feat: optimize scoring prompt and use gpt-4-1106-preview model
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DIYgod
parent
114397ad22
commit
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@ -101,6 +101,7 @@
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"next-themes": "0.2.1",
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"node-id3": "0.2.6",
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"open-graph-scraper": "^6.3.2",
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"openai": "^4.19.0",
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"pangu": "4.0.7",
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"path-to-regexp": "6.2.1",
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"pinyin-pro": "3.18.0",
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@ -187,7 +187,7 @@ dependencies:
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version: 0.16.9
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langchain:
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specifier: 0.0.186
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version: 0.0.186(cheerio@1.0.0-rc.12)(ioredis@5.3.2)(jsdom@22.1.0)(ws@7.5.9)
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version: 0.0.186(cheerio@1.0.0-rc.12)(ioredis@5.3.2)(jsdom@22.1.0)(ws@8.13.0)
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lottie-react:
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specifier: 2.4.0
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version: 2.4.0(react-dom@18.2.0)(react@18.2.0)
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@ -218,6 +218,9 @@ dependencies:
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open-graph-scraper:
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specifier: ^6.3.2
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version: 6.3.2
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openai:
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specifier: ^4.19.0
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version: 4.19.0
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pangu:
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specifier: 4.0.7
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version: 4.0.7
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@ -8447,7 +8450,7 @@ packages:
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engines: {node: '>=6'}
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dev: false
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/langchain@0.0.186(cheerio@1.0.0-rc.12)(ioredis@5.3.2)(jsdom@22.1.0)(ws@7.5.9):
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/langchain@0.0.186(cheerio@1.0.0-rc.12)(ioredis@5.3.2)(jsdom@22.1.0)(ws@8.13.0):
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resolution: {integrity: sha512-uXDipmw9aUrUmDNcFr2XH9ORmshWIlIb/qFKneS1K3X5upMUg7TSbaBxqV9WxuuenLUSYaoTcTy7P/pKkbqXPg==}
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engines: {node: '>=18'}
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peerDependencies:
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@ -8760,12 +8763,12 @@ packages:
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langchainhub: 0.0.6
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langsmith: 0.0.48
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ml-distance: 4.0.1
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openai: 4.17.4
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openai: 4.19.0
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openapi-types: 12.1.3
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p-queue: 6.6.2
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p-retry: 4.6.2
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uuid: 9.0.1
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ws: 7.5.9
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ws: 8.13.0
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yaml: 2.3.4
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zod: 3.22.4
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zod-to-json-schema: 3.21.4(zod@3.22.4)
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@ -10051,8 +10054,8 @@ packages:
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validator: 13.11.0
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dev: false
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/openai@4.17.4:
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resolution: {integrity: sha512-ThRFkl6snLbcAKS58St7N3CaKuI5WdYUvIjPvf4s+8SdymgNtOfzmZcZXVcCefx04oKFnvZJvIcTh3eAFUUhAQ==}
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/openai@4.19.0:
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resolution: {integrity: sha512-cJbl0noZyAaXVKBTMMq6X5BAvP1pm2rWYDBnZes99NL+Zh5/4NmlAwyuhTZEru5SqGGZIoiYKeMPXy4bm9DI0w==}
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hasBin: true
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dependencies:
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'@types/node': 18.18.9
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@ -1,4 +1,5 @@
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import AsyncLock from "async-lock"
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import OpenAI from "openai"
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import countCharacters from "~/lib/character-count"
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import { toGateway } from "~/lib/ipfs-parser"
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@ -6,6 +7,13 @@ import prisma from "~/lib/prisma.server"
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import { cacheGet } from "~/lib/redis.server"
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import { getQuery, NextServerResponse } from "~/lib/server-helper"
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let openai: OpenAI | undefined
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if (process.env.OPENAI_API_KEY) {
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openai = new OpenAI({
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apiKey: process.env.OPENAI_API_KEY,
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})
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}
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const lock = new AsyncLock()
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const getOriginalScore = async (cid: string) => {
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@ -26,41 +34,51 @@ const getOriginalScore = async (cid: string) => {
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}
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if (countCharacters(content) > 300) {
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if (!openai) {
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return {
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number: 100,
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reason: "OpenAI not configured",
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}
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}
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console.time(`fetching score ${cid}`)
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const prompt = `According to rule 1 not too short content, rule 2 good originality and innovation, and rule 3 good fun or logic, give this article a score in the range of 0-100 and explain the reason:
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"${content}"
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Score:`
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const response = await (
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await fetch("https://api.openai.com/v1/chat/completions", {
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method: "POST",
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headers: {
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"Content-Type": "application/json",
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Authorization: `Bearer ${process.env.OPENAI_API_KEY}`,
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const completion = await openai.chat.completions.create({
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messages: [
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{
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role: "user",
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content: `You are an expert in scoring blog posts, you will evaluate this article based on three rules:
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1. Content length should not be too short.
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2. Originality and innovation should be good.
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3. The article should provide either good fun or logical reasoning.
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Based on these criteria, you will assign a score to the article on a scale of 0-100 and provide an explanation for your decision. The evaluation should be presented in JSON format, following the structure: { "score": number, "reason": string }.
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Below you find the post content:
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--------
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${content}
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--------`,
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},
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body: JSON.stringify({
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model: "gpt-4",
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temperature: 0,
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messages: [
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{
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role: "user",
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content: prompt,
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},
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],
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}),
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})
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).json()
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],
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model: "gpt-4-1106-preview",
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temperature: 0,
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response_format: { type: "json_object" },
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})
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let evaluate
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try {
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if (completion.choices?.[0]?.message?.content) {
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evaluate = JSON.parse(completion.choices?.[0]?.message?.content)
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}
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} catch (error) {}
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console.timeEnd(`fetching score ${cid}`)
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return {
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number: parseInt(response.choices?.[0]?.message?.content?.trim()),
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reason: response.choices?.[0]?.message?.content
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?.trim()
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.replace(/^\d+([,.\s]*)/, "")
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.trim()
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.replace(/^Reason:/, "")
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.trim(),
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if (evaluate.reason) {
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return {
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number: evaluate.score,
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reason: evaluate.reason,
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}
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}
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} else {
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return {
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