108 lines
3.2 KiB
TypeScript
108 lines
3.2 KiB
TypeScript
import { cleanUrl } from "@/libs/clean-url"
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import { urlRewriteRuntime } from "@/libs/runtime"
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import { PageAssistHtmlLoader } from "@/loader/html"
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import { pageAssistEmbeddingModel } from "@/models/embedding"
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import {
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defaultEmbeddingModelForRag,
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getOllamaURL
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} from "@/services/ollama"
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import {
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getIsSimpleInternetSearch,
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totalSearchResults
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} from "@/services/search"
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import { getPageAssistTextSplitter } from "@/utils/text-splitter"
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import type { Document } from "@langchain/core/documents"
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import * as cheerio from "cheerio"
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import { MemoryVectorStore } from "langchain/vectorstores/memory"
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export const localBraveSearch = async (query: string) => {
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await urlRewriteRuntime(cleanUrl("https://search.brave.com/search?q=" + query), "duckduckgo")
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const abortController = new AbortController()
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setTimeout(() => abortController.abort(), 10000)
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const htmlString = await fetch(
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"https://search.brave.com/search?q=" + query,
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{
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signal: abortController.signal
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}
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)
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.then((response) => response.text())
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.catch()
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const $ = cheerio.load(htmlString)
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const $results = $("div#results")
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const $snippets = $results.find("div.snippet")
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const searchResults = Array.from($snippets).map((result) => {
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const link = $(result).find("a").attr("href")
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const title = $(result).find("div.title").text()
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const content = $(result).find("div.snippet-description").text()
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return { title, link, content }
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}).filter((result) => result.link && result.title && result.content)
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console.log(searchResults)
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return searchResults
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}
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export const webBraveSearch = async (query: string) => {
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const results = await localBraveSearch(query)
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const TOTAL_SEARCH_RESULTS = await totalSearchResults()
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const searchResults = results.slice(0, TOTAL_SEARCH_RESULTS)
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const isSimpleMode = await getIsSimpleInternetSearch()
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if (isSimpleMode) {
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await getOllamaURL()
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return searchResults.map((result) => {
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return {
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url: result.link,
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content: result.content
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}
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})
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}
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const docs: Document<Record<string, any>>[] = []
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for (const result of searchResults) {
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const loader = new PageAssistHtmlLoader({
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html: "",
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url: result.link
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})
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const documents = await loader.loadByURL()
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documents.forEach((doc) => {
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docs.push(doc)
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})
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}
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const ollamaUrl = await getOllamaURL()
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const embeddingModle = await defaultEmbeddingModelForRag()
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const ollamaEmbedding = await pageAssistEmbeddingModel({
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model: embeddingModle || "",
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baseUrl: cleanUrl(ollamaUrl)
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})
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const textSplitter = await getPageAssistTextSplitter();
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const chunks = await textSplitter.splitDocuments(docs)
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const store = new MemoryVectorStore(ollamaEmbedding)
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await store.addDocuments(chunks)
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const resultsWithEmbeddings = await store.similaritySearch(query, 3)
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const searchResult = resultsWithEmbeddings.map((result) => {
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return {
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url: result.metadata.url,
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content: result.pageContent
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}
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})
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return searchResult
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}
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