From ac3b23f4e3e1e635de95aaf2076c1797c8fa2dc4 Mon Sep 17 00:00:00 2001 From: Saurav Panda Date: Wed, 25 Dec 2024 16:26:20 -0800 Subject: [PATCH 01/14] experiment: webrag for docs --- docs/next.config.mjs | 10 +- docs/package-lock.json | 1414 +++++++++++++++++++++++++- docs/package.json | 8 +- docs/public/context/docs.db | Bin 0 -> 483328 bytes docs/public/sql-wasm.wasm | Bin 0 -> 652953 bytes docs/scripts/extract-docs-context.js | 60 +- 6 files changed, 1445 insertions(+), 47 deletions(-) create mode 100644 docs/public/context/docs.db create mode 100644 docs/public/sql-wasm.wasm diff --git a/docs/next.config.mjs b/docs/next.config.mjs index b73b4e6..6de3e8f 100644 --- a/docs/next.config.mjs +++ b/docs/next.config.mjs @@ -6,9 +6,17 @@ const nextConfig = { config.module.rules.push({ test: /\.json$/, type: 'json', - }) + }); + config.resolve.alias = { + ...config.resolve.alias, + "sharp$": false, + "onnxruntime-node$": false, + } return config }, + experimental: { + esmExternals: true // Enable ES modules + }, // i18n: { // locales: ['en', 'es', 'fr', 'de'], // defaultLocale: 'en', diff --git a/docs/package-lock.json b/docs/package-lock.json index f48f35a..08a23c6 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -1,12 +1,12 @@ { "name": "akiradocs", - "version": "1.0.1", + "version": "1.0.52", "lockfileVersion": 3, "requires": true, "packages": { "": { "name": "akiradocs", - "version": "1.0.1", + "version": "1.0.52", "dependencies": { "@ai-sdk/anthropic": "^1.0.1", "@dnd-kit/core": "^6.1.0", @@ -14,6 +14,7 @@ "@dnd-kit/utilities": "^3.2.2", "@google/generative-ai": "^0.2.1", "@heroicons/react": "^2.1.5", + "@huggingface/transformers": "^3.2.3", "@mdx-js/loader": "^3.1.0", "@mdx-js/react": "^3.1.0", "@million/lint": "^1.0.12", @@ -36,7 +37,9 @@ "@radix-ui/react-toggle": "^1.1.0", "@radix-ui/react-toggle-group": "^1.1.0", "@radix-ui/react-tooltip": "^1.1.3", + "@xenova/transformers": "^2.17.2", "ai": "^4.0.2", + "better-sqlite3": "^11.7.0", "class-variance-authority": "^0.7.0", "clsx": "^2.1.1", "cross-spawn": "^7.0.6", @@ -56,7 +59,10 @@ "react-error-boundary": "^4.1.2", "react-markdown": "^9.0.1", "react-syntax-highlighter": "^15.6.1", + "sharp": "^0.33.5", "sonner": "^1.7.0", + "sql.js-httpvfs": "^0.8.12", + "sqlite-vss": "^0.1.2", "styled-components": "^6.1.13", "tailwind-merge": "^2.5.4", "tailwindcss-animate": "^1.0.7" @@ -529,7 +535,6 @@ }, "node_modules/@clack/prompts/node_modules/is-unicode-supported": { "version": "1.3.0", - "extraneous": true, "inBundle": true, "license": "MIT", "engines": { @@ -610,6 +615,16 @@ "react": ">=16.8.0" } }, + "node_modules/@emnapi/runtime": { + "version": "1.3.1", + "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.3.1.tgz", + "integrity": "sha512-kEBmG8KyqtxJZv+ygbEim+KCGtIq1fC22Ms3S4ziXmYKm8uyoLX0MHONVKwp+9opg390VaKRNt4a7A9NwmpNhw==", + "license": "MIT", + "optional": true, + "dependencies": { + "tslib": "^2.4.0" + } + }, "node_modules/@emotion/is-prop-valid": { 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zC{S5ICXjFd0*^%jpqtpZN33Rh}hXRtf83mA+y`GTwHDQ@KzaO_d3*%qz>7u2iNac@N6tugfh z^#ygWI%29c_s?=eML8k8D)xSdjeem1qP>+>dq)*}Kg(^ejxK*pYUa2Fid#Y3K+J82 izXOyCN(1c#?E>va$=9d<8-+ap|C2&F6eO1RgZ~FsY?bT) literal 0 HcmV?d00001 diff --git a/docs/scripts/extract-docs-context.js b/docs/scripts/extract-docs-context.js index 2bd7f4d..cdc252f 100644 --- a/docs/scripts/extract-docs-context.js +++ b/docs/scripts/extract-docs-context.js @@ -1,6 +1,33 @@ const fs = require('fs/promises'); const path = require('path'); const { glob } = require('glob'); +const sqlite3 = require('better-sqlite3'); + +class EmbeddingPipeline { + static task = 'feature-extraction'; + static model = 'sauravpanda/gte-small-onnx'; + static instance = null; + + static async getInstance(progress_callback = null) { + if (this.instance === null) { + // Optionally set cache directory + const { pipeline, env } = await import('@xenova/transformers'); + this.instance = await pipeline(this.task, this.model, { progress_callback }); + } + return this.instance; + } +} + +async function generateEmbedding(text) { + try { + const extractor = await EmbeddingPipeline.getInstance(); + const output = await extractor(text, { pooling: 'mean', normalize: true }); + return Array.from(output.data); + } catch (error) { + console.error('Error generating embedding:', error); + return null; + } +} async function extractTextFromBlocks(blocks) { if (!Array.isArray(blocks)) { @@ -67,26 +94,47 @@ async function main() { const contextDir = path.join(process.cwd(), 'public', 'context'); await fs.mkdir(contextDir, { recursive: true }); + const dbPath = path.join(contextDir, 'docs.db'); + const db = sqlite3(dbPath); + + db.exec(` + CREATE TABLE IF NOT EXISTS documents ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + path TEXT NOT NULL, + content TEXT NOT NULL, + embedding BLOB, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + `); + + // Initialize the embedding model once + console.log('Initializing embedding model...'); + // const extractor = await pipeline('feature-extraction', 'sauravpanda/gte-small-onnx'); + const enDocsDir = path.join(process.cwd(), 'compiled', 'en'); const docFiles = await glob('**/*.json', { cwd: enDocsDir, ignore: '**/_meta.json' }); - let combinedContent = []; + db.prepare('DELETE FROM documents').run(); + const insert = db.prepare('INSERT INTO documents (path, content, embedding) VALUES (?, ?, ?)'); + + console.log('Processing documents and generating embeddings...'); for (const file of docFiles) { const fullPath = path.join(enDocsDir, file); const content = await processDocFile(fullPath); if (content) { - combinedContent.push(`[Document: ${file}]\n${content}\n-------------`); + const embedding = await generateEmbedding(content); + insert.run(file, content, embedding ? JSON.stringify(embedding) : null); + console.log(`Processed: ${file}`); } } - const outputPath = path.join(contextDir, 'en_docs.txt'); - await fs.writeFile(outputPath, combinedContent.join('\n')); - - console.log('Documentation context has been extracted successfully!'); + db.exec(`CREATE INDEX IF NOT EXISTS idx_embedding ON documents(embedding)`); + console.log('Documentation context and embeddings have been stored in SQLite database!'); + db.close(); } catch (error) { console.error('Error processing documentation:', error); process.exit(1); From aed4e76d06c718864a734805cb5e6c2c3f8c8bcf Mon Sep 17 00:00:00 2001 From: Saurav Panda Date: Wed, 25 Dec 2024 18:16:12 -0800 Subject: [PATCH 02/14] experiment: embedding generation on client side --- docs/next.config.mjs | 8 + docs/public/context/docs.db | Bin 483328 -> 487424 bytes docs/src/app/aiSearch/page.tsx | 390 +++++++++++---------- docs/src/lib/aisearch/embeddings.ts | 34 ++ docs/src/lib/aisearch/embeddings.worker.ts | 31 ++ 5 files changed, 279 insertions(+), 184 deletions(-) create mode 100644 docs/src/lib/aisearch/embeddings.ts create mode 100644 docs/src/lib/aisearch/embeddings.worker.ts diff --git a/docs/next.config.mjs b/docs/next.config.mjs index 6de3e8f..798fa4a 100644 --- a/docs/next.config.mjs +++ b/docs/next.config.mjs @@ -1,5 +1,9 @@ /** @type {import('next').NextConfig} */ // import MillionLint from "@million/lint"; +import path from 'path'; +import { fileURLToPath } from 'url'; + +const __dirname = path.dirname(fileURLToPath(import.meta.url)); const nextConfig = { webpack: (config) => { @@ -7,11 +11,15 @@ const nextConfig = { test: /\.json$/, type: 'json', }); + config.resolve.alias = { ...config.resolve.alias, "sharp$": false, "onnxruntime-node$": false, } + + config.resolve.alias['@huggingface/transformers'] = path.resolve(__dirname, 'node_modules/@huggingface/transformers'); + return config }, experimental: { diff --git a/docs/public/context/docs.db b/docs/public/context/docs.db index 8bbda255ba9afb5459dbc4cad63536bd08fc113f..cf1c9b75d9dea79f8c88e5c71e28137c21242a6d 100644 GIT binary patch delta 2739 zcmZXW3v5)^5kTLackg>^dp~x-AsE(P%-4MFH3nl0tg*rTy~dbN_?b`5pDZNcDv072 z^+}dmBD+f1C_xcQ(wGoP{Ji!~1Ee;;5Fn@yt*R<&C7K3Rs+80zby_6YbY^XSsHSq6NiX<9=|U^c*4PThMxSE}zaJ<*vi_1*lSt&s+5+;*)wSzYtNvu+%BRq#SYRq!p^5L%brZ*D0@7O!|bs%zGRQ4 zQQD*00|_6%`S$!@r2^|K*`?HAS~5`P!;d~;q6|S_PFk>}ps1vvfL4Y^FGN%(4(-n${7C%;@(!j6qoCKx2xULR->M zhGkz)PQTV*Dm`*(O`10Y8nh-?MQWi@E9>34Emp+n^2tOFU|plQBP7Y?V7(OI2{8wo z!aV03LYAhTIi~f^+qk5tCcvgL5YJ-SGos5AyY!b?aD-D!q|MRj zGnBqQghR*dCpgci64Qu0VjMA?m`F?@#uGb;)dcUKPVlo`L@_aj*iGyq4iNmD-wytM zH1=wTp z)F!y0)P~-J_85G58`v(P;*IJ}%DVH8wVhJ5SH4R>9LIVdgdZ1O6mbup{Z=jn5x{w` zh;tt7Itt%Xnf#Db`dAw%lQKSSg<9lTvz5H@vYn61Ue}eO>aPvAr{LZIz%Ie^|H64wOl%L|^9}R|ZaEWBrpq_| z0B0R*3y8dTWr!+luNvhu`a>Pf2$+GE=1wsQd!(=g+QRsmR|HXZiywWc2lSgyi-tWS zpW2bmi*4eEhvg&~?=~KtTu3JuQZ+ZyV={7_Tyc(zs3^kfcH?7DEVd^Mq8LNBRJR8& z`^{45FddTmgzF9!Dd>B61%5kbw8~yVMu&}$NT$`XszA()&H2{_4_;Yn%%Kh4$w;1d ztd$^U;rzMctOtMlshS5O#Me3G#Ut5Zi%tnq(s0JH>Osy81Om}tAmm|wo$B$$!|I+d zbD#rqkaRe%rA(E7gyW9&D#**@?IPTrXBL6l`1vw>D!xC#Tq9M*%~HD%v$q&m4aKFT zlDd~cje0)HMseR4kBEB@9aIo+e(WF)%|cu_*F;b3+)>WvPRCjUVlm1d{X+`Z2b|ok zdJpwOy<5IYW`E>Z`{=Iu##7E&)+z%AIVU9FCYiWnZ3Gd;>V7)7{rcf z%UQ^bu5Zy(FtyTsbD-`an#!!s|LiTRvA)s*%NSvo^?1DrO_4)y<>119R^ATz0vsL$s7Fy zWrX9g9mDO^2eHGis-RO>?5vZ?#x75&M=dcJZ>B7G;_cZG%(nQDJHevER*Ij&YjF zXZLP*f4|+|%l`ISuLoND1BpD1oKX~IZ+mbKC1X8KPC2fdV>I2k&WMTazP=Uh%{J*=1d1Z$;tz1gAiqT=$BFovC?cBc9VUD%Ck zj0!cPzOGa9re$oPBugy5!G$hP?`OBXgb3?_R!7@oF2RD1qgd5r8BJ8Mety?`Af>QW zhencJso;MRK1$Yr*=bgwp9%H=IbM?HIrMT)8ubL2529c%coAd)7m({;08)NCkTTLi z+Yg}G0UT*U=%u1(00&Mm7t90eKm*tfwt=mn9+ZQMG*6|!N6<^(2f=bs4W#TUun}wl z3&DW}Xrw#Kz*rCfF;D}hf$1O@$bd}+go@$_F!mJj{r)@T_7n zu#4<+QL|9Y+egkYE@SBxRWnJSEfBtrbxx<{g{+ zMzX=l)Lt9=a%lX~)Hs)xJtF)bsl$~F5!ENuAi3vtq;oMD;@=Nvx^(ZT&ipP?AtDjp z@6DAN@HB+JpOj0*^a*tjT>$MS#)#LhdaB6J=jLYzJz*{NZIN> zFB{CKqA9{s1BIDWrcS@uu216dC8>3GQ{pC(-YAp84%(u)3!KrcVy&LVX^Y%;_pl{) z+jZ>guv>0Ge8&Gd#g2ZB=^8u!6}mAQ7y57Gt0x&_8IxrIa|MeqMB|~g9>2RlZtIM) zxY8!AZTSY0E5tar^y3`6{vNiC_KjkLg;X?zV4aM`6g&EFj7R$XY;Xb<>tb4Jyxxv~ z2eI9@fABWpa?5N{b6&G_aca4v8ui4d-W!gAoE9=IGd^Tg*}WE$u4WZJ4h>z5BEe#g``%a*IjwZ42o(_ zxyH9%8f)f@)!DR%aakKHI{#Ecr*!ncGF7Zh@b|ok%fm>@y0*w>&G)@Bbt(l*u-^wL z`ITkVQBozII>ayN;!-1*?*6T1ETv=~*3d4jIc6D~D48#6>KrnMbY3HkXGe!(5g;q@ z{ODw%%@Ubl&q}tEOwP-lm)KQxG6c&>6=Z$n9hlxBx3<~9k5Xspo2(zX*3W){6-Of-&KS`8&@>{Yu@ z=;GagV--EcG#R>X%UD6l(>=_mVH+R2>-D56lg!(d!CqGrpxQiN1aOj zk=j95YKzE?m~ez#A$-GyS~Ev{+eyBlBq!NRx^UzZ zma!F9CbYZ5*%nT=YCm#sqgXJPBk9gwQ|C+Xs4}%)m6wJzbe#KTPUjaJsY?_z^BYKt zApIR?Ur*(5Kb<(NwY1&%oHW^Ao-~NO-9Ph?^5p0cgoXkZt{o8v@n2YH9`;Aa(P zK!gj~MUAhznq`i)m$v3;Tt3;`a4R)jBsHRC9*%Ib>0uR>8TAvvLJUP_XAcX>>J)rf zg5-2Fa!A9?4mrM2sX0P?SM11klX1yjI4)h0!pUaU!dc(2j2OiO7}?Eb{n@Ej&=INF z7b)4tTwAqi>UyQ9-Nc6# diff --git a/docs/src/app/aiSearch/page.tsx b/docs/src/app/aiSearch/page.tsx index 5e849e3..7db5382 100644 --- a/docs/src/app/aiSearch/page.tsx +++ b/docs/src/app/aiSearch/page.tsx @@ -1,6 +1,6 @@ "use client" -import { useState } from 'react' +import { useState, useCallback } from 'react' import { SearchHeader } from '@/components/aiSearch/SearchHeader' import { SearchBar } from '@/components/aiSearch/SearchBar' import { LegacyDocsToggle } from '@/components/aiSearch/LegacyDocsToggle' @@ -17,196 +17,218 @@ import { Source } from '@/types/Source' import AILoader from '@/components/aiSearch/AILoader' import { getHeaderConfig } from '@/lib/headerConfig' import { Header } from '@/components/layout/Header' +import { generateEmbedding } from '@/lib/aisearch/embeddings' export default function Home() { - const [query, setQuery] = useState('') - const [aiResponse, setAiResponse] = useState('') - const [isLoading, setIsLoading] = useState(false) - const [error, setError] = useState(null) - const recommendedArticles = getRecommendedArticles() - const searchConfig = getSearchConfig() - const headerConfig = getHeaderConfig() - const config = getAkiradocsConfig() - const [sources, setSources] = useState([]) - - // If AI Search is disabled, show the disabled message - if (!config.navigation.header.items.find((item: any) => item.href === '/aiSearch')?.show) { - return ( -

- ) - } - - const extractSources = (response: string): { cleanResponse: string, sources: Source[] } => { - // Split the response into parts - const parts = response.split('Sources:'); - if (parts.length < 2) { - return { cleanResponse: response, sources: [] }; + const [query, setQuery] = useState('') + const [aiResponse, setAiResponse] = useState('') + const [isLoading, setIsLoading] = useState(false) + const [error, setError] = useState(null) + const recommendedArticles = getRecommendedArticles() + const searchConfig = getSearchConfig() + const headerConfig = getHeaderConfig() + const config = getAkiradocsConfig() + const [sources, setSources] = useState([]) + const [result, setResult] = useState([]) + + const handleGenerateEmbedding = useCallback(async (text: string) => { + try { + setIsLoading(true); + console.log("Loading model for embedding"); + const embedding = await generateEmbedding(text, (progress) => { + console.log(`Progress: ${progress.progress}%`); + }); + return embedding; + } catch (error) { + console.error('Error generating embedding:', error); + throw error; + } finally { + setIsLoading(false); + } + }, []); + + // If AI Search is disabled, show the disabled message + if (!config.navigation.header.items.find((item: any) => item.href === '/aiSearch')?.show) { + return ( +
+
+

AI Search is Disabled

+

+ AI Search is currently disabled. To enable this feature, set "aiSearch": true in your configuration file. +

+ +
+
+ ) } - const cleanResponse = parts[0].trim(); - const sourcesText = parts[1].trim(); - - // Extract sources from the text - const sources: Source[] = sourcesText - .split('\n') - .filter(line => line.trim().length > 0 && line.includes('.json')) // Only process non-empty lines containing .json - .map(line => ({ - title: line.trim().split('/').pop()?.replace('.json', '') || '', // Get the filename without extension - url: line.trim() - })); - - return { cleanResponse, sources }; - }; - - const handleSearch = async (e: React.FormEvent) => { - e.preventDefault() - setIsLoading(true) - setError(null) - setSources([]) - - try { - const contextResponse = await fetch('/context/en_docs.txt'); - if (!contextResponse.ok) { - throw new Error(`Failed to fetch context: ${contextResponse.status}`); - } - const contextData = await contextResponse.text(); - const docsContext = contextData; - - const engine = await CreateMLCEngine( - "Llama-3.2-1B-Instruct-q4f16_1-MLC", - { initProgressCallback: (progress: any) => console.log(progress) - }, - { - context_window_size: 100000, + const extractSources = (response: string): { cleanResponse: string, sources: Source[] } => { + // Split the response into parts + const parts = response.split('Sources:'); + if (parts.length < 2) { + return { cleanResponse: response, sources: [] }; } - ); - - const messages = [ - { - role: "system", - content: `You are a technical documentation assistant specialized in providing accurate, concise answers based on the official documentation. - Your responses should be: - 1. Direct and to the point - 2. Based strictly on the provided documentation context - 3. Include relevant code examples when available - 4. Written in a technical but clear style - - Documentation context: ${docsContext}` - }, - { - role: "user", - content: `Answer the following question using only the provided documentation context. - Question: ${query} - - Requirements for your response: - 1. If the answer isn't clearly supported by the documentation, say "I don't have enough information to answer this question accurately." - 2. Don't make assumptions or provide information not found in the documentation - 3. If relevant, include short code snippets to illustrate your answer. - 4. Only answer questions related to the Documentaion Context. - 5. Try to be concise and to the point. - - After your answer, if you used any sources from the documentation, list them in this format: - ------------- - Sources: - - (<path>)` + + const cleanResponse = parts[0].trim(); + const sourcesText = parts[1].trim(); + + // Extract sources from the text + const sources: Source[] = sourcesText + .split('\n') + .filter(line => line.trim().length > 0 && line.includes('.json')) // Only process non-empty lines containing .json + .map(line => ({ + title: line.trim().split('/').pop()?.replace('.json', '') || '', // Get the filename without extension + url: line.trim() + })); + + return { cleanResponse, sources }; + }; + + const handleSearch = async (e: React.FormEvent) => { + e.preventDefault() + setIsLoading(true) + setError(null) + setSources([]) + + try { + const embeddings = await handleGenerateEmbedding(query); + console.log("Embeddings:", embeddings) + if (embeddings.length === 0) { + throw new Error("No embeddings generated"); + } + const contextResponse = await fetch('/context/en_docs.txt'); + if (!contextResponse.ok) { + throw new Error(`Failed to fetch context: ${contextResponse.status}`); + } + const contextData = await contextResponse.text(); + const docsContext = contextData; + + const engine = await CreateMLCEngine( + "Llama-3.2-1B-Instruct-q4f16_1-MLC", + { initProgressCallback: (progress: any) => console.log(progress) }, + { + context_window_size: 100000, + } + ); + + const messages = [ + { + role: "system", + content: `You are a technical documentation assistant specialized in providing accurate, concise answers based on the official documentation. +Your responses should be: +1. Direct and to the point +2. Based strictly on the provided documentation context +3. Include relevant code examples when available +4. Written in a technical but clear style + +Documentation context: ${docsContext}` + }, + { + role: "user", + content: `Answer the following question using only the provided documentation context. +Question: ${query} + +Requirements for your response: +1. If the answer isn't clearly supported by the documentation, say "I don't have enough information to answer this question accurately." +2. Don't make assumptions or provide information not found in the documentation +3. If relevant, include short code snippets to illustrate your answer. +4. Only answer questions related to the Documentation Context. +5. Try to be concise and to the point. + +After your answer, if you used any sources from the documentation, list them in this format: +------------- +Sources: +- <title> (<path>)` + } + ]; + + console.log("Messages:", messages) + + const chunks = await engine.chat.completions.create({ + messages: messages as ChatCompletionMessageParam[], + stream: true, + stream_options: { include_usage: true } + }); + + let aiContent = ""; + for await (const chunk of chunks) { + const newContent = chunk.choices[0]?.delta.content || ""; + aiContent += newContent; + // Update the response in real-time + setAiResponse(aiContent); + } + console.log("AI Content:", aiContent) + // Extract sources after the full response is received + const { cleanResponse, sources } = extractSources(aiContent); + setAiResponse(cleanResponse); + setSources(sources); + + } catch (error) { + console.error('Error during AI search:', error); + + // Check for WebGPU error + if (error instanceof Error && error.name === 'WebGPUNotAvailableError') { + setError('WebGPU is not yet supported on this browser. Please try using another browser.'); + } else { + setError('Sorry, there was an error processing your request.'); + } + setAiResponse(''); + } finally { + setIsLoading(false) } - ]; - - console.log("messages", messages) - - const chunks = await engine.chat.completions.create({ - messages: messages as ChatCompletionMessageParam[], - stream: true, - stream_options: { include_usage: true } - }); - - let aiContent = ""; - for await (const chunk of chunks) { - const newContent = chunk.choices[0]?.delta.content || ""; - aiContent += newContent; - // Update the response in real-time - setAiResponse(aiContent); - } - - // Extract sources after the full response is received - const { cleanResponse, sources } = extractSources(aiContent); - setAiResponse(cleanResponse); - setSources(sources); - - } catch (error) { - console.error('Error during AI search:', error); - - // Check for WebGPU error - if (error instanceof Error && error.name === 'WebGPUNotAvailableError') { - setError('WebGPU is not yet supported on this browser. Please try using another browser.'); - } else { - setError('Sorry, there was an error processing your request.'); - } - setAiResponse(''); - } finally { - setIsLoading(false) } - } - - const handleBack = () => { - setAiResponse('') - } - - return ( - <div className="flex flex-col min-h-screen"> - <Header {...headerConfig} currentLocale={`en`} currentType={`aiSearch`}/> - <div className="min-h-screen py-12 px-4 sm:px-6 lg:px-8"> - - <div className="max-w-4xl mx-auto"> - <SearchHeader - logo={searchConfig.logo} - title={searchConfig.title} - description={searchConfig.description} - /> - <div className="flex flex-col sm:flex-row space-y-4 sm:space-y-0 sm:space-x-4 justify-center items-center mb-12"> - <SearchBar - query={query} - onQueryChange={setQuery} - onSubmit={handleSearch} - /> - <LegacyDocsToggle/> - </div> - <AnimatePresence> - {isLoading ? ( - <div className="flex flex-col justify-center items-center space-y-4 py-12"> - <AILoader /> - <p className="text-muted-foreground text-sm animate-pulse"> - Loading AI response... - </p> - </div> - ) : error ? ( - <div className="text-center p-4 rounded-lg bg-red-50 text-red-800"> - <p className="text-lg font-medium mb-2">Error</p> - <p>{error}</p> + const handleBack = () => { + setAiResponse('') + } + + return ( + <div className="flex flex-col min-h-screen"> + <Header {...headerConfig} currentLocale={`en`} currentType={`aiSearch`}/> + <div className="min-h-screen py-12 px-4 sm:px-6 lg:px-8"> + + <div className="max-w-4xl mx-auto"> + <SearchHeader + logo={searchConfig.logo} + title={searchConfig.title} + description={searchConfig.description} + /> + <div className="flex flex-col sm:flex-row space-y-4 sm:space-y-0 sm:space-x-4 justify-center items-center mb-12"> + <SearchBar + query={query} + onQueryChange={setQuery} + onSubmit={handleSearch} + /> + <LegacyDocsToggle/> + </div> + + <AnimatePresence> + {isLoading ? ( + <div className="flex flex-col justify-center items-center space-y-4 py-12"> + <AILoader /> + <p className="text-muted-foreground text-sm animate-pulse"> + Loading AI response... + </p> + </div> + ) : error ? ( + <div className="text-center p-4 rounded-lg bg-red-50 text-red-800"> + <p className="text-lg font-medium mb-2">Error</p> + <p>{error}</p> + </div> + ) : aiResponse ? ( + <AIResponse + response={aiResponse} + sources={sources} + onBack={handleBack} + /> + ) : recommendedArticles && ( + <RecommendedArticles articles={recommendedArticles} /> + )} + </AnimatePresence> + </div> </div> - ) : aiResponse ? ( - <AIResponse - response={aiResponse} - sources={sources} - onBack={handleBack} - /> - ) : recommendedArticles && ( - <RecommendedArticles articles={recommendedArticles} /> - )} - </AnimatePresence> - </div> - </div> - </div> - ) + </div> + ) } diff --git a/docs/src/lib/aisearch/embeddings.ts b/docs/src/lib/aisearch/embeddings.ts new file mode 100644 index 0000000..f93f006 --- /dev/null +++ b/docs/src/lib/aisearch/embeddings.ts @@ -0,0 +1,34 @@ +export interface ProgressCallback { + file: string; + progress: number; + loaded: number; + total: number; +} + +export async function generateEmbedding( + text: string, + onProgress?: (progress: ProgressCallback) => void +): Promise<number[]> { + return new Promise((resolve, reject) => { + const worker = new Worker( + new URL('./embeddings.worker.ts', import.meta.url), + { type: 'module' } + ); + + worker.addEventListener('message', (event) => { + if (event.data.status === 'progress' && onProgress) { + onProgress(event.data.progress); + } else if (event.data.status === 'complete') { + resolve(event.data.output); + worker.terminate(); + } + }); + + worker.addEventListener('error', (error) => { + reject(error); + worker.terminate(); + }); + + worker.postMessage({ text }); + }); +} \ No newline at end of file diff --git a/docs/src/lib/aisearch/embeddings.worker.ts b/docs/src/lib/aisearch/embeddings.worker.ts new file mode 100644 index 0000000..8077b1a --- /dev/null +++ b/docs/src/lib/aisearch/embeddings.worker.ts @@ -0,0 +1,31 @@ +import { pipeline } from "@huggingface/transformers"; + +class EmbeddingPipelineSingleton { + static task = 'feature-extraction'; + static model = 'Xenova/gte-small'; + static instance = null; + + static async getInstance(progress_callback = null) { + this.instance ??= pipeline(this.task, this.model, { + progress_callback, + quantized: true + }); + return this.instance; + } +} + +self.addEventListener('message', async (event) => { + const embedder = await EmbeddingPipelineSingleton.getInstance(x => { + self.postMessage({ status: 'progress', progress: x }); + }); + + const output = await embedder(event.data.text, { + pooling: 'mean', + normalize: true + }); + + self.postMessage({ + status: 'complete', + output: Array.from(output.data), + }); +}); \ No newline at end of file From 9eba5db647c0125370c03f5f6b8d80b9d1343833 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Wed, 25 Dec 2024 18:30:12 -0800 Subject: [PATCH 03/14] improved prompt for getting answers --- docs/src/app/aiSearch/page.tsx | 72 +++++++++++++++++++--------------- 1 file changed, 40 insertions(+), 32 deletions(-) diff --git a/docs/src/app/aiSearch/page.tsx b/docs/src/app/aiSearch/page.tsx index 7db5382..1e15957 100644 --- a/docs/src/app/aiSearch/page.tsx +++ b/docs/src/app/aiSearch/page.tsx @@ -91,9 +91,14 @@ export default function Home() { setIsLoading(true) setError(null) setSources([]) - + + const startTime = performance.now() // Add timing start + try { const embeddings = await handleGenerateEmbedding(query); + const embeddingTime = performance.now() // Track embedding time + console.log(`Time taken for embedding generation: ${(embeddingTime - startTime) / 1000}s`) + console.log("Embeddings:", embeddings) if (embeddings.length === 0) { throw new Error("No embeddings generated"); @@ -112,37 +117,38 @@ export default function Home() { context_window_size: 100000, } ); + const engineLoadTime = performance.now() // Track engine load time + console.log(`Time taken for engine initialization: ${(engineLoadTime - embeddingTime) / 1000}s`) const messages = [ - { - role: "system", - content: `You are a technical documentation assistant specialized in providing accurate, concise answers based on the official documentation. -Your responses should be: -1. Direct and to the point -2. Based strictly on the provided documentation context -3. Include relevant code examples when available -4. Written in a technical but clear style - -Documentation context: ${docsContext}` - }, - { - role: "user", - content: `Answer the following question using only the provided documentation context. -Question: ${query} - -Requirements for your response: -1. If the answer isn't clearly supported by the documentation, say "I don't have enough information to answer this question accurately." -2. Don't make assumptions or provide information not found in the documentation -3. If relevant, include short code snippets to illustrate your answer. -4. Only answer questions related to the Documentation Context. -5. Try to be concise and to the point. - -After your answer, if you used any sources from the documentation, list them in this format: -------------- -Sources: -- <title> (<path>)` - } - ]; + { + role: "system", + content: `You are a technical documentation assistant. Answer questions accurately and concisely using only the provided documentation. Include code examples if available.` + }, + { + role: "user", + content: ` + Can you answer in short and concise manner to user based on documentation provided. + + Question: ${query} + + Answer the question using only the provided documentation. + + If the answer isn't in the documentation, say: "I cannot answer this question from the given documentation." + + Do not make assumptions or add information not in the documentation. + + If relevant, include short code snippets. + + Ensure the final output is in markdown format. + + Sources (if used): + - <title> (<path>) + + Documentation: ${docsContext} + ` + } + ]; console.log("Messages:", messages) @@ -156,10 +162,12 @@ Sources: for await (const chunk of chunks) { const newContent = chunk.choices[0]?.delta.content || ""; aiContent += newContent; - // Update the response in real-time setAiResponse(aiContent); } - console.log("AI Content:", aiContent) + + const endTime = performance.now() // Track total time + console.log(`Total time taken for AI search: ${(endTime - startTime) / 1000}s`) + // Extract sources after the full response is received const { cleanResponse, sources } = extractSources(aiContent); 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+ const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0)); + const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0)); + return dotProduct / (magnitudeA * magnitudeB); +} + export default function Home() { const [query, setQuery] = useState('') const [aiResponse, setAiResponse] = useState('') @@ -95,14 +103,11 @@ export default function Home() { const startTime = performance.now() // Add timing start try { + // Generate embedding for the query const embeddings = await handleGenerateEmbedding(query); const embeddingTime = performance.now() // Track embedding time console.log(`Time taken for embedding generation: ${(embeddingTime - startTime) / 1000}s`) - console.log("Embeddings:", embeddings) - if (embeddings.length === 0) { - throw new Error("No embeddings generated"); - } const contextResponse = await fetch('/context/en_docs.txt'); if (!contextResponse.ok) { throw new Error(`Failed to fetch context: ${contextResponse.status}`); @@ -110,11 +115,12 @@ export default function Home() { const contextData = await contextResponse.text(); const docsContext = contextData; + const engine = await CreateMLCEngine( "Llama-3.2-1B-Instruct-q4f16_1-MLC", { initProgressCallback: (progress: any) => console.log(progress) }, { - context_window_size: 100000, + context_window_size: 20000, } ); const engineLoadTime = performance.now() // Track engine load time @@ -155,7 +161,12 @@ export default function Home() { const chunks = await engine.chat.completions.create({ messages: messages as ChatCompletionMessageParam[], stream: true, - stream_options: { include_usage: true } + stream_options: { include_usage: true }, + max_tokens: 1000, + temperature: 0.3, + top_p: 0.9, + frequency_penalty: 0.5, + presence_penalty: 0.5, }); let aiContent = ""; @@ -174,17 +185,18 @@ export default function Home() { setSources(sources); } catch (error) { - console.error('Error during AI search:', error); + console.error('Error during AI search:', error); - // Check for WebGPU error - if (error instanceof Error && error.name === 'WebGPUNotAvailableError') { - setError('WebGPU is not yet supported on this browser. Please try using another browser.'); - } else { - setError('Sorry, there was an error processing your request.'); - } - setAiResponse(''); - } finally { - setIsLoading(false) + // Check for WebGPU error + if (error instanceof Error && error.name === 'WebGPUNotAvailableError') { + setError('WebGPU is not yet supported on this browser. Please try using another browser.'); + } else { + setError('Sorry, there was an error processing your request.'); + } + setAiResponse(''); + setError(error instanceof Error ? error.message : 'An error occurred'); + } finally { + setIsLoading(false); } } From 81fbdb239467627d9f544f32edb1e69cf2423091 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Wed, 25 Dec 2024 22:52:15 -0800 Subject: [PATCH 05/14] fix: sqlite reading fixed --- docs/package-lock.json | 19 ++-- docs/public/context/docs.db | Bin 487424 -> 487424 bytes .../public/{ => sql.js-httpvfs}/sql-wasm.wasm | Bin docs/public/sql.js-httpvfs/sqlite.worker.js | 5 + docs/src/app/aiSearch/page.tsx | 97 +++++++++++++----- docs/src/lib/aisearch/dbWorker.ts | 34 ++++++ 6 files changed, 123 insertions(+), 32 deletions(-) rename docs/public/{ => sql.js-httpvfs}/sql-wasm.wasm (100%) create mode 100644 docs/public/sql.js-httpvfs/sqlite.worker.js create mode 100644 docs/src/lib/aisearch/dbWorker.ts diff --git a/docs/package-lock.json b/docs/package-lock.json index 08a23c6..d588d7c 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -1843,9 +1843,9 @@ } }, "node_modules/@million/install/node_modules/nanoid": { - "version": "5.0.8", - "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-5.0.8.tgz", - "integrity": "sha512-TcJPw+9RV9dibz1hHUzlLVy8N4X9TnwirAjrU08Juo6BNKggzVfP2ZJ/3ZUSq15Xl5i85i+Z89XBO90pB2PghQ==", + "version": "5.0.9", + "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-5.0.9.tgz", + "integrity": "sha512-Aooyr6MXU6HpvvWXKoVoXwKMs/KyVakWwg7xQfv5/S/RIgJMy0Ifa45H9qqYy7pTCszrHzP21Uk4PZq2HpEM8Q==", "funding": [ { "type": "github", @@ -1897,9 +1897,9 @@ } }, "node_modules/@million/lint/node_modules/nanoid": { - "version": "5.0.8", - "resolved": "https://registry.npmjs.org/nanoid/-/nanoid-5.0.8.tgz", - "integrity": "sha512-TcJPw+9RV9dibz1hHUzlLVy8N4X9TnwirAjrU08Juo6BNKggzVfP2ZJ/3ZUSq15Xl5i85i+Z89XBO90pB2PghQ==", + "version": "5.0.9", + "resolved": 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{ const config = getAkiradocsConfig() const [sources, setSources] = useState<Source[]>([]) const [result, setResult] = useState<number[]>([]) - const handleGenerateEmbedding = useCallback(async (text: string) => { try { setIsLoading(true); @@ -100,21 +99,80 @@ export default function Home() { setError(null) setSources([]) - const startTime = performance.now() // Add timing start + const startTime = performance.now() try { // Generate embedding for the query const embeddings = await handleGenerateEmbedding(query); - const embeddingTime = performance.now() // Track embedding time - console.log(`Time taken for embedding generation: ${(embeddingTime - startTime) / 1000}s`) - const contextResponse = await fetch('/context/en_docs.txt'); - if (!contextResponse.ok) { - throw new Error(`Failed to fetch context: ${contextResponse.status}`); - } - const contextData = await contextResponse.text(); - const docsContext = contextData; + // Get database worker + const worker = await getDbWorker(); + // SQL query for similarity search + const similarityQuery = ` + WITH similarity AS ( + SELECT + path, + content, + ( + WITH RECURSIVE + embedding_values(value, rest) AS ( + SELECT '', json($embedding) || ',' + UNION ALL + SELECT + substr(rest, 0, instr(rest, ',')), + substr(rest, instr(rest, ',') + 1) + FROM embedding_values + WHERE rest <> '' + ), + doc_values(value, rest) AS ( + SELECT '', embedding || ',' + UNION ALL + SELECT + substr(rest, 0, instr(rest, ',')), + substr(rest, instr(rest, ',') + 1) + FROM doc_values + WHERE rest <> '' + ) + SELECT + SUM(CAST(ev.value AS FLOAT) * CAST(dv.value AS FLOAT)) / + (SQRT(SUM(CAST(ev.value AS FLOAT) * CAST(ev.value AS FLOAT))) * + SQRT(SUM(CAST(dv.value AS FLOAT) * CAST(dv.value AS FLOAT)))) + FROM embedding_values ev + JOIN doc_values dv ON 1=1 + WHERE ev.value <> '' AND dv.value <> '' + ) as similarity_score + FROM documents + WHERE embedding IS NOT NULL + ) + SELECT path, content, similarity_score + FROM similarity + WHERE similarity_score > 0 + ORDER BY similarity_score DESC + LIMIT 5 + `; + + // const similarityQuery = ` + // SELECT * + // FROM documents + // WHERE embedding IS NOT NULL + // LIMIT 5 + // `; + + const results = await worker.db.query( + similarityQuery + ); + + console.log("RAG top 5 results:", results) + + // Combine relevant documents into context + const docsContext = results + .map((doc: any) => ` + Source: ${doc.path} + ${doc.content} + --- + `) + .join('\n'); const engine = await CreateMLCEngine( "Llama-3.2-1B-Instruct-q4f16_1-MLC", @@ -123,8 +181,9 @@ export default function Home() { context_window_size: 20000, } ); + const engineLoadTime = performance.now() // Track engine load time - console.log(`Time taken for engine initialization: ${(engineLoadTime - embeddingTime) / 1000}s`) + console.log(`Time taken for engine initialization: ${(engineLoadTime - startTime) / 1000}s`) const messages = [ { @@ -185,17 +244,9 @@ export default function Home() { setSources(sources); } catch (error) { - console.error('Error during AI search:', error); - - // Check for WebGPU error - if (error instanceof Error && error.name === 'WebGPUNotAvailableError') { - setError('WebGPU is not yet supported on this browser. Please try using another browser.'); - } else { - setError('Sorry, there was an error processing your request.'); - } - setAiResponse(''); - setError(error instanceof Error ? error.message : 'An error occurred'); - } finally { + console.error('Search error:', error); + setError(error instanceof Error ? error.message : 'An error occurred'); + } finally { setIsLoading(false); } } diff --git a/docs/src/lib/aisearch/dbWorker.ts b/docs/src/lib/aisearch/dbWorker.ts new file mode 100644 index 0000000..510d99d --- /dev/null +++ b/docs/src/lib/aisearch/dbWorker.ts @@ -0,0 +1,34 @@ +import { createDbWorker } from "sql.js-httpvfs"; + +let dbInstance: any = null; + +export async function getDbWorker() { + if (dbInstance) return dbInstance; + + const workerUrl = new URL( + "sql.js-httpvfs/dist/sqlite.worker.js", + import.meta.url + ); + const wasmUrl = new URL( + "sql.js-httpvfs/dist/sql-wasm.wasm", + import.meta.url + ); + + const config = { + from: "inline", + config: { + serverMode: "full", + requestChunkSize: 4096, + url: "/context/docs.db" + } + }; + + const worker = await createDbWorker( + [config], + workerUrl.toString(), + wasmUrl.toString() + ); + + dbInstance = worker; + return dbInstance; +} \ No newline at end of file From 7334969fa3b5e320f0504f4d790f8f62d6ce9c6c Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Wed, 25 Dec 2024 23:04:58 -0800 Subject: [PATCH 06/14] basic rag works --- docs/src/app/aiSearch/page.tsx | 107 +++++++++++++++------------------ 1 file changed, 48 insertions(+), 59 deletions(-) diff --git a/docs/src/app/aiSearch/page.tsx b/docs/src/app/aiSearch/page.tsx index 714e2c4..a17dca9 100644 --- a/docs/src/app/aiSearch/page.tsx +++ b/docs/src/app/aiSearch/page.tsx @@ -21,6 +21,13 @@ import { generateEmbedding } from '@/lib/aisearch/embeddings' import { getDbWorker } from '@/lib/aisearch/dbWorker' function cosineSimilarity(a: number[], b: number[]): number { + if (a.some(isNaN) || b.some(isNaN)) { + console.error("NaN values detected in vectors:"); + console.error("Vector A NaN indices:", a.map((val, i) => isNaN(val) ? i : null).filter(x => x !== null)); + console.error("Vector B NaN indices:", b.map((val, i) => isNaN(val) ? i : null).filter(x => x !== null)); + throw new Error("Invalid vectors containing NaN values"); + } + const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0); const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0)); const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0)); @@ -42,9 +49,7 @@ export default function Home() { try { setIsLoading(true); console.log("Loading model for embedding"); - const embedding = await generateEmbedding(text, (progress) => { - console.log(`Progress: ${progress.progress}%`); - }); + const embedding = await generateEmbedding(text, (progress) => {}); return embedding; } catch (error) { console.error('Error generating embedding:', error); @@ -103,70 +108,54 @@ export default function Home() { try { // Generate embedding for the query - const embeddings = await handleGenerateEmbedding(query); - + const queryEmbedding = await handleGenerateEmbedding(query); + console.log("Query embedding:", queryEmbedding) // Get database worker const worker = await getDbWorker(); - // SQL query for similarity search - const similarityQuery = ` - WITH similarity AS ( - SELECT - path, - content, - ( - WITH RECURSIVE - embedding_values(value, rest) AS ( - SELECT '', json($embedding) || ',' - UNION ALL - SELECT - substr(rest, 0, instr(rest, ',')), - substr(rest, instr(rest, ',') + 1) - FROM embedding_values - WHERE rest <> '' - ), - doc_values(value, rest) AS ( - SELECT '', embedding || ',' - UNION ALL - SELECT - substr(rest, 0, instr(rest, ',')), - substr(rest, instr(rest, ',') + 1) - FROM doc_values - WHERE rest <> '' - ) - SELECT - SUM(CAST(ev.value AS FLOAT) * CAST(dv.value AS FLOAT)) / - (SQRT(SUM(CAST(ev.value AS FLOAT) * CAST(ev.value AS FLOAT))) * - SQRT(SUM(CAST(dv.value AS FLOAT) * CAST(dv.value AS FLOAT)))) - FROM embedding_values ev - JOIN doc_values dv ON 1=1 - WHERE ev.value <> '' AND dv.value <> '' - ) as similarity_score - FROM documents - WHERE embedding IS NOT NULL - ) - SELECT path, content, similarity_score - FROM similarity - WHERE similarity_score > 0 - ORDER BY similarity_score DESC - LIMIT 5 - `; + // Get all documents + const allDocs = await worker.db.query(` + SELECT path, content, embedding + FROM documents + WHERE embedding IS NOT NULL + `); - // const similarityQuery = ` - // SELECT * - // FROM documents - // WHERE embedding IS NOT NULL - // LIMIT 5 - // `; + // Calculate similarity scores and filter results + const similarityThreshold = 0.5; + const scoredDocs = allDocs + .map((doc: any) => { + // Clean the embedding string and parse it + const cleanEmbeddingStr = doc.embedding.replace(/[\[\]]/g, ''); // Remove square brackets + const embeddingArray = cleanEmbeddingStr + .split(',') + .map((val: string) => { + const parsed = parseFloat(val.trim()); + if (isNaN(parsed)) { + console.error(`Invalid embedding value found: "${val}"`); + } + return parsed; + }); + + return { + ...doc, + similarity_score: cosineSimilarity(queryEmbedding, embeddingArray) + }; + }) + .filter((doc: any) => doc.similarity_score > similarityThreshold) + .sort((a: any, b: any) => b.similarity_score - a.similarity_score) + .slice(0, 5); - const results = await worker.db.query( - similarityQuery - ); + console.log("RAG top 5 results:", scoredDocs); - console.log("RAG top 5 results:", results) + // If no relevant documents found, return early + if (scoredDocs.length === 0) { + setAiResponse("I cannot answer this question from the given documentation. The available content doesn't seem relevant enough to provide a accurate answer."); + setIsLoading(false); + return; + } // Combine relevant documents into context - const docsContext = results + const docsContext = scoredDocs .map((doc: any) => ` Source: ${doc.path} ${doc.content} From 29a8caf954992c743aa703279590db4e4afecd08 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Wed, 25 Dec 2024 23:45:02 -0800 Subject: [PATCH 07/14] fix: updated the docs --- docs/package-lock.json | 322 +++++++++++++++++- docs/package.json | 1 + docs/public/context/docs.db | Bin 487424 -> 487424 bytes docs/src/app/aiSearch/page.tsx | 51 +-- docs/src/components/aiSearch/AIResponse.tsx | 32 +- .../components/aiSearch/AIResponseSources.tsx | 2 +- 6 files changed, 383 insertions(+), 25 deletions(-) diff --git a/docs/package-lock.json b/docs/package-lock.json index d588d7c..3c80bc7 100644 --- a/docs/package-lock.json +++ b/docs/package-lock.json @@ -59,6 +59,7 @@ "react-error-boundary": "^4.1.2", "react-markdown": "^9.0.1", "react-syntax-highlighter": "^15.6.1", + "remark-gfm": "^4.0.0", "sharp": "^0.33.5", "sonner": "^1.7.0", "sql.js-httpvfs": "^0.8.12", @@ -6677,6 +6678,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/fault/-/fault-1.0.4.tgz", "integrity": "sha512-CJ0HCB5tL5fYTEA7ToAq5+kTwd++Borf1/bifxd9iT70QcXr4MRrO3Llf8Ifs70q+SJcGHFtnIE/Nw6giCtECA==", + "license": "MIT", "dependencies": { "format": "^0.2.0" }, @@ -7245,6 +7247,7 @@ "version": "2.2.5", "resolved": "https://registry.npmjs.org/hast-util-parse-selector/-/hast-util-parse-selector-2.2.5.tgz", "integrity": "sha512-7j6mrk/qqkSehsM92wQjdIgWM2/BW61u/53G6xmC8i1OmEdKLHbk419QKQUjz6LglWsfqoiHmyMRkP1BGjecNQ==", + "license": "MIT", "funding": { "type": "opencollective", "url": "https://opencollective.com/unified" @@ -7332,6 +7335,7 @@ "version": "6.0.0", "resolved": "https://registry.npmjs.org/hastscript/-/hastscript-6.0.0.tgz", "integrity": 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"1.1.4", "resolved": "https://registry.npmjs.org/character-entities-legacy/-/character-entities-legacy-1.1.4.tgz", "integrity": "sha512-3Xnr+7ZFS1uxeiUDvV02wQ+QDbc55o97tIV5zHScSPJpcLm/r0DFPcoY3tYRp+VZukxuMeKgXYmsXQHO05zQeA==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" @@ -10709,6 +10986,7 @@ "version": "1.1.4", "resolved": "https://registry.npmjs.org/character-reference-invalid/-/character-reference-invalid-1.1.4.tgz", "integrity": "sha512-mKKUkUbhPpQlCOfIuZkvSEgktjPFIsZKRRbC6KWVEMvlzblj3i3asQv5ODsrwt0N3pHAEvjP8KTQPHkp0+6jOg==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" @@ -10718,6 +10996,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/is-alphabetical/-/is-alphabetical-1.0.4.tgz", "integrity": "sha512-DwzsA04LQ10FHTZuL0/grVDk4rFoVH1pjAToYwBrHSxcrBIGQuXrQMtD5U1b0U2XVgKZCTLLP8u2Qxqhy3l2Vg==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" @@ -10727,6 +11006,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/is-alphanumerical/-/is-alphanumerical-1.0.4.tgz", "integrity": "sha512-UzoZUr+XfVz3t3v4KyGEniVL9BDRoQtY7tOyrRybkVNjDFWyo1yhXNGrrBTQxp3ib9BLAWs7k2YKBQsFRkZG9A==", + "license": "MIT", "dependencies": { "is-alphabetical": "^1.0.0", "is-decimal": "^1.0.0" @@ -10740,6 +11020,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/is-decimal/-/is-decimal-1.0.4.tgz", "integrity": "sha512-RGdriMmQQvZ2aqaQq3awNA6dCGtKpiDFcOzrTWrDAT2MiWrKQVPmxLGHl7Y2nNu6led0kEyoX0enY0qXYsv9zw==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" @@ -10749,6 +11030,7 @@ "version": "1.0.4", "resolved": "https://registry.npmjs.org/is-hexadecimal/-/is-hexadecimal-1.0.4.tgz", "integrity": "sha512-gyPJuv83bHMpocVYoqof5VDiZveEoGoFL8m3BXNb2VW8Xs+rz9kqO8LOQ5DH6EsuvilT1ApazU0pyl+ytbPtlw==", + "license": "MIT", "funding": { "type": "github", "url": "https://github.com/sponsors/wooorm" @@ -10758,6 +11040,7 @@ "version": "2.0.0", "resolved": "https://registry.npmjs.org/parse-entities/-/parse-entities-2.0.0.tgz", "integrity": "sha512-kkywGpCcRYhqQIchaWqZ875wzpS/bMKhz5HnN3p7wveJTkTtyAB/AlnS0f8DFSqYW1T82t6yEAkEcB+A1I3MbQ==", + "license": "MIT", "dependencies": { "character-entities": "^1.0.0", "character-entities-legacy": "^1.0.0", @@ -10775,6 +11058,7 @@ "version": "1.27.0", "resolved": "https://registry.npmjs.org/prismjs/-/prismjs-1.27.0.tgz", "integrity": "sha512-t13BGPUlFDR7wRB5kQDG4jjl7XeuH6jbJGt11JHPL96qwsEHNX2+68tFXqc1/k+/jALsbSWJKUOT/hcYAZ5LkA==", + "license": "MIT", "engines": { "node": ">=6" } @@ -10840,6 +11124,24 @@ "url": "https://opencollective.com/unified" } }, + "node_modules/remark-gfm": { + "version": "4.0.0", + "resolved": "https://registry.npmjs.org/remark-gfm/-/remark-gfm-4.0.0.tgz", + "integrity": "sha512-U92vJgBPkbw4Zfu/IiW2oTZLSL3Zpv+uI7My2eq8JxKgqraFdU8YUGicEJCEgSbeaG+QDFqIcwwfMTOEelPxuA==", + "license": "MIT", + "dependencies": { + "@types/mdast": "^4.0.0", + "mdast-util-gfm": "^3.0.0", + "micromark-extension-gfm": "^3.0.0", + "remark-parse": "^11.0.0", + "remark-stringify": "^11.0.0", + "unified": "^11.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/remark-mdx": { "version": "3.1.0", "resolved": "https://registry.npmjs.org/remark-mdx/-/remark-mdx-3.1.0.tgz", @@ -10884,6 +11186,21 @@ "url": "https://opencollective.com/unified" } }, + "node_modules/remark-stringify": { + "version": "11.0.0", + "resolved": "https://registry.npmjs.org/remark-stringify/-/remark-stringify-11.0.0.tgz", + "integrity": "sha512-1OSmLd3awB/t8qdoEOMazZkNsfVTeY4fTsgzcQFdXNq8ToTN4ZGwrMnlda4K6smTFKD+GRV6O48i6Z4iKgPPpw==", + "license": "MIT", + "dependencies": { + "@types/mdast": "^4.0.0", + "mdast-util-to-markdown": "^2.0.0", + "unified": "^11.0.0" + }, + "funding": { + "type": "opencollective", + "url": "https://opencollective.com/unified" + } + }, "node_modules/require-directory": { "version": "2.1.1", "resolved": "https://registry.npmjs.org/require-directory/-/require-directory-2.1.1.tgz", @@ -13010,6 +13327,7 @@ "version": "4.0.2", "resolved": "https://registry.npmjs.org/xtend/-/xtend-4.0.2.tgz", "integrity": "sha512-LKYU1iAXJXUgAXn9URjiu+MWhyUXHsvfp7mcuYm9dSUKK0/CjtrUwFAxD82/mCWbtLsGjFIad0wIsod4zrTAEQ==", + "license": "MIT", "engines": { "node": ">=0.4" } diff --git a/docs/package.json b/docs/package.json index f888a23..3d82ee4 100644 --- a/docs/package.json +++ b/docs/package.json @@ -65,6 +65,7 @@ "react-error-boundary": "^4.1.2", "react-markdown": "^9.0.1", "react-syntax-highlighter": "^15.6.1", + "remark-gfm": "^4.0.0", "sharp": "^0.33.5", "sonner": "^1.7.0", "sql.js-httpvfs": "^0.8.12", diff --git a/docs/public/context/docs.db b/docs/public/context/docs.db index 2a63d61a8ae75914ff8dc72b60ca978921fe4a5b..bd0a1dcdb9e383925e105c2337d874e9f494188d 100644 GIT binary patch delta 2663 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zfzm~I6@k9t658AKHqQS?lsRB_Iy6ztmV9gyclX%Pc2TkySXcyZJ-8?r)#Ris(r#p| z`uK6zc#RW+_xCZOR%15{3$^-Dt;quEHcgthB-n+)Y8}ri+;`CFbatFut0Y5OkNmZb z901mZj<kzZVC<BnUXFalbe^3uv=+uL&(xdyGFY<AR}AFn$6%Mtu28<@a;kn57AP!B S<_o?^(T~8s2$n&~i~j{Azq&X8 diff --git a/docs/src/app/aiSearch/page.tsx b/docs/src/app/aiSearch/page.tsx index a17dca9..7699c50 100644 --- a/docs/src/app/aiSearch/page.tsx +++ b/docs/src/app/aiSearch/page.tsx @@ -37,6 +37,7 @@ function cosineSimilarity(a: number[], b: number[]): number { export default function Home() { const [query, setQuery] = useState('') const [aiResponse, setAiResponse] = useState('') + const [loaderText, setLoaderText] = useState('Loading database ...') const [isLoading, setIsLoading] = useState(false) const [error, setError] = useState<string | null>(null) const recommendedArticles = getRecommendedArticles() @@ -44,7 +45,6 @@ export default function Home() { const headerConfig = getHeaderConfig() const config = getAkiradocsConfig() const [sources, setSources] = useState<Source[]>([]) - const [result, setResult] = useState<number[]>([]) const handleGenerateEmbedding = useCallback(async (text: string) => { try { setIsLoading(true); @@ -54,8 +54,9 @@ export default function Home() { } catch (error) { console.error('Error generating embedding:', error); throw error; - } finally { - setIsLoading(false); + } + finally { + setLoaderText('Searching database for relevant information ...') } }, []); @@ -154,12 +155,15 @@ export default function Home() { return; } + setLoaderText('Loading the AI model ...') + // Combine relevant documents into context const docsContext = scoredDocs .map((doc: any) => ` Source: ${doc.path} + --- Content --- ${doc.content} - --- + --- End of Content --- `) .join('\n'); @@ -171,9 +175,11 @@ export default function Home() { } ); + + const engineLoadTime = performance.now() // Track engine load time console.log(`Time taken for engine initialization: ${(engineLoadTime - startTime) / 1000}s`) - + setLoaderText('Processing information and generating AI response ...') const messages = [ { role: "system", @@ -187,19 +193,22 @@ export default function Home() { Question: ${query} Answer the question using only the provided documentation. - - If the answer isn't in the documentation, say: "I cannot answer this question from the given documentation." - + Do not make assumptions or add information not in the documentation. - If relevant, include short code snippets. + If relevant, include short code snippets. Only add code snippets if it is helpful to the question. + + Dont add any notes or comments to the answer. + + Make sure the question is answered properly avoiding unnecessary information. - Ensure the final output is in markdown format. + Ensure the final output is in markdown format. Make sure it pretty and clean. Sources (if used): - <title> (<path>) - Documentation: ${docsContext} + Here is the documentation, only answer based on this information: + ${docsContext} ` } ]; @@ -210,8 +219,8 @@ export default function Home() { messages: messages as ChatCompletionMessageParam[], stream: true, stream_options: { include_usage: true }, - max_tokens: 1000, - temperature: 0.3, + max_tokens: 500, + temperature: 0.1, top_p: 0.9, frequency_penalty: 0.5, presence_penalty: 0.5, @@ -221,17 +230,19 @@ export default function Home() { for await (const chunk of chunks) { const newContent = chunk.choices[0]?.delta.content || ""; aiContent += newContent; - setAiResponse(aiContent); + + // Process partial content for streaming + const { cleanResponse } = extractSources(aiContent); + setAiResponse(cleanResponse); } + // Only extract and set sources after streaming is complete + const { sources } = extractSources(aiContent); + setSources(sources); + const endTime = performance.now() // Track total time console.log(`Total time taken for AI search: ${(endTime - startTime) / 1000}s`) - // Extract sources after the full response is received - const { cleanResponse, sources } = extractSources(aiContent); - setAiResponse(cleanResponse); - setSources(sources); - } catch (error) { console.error('Search error:', error); setError(error instanceof Error ? error.message : 'An error occurred'); @@ -269,7 +280,7 @@ export default function Home() { <div className="flex flex-col justify-center items-center space-y-4 py-12"> <AILoader /> <p className="text-muted-foreground text-sm animate-pulse"> - Loading AI response... + {loaderText} </p> </div> ) : error ? ( diff --git a/docs/src/components/aiSearch/AIResponse.tsx b/docs/src/components/aiSearch/AIResponse.tsx index ca46310..e78f31f 100644 --- a/docs/src/components/aiSearch/AIResponse.tsx +++ b/docs/src/components/aiSearch/AIResponse.tsx @@ -8,6 +8,7 @@ import { AIResponseActions } from "./AIResponseActions" import { AIResponseSources } from "./AIResponseSources" import ReactMarkdown from 'react-markdown' import { Source } from "@/types/Source" +import { memo } from 'react' interface AIResponseProps { response: string @@ -15,6 +16,20 @@ interface AIResponseProps { onBack: () => void } +// Memoize the markdown component to prevent unnecessary re-renders +const MemoizedMarkdown = memo(({ content }: { content: string }) => ( + <ReactMarkdown + components={{ + pre({ children }) { + return <div className="not-prose">{children}</div> + } + }} + > + {content} + </ReactMarkdown> +)) +MemoizedMarkdown.displayName = 'MemoizedMarkdown' + export function AIResponse({ response, sources, onBack }: AIResponseProps) { return ( <motion.div @@ -35,8 +50,21 @@ export function AIResponse({ response, sources, onBack }: AIResponseProps) { </Button> </CardHeader> <CardContent className="pt-6"> - <div className="prose dark:prose-invert max-w-none"> - <ReactMarkdown>{response}</ReactMarkdown> + <div className="prose dark:prose-invert max-w-none + prose-headings:font-semibold + prose-h1:text-xl prose-h1:mt-8 prose-h1:mb-4 + prose-h2:text-lg prose-h2:mt-6 prose-h2:mb-3 + prose-p:my-4 prose-p:leading-relaxed + prose-ul:space-y-2 prose-ul:list-disc prose-ul:pl-6 + prose-ol:space-y-2 prose-ol:list-decimal prose-ol:pl-6 + prose-li:pl-2 + [&_pre]:bg-secondary [&_pre]:p-4 [&_pre]:rounded-lg [&_pre]:my-4 + [&_code:not(pre_code)]:bg-secondary [&_code:not(pre_code)]:px-1.5 [&_code:not(pre_code)]:py-0.5 [&_code:not(pre_code)]:rounded-sm + [&_pre_code]:bg-transparent [&_pre_code]:p-0 [&_pre_code]:rounded-none + [&>*:first-child]:mt-0 + [&>p>strong]:block [&>p>strong]:mt-8 [&>p>strong]:mb-4 [&>p>strong]:text-lg + [&>p:has(>strong:only-child)]:m-0"> + <MemoizedMarkdown content={response} /> </div> </CardContent> <CardFooter className="flex flex-col items-start space-y-4"> diff --git a/docs/src/components/aiSearch/AIResponseSources.tsx b/docs/src/components/aiSearch/AIResponseSources.tsx index 5b160ed..e4577ca 100644 --- a/docs/src/components/aiSearch/AIResponseSources.tsx +++ b/docs/src/components/aiSearch/AIResponseSources.tsx @@ -12,7 +12,7 @@ export function AIResponseSources({ sources }: AIResponseSourcesProps) { {sources.map((source, index) => ( <li key={index}> <a - href={source.url} + href={`${window.location.origin}/en/${source.url.replace('*%20', '')}?utm_source=akiradocs`} target="_blank" rel="noopener noreferrer" className="text-sm text-indigo-600 dark:text-indigo-400 hover:underline" From 8b92e0de3ca13839cc2a2495eb90249b74fa7e19 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Wed, 25 Dec 2024 23:51:24 -0800 Subject: [PATCH 08/14] deployment bug fix --- docs/src/lib/aisearch/dbWorker.ts | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/src/lib/aisearch/dbWorker.ts b/docs/src/lib/aisearch/dbWorker.ts index 510d99d..9e6a3b2 100644 --- a/docs/src/lib/aisearch/dbWorker.ts +++ b/docs/src/lib/aisearch/dbWorker.ts @@ -1,4 +1,5 @@ import { createDbWorker } from "sql.js-httpvfs"; +import { SplitFileConfig } from "sql.js-httpvfs/dist/sqlite.worker"; let dbInstance: any = null; @@ -14,8 +15,8 @@ export async function getDbWorker() { import.meta.url ); - const config = { - from: "inline", + const config: SplitFileConfig = { + from: "inline" as const, config: { serverMode: "full", requestChunkSize: 4096, From 35838912faff70536772d71a8661933139a01431 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Thu, 26 Dec 2024 00:05:47 -0800 Subject: [PATCH 09/14] fix: update the page context and fixed --- docs/public/context/docs.db | Bin 487424 -> 487424 bytes docs/src/app/aiSearch/page.tsx | 14 ++++++++------ docs/src/lib/aisearch/embeddings.worker.ts | 11 +++++------ 3 files changed, 13 insertions(+), 12 deletions(-) diff --git a/docs/public/context/docs.db b/docs/public/context/docs.db index bd0a1dcdb9e383925e105c2337d874e9f494188d..1ac6b70e9ec8f48794dbb1247b5ef31e21ddded9 100644 GIT binary patch delta 2574 zcmYk8dr(x@8Nl~DkGl)*WtUKmBFKs+K2a1=6qH2)AE+!ULF#MO$QxXq8xfG(iPPKZ z*n(}{wize1la`oN!?Iz&NsIwC)K~1#q<^+Ev@^Cdok>b<r)CC$eusyFncw*?XTLq? zd*5vzj&C21?^p;oI^%Z0jiC2^2h0oEo!^gylF(0MH_MdOW{bsQ$u^s5-ETJ1ddRG& zHN~u@wZg2S^;NT+*5zg?t->tnbxk;+tvC56lZR}nlrTTs!FzRR4H_N>65UJ===j+s z?q(>Q)@P20)U+)r8|d5CZsD>zQMmYzbnpcYAA<<=-qE^rd?%hCW%DCR$!0Mv_C!%) z%Z`eUOksYl&|-EPK8#)jUjytz|4T~7VTn-kI3~uCv{v^!#CQTjuU9?+bxd)8j2K_W z@Tdo-Hj?{6jB|!HK0dXP_i2L|D`$9qKzjml^p(h&>myTa*`h=%WPXbo6>U$X>$QF^ z#;pvix=2}&@<#M0Dg*qOy<8b^rawxI$1^<HE~XS>FlD7ucm@4T$s}B4SF#YJ*N}WU zO5J9qE25&A-=3)M)CxsO&^XD^U#B!RG<{0$Ler(>VJvJSX|3)5ju>CT@W{}#C-wc& zVtfhfJ5K*A+Y&Lcg%ssZdUk}ezUbHS?p65O0d#r+gO^Zri02MFqmP+AmR5`2XNL*~ zr(W1P8C~n~*-l}s9iD=<@Y}%tb@DblMz^WkPSH7YFgi+^ni}pBqf?1rPup`b_j}@F z9Y5X5a@YlRq58|8W);ajL>{4@=S`!^PU&dG5n?y7i%>eN3H4bfv5DA7*oXy04x#e7 zh<KeSA<hsPM7qUt!BRvIhY3k+BeoK$gsRbOLLJ~C)CH;=>WD0&ny4c7PqU6>0kLmd z9w7M&v5YuGR1zx)WmqDijH{vhEvmW72-Q*%genCUrxe0VoFtUT#f0+SN+b}A36<2N z#4+MDp~Aj{*iNh=l8B|m65<4LoKTUhAXKgwD$imF)$I+$YeXTTEPs{QOl%?663Ij^ zp)OrcC~us^9Kuf=B<ADLI!KK|>0;eFR0NHKESxO&aA?E46#0o&u6j^rwxT^<{zpK0 z@9?a;csV47xD&1I@*N$TgT`T+7(OvQF;p;O1Nm>u40N}PyAD`@I}@!R>3Hk9%w@=j z#W&xMmUt8+V#0xZg#Av3x}b50jvCSa;E-zgq;wjq!G&U2quG@}l961BLjnxIi@##O ziJwvX6D-3$y%5&%<0O&G!Xt1Epd8=(H$0NEVWi0{!{%pj(FR+>PVue|^+BVET}hS# z#Q^+^Ln;3EF3hom^FvOK@eVd^qjDynoa4_O;dW>d(1NXR!Y4u=dA!j)kK29#w>7DX zc@27=@*W*OOw#hnNvHO<4pnL>^a<^{4rP-aycV+#fa>Zo>1MDVMY2ZcdbCkT%;c=q z=^)PQEs;aE1WqQN;o?D@S&QBGMYpim3@$c(HcRmjYsbn1H|~ph=zA=F%w*|9mw7g1 z%U&hnWOzVc)4?A!iWzLc-P<|pc=MLr#b6T_{95}!$IvB>Cd&Dsaf%}ChFM8VL*W4m z@MaWVc<xXWDT7QD&k|m|3AS|$H^oOJ^I5NloNm#j(PkHI+GpMwZkNjyxVcwbfyq7m zO9$+rw5lKu^HGOTN`Z+9o(j!<sbU3}w!sYZ9G>Rl9+QiHpJcv)Yc-L^W%_3;O+Qve zh*T!G-rsLF;cw@NVga78$2vMyBOAgV)~Q2H(5PjQi)nw;?(6vZKDLR$?od(PH65>4 zTdA~N@+}=&f<_r-#7d8CLE|)oJt&5#xcWGIPltw}QOIB~=B(Fl>e&Ak<T5LdwM6`# z#|DikE*@u?jiI#N_cWRHd8FBZd2Vg42J6CJHr*R)7!`C+_J$asXiR05HV?XXoUN{I ztSN6e7Z}Kti*aoa{Lv8}f>B5Gi_9|`a1sBr6m=ST;!B5Fk7shVUq;9SkDARoyn07S zO{zYAEtGlZwvL1QteKR-Wa|eyl&e>GNOV!ny%97{Fet+O^&+T;`o{8@%Hwx2=ajsu zhx#v3?$1uTSrWM2A?Jn``2I=V=i#&J{V3(1QyrX8Ep|Q%FSKcI0&=hR3mqzhMyYCc zIje5Gq%TSTG}RAD<Zek7U#$iTbr&O}&P^KiI_5tmqp<HUY`<!enI2P7D<e(H1M(hi zmL-*e+xgnuz6J6hjw>B<sjbFlDK%rpN(1qi<wD%qAs>QVd@tUt#(9_JyWD>4;R&<K zW+}W<Fdi|b^W<l8m$Yxc<1{O6mLnH8kF^F?UX`(U@GbcW<dVSxvjUe~m49ZiFzl4m zC6(IieP4%0H6ox2clPp+^w6zixeU$*d_R|a(bFS(D0%dzoEksjc7{6C28}!hK1}Zw zgE~HFW$EO;SKQE{Y4R6V9lF2Z>KE2Ak;$MjBnBoNA>Q-N%ppTEs6*-GoM^(V%R<eG IujE$te~K8OcK`qY delta 2591 zcmZXWe^6A{702JX@7}iy?qe5IP5gl_m{idaR~AsfA4^0qDzJbH@=F#F{E?vGkL)z9 z<GxIszG<cn8Y4|p8^%~on<nA;WKZ)G{6YK`S{vIxI@4xoXZ$CBIE=~EOrxad?t(JY zo%!7R_MQ9gx%Zy$IqyYB)1sqkJ#*mLk-2N(m=%7<4YO>Q=cma~W*?iKFZ`R$Hbqg& z%ob{k%&pW4vyR$!vzFR=GeoV|3{snBmQ%~kva{ar-Ozs4_iB8Sf@g)EqLYPjt|aAf zZW-UI{F>LS!@n+8mk3civeMjw^KSCXnpjEW99YWu2ReQ-qLnaI`?a@q*kT#A3^IDp z_h>V4R-gFF4avBUX&>o0_;YdF4Rw}L!;pu?n>sXFMwqSxj=S!pY+pgPpC)x7_c>@; 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Answer questions accurately and concisely using only the provided documentation. Include code examples if available.` + content: `You are a technical documentation assistant for AkiraDocs. Your purpose is to: +1. Provide accurate, helpful answers using ONLY the provided documentation +2. Stay positive and factual based on the documentation provided.` }, { role: "user", content: ` - Can you answer in short and concise manner to user based on documentation provided. - + Please provide a helpful answer which is short and concise to the following question using only the provided documentation. + Question: ${query} Answer the question using only the provided documentation. @@ -219,9 +221,9 @@ export default function Home() { messages: messages as ChatCompletionMessageParam[], stream: true, stream_options: { include_usage: true }, - max_tokens: 500, - temperature: 0.1, - top_p: 0.9, + max_tokens: 300, + temperature: 0.3, + top_p: 0.95, frequency_penalty: 0.5, presence_penalty: 0.5, }); diff --git a/docs/src/lib/aisearch/embeddings.worker.ts b/docs/src/lib/aisearch/embeddings.worker.ts index 8077b1a..b6aecdd 100644 --- a/docs/src/lib/aisearch/embeddings.worker.ts +++ b/docs/src/lib/aisearch/embeddings.worker.ts @@ -1,14 +1,13 @@ -import { pipeline } from "@huggingface/transformers"; +import { pipeline, ProgressCallback, type PipelineType } from "@huggingface/transformers"; class EmbeddingPipelineSingleton { - static task = 'feature-extraction'; + static task: PipelineType = 'feature-extraction'; static model = 'Xenova/gte-small'; - static instance = null; + static instance: Promise<any> | null = null; - static async getInstance(progress_callback = null) { + static async getInstance(progress_callback: ProgressCallback | null = null) { this.instance ??= pipeline(this.task, this.model, { - progress_callback, - quantized: true + progress_callback: progress_callback ?? undefined }); return this.instance; } From 108e0227ad7b357b8b8c23f9558965fa2580bc2f Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Thu, 26 Dec 2024 00:27:35 -0800 Subject: [PATCH 10/14] updated model and reduced logs --- docs/src/app/aiSearch/page.tsx | 11 +++++++---- docs/src/lib/aisearch/embeddings.worker.ts | 2 +- 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/docs/src/app/aiSearch/page.tsx b/docs/src/app/aiSearch/page.tsx index 90dca7b..0239b37 100644 --- a/docs/src/app/aiSearch/page.tsx +++ b/docs/src/app/aiSearch/page.tsx @@ -48,7 +48,7 @@ export default function Home() { const handleGenerateEmbedding = useCallback(async (text: string) => { try { setIsLoading(true); - console.log("Loading model for embedding"); + // console.log("Loading model for embedding"); const embedding = await generateEmbedding(text, (progress) => {}); return embedding; } catch (error) { @@ -110,7 +110,7 @@ export default function Home() { try { // Generate embedding for the query const queryEmbedding = await handleGenerateEmbedding(query); - console.log("Query embedding:", queryEmbedding) + // console.log("Query embedding:", queryEmbedding) // Get database worker const worker = await getDbWorker(); @@ -169,7 +169,10 @@ export default function Home() { const engine = await CreateMLCEngine( "Llama-3.2-1B-Instruct-q4f16_1-MLC", - { initProgressCallback: (progress: any) => console.log(progress) }, + { initProgressCallback: (progress: any) => { + console.log(progress) + setLoaderText(`Loading the AI model ${Math.round(progress.progress * 100)}% ...`) + } }, { context_window_size: 20000, } @@ -215,7 +218,7 @@ export default function Home() { } ]; - console.log("Messages:", messages) + // console.log("Messages:", messages) const chunks = await engine.chat.completions.create({ messages: messages as ChatCompletionMessageParam[], diff --git a/docs/src/lib/aisearch/embeddings.worker.ts b/docs/src/lib/aisearch/embeddings.worker.ts index b6aecdd..52b26d6 100644 --- a/docs/src/lib/aisearch/embeddings.worker.ts +++ b/docs/src/lib/aisearch/embeddings.worker.ts @@ -2,7 +2,7 @@ import { pipeline, ProgressCallback, type PipelineType } from "@huggingface/tran class EmbeddingPipelineSingleton { static task: PipelineType = 'feature-extraction'; - static model = 'Xenova/gte-small'; + static model = 'sauravpanda/gte-small-onnx'; static instance: Promise<any> | null = null; static async getInstance(progress_callback: ProgressCallback | null = null) { From 1a64c4f0bf327a1a023a697573252eabbb811e0f Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Thu, 26 Dec 2024 00:41:05 -0800 Subject: [PATCH 11/14] updated main docs package --- .../components/aiSearch/AIResponseSources.tsx | 2 +- packages/akiradocs/compiled/manifest.json | 3 +- packages/akiradocs/next.config.mjs | 18 +- packages/akiradocs/package-lock.json | 1384 ++++++++++++++++- packages/akiradocs/package.json | 5 + packages/akiradocs/public/context/docs.db | Bin 0 -> 122880 bytes .../akiradocs/scripts/extract-docs-context.js | 60 +- packages/akiradocs/src/app/aiSearch/page.tsx | 197 ++- .../src/components/aiSearch/AIResponse.tsx | 32 +- .../components/aiSearch/AIResponseSources.tsx | 2 +- .../akiradocs/src/lib/aisearch/dbWorker.ts | 35 + .../akiradocs/src/lib/aisearch/embeddings.ts | 34 + .../src/lib/aisearch/embeddings.worker.ts | 30 + 13 files changed, 1697 insertions(+), 105 deletions(-) create mode 100644 packages/akiradocs/public/context/docs.db create mode 100644 packages/akiradocs/src/lib/aisearch/dbWorker.ts create mode 100644 packages/akiradocs/src/lib/aisearch/embeddings.ts create mode 100644 packages/akiradocs/src/lib/aisearch/embeddings.worker.ts diff --git a/docs/src/components/aiSearch/AIResponseSources.tsx b/docs/src/components/aiSearch/AIResponseSources.tsx index e4577ca..d146d15 100644 --- a/docs/src/components/aiSearch/AIResponseSources.tsx +++ b/docs/src/components/aiSearch/AIResponseSources.tsx @@ -12,7 +12,7 @@ export function AIResponseSources({ sources }: AIResponseSourcesProps) { {sources.map((source, index) => ( <li key={index}> <a - href={`${window.location.origin}/en/${source.url.replace('*%20', '')}?utm_source=akiradocs`} + href={`${window.location.origin}/en/${source.url.replace('%20', '')}?utm_source=akiradocs`} target="_blank" rel="noopener noreferrer" className="text-sm text-indigo-600 dark:text-indigo-400 hover:underline" diff --git a/packages/akiradocs/compiled/manifest.json b/packages/akiradocs/compiled/manifest.json index 9b82fb7..d96ba5d 100644 --- a/packages/akiradocs/compiled/manifest.json +++ b/packages/akiradocs/compiled/manifest.json @@ -37,6 +37,7 @@ "fr/articles/welcome.json", "fr/docs/_meta.json", "fr/docs/getting-started/quickstart.json", - "fr/docs/introduction.json" + "fr/docs/introduction.json", + "manifest.json" ] } \ No newline at end of file diff --git a/packages/akiradocs/next.config.mjs b/packages/akiradocs/next.config.mjs index b73b4e6..798fa4a 100644 --- a/packages/akiradocs/next.config.mjs +++ b/packages/akiradocs/next.config.mjs @@ -1,14 +1,30 @@ /** @type {import('next').NextConfig} */ // import MillionLint from "@million/lint"; +import path from 'path'; +import { fileURLToPath } from 'url'; + +const __dirname = path.dirname(fileURLToPath(import.meta.url)); const nextConfig = { webpack: (config) => { config.module.rules.push({ test: /\.json$/, type: 'json', - }) + }); + + config.resolve.alias = { + ...config.resolve.alias, + "sharp$": false, + "onnxruntime-node$": false, + } + + config.resolve.alias['@huggingface/transformers'] = path.resolve(__dirname, 'node_modules/@huggingface/transformers'); + return config }, + experimental: { + esmExternals: true // Enable ES modules + }, // i18n: { // locales: ['en', 'es', 'fr', 'de'], // defaultLocale: 'en', diff --git a/packages/akiradocs/package-lock.json b/packages/akiradocs/package-lock.json index f48f35a..cd20ac1 100644 --- a/packages/akiradocs/package-lock.json +++ b/packages/akiradocs/package-lock.json @@ -14,6 +14,7 @@ "@dnd-kit/utilities": "^3.2.2", "@google/generative-ai": "^0.2.1", "@heroicons/react": "^2.1.5", + "@huggingface/transformers": "^3.2.3", "@mdx-js/loader": "^3.1.0", "@mdx-js/react": "^3.1.0", "@million/lint": "^1.0.12", @@ -36,7 +37,9 @@ "@radix-ui/react-toggle": "^1.1.0", "@radix-ui/react-toggle-group": "^1.1.0", "@radix-ui/react-tooltip": "^1.1.3", + "@xenova/transformers": "^2.17.2", "ai": "^4.0.2", + "better-sqlite3": "^11.7.0", "class-variance-authority": "^0.7.0", "clsx": "^2.1.1", "cross-spawn": "^7.0.6", @@ -56,7 +59,9 @@ "react-error-boundary": "^4.1.2", "react-markdown": "^9.0.1", "react-syntax-highlighter": "^15.6.1", + "sharp": "^0.33.5", "sonner": "^1.7.0", + "sql.js-httpvfs": "^0.8.12", "styled-components": "^6.1.13", "tailwind-merge": "^2.5.4", "tailwindcss-animate": "^1.0.7" @@ -529,7 +534,6 @@ }, "node_modules/@clack/prompts/node_modules/is-unicode-supported": { "version": "1.3.0", - "extraneous": true, "inBundle": true, "license": "MIT", "engines": { @@ -610,6 +614,16 @@ "react": ">=16.8.0" } }, + "node_modules/@emnapi/runtime": { + "version": "1.3.1", + "resolved": "https://registry.npmjs.org/@emnapi/runtime/-/runtime-1.3.1.tgz", + "integrity": "sha512-kEBmG8KyqtxJZv+ygbEim+KCGtIq1fC22Ms3S4ziXmYKm8uyoLX0MHONVKwp+9opg390VaKRNt4a7A9NwmpNhw==", + "license": "MIT", + "optional": true, + "dependencies": { + 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b/packages/akiradocs/scripts/extract-docs-context.js @@ -1,6 +1,33 @@ const fs = require('fs/promises'); const path = require('path'); const { glob } = require('glob'); +const sqlite3 = require('better-sqlite3'); + +class EmbeddingPipeline { + static task = 'feature-extraction'; + static model = 'sauravpanda/gte-small-onnx'; + static instance = null; + + static async getInstance(progress_callback = null) { + if (this.instance === null) { + // Optionally set cache directory + const { pipeline, env } = await import('@xenova/transformers'); + this.instance = await pipeline(this.task, this.model, { progress_callback }); + } + return this.instance; + } +} + +async function generateEmbedding(text) { + try { + const extractor = await EmbeddingPipeline.getInstance(); + const output = await extractor(text, { pooling: 'mean', normalize: true }); + return Array.from(output.data); + } catch (error) { + console.error('Error generating embedding:', error); + return null; + } +} async function extractTextFromBlocks(blocks) { if (!Array.isArray(blocks)) { @@ -67,26 +94,47 @@ async function main() { const contextDir = path.join(process.cwd(), 'public', 'context'); await fs.mkdir(contextDir, { recursive: true }); + const dbPath = path.join(contextDir, 'docs.db'); + const db = sqlite3(dbPath); + + db.exec(` + CREATE TABLE IF NOT EXISTS documents ( + id INTEGER PRIMARY KEY AUTOINCREMENT, + path TEXT NOT NULL, + content TEXT NOT NULL, + embedding BLOB, + created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP + ) + `); + + // Initialize the embedding model once + console.log('Initializing embedding model...'); + // const extractor = await pipeline('feature-extraction', 'sauravpanda/gte-small-onnx'); + const enDocsDir = path.join(process.cwd(), 'compiled', 'en'); const docFiles = await glob('**/*.json', { cwd: enDocsDir, ignore: '**/_meta.json' }); - let combinedContent = []; + db.prepare('DELETE FROM documents').run(); + const insert = db.prepare('INSERT INTO documents (path, content, embedding) VALUES (?, ?, ?)'); + + console.log('Processing documents and generating embeddings...'); for (const file of docFiles) { const fullPath = path.join(enDocsDir, file); const content = await processDocFile(fullPath); if (content) { - combinedContent.push(`[Document: ${file}]\n${content}\n-------------`); + const embedding = await generateEmbedding(content); + insert.run(file, content, embedding ? JSON.stringify(embedding) : null); + console.log(`Processed: ${file}`); } } - const outputPath = path.join(contextDir, 'en_docs.txt'); - await fs.writeFile(outputPath, combinedContent.join('\n')); - - console.log('Documentation context has been extracted successfully!'); + db.exec(`CREATE INDEX IF NOT EXISTS idx_embedding ON documents(embedding)`); + console.log('Documentation context and embeddings have been stored in SQLite database!'); + db.close(); } catch (error) { console.error('Error processing documentation:', error); process.exit(1); diff --git a/packages/akiradocs/src/app/aiSearch/page.tsx b/packages/akiradocs/src/app/aiSearch/page.tsx index 5e849e3..e7988c5 100644 --- a/packages/akiradocs/src/app/aiSearch/page.tsx +++ b/packages/akiradocs/src/app/aiSearch/page.tsx @@ -1,6 +1,6 @@ "use client" -import { useState } from 'react' +import { useState, useCallback } from 'react' import { SearchHeader } from '@/components/aiSearch/SearchHeader' import { SearchBar } from '@/components/aiSearch/SearchBar' import { LegacyDocsToggle } from '@/components/aiSearch/LegacyDocsToggle' @@ -17,10 +17,27 @@ import { Source } from '@/types/Source' import AILoader from '@/components/aiSearch/AILoader' import { getHeaderConfig } from '@/lib/headerConfig' import { Header } from '@/components/layout/Header' +import { generateEmbedding } from '@/lib/aisearch/embeddings' +import { getDbWorker } from '@/lib/aisearch/dbWorker' + +function cosineSimilarity(a: number[], b: number[]): number { + if (a.some(isNaN) || b.some(isNaN)) { + console.error("NaN values detected in vectors:"); + console.error("Vector A NaN indices:", a.map((val, i) => isNaN(val) ? i : null).filter(x => x !== null)); + console.error("Vector B NaN indices:", b.map((val, i) => isNaN(val) ? i : null).filter(x => x !== null)); + throw new Error("Invalid vectors containing NaN values"); + } + + const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0); + const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0)); + const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0)); + return dotProduct / (magnitudeA * magnitudeB); +} export default function Home() { const [query, setQuery] = useState('') const [aiResponse, setAiResponse] = useState('') + const [loaderText, setLoaderText] = useState('Loading database ...') const [isLoading, setIsLoading] = useState(false) const [error, setError] = useState<string | null>(null) const recommendedArticles = getRecommendedArticles() @@ -28,6 +45,20 @@ export default function Home() { const headerConfig = getHeaderConfig() const config = getAkiradocsConfig() const [sources, setSources] = useState<Source[]>([]) + const handleGenerateEmbedding = useCallback(async (text: string) => { + try { + setIsLoading(true); + // console.log("Loading model for embedding"); + const embedding = await generateEmbedding(text, (progress) => {}); + return embedding; + } catch (error) { + console.error('Error generating embedding:', error); + throw error; + } + finally { + setLoaderText('Searching database for relevant information ...') + } + }, []); // If AI Search is disabled, show the disabled message if (!config.navigation.header.items.find((item: any) => item.href === '/aiSearch')?.show) { @@ -74,87 +105,157 @@ export default function Home() { setError(null) setSources([]) - try { - const contextResponse = await fetch('/context/en_docs.txt'); - if (!contextResponse.ok) { - throw new Error(`Failed to fetch context: ${contextResponse.status}`); - } - const contextData = await contextResponse.text(); - const docsContext = contextData; + const startTime = performance.now() + + try { + // Generate embedding for the query + const queryEmbedding = await handleGenerateEmbedding(query); + // console.log("Query embedding:", queryEmbedding) + // Get database worker + const worker = await getDbWorker(); + + // Get all documents + const allDocs = await worker.db.query(` + SELECT path, content, embedding + FROM documents + WHERE embedding IS NOT NULL + `); + + // Calculate similarity scores and filter results + const similarityThreshold = 0.5; + const scoredDocs = allDocs + .map((doc: any) => { + // Clean the embedding string and parse it + const cleanEmbeddingStr = doc.embedding.replace(/[\[\]]/g, ''); // Remove square brackets + const embeddingArray = cleanEmbeddingStr + .split(',') + .map((val: string) => { + const parsed = parseFloat(val.trim()); + if (isNaN(parsed)) { + console.error(`Invalid embedding value found: "${val}"`); + } + return parsed; + }); + + return { + ...doc, + similarity_score: cosineSimilarity(queryEmbedding, embeddingArray) + }; + }) + .filter((doc: any) => doc.similarity_score > similarityThreshold) + .sort((a: any, b: any) => b.similarity_score - a.similarity_score) + .slice(0, 5); + + console.log("RAG top 5 results:", scoredDocs); + + // If no relevant documents found, return early + if (scoredDocs.length === 0) { + setAiResponse("I cannot answer this question from the given documentation. The available content doesn't seem relevant enough to provide a accurate answer."); + setIsLoading(false); + return; + } + + setLoaderText('Loading the AI model ...') + + // Combine relevant documents into context + const docsContext = scoredDocs + .map((doc: any) => ` + Source: ${doc.path} + --- Content --- + ${doc.content} + --- End of Content --- + `) + .join('\n'); const engine = await CreateMLCEngine( "Llama-3.2-1B-Instruct-q4f16_1-MLC", - { initProgressCallback: (progress: any) => console.log(progress) - }, + { initProgressCallback: (progress: any) => { + console.log(progress) + setLoaderText(`Loading the AI model ${Math.round(progress.progress * 100)}% ...`) + } }, { - context_window_size: 100000, + context_window_size: 20000, } ); + + + const engineLoadTime = performance.now() // Track engine load time + console.log(`Time taken for engine initialization: ${(engineLoadTime - startTime) / 1000}s`) + setLoaderText('Processing information and generating AI response ...') const messages = [ { role: "system", - content: `You are a technical documentation assistant specialized in providing accurate, concise answers based on the official documentation. - Your responses should be: - 1. Direct and to the point - 2. Based strictly on the provided documentation context - 3. Include relevant code examples when available - 4. Written in a technical but clear style - - Documentation context: ${docsContext}` + content: `You are a technical documentation assistant for AkiraDocs. Your purpose is to: +1. Provide accurate, helpful answers using ONLY the provided documentation +2. Stay positive and factual based on the documentation provided. +3. Make sure the markdown answer is pretty, clean and easy to read.` }, { role: "user", - content: `Answer the following question using only the provided documentation context. + content: ` + Please provide a helpful answer which is short and concise to the following question using only the provided documentation. + Question: ${query} - Requirements for your response: - 1. If the answer isn't clearly supported by the documentation, say "I don't have enough information to answer this question accurately." - 2. Don't make assumptions or provide information not found in the documentation - 3. If relevant, include short code snippets to illustrate your answer. - 4. Only answer questions related to the Documentaion Context. - 5. Try to be concise and to the point. + Answer the question using only the provided documentation. + + Do not make assumptions or add information not in the documentation. + + If relevant, include short code snippets. Only add code snippets if it is helpful to the question. + + Dont add any notes or comments to the answer. + + Make sure the question is answered properly avoiding unnecessary information. + + Ensure the final output is in markdown format. Make sure it pretty and clean. - After your answer, if you used any sources from the documentation, list them in this format: + End of the answer, if you used any sources from the documentation, list them in this format: ------------- Sources: - - <title> (<path>)` + - <title> (<path>) + + Here is the documentation, only answer based on this information: + ${docsContext} + ` } ]; - console.log("messages", messages) + // console.log("Messages:", messages) const chunks = await engine.chat.completions.create({ messages: messages as ChatCompletionMessageParam[], stream: true, - stream_options: { include_usage: true } + stream_options: { include_usage: true }, + max_tokens: 300, + temperature: 0.7, + top_p: 0.95, + frequency_penalty: 0.5, + presence_penalty: 0.5, }); let aiContent = ""; for await (const chunk of chunks) { const newContent = chunk.choices[0]?.delta.content || ""; aiContent += newContent; - // Update the response in real-time - setAiResponse(aiContent); - } - - // Extract sources after the full response is received - const { cleanResponse, sources } = extractSources(aiContent); + + // Process partial content for streaming + const { cleanResponse } = extractSources(aiContent); setAiResponse(cleanResponse); + } + + // Only extract and set sources after streaming is complete + const { sources } = extractSources(aiContent); setSources(sources); - } catch (error) { - console.error('Error during AI search:', error); - - // Check for WebGPU error - if (error instanceof Error && error.name === 'WebGPUNotAvailableError') { - setError('WebGPU is not yet supported on this browser. Please try using another browser.'); - } else { - setError('Sorry, there was an error processing your request.'); - } - setAiResponse(''); + const endTime = performance.now() // Track total time + console.log(`Total time taken for AI search: ${(endTime - startTime) / 1000}s`) + + } catch (error) { + console.error('Search error:', error); + setError(error instanceof Error ? error.message : 'An error occurred'); } finally { - setIsLoading(false) + setIsLoading(false); } } @@ -187,7 +288,7 @@ export default function Home() { <div className="flex flex-col justify-center items-center space-y-4 py-12"> <AILoader /> <p className="text-muted-foreground text-sm animate-pulse"> - Loading AI response... + {loaderText} </p> </div> ) : error ? ( diff --git a/packages/akiradocs/src/components/aiSearch/AIResponse.tsx b/packages/akiradocs/src/components/aiSearch/AIResponse.tsx index ca46310..e78f31f 100644 --- a/packages/akiradocs/src/components/aiSearch/AIResponse.tsx +++ b/packages/akiradocs/src/components/aiSearch/AIResponse.tsx @@ -8,6 +8,7 @@ import { AIResponseActions } from "./AIResponseActions" import { AIResponseSources } from "./AIResponseSources" import ReactMarkdown from 'react-markdown' import { Source } from "@/types/Source" +import { memo } from 'react' interface AIResponseProps { response: string @@ -15,6 +16,20 @@ interface AIResponseProps { onBack: () => void } +// Memoize the markdown component to prevent unnecessary re-renders +const MemoizedMarkdown = memo(({ content }: { content: string }) => ( + <ReactMarkdown + components={{ + pre({ children }) { + return <div className="not-prose">{children}</div> + } + }} + > + {content} + </ReactMarkdown> +)) +MemoizedMarkdown.displayName = 'MemoizedMarkdown' + export function AIResponse({ response, sources, onBack }: AIResponseProps) { return ( <motion.div @@ -35,8 +50,21 @@ export function AIResponse({ response, sources, onBack }: AIResponseProps) { </Button> </CardHeader> <CardContent className="pt-6"> - <div className="prose dark:prose-invert max-w-none"> - <ReactMarkdown>{response}</ReactMarkdown> + <div className="prose dark:prose-invert max-w-none + prose-headings:font-semibold + prose-h1:text-xl prose-h1:mt-8 prose-h1:mb-4 + prose-h2:text-lg prose-h2:mt-6 prose-h2:mb-3 + prose-p:my-4 prose-p:leading-relaxed + prose-ul:space-y-2 prose-ul:list-disc prose-ul:pl-6 + prose-ol:space-y-2 prose-ol:list-decimal prose-ol:pl-6 + prose-li:pl-2 + [&_pre]:bg-secondary [&_pre]:p-4 [&_pre]:rounded-lg [&_pre]:my-4 + [&_code:not(pre_code)]:bg-secondary [&_code:not(pre_code)]:px-1.5 [&_code:not(pre_code)]:py-0.5 [&_code:not(pre_code)]:rounded-sm + [&_pre_code]:bg-transparent [&_pre_code]:p-0 [&_pre_code]:rounded-none + [&>*:first-child]:mt-0 + [&>p>strong]:block [&>p>strong]:mt-8 [&>p>strong]:mb-4 [&>p>strong]:text-lg + [&>p:has(>strong:only-child)]:m-0"> + <MemoizedMarkdown content={response} /> </div> </CardContent> <CardFooter className="flex flex-col items-start space-y-4"> diff --git a/packages/akiradocs/src/components/aiSearch/AIResponseSources.tsx b/packages/akiradocs/src/components/aiSearch/AIResponseSources.tsx index 5b160ed..d146d15 100644 --- a/packages/akiradocs/src/components/aiSearch/AIResponseSources.tsx +++ b/packages/akiradocs/src/components/aiSearch/AIResponseSources.tsx @@ -12,7 +12,7 @@ export function AIResponseSources({ sources }: AIResponseSourcesProps) { {sources.map((source, index) => ( <li key={index}> <a - href={source.url} + href={`${window.location.origin}/en/${source.url.replace('%20', '')}?utm_source=akiradocs`} target="_blank" rel="noopener noreferrer" className="text-sm text-indigo-600 dark:text-indigo-400 hover:underline" diff --git a/packages/akiradocs/src/lib/aisearch/dbWorker.ts b/packages/akiradocs/src/lib/aisearch/dbWorker.ts new file mode 100644 index 0000000..9e6a3b2 --- /dev/null +++ b/packages/akiradocs/src/lib/aisearch/dbWorker.ts @@ -0,0 +1,35 @@ +import { createDbWorker } from "sql.js-httpvfs"; +import { SplitFileConfig } from "sql.js-httpvfs/dist/sqlite.worker"; + +let dbInstance: any = null; + +export async function getDbWorker() { + if (dbInstance) return dbInstance; + + const workerUrl = new URL( + "sql.js-httpvfs/dist/sqlite.worker.js", + import.meta.url + ); + const wasmUrl = new URL( + "sql.js-httpvfs/dist/sql-wasm.wasm", + import.meta.url + ); + + const config: SplitFileConfig = { + from: "inline" as const, + config: { + serverMode: "full", + requestChunkSize: 4096, + url: "/context/docs.db" + } + }; + + const worker = await createDbWorker( + [config], + workerUrl.toString(), + wasmUrl.toString() + ); + + dbInstance = worker; + return dbInstance; +} \ No newline at end of file diff --git a/packages/akiradocs/src/lib/aisearch/embeddings.ts b/packages/akiradocs/src/lib/aisearch/embeddings.ts new file mode 100644 index 0000000..f93f006 --- /dev/null +++ b/packages/akiradocs/src/lib/aisearch/embeddings.ts @@ -0,0 +1,34 @@ +export interface ProgressCallback { + file: string; + progress: number; + loaded: number; + total: number; +} + +export async function generateEmbedding( + text: string, + onProgress?: (progress: ProgressCallback) => void +): Promise<number[]> { + return new Promise((resolve, reject) => { + const worker = new Worker( + new URL('./embeddings.worker.ts', import.meta.url), + { type: 'module' } + ); + + worker.addEventListener('message', (event) => { + if (event.data.status === 'progress' && onProgress) { + onProgress(event.data.progress); + } else if (event.data.status === 'complete') { + resolve(event.data.output); + worker.terminate(); + } + }); + + worker.addEventListener('error', (error) => { + reject(error); + worker.terminate(); + }); + + worker.postMessage({ text }); + }); +} \ No newline at end of file diff --git a/packages/akiradocs/src/lib/aisearch/embeddings.worker.ts b/packages/akiradocs/src/lib/aisearch/embeddings.worker.ts new file mode 100644 index 0000000..52b26d6 --- /dev/null +++ b/packages/akiradocs/src/lib/aisearch/embeddings.worker.ts @@ -0,0 +1,30 @@ +import { pipeline, ProgressCallback, type PipelineType } from "@huggingface/transformers"; + +class EmbeddingPipelineSingleton { + static task: PipelineType = 'feature-extraction'; + static model = 'sauravpanda/gte-small-onnx'; + static instance: Promise<any> | null = null; + + static async getInstance(progress_callback: ProgressCallback | null = null) { + this.instance ??= pipeline(this.task, this.model, { + progress_callback: progress_callback ?? undefined + }); + return this.instance; + } +} + +self.addEventListener('message', async (event) => { + const embedder = await EmbeddingPipelineSingleton.getInstance(x => { + self.postMessage({ status: 'progress', progress: x }); + }); + + const output = await embedder(event.data.text, { + pooling: 'mean', + normalize: true + }); + + self.postMessage({ + status: 'complete', + output: Array.from(output.data), + }); +}); \ No newline at end of file From a70e052977c1832a613ab6ae2602e41bb46e9677 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Thu, 26 Dec 2024 00:43:37 -0800 Subject: [PATCH 12/14] updated main package with web RAG --- packages/akiradocs/public/context/docs.db | Bin 122880 -> 122880 bytes .../public/sql.js-httpvfs/sql-wasm.wasm | Bin 0 -> 652953 bytes .../public/sql.js-httpvfs/sqlite.worker.js | 5 +++++ packages/akiradocs/src/app/aiSearch/page.tsx | 2 +- 4 files changed, 6 insertions(+), 1 deletion(-) create mode 100644 packages/akiradocs/public/sql.js-httpvfs/sql-wasm.wasm create mode 100644 packages/akiradocs/public/sql.js-httpvfs/sqlite.worker.js diff --git a/packages/akiradocs/public/context/docs.db b/packages/akiradocs/public/context/docs.db index 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u,l=eh.lookupNode(r,s),c=ea.relative(e,i);if("."!==c.charAt(0))throw new eh.ErrnoError(28);if("."!==(c=ea.relative(t,o)).charAt(0))throw new eh.ErrnoError(55);try{u=eh.lookupNode(n,a)}catch(e){}if(l!==u){var f=eh.isDir(l.mode),d=eh.mayDelete(r,s,f);if(d||(d=u?eh.mayDelete(n,a,f):eh.mayCreate(n,a)))throw new eh.ErrnoError(d);if(!r.node_ops.rename)throw new eh.ErrnoError(63);if(eh.isMountpoint(l)||u&&eh.isMountpoint(u))throw new eh.ErrnoError(10);if(n!==r&&(d=eh.nodePermissions(r,"w")))throw new eh.ErrnoError(d);eh.hashRemoveNode(l);try{r.node_ops.rename(l,n,a)}catch(e){throw e}finally{eh.hashAddNode(l)}}},rmdir:e=>{var t=eh.lookupPath(e,{parent:!0}).node,r=es.basename(e),n=eh.lookupNode(t,r),o=eh.mayDelete(t,r,!0);if(o)throw new eh.ErrnoError(o);if(!t.node_ops.rmdir)throw new eh.ErrnoError(63);if(eh.isMountpoint(n))throw new eh.ErrnoError(10);t.node_ops.rmdir(t,r),eh.destroyNode(n)},readdir:e=>{var t=eh.lookupPath(e,{follow:!0}).node;if(!t.node_ops.readdir)throw new 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13/14] released package with webRag --- docs/package.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/package.json b/docs/package.json index 3d82ee4..6908e7e 100644 --- a/docs/package.json +++ b/docs/package.json @@ -1,6 +1,6 @@ { "name": "akiradocs", - "version": "1.0.52", + "version": "1.0.53", "private": true, "scripts": { "translate": "npm run compile && node scripts/translate.js", From 5057f7edf51672bf13fa31e5cd990b1522fb2cc1 Mon Sep 17 00:00:00 2001 From: Saurav Panda <sgp65@cornell.edu> Date: Thu, 26 Dec 2024 00:49:05 -0800 Subject: [PATCH 14/14] updated create-app version --- packages/create-app/package.json | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/packages/create-app/package.json b/packages/create-app/package.json index f6c40d3..9f8a964 100644 --- a/packages/create-app/package.json +++ b/packages/create-app/package.json @@ -1,6 +1,6 @@ { "name": "create-akiradocs", - "version": "1.0.52", + "version": "1.0.53", "description": "Create Akira Docs documentation sites with one command", "main": "./dist/index.js", "type": "module",