "# For the initial write \n", 359 | "# for sublist in [splits_scrape+splits_new]:\n", 360 | "# For updates -- \n", 361 | "for sublist in splits_new:\n", 362 | " splits_all.extend(sublist)\n", 363 | "\n", 364 | "metadatas_all = []\n", 365 | "# For the initial write \n", 366 | "# for sublist in [metadatas_scrape+metadatas_new]:\n", 367 | "# For updates -- \n", 368 | "for sublist in metadatas_new:\n", 369 | " metadatas_all.extend(sublist)" 370 | ] 371 | }, 372 | { 373 | "cell_type": "markdown", 374 | "metadata": {}, 375 | "source": [ 376 | "`7. Embed full dataset in Pinecone VectorDB -`" 377 | ] 378 | }, 379 | { 380 | "cell_type": "code", 381 | "execution_count": null, 382 | "metadata": {}, 383 | "outputs": [], 384 | "source": [ 385 | "# Pinecone\n", 386 | "pinecone.init(\n", 387 | " api_key=os.environ.get('PINECONE_API_KEY'), \n", 388 | " environment=\"us-east1-gcp\" \n", 389 | ")\n", 390 | "\n", 391 | "# Initialize with small set of data - \n", 392 | "# p = Pinecone.from_texts(splits_all[0:2], \n", 393 | "# embeddings, \n", 394 | "# index_name=index_name, \n", 395 | "# metadatas=metadatas_all[0:2])\n", 396 | "\n", 397 | "# Update - \n", 398 | "index_name = \"lex-gpt\"\n", 399 | "embeddings = OpenAIEmbeddings()\n", 400 | "p = Pinecone.from_existing_index(index_name=index_name,embedding=embeddings)" 401 | ] 402 | }, 403 | { 404 | "cell_type": "code", 405 | "execution_count": null, 406 | "metadata": {}, 407 | "outputs": [], 408 | "source": [ 409 | "# Add data in chunk to avoid data ingest errors\n", 410 | "chunk_size = 100\n", 411 | "last_chunk = 0\n", 412 | "num_chunks = math.ceil(len(splits_all) / chunk_size)\n", 413 | "for i in range(last_chunk,num_chunks):\n", 414 | " \n", 415 | " print(i)\n", 416 | " start_time = time.time()\n", 417 | " start_idx = i * chunk_size\n", 418 | " end_idx = min(start_idx + chunk_size, len(splits_all))\n", 419 | " \n", 420 | " # Extract the current chunk\n", 421 | " current_splits = splits_all[start_idx:end_idx]\n", 422 | " current_metadatas = metadatas_all[start_idx:end_idx]\n", 423 | " \n", 424 | " # Add the current chunk to the vector database\n", 425 | " p.add_texts(texts = current_splits, metadatas=current_metadatas)\n", 426 | " end_time = time.time()\n", 427 | " elapsed_time = end_time - start_time\n", 428 | " print(f\"Elapsed time: {elapsed_time} seconds\")\n", 429 | " print(\"--------\")" 430 | ] 431 | }, 432 | { 433 | "cell_type": "markdown", 434 | "metadata": {}, 435 | "source": [ 436 | "`8. Read in VectorDB for testing` " 437 | ] 438 | }, 439 | { 440 | "cell_type": "code", 441 | "execution_count": null, 442 | "metadata": {}, 443 | "outputs": [], 444 | "source": [ 445 | "# Pinecone\n", 446 | "pinecone.init(\n", 447 | " api_key=os.environ.get('PINECONE_API_KEY'), \n", 448 | " environment=\"us-east1-gcp\" \n", 449 | ")\n", 450 | "index_name = \"lex-gpt\"\n", 451 | "embeddings = OpenAIEmbeddings()\n", 452 | "p = Pinecone.from_existing_index(index_name=index_name,embedding=embeddings)" 453 | ] 454 | }, 455 | { 456 | "cell_type": "markdown", 457 | "metadata": {}, 458 | "source": [ 459 | "`9. Run VectorDBQAWithSourcesChain`" 460 | ] 461 | }, 462 | { 463 | "cell_type": "code", 464 | "execution_count": null, 465 | "metadata": {}, 466 | "outputs": [], 467 | "source": [ 468 | "def run_vectordb_sources_chain(llm,query,docstore):\n", 469 | "\n", 470 | " start_time = time.time()\n", 471 | " chain = VectorDBQAWithSourcesChain.from_chain_type(llm, chain_type=\"stuff\", vectorstore=docstore)\n", 472 | " a = chain({\"question\": query},return_only_outputs=True)\n", 473 | " print(a[\"answer\"])\n", 474 | " print(a[\"sources\"])\n", 475 | " end_time = time.time()\n", 476 | " elapsed_time = end_time - start_time\n", 477 | " print(f\"Elapsed time: {elapsed_time} seconds\")\n", 478 | " print(\"--------\")\n", 479 | "\n", 480 | "llm = OpenAIChat(temperature=0)\n", 481 | "q = \"What does Paul Rosolie say about the Amazon Jungle?\"\n", 482 | "run_vectordb_sources_chain(llm,q,p)" 483 | ] 484 | }, 485 | { 486 | "cell_type": "code", 487 | "execution_count": null, 488 | "metadata": {}, 489 | "outputs": [], 490 | "source": [] 491 | } 492 | ], 493 | "metadata": { 494 | "kernelspec": { 495 | "display_name": "Python 3 (ipykernel)", 496 | "language": "python", 497 | "name": "python3" 498 | }, 499 | "language_info": { 500 | "codemirror_mode": { 501 | "name": "ipython", 502 | "version": 3 503 | }, 504 | "file_extension": ".py", 505 | "mimetype": "text/x-python", 506 | "name": "python", 507 | "nbconvert_exporter": "python", 508 | "pygments_lexer": "ipython3", 509 | "version": "3.9.16" 510 | } 511 | }, 512 | "nbformat": 4, 513 | "nbformat_minor": 2 514 | } 515 | -------------------------------------------------------------------------------- /scripts/run_whisper.py: -------------------------------------------------------------------------------- 1 | # trascribe audio 2 | import time,os 3 | import logging 4 | import whisper 5 | import numpy as np 6 | import pandas as pd 7 | 8 | # Set up logger 9 | logging.basicConfig(filename='whisper.log', filemode='w', level=logging.DEBUG) 10 | 11 | # Read the csv file 12 | new_ep=pd.read_csv("audio_transcription/episodes.csv",index_col=None) 13 | 14 | # Run whisper on each audio file 15 | for ix in new_ep.index: 16 | 17 | # get data 18 | ep_number=int(new_ep.loc[ix,'number']) 19 | print("EPISODE: %s"%ep_number) 20 | logging.info("EPISODE: %s", ep_number) 21 | 22 | # get audio 23 | audio_file_path='audio/%s.m4a'%str(ep_number) 24 | out_file_path='audio_transcription/0%s.txt'%str(ep_number) 25 | 26 | print(f"Processing file: {audio_file_path}") 27 | logging.info(f"Processing file: {audio_file_path}") 28 | start_time = time.time() 29 | 30 | # load Whisper model and transcribe audio file 31 | model = whisper.load_model("medium") 32 | result = model.transcribe(audio_file_path) 33 | 34 | # write 35 | with open(out_file_path, "w") as f: 36 | for seg in result['segments']: 37 | ts = np.round(seg['start'],1) 38 | f.write(new_ep.loc[ix,'link'] + "&t=%ss"%ts + "\t" + str(ts) + "\t" + seg['text'] + "\n") 39 | 40 | end_time = time.time() 41 | time_diff = end_time - start_time 42 | print(f"Time taken: {time_diff:.2f} seconds") 43 | logging.info(f"File processed: {audio_file_path}") 44 | logging.info(f"Time taken: {time_diff:.2f} seconds") -------------------------------------------------------------------------------- /styles/globals.css: -------------------------------------------------------------------------------- 1 | @tailwind base; 2 | @tailwind components; 3 | @tailwind utilities; 4 | -------------------------------------------------------------------------------- /tailwind.config.js: -------------------------------------------------------------------------------- 1 | /** @type {import('tailwindcss').Config} */ 2 | module.exports = { 3 | content: ["./app/**/*.{js,ts,jsx,tsx}", "./pages/**/*.{js,ts,jsx,tsx}", "./components/**/*.{js,ts,jsx,tsx}"], 4 | theme: { 5 | extend: {} 6 | }, 7 | plugins: [] 8 | }; 9 | -------------------------------------------------------------------------------- /tsconfig.json: -------------------------------------------------------------------------------- 1 | { 2 | "compilerOptions": { 3 | "target": "ES2020", 4 | "lib": ["dom", "dom.iterable", "esnext"], 5 | "allowJs": true, 6 | "skipLibCheck": true, 7 | "strict": true, 8 | "forceConsistentCasingInFileNames": true, 9 | "noEmit": true, 10 | "esModuleInterop": true, 11 | "module": "nodenext", 12 | "moduleResolution": "node", 13 | "resolveJsonModule": true, 14 | "isolatedModules": true, 15 | "jsx": "preserve", 16 | "incremental": true, 17 | "baseUrl": ".", 18 | "paths": { 19 | "@/*": ["./*"] 20 | } 21 | }, 22 | "include": ["next-env.d.ts", "**/*.ts", "**/*.tsx"], 23 | "exclude": ["node_modules"] 24 | } 25 | -------------------------------------------------------------------------------- /types/index.ts: -------------------------------------------------------------------------------- 1 | export enum OpenAIModel { 2 | DAVINCI_TURBO = "gpt-3.5-turbo" 3 | } 4 | 5 | export type LEXChunk = { 6 | pageContent: string; 7 | source: string; 8 | metadata: Metadata; 9 | length: number; 10 | }; 11 | 12 | interface Metadata { 13 | id: string; 14 | title: string; 15 | link: string; 16 | } 17 | --------------------------------------------------------------------------------