768ff2e555
Refactor embedding models and their handling to improve performance and simplify the process. Add a new model selection mechanism, and enhance the UI for model selection, offering clearer and more user-friendly options for embedding models. Refactor embeddings to use a common model for page assist and RAG, further improving performance and streamlining the workflow.
116 lines
3.2 KiB
TypeScript
116 lines
3.2 KiB
TypeScript
import { pageAssistEmbeddingModel } from "@/models/embedding"
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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 type { Document } from "@langchain/core/documents"
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import { RecursiveCharacterTextSplitter } from "langchain/text_splitter"
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import { MemoryVectorStore } from "langchain/vectorstores/memory"
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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 {
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defaultEmbeddingChunkOverlap,
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defaultEmbeddingChunkSize,
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defaultEmbeddingModelForRag,
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getOllamaURL
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} from "~/services/ollama"
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export const localGoogleSearch = async (query: string) => {
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await urlRewriteRuntime(
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cleanUrl("https://www.google.com/search?hl=en&q=" + query),
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"google"
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)
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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://www.google.com/search?hl=en&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 parser = new DOMParser()
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const doc = parser.parseFromString(htmlString, "text/html")
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const searchResults = Array.from(doc.querySelectorAll("div.g")).map(
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(result) => {
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const title = result.querySelector("h3")?.textContent
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const link = result.querySelector("a")?.getAttribute("href")
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const content = Array.from(result.querySelectorAll("span"))
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.map((span) => span.textContent)
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.join(" ")
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return { title, link, content }
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}
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)
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return searchResults
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}
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export const webGoogleSearch = async (query: string) => {
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const results = await localGoogleSearch(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 chunkSize = await defaultEmbeddingChunkSize()
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const chunkOverlap = await defaultEmbeddingChunkOverlap()
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const textSplitter = new RecursiveCharacterTextSplitter({
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chunkSize,
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chunkOverlap
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})
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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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