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import { searchWeb } from "$lib/server/websearch/searchWeb"; |
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import type { Message } from "$lib/types/Message"; |
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import type { WebSearch, WebSearchSource } from "$lib/types/WebSearch"; |
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import { generateQuery } from "$lib/server/websearch/generateQuery"; |
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import { parseWeb } from "$lib/server/websearch/parseWeb"; |
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import { chunk } from "$lib/utils/chunk"; |
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import { |
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MAX_SEQ_LEN as CHUNK_CAR_LEN, |
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findSimilarSentences, |
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} from "$lib/server/websearch/sentenceSimilarity"; |
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import type { Conversation } from "$lib/types/Conversation"; |
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import type { MessageUpdate } from "$lib/types/MessageUpdate"; |
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import { getWebSearchProvider } from "./searchWeb"; |
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const MAX_N_PAGES_SCRAPE = 10 as const; |
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const MAX_N_PAGES_EMBED = 5 as const; |
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const DOMAIN_BLOCKLIST = ["youtube.com", "twitter.com"]; |
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export async function runWebSearch( |
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conv: Conversation, |
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prompt: string, |
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updatePad: (upd: MessageUpdate) => void |
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) { |
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const messages = (() => { |
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return [...conv.messages, { content: prompt, from: "user", id: crypto.randomUUID() }]; |
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})() satisfies Message[]; |
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const webSearch: WebSearch = { |
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prompt: prompt, |
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searchQuery: "", |
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results: [], |
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context: "", |
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contextSources: [], |
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createdAt: new Date(), |
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updatedAt: new Date(), |
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}; |
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function appendUpdate(message: string, args?: string[], type?: "error" | "update") { |
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updatePad({ type: "webSearch", messageType: type ?? "update", message: message, args: args }); |
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} |
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try { |
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webSearch.searchQuery = await generateQuery(messages); |
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const searchProvider = getWebSearchProvider(); |
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appendUpdate(`Searching ${searchProvider}`, [webSearch.searchQuery]); |
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const results = await searchWeb(webSearch.searchQuery); |
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webSearch.results = |
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(results.organic_results && |
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results.organic_results.map((el: { title?: string; link: string; text?: string }) => { |
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const { title, link, text } = el; |
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const { hostname } = new URL(link); |
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return { title, link, hostname, text }; |
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})) ?? |
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[]; |
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webSearch.results = webSearch.results |
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.filter(({ link }) => !DOMAIN_BLOCKLIST.some((el) => link.includes(el))) |
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.slice(0, MAX_N_PAGES_SCRAPE); |
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let paragraphChunks: { source: WebSearchSource; text: string }[] = []; |
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if (webSearch.results.length > 0) { |
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appendUpdate("Browsing results"); |
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const promises = webSearch.results.map(async (result) => { |
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const { link } = result; |
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let text = result.text ?? ""; |
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if (!text) { |
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try { |
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text = await parseWeb(link); |
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appendUpdate("Browsing webpage", [link]); |
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} catch (e) { |
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} |
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} |
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const MAX_N_CHUNKS = 100; |
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const texts = chunk(text, CHUNK_CAR_LEN).slice(0, MAX_N_CHUNKS); |
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return texts.map((t) => ({ source: result, text: t })); |
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}); |
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const nestedParagraphChunks = (await Promise.all(promises)).slice(0, MAX_N_PAGES_EMBED); |
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paragraphChunks = nestedParagraphChunks.flat(); |
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if (!paragraphChunks.length) { |
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throw new Error("No text found on the first 5 results"); |
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} |
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} else { |
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throw new Error("No results found for this search query"); |
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} |
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appendUpdate("Extracting relevant information"); |
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const topKClosestParagraphs = 8; |
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const texts = paragraphChunks.map(({ text }) => text); |
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const indices = await findSimilarSentences(prompt, texts, { |
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topK: topKClosestParagraphs, |
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}); |
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webSearch.context = indices.map((idx) => texts[idx]).join(""); |
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const usedSources = new Set<string>(); |
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for (const idx of indices) { |
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const { source } = paragraphChunks[idx]; |
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if (!usedSources.has(source.link)) { |
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usedSources.add(source.link); |
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webSearch.contextSources.push(source); |
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} |
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} |
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updatePad({ |
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type: "webSearch", |
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messageType: "sources", |
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message: "sources", |
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sources: webSearch.contextSources, |
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}); |
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} catch (searchError) { |
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if (searchError instanceof Error) { |
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appendUpdate( |
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"An error occurred with the web search", |
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[JSON.stringify(searchError.message)], |
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"error" |
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); |
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} |
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} |
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return webSearch; |
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} |
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